llama-run : improve progress bar (#10821 )

Set default width to whatever the terminal is. Also fixed a small bug around default n_gpu_layers value. Signed-off-by: Eric Curtin <ecurtin@redhat.com>
ggml : fix arm build (#10890 )
2026-07-01 10:07:44 +02:00 · 2024-12-19 03:58:00 +01:00 · 2024-12-18 23:21:42 +01:00 · 2024-12-18 19:27:21 +02:00 · 2024-12-18 15:00:07 +01:00 · 2024-12-18 13:01:41 +02:00
201 changed files with 24174 additions and 5364 deletions
@@ -31,6 +31,7 @@
  # Increases the runtime closure size by ~700M
  useMpi ? false,
  useRocm ? config.rocmSupport,
+  rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
  enableCurl ? true,
  useVulkan ? false,
  llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
@@ -188,7 +189,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
    ]
    ++ optionals useRocm [
      (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang")
-      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets))
+      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" rocmGpuTargets)
    ]
    ++ optionals useMetalKit [
      (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
@@ -8,11 +8,11 @@ arg1="$1"
 shift

 if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    python3 ./convert_hf_to_gguf.py "$@"
+    exec python3 ./convert_hf_to_gguf.py "$@"
 elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
-    ./llama-quantize "$@"
+    exec ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
-    ./llama-cli "$@"
+    exec ./llama-cli "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
    echo "Converting PTH to GGML..."
    for i in `ls $1/$2/ggml-model-f16.bin*`; do
@@ -20,11 +20,11 @@ elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
            echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
        else
            echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
-            ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
+            exec ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
        fi
    done
 elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
-    ./llama-server "$@"
+    exec ./llama-server "$@"
 else
    echo "Unknown command: $arg1"
    echo "Available commands: "
@@ -317,7 +317,7 @@ jobs:
          wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
          sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
          sudo apt-get update -y
-          sudo apt-get install -y build-essential vulkan-sdk
+          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk

      - name: Build
        id: cmake_build
@@ -327,6 +327,12 @@ jobs:
          cmake -DGGML_VULKAN=ON ..
          cmake --build . --config Release -j $(nproc)

+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose --timeout 900
+
  ubuntu-22-cmake-hip:
    runs-on: ubuntu-22.04
    container: rocm/dev-ubuntu-22.04:6.0.2
@@ -552,35 +558,44 @@ jobs:
            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO

-# TODO: tmp disabled. see for possible re-enable:
-#       https://github.com/ggerganov/llama.cpp/pull/10525
-#  macOS-latest-swift:
-#    runs-on: macos-latest
-#
-#    strategy:
-#      matrix:
-#        destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS']
-#
-#    steps:
-#      - name: Clone
-#        id: checkout
-#        uses: actions/checkout@v4
-#
-#      - name: Dependencies
-#        id: depends
-#        continue-on-error: true
-#        run: |
-#          brew update
-#
-#      - name: xcodebuild for swift package
-#        id: xcodebuild
-#        run: |
-#          xcodebuild -scheme llama -destination "${{ matrix.destination }}"
-#
-#      - name: Build Swift Example
-#        id: make_build_swift_example
-#        run: |
-#            make swift
+  macOS-latest-swift:
+    runs-on: macos-latest
+
+    strategy:
+      matrix:
+        destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS']
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+
+      - name: Build llama.cpp with CMake
+        id: cmake_build
+        run: |
+          sysctl -a
+          mkdir build
+          cd build
+          cmake -G Xcode .. \
+            -DGGML_METAL_USE_BF16=ON \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_BUILD_EXAMPLES=OFF \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DLLAMA_BUILD_SERVER=OFF \
+            -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+          sudo cmake --install . --config Release
+
+      - name: xcodebuild for swift package
+        id: xcodebuild
+        run: |
+          xcodebuild -scheme llama-Package -destination "${{ matrix.destination }}"

  windows-msys2:
    runs-on: windows-latest
@@ -653,6 +668,8 @@ jobs:
            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
          - build: 'msvc-arm64'
            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
+          - build: 'llvm-arm64-opencl-adreno'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'

    steps:
      - name: Clone
@@ -694,6 +711,28 @@ jobs:
        run: |
          choco install ninja

+      - name: Install OpenCL Headers and Libs
+        id: install_opencl
+        if: ${{ matrix.build == 'llvm-arm64-opencl-adreno' }}
+        run: |
+          git clone https://github.com/KhronosGroup/OpenCL-Headers
+          cd OpenCL-Headers
+          mkdir build && cd build
+          cmake .. `
+            -DBUILD_TESTING=OFF `
+            -DOPENCL_HEADERS_BUILD_TESTING=OFF `
+            -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
+            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
+          cmake --build . --target install
+          git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
+          cd OpenCL-ICD-Loader
+          mkdir build-arm64-release && cd build-arm64-release
+          cmake .. `
+            -A arm64 `
+            -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" `
+            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
+          cmake --build . --target install --config release
+
      - name: Build
        id: cmake_build
        run: |
@@ -723,7 +762,7 @@ jobs:
      - name: Test
        id: cmake_test
        # not all machines have native AVX-512
-        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
+        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
        run: |
          cd build
          ctest -L main -C Release --verbose --timeout 900
@@ -1104,6 +1143,29 @@ jobs:
      - name: Checkout code
        uses: actions/checkout@v4

+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          mkdir build
+          cd build
+          cmake -G Xcode .. \
+            -DGGML_METAL_USE_BF16=ON \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_BUILD_EXAMPLES=OFF \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DLLAMA_BUILD_SERVER=OFF \
+            -DCMAKE_SYSTEM_NAME=iOS \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+          sudo cmake --install . --config Release
+
+      - name: xcodebuild for swift package
+        id: xcodebuild
+        run: |
+          xcodebuild -scheme llama-Package -destination 'generic/platform=iOS'
+
      - name: Build Xcode project
        run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build

@@ -1131,23 +1193,6 @@ jobs:

          ./gradlew build --no-daemon

-#  freeBSD-latest:
-#    runs-on: macos-12
-#    steps:
-#    - name: Clone
-#      uses: actions/checkout@v4
-#
-#    - name: Build
-#      uses: cross-platform-actions/action@v0.19.0
-#      with:
-#        operating_system: freebsd
-#        version: '13.2'
-#        hypervisor: 'qemu'
-#        run: |
-#            sudo pkg update
-#            sudo pkg install -y gmake automake autoconf pkgconf llvm15 openblas
-#            gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu`
-
  release:
    if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}

@@ -79,7 +79,7 @@ jobs:
      # Setup nodejs (to be used for verifying bundled index.html)
      - uses: actions/setup-node@v4
        with:
-          node-version: 22
+          node-version: '22.11.0'

      - name: Verify bundled index.html
        id: verify_server_index_html
@@ -46,11 +46,9 @@ if (WIN32)
    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
 endif()

-if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC")
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/source-charset:utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/source-charset:utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/execution-charset:utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/execution-charset:utf-8>")
+if (MSVC)
+    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
+    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
 endif()

 #
@@ -31,6 +31,13 @@
    { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16":    "ON" } },
    { "name": "vulkan",   "hidden": true, "cacheVariables": { "GGML_VULKAN":      "ON" } },

+    {
+        "name": "x64-windows-llvm", "hidden": true,
+        "cacheVariables": {
+            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/x64-windows-llvm.cmake"
+        }
+    },
+
    {
        "name": "arm64-windows-msvc", "hidden": true,
        "architecture": { "value": "arm64",    "strategy": "external" },
@@ -70,6 +77,11 @@
    { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg" ] },
    { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg", "static" ] },

+    { "name": "x64-windows-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] },
+    { "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] },
+    { "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] },
+    { "name": "x64-windows-llvm+static-release", "inherits": [ "base", "x64-windows-llvm", "reldbg", "static" ] },
+
    { "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] },
    { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
    { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
@@ -1,3 +1,5 @@
 # collaborators can optionally add themselves here to indicate their availability for reviewing related PRs

-ci/ @ggerganov
+/ci/ @ggerganov
+/.devops/ @ngxson
+/examples/server/ @ngxson
@@ -22,6 +22,7 @@ BUILD_TARGETS = \
 	llama-infill \
 	llama-llava-cli \
 	llama-minicpmv-cli\
+	llama-qwen2vl-cli\
 	llama-lookahead \
 	llama-lookup \
 	llama-lookup-create \
@@ -445,6 +446,10 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
 	MK_CFLAGS     += -march=native -mtune=native
 	HOST_CXXFLAGS += -march=native -mtune=native

+	# Usage AMX build test
+	#MK_CFLAGS     += -march=graniterapids -mtune=graniterapids
+	#HOST_CXXFLAGS += -march=graniterapids -mtune=graniterapids
+
 	# Usage AVX-only
 	#MK_CFLAGS   += -mfma -mf16c -mavx
 	#MK_CXXFLAGS += -mfma -mf16c -mavx
@@ -948,7 +953,6 @@ DIR_COMMON = common

 OBJ_GGML = \
 	$(DIR_GGML)/src/ggml.o \
-	$(DIR_GGML)/src/ggml-aarch64.o \
 	$(DIR_GGML)/src/ggml-alloc.o \
 	$(DIR_GGML)/src/ggml-backend.o \
 	$(DIR_GGML)/src/ggml-backend-reg.o \
@@ -956,9 +960,11 @@ OBJ_GGML = \
 	$(DIR_GGML)/src/ggml-quants.o \
 	$(DIR_GGML)/src/ggml-threading.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o \
+	$(DIR_GGML)/src/ggml-cpu/ggml-cpu_cpp.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-aarch64.o \
+	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-hbm.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \
+	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-traits.o \
 	$(OBJ_GGML_EXT)

 OBJ_LLAMA = \
@@ -1098,17 +1104,10 @@ DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d)
 # Default target
 all: $(BUILD_TARGETS)

+# force c++ build for source file that have same name as c file
 # Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files
-#       g++ -M -I ./ggml/include/ -I ./ggml/src ggml/src/ggml-cpu/ggml-cpu.cpp | grep ggml
-$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o: \
-	ggml/src/ggml-cpu/ggml-cpu.cpp \
-	ggml/include/ggml-backend.h \
-	ggml/include/ggml.h \
-	ggml/include/ggml-alloc.h \
-	ggml/src/ggml-backend-impl.h \
-	ggml/include/ggml-cpu.h \
-	ggml/src/ggml-impl.h
-	$(CXX) $(CXXFLAGS)   -c $< -o $@
+$(DIR_GGML)/%_cpp.o: $(DIR_GGML)/%.cpp
+	$(CXX) $(CXXFLAGS) -MMD -c $< -o $@

 # Rules for building object files
 $(DIR_GGML)/%.o: $(DIR_GGML)/%.c
@@ -1406,6 +1405,14 @@ llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual

+llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \
+	examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
+
 ifeq ($(UNAME_S),Darwin)
 swift: examples/batched.swift
 	(cd examples/batched.swift; make build)
@@ -2,59 +2,6 @@

 import PackageDescription

-var sources = [
-    "src/llama.cpp",
-    "src/llama-vocab.cpp",
-    "src/llama-grammar.cpp",
-    "src/llama-sampling.cpp",
-    "src/unicode.cpp",
-    "src/unicode-data.cpp",
-    "ggml/src/ggml.c",
-    "ggml/src/ggml-aarch64.c",
-    "ggml/src/ggml-alloc.c",
-    "ggml/src/ggml-backend.cpp",
-    "ggml/src/ggml-backend-reg.cpp",
-    "ggml/src/ggml-cpu/ggml-cpu.c",
-    "ggml/src/ggml-cpu/ggml-cpu.cpp",
-    "ggml/src/ggml-cpu/ggml-cpu-aarch64.c",
-    "ggml/src/ggml-cpu/ggml-cpu-quants.c",
-    "ggml/src/ggml-threading.cpp",
-    "ggml/src/ggml-quants.c",
-]
-
-var resources: [Resource] = []
-var linkerSettings: [LinkerSetting] = []
-var cSettings: [CSetting] =  [
-    .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
-    .unsafeFlags(["-fno-objc-arc"]),
-    .headerSearchPath("ggml/src"),
-    .headerSearchPath("ggml/src/ggml-cpu"),
-    // NOTE: NEW_LAPACK will required iOS version 16.4+
-    // We should consider add this in the future when we drop support for iOS 14
-    // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
-    // .define("ACCELERATE_NEW_LAPACK"),
-    // .define("ACCELERATE_LAPACK_ILP64")
-    .define("GGML_USE_CPU"),
-]
-
-
-#if canImport(Darwin)
-sources.append("ggml/src/ggml-common.h")
-sources.append("ggml/src/ggml-metal/ggml-metal.m")
-resources.append(.process("ggml/src/ggml-metal/ggml-metal.metal"))
-linkerSettings.append(.linkedFramework("Accelerate"))
-cSettings.append(
-    contentsOf: [
-        .define("GGML_USE_ACCELERATE"),
-        .define("GGML_USE_METAL"),
-    ]
-)
-#endif
-
-#if os(Linux)
-    cSettings.append(.define("_GNU_SOURCE"))
-#endif
-
 let package = Package(
    name: "llama",
    platforms: [
@@ -67,26 +14,6 @@ let package = Package(
        .library(name: "llama", targets: ["llama"]),
    ],
    targets: [
-        .target(
-            name: "llama",
-            path: ".",
-            exclude: [
-               "build",
-               "cmake",
-               "examples",
-               "scripts",
-               "models",
-               "tests",
-               "CMakeLists.txt",
-               "Makefile",
-               "ggml/src/ggml-metal-embed.metal"
-            ],
-            sources: sources,
-            resources: resources,
-            publicHeadersPath: "spm-headers",
-            cSettings: cSettings,
-            linkerSettings: linkerSettings
-        )
-    ],
-    cxxLanguageStandard: .cxx17
+        .systemLibrary(name: "llama", pkgConfig: "llama"),
+    ]
 )
@@ -98,6 +98,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
 - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
 - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
+- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)

 #### Multimodal

@@ -110,6 +111,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
 - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
 - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
+- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)

 </details>

@@ -219,7 +221,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
 | [MUSA](docs/build.md#musa) | Moore Threads MTT GPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
-| [hipBLAS](docs/build.md#hipblas) | AMD GPU |
+| [HIP](docs/build.md#hip) | AMD GPU |
 | [Vulkan](docs/build.md#vulkan) | GPU |
 | [CANN](docs/build.md#cann) | Ascend NPU |

@@ -412,7 +414,7 @@ To learn more about model quantization, [read this documentation](examples/quant
 [^1]: [examples/perplexity/README.md](examples/perplexity/README.md)
 [^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)

-## [`llama-bench`](example/bench)
+## [`llama-bench`](examples/llama-bench)

 #### Benchmark the performance of the inference for various parameters.

@@ -433,6 +435,20 @@ To learn more about model quantization, [read this documentation](examples/quant

    </details>

+## [`llama-run`](examples/run)
+
+#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3].
+
+- <details>
+    <summary>Run a model with a specific prompt (by default it's pulled from Ollama registry)</summary>
+
+    ```bash
+    llama-run granite-code
+    ```
+
+    </details>
+
+[^3]: [RamaLama](https://github.com/containers/ramalama)

 ## [`llama-simple`](examples/simple)

@@ -0,0 +1,4 @@
+#pragma once
+
+#include <llama.h>
+
@@ -0,0 +1,5 @@
+module llama [system] {
+    header "llama.h"
+    link "llama"
+    export *
+}
@@ -6,5 +6,5 @@ includedir=${prefix}/include
 Name: llama
 Description: Port of Facebook's LLaMA model in C/C++
 Version: @PROJECT_VERSION@
-Libs: -L${libdir} -lllama
+Libs: -L${libdir} -lggml  -lggml-base -lllama
 Cflags: -I${includedir}
@@ -0,0 +1,11 @@
+set( CMAKE_SYSTEM_NAME Windows )
+set( CMAKE_SYSTEM_PROCESSOR x86_64 )
+
+set( CMAKE_C_COMPILER    clang )
+set( CMAKE_CXX_COMPILER  clang++ )
+
+set( arch_c_flags "-march=native" )
+
+set( CMAKE_C_FLAGS_INIT   "${arch_c_flags}" )
+set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags}" )
+
@@ -81,7 +81,7 @@ set(LLAMA_COMMON_EXTRA_LIBS build_info)
 # Use curl to download model url
 if (LLAMA_CURL)
    find_package(CURL REQUIRED)
-    add_definitions(-DLLAMA_USE_CURL)
+    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
    include_directories(${CURL_INCLUDE_DIRS})
    find_library(CURL_LIBRARY curl REQUIRED)
    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
@@ -119,32 +119,65 @@ std::string common_arg::to_string() {
 // utils
 //

-static void common_params_handle_model_default(common_params & params) {
-    if (!params.hf_repo.empty()) {
+static void common_params_handle_model_default(
+        std::string & model,
+        std::string & model_url,
+        std::string & hf_repo,
+        std::string & hf_file) {
+    if (!hf_repo.empty()) {
        // short-hand to avoid specifying --hf-file -> default it to --model
-        if (params.hf_file.empty()) {
-            if (params.model.empty()) {
+        if (hf_file.empty()) {
+            if (model.empty()) {
                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
            }
-            params.hf_file = params.model;
-        } else if (params.model.empty()) {
+            hf_file = model;
+        } else if (model.empty()) {
            // this is to avoid different repo having same file name, or same file name in different subdirs
-            std::string filename = params.hf_repo + "_" + params.hf_file;
+            std::string filename = hf_repo + "_" + hf_file;
            // to make sure we don't have any slashes in the filename
            string_replace_all(filename, "/", "_");
-            params.model = fs_get_cache_file(filename);
+            model = fs_get_cache_file(filename);
        }
-    } else if (!params.model_url.empty()) {
-        if (params.model.empty()) {
-            auto f = string_split<std::string>(params.model_url, '#').front();
+    } else if (!model_url.empty()) {
+        if (model.empty()) {
+            auto f = string_split<std::string>(model_url, '#').front();
            f = string_split<std::string>(f, '?').front();
-            params.model = fs_get_cache_file(string_split<std::string>(f, '/').back());
+            model = fs_get_cache_file(string_split<std::string>(f, '/').back());
        }
-    } else if (params.model.empty()) {
-        params.model = DEFAULT_MODEL_PATH;
+    } else if (model.empty()) {
+        model = DEFAULT_MODEL_PATH;
    }
 }

+const std::vector<ggml_type> kv_cache_types = {
+    GGML_TYPE_F32,
+    GGML_TYPE_F16,
+    GGML_TYPE_BF16,
+    GGML_TYPE_Q8_0,
+    GGML_TYPE_Q4_0,
+    GGML_TYPE_Q4_1,
+    GGML_TYPE_IQ4_NL,
+    GGML_TYPE_Q5_0,
+    GGML_TYPE_Q5_1,
+};
+
+static ggml_type kv_cache_type_from_str(const std::string & s) {
+    for (const auto & type : kv_cache_types) {
+        if (ggml_type_name(type) == s) {
+            return type;
+        }
+    }
+    throw std::runtime_error("Unsupported cache type: " + s);
+}
+
+static std::string get_all_kv_cache_types() {
+    std::ostringstream msg;
+    for (const auto & type : kv_cache_types) {
+        msg << ggml_type_name(type) << (&type == &kv_cache_types.back() ? "" : ", ");
+    }
+    return msg.str();
+}
+
 //
 // CLI argument parsing functions
 //
@@ -247,7 +280,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
    }

-    common_params_handle_model_default(params);
+    // TODO: refactor model params in a common struct
+    common_params_handle_model_default(params.model,         params.model_url,         params.hf_repo,         params.hf_file);
+    common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file);

    if (params.escape) {
        string_process_escapes(params.prompt);
@@ -591,7 +626,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params) {
            params.ctx_shift = false;
        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
    add_opt(common_arg(
        {"--chunks"}, "N",
        string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
@@ -786,7 +821,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params) {
            params.warmup = false;
        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
    add_opt(common_arg(
        {"--spm-infill"},
        string_format(
@@ -813,7 +848,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_sparam());
    add_opt(common_arg(
-        {"--sampling-seq"}, "SEQUENCE",
+        {"--sampling-seq", "--sampler-seq"}, "SEQUENCE",
        string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
        [](common_params & params, const std::string & value) {
            params.sampling.samplers = common_sampler_types_from_chars(value);
@@ -826,13 +861,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.sampling.ignore_eos = true;
        }
    ).set_sparam());
-    add_opt(common_arg(
-        {"--penalize-nl"},
-        string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"),
-        [](common_params & params) {
-            params.sampling.penalize_nl = true;
-        }
-    ).set_sparam());
    add_opt(common_arg(
        {"--temp"}, "N",
        string_format("temperature (default: %.1f)", (double)params.sampling.temp),
@@ -887,6 +915,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        {"--repeat-last-n"}, "N",
        string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
        [](common_params & params, int value) {
+            if (value < -1) {
+                throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value));
+            }
            params.sampling.penalty_last_n = value;
            params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
        }
@@ -941,6 +972,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        {"--dry-penalty-last-n"}, "N",
        string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
        [](common_params & params, int value) {
+            if (value < -1) {
+                throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value));
+            }
            params.sampling.dry_penalty_last_n = value;
        }
    ).set_sparam());
@@ -1174,18 +1208,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
    add_opt(common_arg(
        {"-ctk", "--cache-type-k"}, "TYPE",
-        string_format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
+        string_format(
+            "KV cache data type for K\n"
+            "allowed values: %s\n"
+            "(default: %s)",
+            get_all_kv_cache_types().c_str(),
+            ggml_type_name(params.cache_type_k)
+        ),
        [](common_params & params, const std::string & value) {
-            // TODO: get the type right here
-            params.cache_type_k = value;
+            params.cache_type_k = kv_cache_type_from_str(value);
        }
    ).set_env("LLAMA_ARG_CACHE_TYPE_K"));
    add_opt(common_arg(
        {"-ctv", "--cache-type-v"}, "TYPE",
-        string_format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
+        string_format(
+            "KV cache data type for V\n"
+            "allowed values: %s\n"
+            "(default: %s)",
+            get_all_kv_cache_types().c_str(),
+            ggml_type_name(params.cache_type_v)
+        ),
        [](common_params & params, const std::string & value) {
-            // TODO: get the type right here
-            params.cache_type_v = value;
+            params.cache_type_v = kv_cache_type_from_str(value);
        }
    ).set_env("LLAMA_ARG_CACHE_TYPE_V"));
    add_opt(common_arg(
@@ -1543,6 +1587,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.hf_file = value;
        }
    ).set_env("LLAMA_ARG_HF_FILE"));
+    add_opt(common_arg(
+        {"-hfrv", "--hf-repo-v"}, "REPO",
+        "Hugging Face model repository for the vocoder model (default: unused)",
+        [](common_params & params, const std::string & value) {
+            params.vocoder.hf_repo = value;
+        }
+    ).set_env("LLAMA_ARG_HF_REPO_V"));
+    add_opt(common_arg(
+        {"-hffv", "--hf-file-v"}, "FILE",
+        "Hugging Face model file for the vocoder model (default: unused)",
+        [](common_params & params, const std::string & value) {
+            params.vocoder.hf_file = value;
+        }
+    ).set_env("LLAMA_ARG_HF_FILE_V"));
    add_opt(common_arg(
        {"-hft", "--hf-token"}, "TOKEN",
        "Hugging Face access token (default: value from HF_TOKEN environment variable)",
@@ -1711,6 +1769,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.public_path = value;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH"));
+    add_opt(common_arg(
+        {"--no-webui"},
+        string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
+        [](common_params & params) {
+            params.webui = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI"));
    add_opt(common_arg(
        {"--embedding", "--embeddings"},
        string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
@@ -2076,35 +2141,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params, int value) {
            params.speculative.n_max = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MAX"));
    add_opt(common_arg(
        {"--draft-min", "--draft-n-min"}, "N",
        string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min),
        [](common_params & params, int value) {
            params.speculative.n_min = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MIN"));
    add_opt(common_arg(
        {"--draft-p-split"}, "P",
        string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split),
        [](common_params & params, const std::string & value) {
            params.speculative.p_split = std::stof(value);
        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT"));
    add_opt(common_arg(
        {"--draft-p-min"}, "P",
        string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min),
        [](common_params & params, const std::string & value) {
            params.speculative.p_min = std::stof(value);
        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_P_MIN"));
    add_opt(common_arg(
        {"-cd", "--ctx-size-draft"}, "N",
        string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),
        [](common_params & params, int value) {
            params.speculative.n_ctx = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT"));
    add_opt(common_arg(
        {"-devd", "--device-draft"}, "<dev1,dev2,..>",
        "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
@@ -2124,14 +2189,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n");
            }
        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT"));
    add_opt(common_arg(
        {"-md", "--model-draft"}, "FNAME",
        "draft model for speculative decoding (default: unused)",
        [](common_params & params, const std::string & value) {
            params.speculative.model = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
+
+    add_opt(common_arg(
+        {"-mv", "--model-vocoder"}, "FNAME",
+        "vocoder model for audio generation (default: unused)",
+        [](common_params & params, const std::string & value) {
+            params.vocoder.model = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));

    return ctx_arg;
 }
@@ -940,6 +940,25 @@ struct common_init_result common_init_from_params(common_params & params) {
        params.sampling.ignore_eos = false;
    }

+    if (params.sampling.ignore_eos) {
+        for (llama_token i = 0; i < llama_n_vocab(model); i++) {
+            if (llama_token_is_eog(model, i)) {
+                LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
+                params.sampling.logit_bias.push_back({i, -INFINITY});
+            }
+        }
+    }
+
+    if (params.sampling.penalty_last_n == -1) {
+        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+        params.sampling.penalty_last_n = llama_n_ctx(lctx);
+    }
+
+    if (params.sampling.dry_penalty_last_n == -1) {
+        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
+    }
+
    if (params.warmup) {
        LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);

@@ -1015,38 +1034,6 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
    return mparams;
 }

-static ggml_type kv_cache_type_from_str(const std::string & s) {
-    if (s == "f32") {
-        return GGML_TYPE_F32;
-    }
-    if (s == "f16") {
-        return GGML_TYPE_F16;
-    }
-    if (s == "bf16") {
-        return GGML_TYPE_BF16;
-    }
-    if (s == "q8_0") {
-        return GGML_TYPE_Q8_0;
-    }
-    if (s == "q4_0") {
-        return GGML_TYPE_Q4_0;
-    }
-    if (s == "q4_1") {
-        return GGML_TYPE_Q4_1;
-    }
-    if (s == "iq4_nl") {
-        return GGML_TYPE_IQ4_NL;
-    }
-    if (s == "q5_0") {
-        return GGML_TYPE_Q5_0;
-    }
-    if (s == "q5_1") {
-        return GGML_TYPE_Q5_1;
-    }
-
-    throw std::runtime_error("Unsupported cache type: " + s);
-}
-
 struct llama_context_params common_context_params_to_llama(const common_params & params) {
    auto cparams = llama_context_default_params();

@@ -1081,8 +1068,8 @@ struct llama_context_params common_context_params_to_llama(const common_params &
        cparams.pooling_type  = LLAMA_POOLING_TYPE_RANK;
    }

-    cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
-    cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
+    cparams.type_k = params.cache_type_k;
+    cparams.type_v = params.cache_type_v;

    return cparams;
 }
@@ -1108,13 +1095,7 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 #define CURL_MAX_RETRY 3
 #define CURL_RETRY_DELAY_SECONDS 2

-
-static bool starts_with(const std::string & str, const std::string & prefix) {
-    // While we wait for C++20's std::string::starts_with...
-    return str.rfind(prefix, 0) == 0;
-}
-
-static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
+static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) {
    int remaining_attempts = max_attempts;

    while (remaining_attempts > 0) {
@@ -1138,7 +1119,6 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_
 }

 static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
-
    // Initialize libcurl
    std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
    if (!curl) {
@@ -1211,11 +1191,13 @@ static bool common_download_file(const std::string & url, const std::string & pa
        std::string etag;
        std::string last_modified;
    };
+
    common_load_model_from_url_headers headers;
+
    {
        typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
        auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
-            common_load_model_from_url_headers *headers = (common_load_model_from_url_headers *) userdata;
+            common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;

            static std::regex header_regex("([^:]+): (.*)\r\n");
            static std::regex etag_regex("ETag", std::regex_constants::icase);
@@ -1799,7 +1781,9 @@ void common_embd_normalize(const float * inp, float * out, int n, int embd_norm)
            break;
        case 0: // max absolute
            for (int i = 0; i < n; i++) {
-                if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
+                if (sum < std::abs(inp[i])) {
+                    sum = std::abs(inp[i]);
+                }
            }
            sum /= 32760.0; // make an int16 range
            break;
@@ -37,9 +37,9 @@ using llama_tokens = std::vector<llama_token>;

 // build info
 extern int LLAMA_BUILD_NUMBER;
-extern char const * LLAMA_COMMIT;
-extern char const * LLAMA_COMPILER;
-extern char const * LLAMA_BUILD_TARGET;
+extern const char * LLAMA_COMMIT;
+extern const char * LLAMA_COMPILER;
+extern const char * LLAMA_BUILD_TARGET;

 struct common_control_vector_load_info;

@@ -80,6 +80,7 @@ enum llama_example {
    LLAMA_EXAMPLE_LLAVA,
    LLAMA_EXAMPLE_LOOKUP,
    LLAMA_EXAMPLE_PARALLEL,
+    LLAMA_EXAMPLE_TTS,

    LLAMA_EXAMPLE_COUNT,
 };
@@ -95,6 +96,7 @@ enum common_sampler_type {
    COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
    COMMON_SAMPLER_TYPE_XTC         = 8,
    COMMON_SAMPLER_TYPE_INFILL      = 9,
+    COMMON_SAMPLER_TYPE_PENALTIES   = 10,
 };

 // dimensionality reduction methods, used by cvector-generator
@@ -130,7 +132,6 @@ struct common_params_sampling {
    int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
    float   mirostat_tau       = 5.00f; // target entropy
    float   mirostat_eta       = 0.10f; // learning rate
-    bool    penalize_nl        = false; // consider newlines as a repeatable token
    bool    ignore_eos         = false;
    bool    no_perf            = false; // disable performance metrics
    bool    timing_per_token   = false;
@@ -139,6 +140,7 @@ struct common_params_sampling {


    std::vector<enum common_sampler_type> samplers = {
+        COMMON_SAMPLER_TYPE_PENALTIES,
        COMMON_SAMPLER_TYPE_DRY,
        COMMON_SAMPLER_TYPE_TOP_K,
        COMMON_SAMPLER_TYPE_TYPICAL_P,
@@ -158,6 +160,7 @@ struct common_params_sampling {

 struct common_params_speculative {
    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+
    int32_t n_ctx        =     0; // draft context size
    int32_t n_max        =    16; // maximum number of tokens to draft during speculative decoding
    int32_t n_min        =     5; // minimum number of draft tokens to use for speculative decoding
@@ -171,6 +174,14 @@ struct common_params_speculative {
    std::string model = ""; // draft model for speculative decoding                          // NOLINT
 };

+struct common_params_vocoder {
+    std::string hf_repo = ""; // HF repo                                                     // NOLINT
+    std::string hf_file = ""; // HF file                                                     // NOLINT
+
+    std::string model     = ""; // model path                                                // NOLINT
+    std::string model_url = ""; // model url to download                                     // NOLINT
+};
+
 struct common_params {
    int32_t n_predict             =    -1; // new tokens to predict
    int32_t n_ctx                 =  4096; // context size
@@ -193,11 +204,13 @@ struct common_params {
    float   defrag_thold          =  0.1f; // KV cache defragmentation threshold

    // offload params
-    std::vector<ggml_backend_dev_t> devices;         // devices to use for offloading
-    int32_t n_gpu_layers                    =    -1; // number of layers to store in VRAM (-1 - use default)
-    int32_t main_gpu                        =     0; // the GPU that is used for scratch and small tensors
-    float   tensor_split[128]               =   {0}; // how split tensors should be distributed across GPUs
-    enum llama_split_mode        split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
+    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+
+    int32_t n_gpu_layers      = -1;  // number of layers to store in VRAM (-1 - use default)
+    int32_t main_gpu          = 0;   // the GPU that is used for scratch and small tensors
+    float   tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
+
+    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs

    struct cpu_params cpuparams;
    struct cpu_params cpuparams_batch;
@@ -211,11 +224,12 @@ struct common_params {
    enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
    enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings

-    struct common_params_sampling sampling;
+    struct common_params_sampling    sampling;
    struct common_params_speculative speculative;
+    struct common_params_vocoder     vocoder;

    std::string model                = ""; // model path                                                    // NOLINT
-    std::string model_alias          = "unknown"; // model alias                                            // NOLINT
+    std::string model_alias          = ""; // model alias                                                   // NOLINT
    std::string model_url            = ""; // model url to download                                         // NOLINT
    std::string hf_token             = ""; // HF token                                                      // NOLINT
    std::string hf_repo              = ""; // HF repo                                                       // NOLINT
@@ -286,8 +300,8 @@ struct common_params {
    bool warmup            = true;  // warmup run
    bool check_tensors     = false; // validate tensor data

-    std::string cache_type_k = "f16"; // KV cache data type for the K
-    std::string cache_type_v = "f16"; // KV cache data type for the V
+    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
+    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V

    // multimodal models (see examples/llava)
    std::string mmproj = "";        // path to multimodal projector                                         // NOLINT
@@ -437,6 +451,11 @@ std::vector<std::string> string_split<std::string>(const std::string & input, ch
    return parts;
 }

+static bool string_starts_with(const std::string & str,
+                               const std::string & prefix) {  // While we wait for C++20's std::string::starts_with...
+    return str.rfind(prefix, 0) == 0;
+}
+
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
 void string_process_escapes(std::string & input);

@@ -588,7 +607,8 @@ void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_si
 // Embedding utils
 //

-void common_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
+// TODO: repace embd_norm with an enum
+void common_embd_normalize(const float * inp, float * out, int n, int embd_norm);

 float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);

@@ -161,32 +161,20 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                params.logit_bias.size(),
                params.logit_bias.data()));

-    llama_sampler_chain_add(result->chain,
-            llama_sampler_init_penalties(
-                llama_n_vocab  (model),
-                llama_token_eos(model),
-                llama_token_nl (model),
-                params.penalty_last_n,
-                params.penalty_repeat,
-                params.penalty_freq,
-                params.penalty_present,
-                params.penalize_nl,
-                params.ignore_eos));
-
    if (params.mirostat == 0) {
        for (const auto & cnstr : params.samplers) {
            switch (cnstr) {
-                    case COMMON_SAMPLER_TYPE_DRY:
+                case COMMON_SAMPLER_TYPE_DRY:
                    {
-                        std::vector<const char*> c_breakers;
+                        std::vector<const char *> c_breakers;
                        c_breakers.reserve(params.dry_sequence_breakers.size());
-                        for (const auto& str : params.dry_sequence_breakers) {
+                        for (const auto & str : params.dry_sequence_breakers) {
                            c_breakers.push_back(str.c_str());
                        }

                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                    }
-                        break;
+                    break;
                case COMMON_SAMPLER_TYPE_TOP_K:
                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_k    (params.top_k));
                    break;
@@ -208,6 +196,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                case COMMON_SAMPLER_TYPE_INFILL:
                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (model));
                    break;
+                case COMMON_SAMPLER_TYPE_PENALTIES:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    break;
                default:
                    GGML_ASSERT(false && "unknown sampler type");
            }
@@ -415,6 +406,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
        case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
        case COMMON_SAMPLER_TYPE_XTC:         return 'x';
        case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
+        case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
        default : return '?';
    }
 }
@@ -429,6 +421,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
        case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
        case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
        case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
+        case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
        default : return "";
    }
 }
@@ -443,6 +436,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
        { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
        { "xtc",         COMMON_SAMPLER_TYPE_XTC },
        { "infill",      COMMON_SAMPLER_TYPE_INFILL },
+        { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
    };

    // since samplers names are written multiple ways
@@ -489,6 +483,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
    };

    std::vector<common_sampler_type> samplers;
@@ -62,6 +62,10 @@ struct common_speculative * common_speculative_init(
 }

 void common_speculative_free(struct common_speculative * spec) {
+    if (spec == nullptr) {
+        return;
+    }
+
    common_sampler_free(spec->smpl);

    llama_batch_free(spec->batch);
@@ -221,17 +221,17 @@ class Model:
            self.gguf_writer.add_context_length(n_ctx)
            logger.info(f"gguf: context length = {n_ctx}")

-        n_embd = self.find_hparam(["hidden_size", "n_embd"])
-        self.gguf_writer.add_embedding_length(n_embd)
-        logger.info(f"gguf: embedding length = {n_embd}")
+        if (n_embd := self.find_hparam(["hidden_size", "n_embd"], optional=True)) is not None:
+            self.gguf_writer.add_embedding_length(n_embd)
+            logger.info(f"gguf: embedding length = {n_embd}")

        if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
            self.gguf_writer.add_feed_forward_length(n_ff)
            logger.info(f"gguf: feed forward length = {n_ff}")

-        n_head = self.find_hparam(["num_attention_heads", "n_head"])
-        self.gguf_writer.add_head_count(n_head)
-        logger.info(f"gguf: head count = {n_head}")
+        if (n_head := self.find_hparam(["num_attention_heads", "n_head"], optional=True)) is not None:
+            self.gguf_writer.add_head_count(n_head)
+            logger.info(f"gguf: head count = {n_head}")

        if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
            self.gguf_writer.add_head_count_kv(n_head_kv)
@@ -296,7 +296,9 @@ class Model:
                    break

            for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
-                data = data_torch.squeeze().numpy()
+                # TODO: why do we squeeze here?
+                # data = data_torch.squeeze().numpy()
+                data = data_torch.numpy()

                # if data ends up empty, it means data_torch was a scalar tensor -> restore
                if len(data.shape) == 0:
@@ -324,6 +326,8 @@ class Model:
                            gguf.MODEL_TENSOR.TIME_MIX_W2,
                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
+                            gguf.MODEL_TENSOR.POSNET_NORM1,
+                            gguf.MODEL_TENSOR.POSNET_NORM2,
                        )
                    )
                    or not new_name.endswith(".weight")
@@ -658,6 +662,15 @@ class Model:
        if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450":
            # ref: https://huggingface.co/facebook/chameleon-7b
            res = "chameleon"
+        if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
+            # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
+            res = "minerva-7b"
+        if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65":
+            # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
+            res = "roberta-bpe"
+        if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
+            # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
+            res = "gigachat"

        if res is None:
            logger.warning("\n")
@@ -680,6 +693,9 @@ class Model:
        return res
        # Marker: End get_vocab_base_pre

+    def _set_vocab_none(self) -> None:
+        self.gguf_writer.add_tokenizer_model("none")
+
    def _set_vocab_gpt2(self) -> None:
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
@@ -1986,6 +2002,75 @@ class Qwen2Model(Model):
        except FileNotFoundError:
            self._set_vocab_gpt2()

+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "yarn":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+                self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
+
+
+@Model.register("Qwen2VLForConditionalGeneration")
+class Qwen2VLModel(Model):
+    model_arch = gguf.MODEL_ARCH.QWEN2VL
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        mrope_section = self.hparams["rope_scaling"]["mrope_section"]
+        mrope_section += [0] * max(0, 4 - len(mrope_section))
+        self.gguf_writer.add_rope_dimension_sections(mrope_section)
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
+        for name, data in super().get_tensors():
+            if name.startswith("visual."):
+                continue
+            yield name, data
+
+
+@Model.register("WavTokenizerDec")
+class WavTokenizerDecModel(Model):
+    model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        if \
+                name.endswith("codebook.cluster_size") or \
+                name.endswith("codebook.embed_avg") or \
+                name.endswith("codebook.inited"):
+            logger.debug(f"Skipping {name!r}")
+            return []
+
+        logger.info(f"{self.map_tensor_name(name)} -> {data_torch.shape}")
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def set_vocab(self):
+        self._set_vocab_none()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size         (self.hparams["vocab_size"])
+        self.gguf_writer.add_features_length    (self.hparams["n_embd_features"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"])
+        self.gguf_writer.add_group_norm_eps     (self.hparams["group_norm_epsilon"])
+        self.gguf_writer.add_group_norm_groups  (self.hparams["group_norm_groups"])
+
+        self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"])
+        self.gguf_writer.add_posnet_block_count     (self.hparams["posnet"]["n_layer"])
+
+        self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"])
+        self.gguf_writer.add_convnext_block_count     (self.hparams["convnext"]["n_layer"])
+
+        self.gguf_writer.add_causal_attention(False)
+

@Model.register("Qwen2MoeForCausalLM")
 class Qwen2MoeModel(Model):
@@ -2530,7 +2615,7 @@ class InternLM2Model(Model):
            return [(self.map_tensor_name(name), data_torch)]


-@Model.register("BertModel", "CamembertModel")
+@Model.register("BertModel", "CamembertModel", "RobertaModel")
 class BertModel(Model):
    model_arch = gguf.MODEL_ARCH.BERT

@@ -2571,7 +2656,8 @@ class BertModel(Model):

        # we need this to validate the size of the token_type embeddings
        # though currently we are passing all zeros to the token_type embeddings
-        self.gguf_writer.add_token_type_count(2)  # "Sequence A" or "Sequence B"
+        # "Sequence A" or "Sequence B"
+        self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))

        # convert to phantom space vocab
        def phantom(tok):
@@ -3389,6 +3475,97 @@ class ArcticModel(Model):
                raise ValueError(f"Unprocessed experts: {experts}")


+@Model.register("DeepseekForCausalLM")
+class DeepseekModel(Model):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if "head_dim" in hparams:
+            rope_dim = hparams["head_dim"]
+        else:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_weights_scale(1.0)
+        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
+        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = DeepseekModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head)
+
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
@Model.register("DeepseekV2ForCausalLM")
 class DeepseekV2Model(Model):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
@@ -102,6 +102,9 @@ models = [
    {"name": "exaone",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
    {"name": "phi-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
    {"name": "chameleon",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
+    {"name": "minerva-7b",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
+    {"name": "roberta-bpe",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
+    {"name": "gigachat",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
 ]


@@ -55,7 +55,14 @@ cmake --build build --config Release
    cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
    cmake --build build-arm64-windows-llvm-release
    ```
-    Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_4_8 CPU kernels.
+    Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_N_M CPU kernels.
+
+    For building with ninja generator and clang compiler as default:
+      -set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64
+      ```bash
+      cmake --preset x64-windows-llvm-release
+      cmake --build build-x64-windows-llvm-release
+      ```

 ## BLAS Build

@@ -20,7 +20,12 @@ else()
    add_subdirectory(batched)
    add_subdirectory(embedding)
    add_subdirectory(eval-callback)
-    add_subdirectory(gbnf-validator)
+
+    if (NOT WIN32)
+        # disabled on Windows because it uses internal functions not exported with LLAMA_API
+        add_subdirectory(gbnf-validator)
+    endif()
+
    add_subdirectory(gguf-hash)
    add_subdirectory(gguf-split)
    add_subdirectory(gguf)
@@ -46,12 +51,17 @@ else()
    add_subdirectory(speculative)
    add_subdirectory(speculative-simple)
    add_subdirectory(tokenize)
+    add_subdirectory(tts)
+    add_subdirectory(gen-docs)
    if (NOT GGML_BACKEND_DL)
        # these examples use the backends directly and cannot be built with dynamic loading
        add_subdirectory(convert-llama2c-to-ggml)
        add_subdirectory(cvector-generator)
        add_subdirectory(export-lora)
-        add_subdirectory(quantize-stats)
+        if (NOT WIN32)
+            # disabled on Windows because it uses internal functions not exported with LLAMA_API
+            add_subdirectory(quantize-stats)
+        endif()
        add_subdirectory(llava)
        if (GGML_RPC)
            add_subdirectory(rpc)
@@ -65,6 +65,7 @@ int main(int argc, char ** argv) {
    llama_context * ctx = llama_new_context_with_model(model, ctx_params);

    auto sparams = llama_sampler_chain_default_params();
+    sparams.no_perf = false;

    llama_sampler * smpl = llama_sampler_chain_init(sparams);

@@ -287,7 +287,7 @@ struct split_strategy {
    }

    void print_info() {
-        printf("n_split: %ld\n", ctx_outs.size());
+        printf("n_split: %zu\n", ctx_outs.size());
        int i_split = 0;
        for (auto & ctx_out : ctx_outs) {
            // re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
@@ -297,7 +297,7 @@ struct split_strategy {
                total_size += ggml_nbytes(t);
            }
            total_size = total_size / 1000 / 1000; // convert to megabytes
-            printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
+            printf("split %05d: n_tensors = %d, total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
            i_split++;
        }
    }
@@ -75,7 +75,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
        }

        std::vector<float> emb_norm(emb_unorm.size());
-        common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
+        common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd, 2);
        result.push_back(emb_norm);

 #ifdef GRIT_DEBUG
@@ -1521,7 +1521,7 @@ int main(int argc, char ** argv) {
    for (const auto & inst : params_instances) {
        params_idx++;
        if (params.progress) {
-            fprintf(stderr, "llama-bench: benchmark %d/%ld: starting\n", params_idx, params_count);
+            fprintf(stderr, "llama-bench: benchmark %d/%zu: starting\n", params_idx, params_count);
        }
        // keep the same model between tests when possible
        if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
@@ -1573,14 +1573,14 @@ int main(int argc, char ** argv) {
        // warmup run
        if (t.n_prompt > 0) {
            if (params.progress) {
-                fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count);
+                fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count);
            }
            //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
            test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
        }
        if (t.n_gen > 0) {
            if (params.progress) {
-                fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count);
+                fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count);
            }
            test_gen(ctx, 1, t.n_threads);
        }
@@ -1592,14 +1592,14 @@ int main(int argc, char ** argv) {

            if (t.n_prompt > 0) {
                if (params.progress) {
-                    fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count,
+                    fprintf(stderr, "llama-bench: benchmark %d/%zu: prompt run %d/%d\n", params_idx, params_count,
                            i + 1, params.reps);
                }
                test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
            }
            if (t.n_gen > 0) {
                if (params.progress) {
-                    fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count,
+                    fprintf(stderr, "llama-bench: benchmark %d/%zu: generation run %d/%d\n", params_idx, params_count,
                            i + 1, params.reps);
                }
                test_gen(ctx, t.n_gen, t.n_threads);
@@ -19,6 +19,7 @@ android {
        externalNativeBuild {
            cmake {
                arguments += "-DLLAMA_BUILD_COMMON=ON"
+                arguments += "-DGGML_LLAMAFILE=OFF"
                arguments += "-DCMAKE_BUILD_TYPE=Release"
                cppFlags += listOf()
                arguments += listOf()
@@ -210,20 +210,20 @@ actor LlamaContext {

            llama_kv_cache_clear(context)

-            let t_pp_start = ggml_time_us()
+            let t_pp_start = DispatchTime.now().uptimeNanoseconds / 1000;

            if llama_decode(context, batch) != 0 {
                print("llama_decode() failed during prompt")
            }
            llama_synchronize(context)

-            let t_pp_end = ggml_time_us()
+            let t_pp_end = DispatchTime.now().uptimeNanoseconds / 1000;

            // bench text generation

            llama_kv_cache_clear(context)

-            let t_tg_start = ggml_time_us()
+            let t_tg_start = DispatchTime.now().uptimeNanoseconds / 1000;

            for i in 0..<tg {
                llama_batch_clear(&batch)
@@ -238,7 +238,7 @@ actor LlamaContext {
                llama_synchronize(context)
            }

-            let t_tg_end = ggml_time_us()
+            let t_tg_end = DispatchTime.now().uptimeNanoseconds / 1000;

            llama_kv_cache_clear(context)

@@ -7,6 +7,7 @@
 	objects = {

 /* Begin PBXBuildFile section */
+		1809696D2D05A39F00400EE8 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = 1809696C2D05A39F00400EE8 /* llama */; };
 		549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; };
 		79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */; };
 		7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; };
@@ -17,7 +18,6 @@
 		8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
 		8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
 		8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
-		DF810E132B4A5BA200301144 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = DF810E122B4A5BA200301144 /* llama */; };
 		F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
 /* End PBXBuildFile section */

@@ -42,7 +42,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
-				DF810E132B4A5BA200301144 /* llama in Frameworks */,
+				1809696D2D05A39F00400EE8 /* llama in Frameworks */,
 				549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
 				8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */,
 			);
@@ -151,7 +151,7 @@
 			);
 			name = llama.swiftui;
 			packageProductDependencies = (
-				DF810E122B4A5BA200301144 /* llama */,
+				1809696C2D05A39F00400EE8 /* llama */,
 			);
 			productName = llama.swiftui;
 			productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */;
@@ -429,7 +429,7 @@
 /* End XCConfigurationList section */

 /* Begin XCSwiftPackageProductDependency section */
-		DF810E122B4A5BA200301144 /* llama */ = {
+		1809696C2D05A39F00400EE8 /* llama */ = {
 			isa = XCSwiftPackageProductDependency;
 			productName = llama;
 		};
@@ -43,3 +43,10 @@ set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-minicpmv-cli)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+set(TARGET llama-qwen2vl-cli)
+add_executable(${TARGET} qwen2vl-cli.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
@@ -102,7 +102,9 @@ static std::string format(const char * fmt, ...) {
 #define KEY_HAS_LLAVA_PROJ      "clip.has_llava_projector"
 #define KEY_HAS_MINICPMV_PROJ   "clip.has_minicpmv_projector"
 #define KEY_MINICPMV_VERSION    "clip.minicpmv_version"
+#define KEY_HAS_QWEN2VL_MERGER  "clip.has_qwen2vl_merger"
 #define KEY_USE_GELU            "clip.use_gelu"
+#define KEY_USE_SILU            "clip.use_silu"
 #define KEY_N_EMBD              "clip.%s.embedding_length"
 #define KEY_N_FF                "clip.%s.feed_forward_length"
 #define KEY_N_BLOCK             "clip.%s.block_count"
@@ -129,7 +131,8 @@ static std::string format(const char * fmt, ...) {
 #define TN_TOKEN_EMBD      "%s.token_embd.weight"
 #define TN_POS_EMBD        "%s.position_embd.weight"
 #define TN_CLASS_EMBD      "v.class_embd"
-#define TN_PATCH_EMBD      "v.patch_embd.weight"
+#define TN_PATCH_EMBD      "v.patch_embd.weight"  // not rename tensor with ".0" postfix for backwrad compat
+#define TN_PATCH_EMBD_1    "v.patch_embd.weight.1"
 #define TN_PATCH_BIAS      "v.patch_embd.bias"
 #define TN_ATTN_K          "%s.blk.%d.attn_k.%s"
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
@@ -163,6 +166,7 @@ enum projector_type {
    PROJECTOR_TYPE_LDP,
    PROJECTOR_TYPE_LDPV2,
    PROJECTOR_TYPE_RESAMPLER,
+    PROJECTOR_TYPE_MERGER,
    PROJECTOR_TYPE_UNKNOWN,
 };

@@ -171,6 +175,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
    { PROJECTOR_TYPE_LDP, "ldp" },
    { PROJECTOR_TYPE_LDPV2, "ldpv2"},
    { PROJECTOR_TYPE_RESAMPLER, "resampler"},
+    { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
 };


@@ -463,7 +468,8 @@ struct clip_vision_model {

    // embeddings
    struct ggml_tensor * class_embedding;
-    struct ggml_tensor * patch_embeddings;
+    struct ggml_tensor * patch_embeddings_0;
+    struct ggml_tensor * patch_embeddings_1;  // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
    struct ggml_tensor * patch_bias;
    struct ggml_tensor * position_embeddings;

@@ -553,6 +559,7 @@ struct clip_ctx {
    bool has_vision_encoder  = false;
    bool has_llava_projector = false;
    bool has_minicpmv_projector = false;
+    bool has_qwen2vl_merger = false;
    int minicpmv_version = 2;

    struct clip_vision_model vision_model;
@@ -561,6 +568,7 @@ struct clip_ctx {
    float image_mean[3];
    float image_std[3];
    bool use_gelu = false;
+    bool use_silu = false;
    int32_t ftype = 1;

    bool has_class_embedding = true;
@@ -606,14 +614,26 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
            image_size_height = imgs->data->ny;
        }
    }
+    else if (ctx->has_qwen2vl_merger) {
+        // use the image's native resolution when image is avaible
+        if (is_inf) {
+        // if (imgs->data->nx && imgs->data->ny) {
+            image_size_width  = imgs->data->nx;
+            image_size_height = imgs->data->ny;
+        }
+    }
    const int patch_size           = hparams.patch_size;
    const int num_patches          = ((image_size_width / patch_size) * (image_size_height / patch_size));
+    const int patches_w            = image_size_width / patch_size;
+    const int patches_h            = image_size_height / patch_size;
    const int num_positions        = num_patches + (ctx->has_class_embedding ? 1 : 0);
+    const int num_position_ids     = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions;
    const int hidden_size          = hparams.hidden_size;
    const int n_head               = hparams.n_head;
    const int d_head               = hidden_size / n_head;
    int n_layer                    = hparams.n_layer;
    const float eps                = hparams.eps;
+    int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};

    const int batch_size = imgs->size;

@@ -634,10 +654,30 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
    ggml_set_name(inp_raw, "inp_raw");
    ggml_set_input(inp_raw);

-    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);

-    inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
-    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
+    if (ctx->has_qwen2vl_merger) {
+        GGML_ASSERT(image_size_width % (patch_size * 2) == 0);
+        GGML_ASSERT(image_size_height % (patch_size * 2) == 0);
+
+        auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+        inp = ggml_add(ctx0, inp, inp_1);
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3));  // [w, h, c, b] -> [c, w, h, b]
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            hidden_size * 2, patches_w / 2, patches_h, batch_size);
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2));
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3));
+        inp = ggml_reshape_3d(
+            ctx0, inp,
+            hidden_size, patches_w * patches_h, batch_size);
+    }
+    else {
+        inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
+    }

    if (ctx->has_patch_bias) {
        // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
@@ -659,12 +699,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
        }
    }

-    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
+    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
    ggml_set_name(positions, "positions");
    ggml_set_input(positions);

-    embeddings =
-        ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
+    if (!ctx->has_qwen2vl_merger) { // qwen2vl use rope position embedding
+        embeddings =
+            ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
+    }

    if (ctx->has_minicpmv_projector) {
        int pos_w = image_size_width/patch_size;
@@ -688,7 +730,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
    }

    // loop over layers
-    if (ctx->has_minicpmv_projector) {
+    if (ctx->has_minicpmv_projector || ctx->has_qwen2vl_merger) {
+        // TODO: figure out why we doing thing in this way ???
        n_layer += 1;
    }
    for (int il = 0; il < n_layer - 1; il++) {
@@ -710,8 +753,13 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
            struct ggml_tensor * Q =
                ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);

-            Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
            Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
+            if (ctx->has_qwen2vl_merger) {
+                Q = ggml_rope_multi(
+                    ctx0, Q, positions, nullptr,
+                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            }
+            Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
            Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
            Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);

@@ -719,6 +767,11 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
                ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);

            K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
+            if (ctx->has_qwen2vl_merger) {
+                K = ggml_rope_multi(
+                    ctx0, K, positions, nullptr,
+                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            }
            K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
            K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);

@@ -758,6 +811,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32

        if (ctx->use_gelu) {
            cur = ggml_gelu_inplace(ctx0, cur);
+        } else if (ctx->use_silu) {
+            cur = ggml_silu_inplace(ctx0, cur);
        } else {
            cur = ggml_gelu_quick_inplace(ctx0, cur);
        }
@@ -769,6 +824,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
        cur = ggml_add(ctx0, embeddings, cur);

        embeddings = cur;
+
    }

    // post-layernorm
@@ -840,7 +896,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
                mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
                mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
                // stride = 1, padding = 1, bias is nullptr
-                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
+                block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);

                // layer norm
                // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
@@ -888,7 +944,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
            // block_2
            {
                // stride = 2
-                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
+                block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);

                // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
                // layer norm
@@ -949,7 +1005,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
            // mlp_2 ne [24, 24, 2048, 1]
            mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
            // weight ne = [3, 3, 2048, 1]
-            struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
+            struct ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
            peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
            peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
            mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
@@ -1030,6 +1086,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
            GGML_ASSERT(false);
        }
    }
+    else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
+
+        embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+        embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+
+        // GELU activation
+        embeddings = ggml_gelu(ctx0, embeddings);
+
+        // Second linear layer
+        embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings);
+        embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
+    }

    // build the graph
    ggml_build_forward_expand(gf, embeddings);
@@ -1206,6 +1275,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
        }

+        idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER);
+        if (idx != -1) {
+            new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx);
+        }
        // GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search

        GGML_ASSERT(new_clip->has_vision_encoder);
@@ -1214,6 +1287,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        idx = get_key_idx(ctx, KEY_USE_GELU);
        new_clip->use_gelu = gguf_get_val_bool(ctx, idx);

+        try {
+            idx = get_key_idx(ctx, KEY_USE_SILU);
+            new_clip->use_silu = gguf_get_val_bool(ctx, idx);
+        } catch (std::runtime_error & /*e*/) {
+            new_clip->use_silu = false;
+        }
+
        if (verbosity >= 1) {
            LOG_INF("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
            LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
@@ -1389,11 +1469,16 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        }

        try {
-            vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
+            vision_model.patch_embeddings_0    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
            vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
        } catch(const std::exception& /*e*/) {
            LOG_ERR("%s: failed to load vision model tensors\n", __func__);
        }
+        try {
+            vision_model.patch_embeddings_1    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD_1);
+        } catch(const std::exception& /*e*/) {
+            new_clip->has_qwen2vl_merger = false;
+        }

        // LLaVA projection
        if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) {
@@ -1481,6 +1566,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
            vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
        }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) {
+            vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
+            vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
+            vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
+            vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
+        }
        else {
            std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
            throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -1519,6 +1610,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
        clip_image_f32_batch batch;
        batch.size = 1;
+        batch.data = nullptr;
        ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false);
        ggml_gallocr_reserve(new_clip->compute_alloc, gf);
        size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
@@ -1532,6 +1624,10 @@ void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size
    ctx_clip->load_image_size = load_image_size;
 }

+struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip) {
+    return ctx_clip->load_image_size;
+}
+
 struct clip_image_size * clip_image_size_init() {
    struct clip_image_size * load_image_size = new struct clip_image_size();
    load_image_size->width = 448;
@@ -1984,6 +2080,23 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
        }
        return true;
    }
+    else if (ctx->has_qwen2vl_merger) {
+        clip_image_u8 * resized = clip_image_u8_init();
+        auto patch_size = clip_patch_size(ctx) * 2;
+        int nx = ceil((float)img->nx / patch_size) * patch_size;
+        int ny = ceil((float)img->ny / patch_size) * patch_size;
+        bicubic_resize(*img, *resized, nx, ny);
+
+        res_imgs->data = new clip_image_f32[1];
+        // clip_image_f32 * res = clip_image_f32_init();
+        normalize_image_u8_to_f32(resized, res_imgs->data, ctx->image_mean, ctx->image_std);
+        // res_imgs->data[0] = *res;
+        res_imgs->size = 1;
+
+        // clip_image_f32_free(res);
+        clip_image_u8_free(resized);
+        return true;
+    }

    bool pad_to_square = true;
    if (!ctx->has_vision_encoder) {
@@ -2173,6 +2286,13 @@ size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
    return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
 }

+size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) {
+    clip_image_f32 img;
+    img.nx = img_w;
+    img.ny = img_h;
+    return clip_n_patches_by_img(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float);
+}
+
 int32_t clip_image_size(const struct clip_ctx * ctx) {
    return ctx->vision_model.hparams.image_size;
 }
@@ -2194,6 +2314,13 @@ const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
 }

 int clip_n_patches(const struct clip_ctx * ctx) {
+    clip_image_f32 img;
+    img.nx = ctx->vision_model.hparams.image_size;
+    img.ny = ctx->vision_model.hparams.image_size;
+    return clip_n_patches_by_img(ctx, &img);
+}
+
+int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
    const auto & params = ctx->vision_model.hparams;

    int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
@@ -2207,6 +2334,11 @@ int clip_n_patches(const struct clip_ctx * ctx) {
        else if (ctx->minicpmv_version == 3) {
            n_patches = 64;
        }
+    } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        int patch_size = params.patch_size * 2;
+        int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
+        int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
+        n_patches = x_patch * y_patch;
    }

    return n_patches;
@@ -2335,7 +2467,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
    const int image_size = hparams.image_size;
    int image_size_width  = image_size;
    int image_size_height = image_size;
-    if (ctx->has_minicpmv_projector) {
+    if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) {
        image_size_width  = imgs->data[0].nx;
        image_size_height = imgs->data[0].ny;
    }
@@ -2355,7 +2487,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
        for (size_t i = 0; i < imgs->size; i++) {
            const int nx = imgs->data[i].nx;
            const int ny = imgs->data[i].ny;
-            if (!ctx->has_minicpmv_projector) {
+            if (!(ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger)) {
                GGML_ASSERT(nx == image_size && ny == image_size);
            }

@@ -2413,9 +2545,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
            auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));

            float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed));
-            for(int i=0;i<pos_w * pos_h;++i){
-                for(int j=0;j<embed_dim;++j){
-                    pos_embed_data[i*embed_dim+j]=pos_embed_t[i][j];
+            for(int i=0;i < pos_w * pos_h; ++i){
+                for(int j=0; j < embed_dim; ++j){
+                    pos_embed_data[i * embed_dim + j] = pos_embed_t[i][j];
                }
            }

@@ -2435,7 +2567,34 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
            }
        }

-        {
+        if (ctx->has_qwen2vl_merger) {
+            struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
+
+            const int pw = image_size_width / patch_size;
+            const int ph = image_size_height / patch_size;
+            int* positions_data = (int*)malloc(ggml_nbytes(positions));
+
+            int ptr = 0;
+            for (int y = 0; y < ph; y+=2)
+            {
+                for (int x = 0; x < pw; x+=2)
+                {
+                    for (int dy = 0; dy < 2; dy++) {
+                        for (int dx = 0; dx < 2; dx++) {
+                            positions_data[ptr]                 = y + dy;
+                            positions_data[num_patches + ptr]     = x + dx;
+                            positions_data[num_patches * 2 + ptr] = y + dy;
+                            positions_data[num_patches * 3 + ptr] = x + dx;
+                            ptr++;
+                        }
+                    }
+                }
+            }
+
+            ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
+            free(positions_data);
+        }
+        else {
            struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");

            int* positions_data = (int*)malloc(ggml_nbytes(positions));
@@ -2444,16 +2603,16 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
            }
            ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
            free(positions_data);
-        }

-        {
-            struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
-            int* patches_data = (int*)malloc(ggml_nbytes(patches));
-            for (int i = 0; i < num_patches; i++) {
-                patches_data[i] = i + 1;
+            {
+                struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
+                int* patches_data = (int*)malloc(ggml_nbytes(patches));
+                for (int i = 0; i < num_patches; i++) {
+                    patches_data[i] = i + 1;
+                }
+                ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
+                free(patches_data);
            }
-            ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
-            free(patches_data);
        }
    }

@@ -2626,6 +2785,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
            return 3584;
        }
    }
+    if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        return ctx->vision_model.mm_1_b->ne[0];
+    }

    std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
    throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -2637,3 +2799,21 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
    }
    return 0;
 }
+
+bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
+    return ctx->has_qwen2vl_merger;
+}
+
+
+bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) {
+    clip_image_f32 clip_img;
+    clip_img.buf.resize(h * w * 3);
+    for (int i = 0; i < h*w*3; i++)
+    {
+        clip_img.buf[i] = img[i];
+    }
+    clip_img.nx = w;
+    clip_img.ny = h;
+    clip_image_encode(ctx, n_threads, &clip_img, vec);
+    return true;
+}
@@ -45,6 +45,7 @@ CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity
 CLIP_API void clip_free(struct clip_ctx * ctx);

 CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
+CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w);

 CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx);
 CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx);
@@ -55,11 +56,13 @@ CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);

 CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);

-CLIP_API int clip_n_patches    (const struct clip_ctx * ctx);
-CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+CLIP_API int clip_n_patches        (const struct clip_ctx * ctx);
+CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img);
+CLIP_API int clip_n_mmproj_embd    (const struct clip_ctx * ctx);

 CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
 CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
+CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);

 CLIP_API struct clip_image_size * clip_image_size_init();
 CLIP_API struct clip_image_u8  * clip_image_u8_init ();
@@ -86,6 +89,9 @@ CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, cons
 CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);

 CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx);
+CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
+
+CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);

 #ifdef __cplusplus
 }
@@ -259,25 +259,33 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli

    const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);

-    if (clip_is_minicpmv(ctx_clip)) {
+    if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) {
        std::vector<float *> image_embd_v;
        image_embd_v.resize(img_res_v.size);
        struct clip_image_size * load_image_size = clip_image_size_init();
+
        for (size_t i = 0; i < img_res_v.size; i++) {
            const int64_t t_img_enc_step_start_us = ggml_time_us();
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip));
+            image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny));
            int patch_size=14;
            load_image_size->width = img_res_v.data[i].nx;
            load_image_size->height = img_res_v.data[i].ny;
            clip_add_load_image_size(ctx_clip, load_image_size);
+
            bool encoded = false;
-            int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
-            if (has_minicpmv_projector == 2) {
-                encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
-            }
-            else if (has_minicpmv_projector == 3) {
+            if (clip_is_qwen2vl(ctx_clip)) {
                encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
            }
+            else {
+                int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
+                if (has_minicpmv_projector == 2) {
+                    encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
+                }
+                else if (has_minicpmv_projector == 3) {
+                    encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
+                }
+            }
+
            if (!encoded) {
                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
                return false;
@@ -290,8 +298,11 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli

        int n_img_pos_out = 0;
        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            std::memcpy(image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), image_embd_v[i], clip_embd_nbytes(ctx_clip));
-            n_img_pos_out += clip_n_patches(ctx_clip);
+            std::memcpy(
+                image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
+                image_embd_v[i],
+                clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny));
+            n_img_pos_out += clip_n_patches_by_img(ctx_clip, &img_res_v.data[i]);
        }
        *n_img_pos = n_img_pos_out;
        for (size_t i = 0; i < image_embd_v.size(); i++) {
@@ -387,7 +398,13 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
    if (clip_is_minicpmv(ctx_clip)) {
        num_max_patches = 10;
    }
-    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
+    float * image_embd;
+    if (clip_is_qwen2vl(ctx_clip)) {
+        // qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
+        image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny));
+    } else {
+        image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
+    }
    if (!image_embd) {
        LOG_ERR("Unable to allocate memory for image embeddings\n");
        return false;
@@ -0,0 +1,165 @@
+import argparse
+from typing import Dict
+
+import torch
+import numpy as np
+from gguf import *
+from transformers import (
+    Qwen2VLForConditionalGeneration,
+    Qwen2VLProcessor,
+    AutoProcessor,
+    Qwen2VLConfig
+)
+
+
+VISION = "clip.vision"
+
+
+def k(raw_key: str, arch: str) -> str:
+    return raw_key.format(arch=arch)
+
+
+def to_gguf_name(name: str) -> str:
+    og = name
+    name = name.replace("text_model", "t").replace("vision_model", "v")
+    name = name.replace("blocks", "blk").replace("embeddings.", "")
+    name = name.replace("attn.", "attn_")
+    name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.")
+    # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln")
+    name = name.replace("norm1", "ln1").replace("norm2", "ln2")
+    name = name.replace("merger.mlp", 'mm')
+    print(f"[to_gguf_name] {og} --> {name}")
+    return name
+
+
+def find_vision_tensors(qwen2vl, dtype) -> Dict[str, np.ndarray]:
+    vision_model = qwen2vl.visual
+    tensor_map = {}
+    for name, ten in vision_model.state_dict().items():
+        ten = ten.numpy()
+        if 'qkv' in name:
+            if ten.ndim == 2: # weight
+                c3, _ = ten.shape
+            else:             # bias
+                c3 = ten.shape[0]
+            assert c3 % 3 == 0
+            c = c3 // 3
+            wq = ten[:c]
+            wk = ten[c: c * 2]
+            wv = ten[c * 2:]
+            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq
+            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk
+            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv
+        elif 'merger' in name:
+            if name.endswith("ln_q.weight"):
+                tensor_map['v.post_ln.weight'] = ten
+            elif name.endswith("ln_q.bias"):
+                tensor_map['v.post_ln.bias'] = ten
+            else:
+                # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias"
+                tensor_map[to_gguf_name(name)] = ten
+        elif 'patch_embed.proj.weight' in name:
+            # NOTE: split Conv3D into Conv2Ds
+            c1, c2, kt, kh, kw = ten.shape
+            assert kt == 2, "Current implmentation only support temporal_patch_size of 2"
+            tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...]
+            tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...]
+        else:
+            tensor_map[to_gguf_name(f"vision_model.{name}")] = ten
+
+    for new_name, ten in tensor_map.items():
+        if ten.ndim <= 1 or new_name.endswith("_norm.weight"):
+            tensor_map[new_name] = ten.astype(np.float32)
+        else:
+            tensor_map[new_name] = ten.astype(dtype)
+    tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32)  # dummy tensor, just here as a placeholder
+    return tensor_map
+
+
+def main(args):
+    if args.data_type == 'fp32':
+        dtype = torch.float32
+        np_dtype = np.float32
+        ftype = 0
+    elif args.data_type == 'fp16':
+        dtype = torch.float32
+        np_dtype = np.float16
+        ftype = 1
+    else:
+        raise ValueError()
+
+    local_model = False
+    model_path = ""
+    model_name = args.model_name
+    print("model_name: ", model_name)
+    qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained(
+        model_name, torch_dtype=dtype, device_map="cpu"
+    )
+    cfg: Qwen2VLConfig = qwen2vl.config  # type: ignore[reportAssignmentType]
+    vcfg = cfg.vision_config
+
+    if os.path.isdir(model_name):
+        local_model = True
+        if model_name.endswith(os.sep):
+            model_name = model_name[:-1]
+        model_path = model_name
+        model_name = os.path.basename(model_name)
+    fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf"
+
+    fout = GGUFWriter(path=fname_out, arch="clip")
+    fout.add_description("image encoder for Qwen2VL")
+
+    fout.add_file_type(ftype)
+    fout.add_bool("clip.has_text_encoder", False)
+    fout.add_bool("clip.has_vision_encoder", True)
+    fout.add_bool("clip.has_qwen2vl_merger", True)
+    fout.add_string("clip.projector_type", "qwen2vl_merger")
+
+    print(cfg.vision_config)
+    if 'silu' in cfg.vision_config.hidden_act.lower():
+        fout.add_bool("clip.use_silu", True)
+        fout.add_bool("clip.use_gelu", False)
+    elif 'gelu' in cfg.vision_config.hidden_act.lower():
+        fout.add_bool("clip.use_silu", False)
+        fout.add_bool("clip.use_gelu", 'quick' not in cfg.vision_config.hidden_act.lower())
+    else:
+        raise ValueError()
+
+    tensor_map = find_vision_tensors(qwen2vl, np_dtype)
+    for name, data in tensor_map.items():
+        fout.add_tensor(name, data)
+
+    fout.add_uint32("clip.vision.patch_size", vcfg.patch_size)
+    fout.add_uint32("clip.vision.image_size", 14 * 40)  # some reasonable size that is divable by (14*2)
+    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim)
+    fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size)
+    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads)
+    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
+    fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth)
+    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 0)  # not sure what this does, put 0 here as a placeholder
+    fout.add_name(model_name)
+    """
+    HACK: Since vision rope related parameter aren't stored in the `Qwen2VLConfig,
+            it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`.
+    """
+
+    if local_model:
+        processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path)
+    else:
+        processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name)
+    fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue]
+    fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue]
+
+    fout.write_header_to_file()
+    fout.write_kv_data_to_file()
+    fout.write_tensors_to_file()
+    fout.close()
+    print("save model as: ", fname_out)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct")
+    parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32")
+    args = parser.parse_args()
+    main(args)
@@ -0,0 +1,581 @@
+#include "arg.h"
+#include "base64.hpp"
+#include "log.h"
+#include "common.h"
+#include "sampling.h"
+#include "clip.h"
+#include "llava.h"
+#include "llama.h"
+#include "ggml.h"
+
+#ifdef GGML_USE_CUDA
+#include "ggml-cuda.h"
+#endif
+#ifdef NDEBUG
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+#endif
+
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <vector>
+#include <algorithm>
+#include <iostream>
+#include <fstream>
+
+
+static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
+                                     int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
+    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
+    const int patch_size = 14 * 2;
+    const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
+    const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
+    auto img_tokens = image_embed->n_image_pos;
+    // llama_pos mrope_pos[img_tokens * 4];
+    std::vector<llama_pos> mrope_pos;
+    mrope_pos.resize(img_tokens * 4);
+
+    for (int y = 0; y < ph; y++)
+    {
+        for (int x = 0; x < pw; x++)
+        {
+            int i = y * pw + x;
+            mrope_pos[i] = *st_pos_id;
+            mrope_pos[i + img_tokens] = *st_pos_id + y;
+            mrope_pos[i + img_tokens * 2] = *st_pos_id + x;
+            mrope_pos[i + img_tokens * 3] = 0;
+        }
+    }
+    *st_pos_id += std::max(pw, ph);
+
+    int processed = 0;
+    std::vector<llama_pos> batch_mrope_pos;
+    batch_mrope_pos.resize(img_tokens * 4);
+
+    for (int i = 0; i < img_tokens; i += n_batch) {
+        int n_eval = img_tokens - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+
+        // llama_pos batch_mrope_pos[n_eval * 4];
+        std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0);
+        memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos));
+
+        llama_batch batch = {
+            int32_t(n_eval),                // n_tokens
+            nullptr,                        // token
+            (image_embed->embed+i*n_embd),  // embed
+            batch_mrope_pos.data(),         // pos
+            nullptr,  // n_seq_id
+            nullptr,  // seq_id
+            nullptr,  // logits
+        };
+
+        if (llama_decode(ctx_llama, batch)) {
+            LOG_ERR("%s : failed to eval\n", __func__);
+            return false;
+        }
+        *n_past += n_eval;
+        processed += n_eval;
+    }
+    return true;
+}
+
+
+static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past, int * st_pos_id) {
+    int N = (int) tokens.size();
+    std::vector<llama_pos> pos;
+    for (int i = 0; i < N; i += n_batch) {
+        int n_eval = (int) tokens.size() - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        auto batch = llama_batch_get_one(&tokens[i], n_eval);
+        // TODO: add mrope pos ids somewhere else
+        pos.resize(batch.n_tokens * 4);
+        std::fill(pos.begin(), pos.end(), 0);
+        for (int j = 0; j < batch.n_tokens * 3; j ++) {
+            pos[j] = *st_pos_id + (j % batch.n_tokens);
+        }
+        batch.pos = pos.data();
+
+        if (llama_decode(ctx_llama, batch)) {
+            LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
+            return false;
+        }
+        *n_past += n_eval;
+        *st_pos_id += n_eval;
+    }
+    return true;
+}
+
+static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) {
+    std::vector<llama_token> tokens;
+    tokens.push_back(id);
+    return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id);
+}
+
+static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){
+    std::string              str2     = str;
+    std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
+    eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id);
+    return true;
+}
+
+static const char * sample(struct common_sampler * smpl,
+                           struct llama_context * ctx_llama,
+                           int * n_past, int * st_pos_id) {
+    const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
+    common_sampler_accept(smpl, id, true);
+    static std::string ret;
+    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
+        ret = "</s>";
+    } else {
+        ret = common_token_to_piece(ctx_llama, id);
+    }
+    eval_id(ctx_llama, id, n_past, st_pos_id);
+    return ret.c_str();
+}
+
+static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
+static const char* IMG_BASE64_TAG_END = "\">";
+
+static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
+    begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
+    end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
+}
+
+static bool prompt_contains_image(const std::string& prompt) {
+    size_t begin, end;
+    find_image_tag_in_prompt(prompt, begin, end);
+    return (begin != std::string::npos);
+}
+
+// replaces the base64 image tag in the prompt with `replacement`
+static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
+    size_t img_base64_str_start, img_base64_str_end;
+    find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
+    if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
+        LOG_ERR("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
+        return NULL;
+    }
+
+    auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
+    auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
+    auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
+
+    auto required_bytes = base64::required_encode_size(base64_str.size());
+    auto img_bytes = std::vector<unsigned char>(required_bytes);
+    base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
+
+    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
+    if (!embed) {
+        LOG_ERR("%s: could not load image from base64 string.\n", __func__);
+        return NULL;
+    }
+
+    return embed;
+}
+
+static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
+    size_t begin, end;
+    find_image_tag_in_prompt(prompt, begin, end);
+    if (begin == std::string::npos || end == std::string::npos) {
+        return prompt;
+    }
+    auto pre = prompt.substr(0, begin);
+    auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
+    return pre + replacement + post;
+}
+
+struct llava_context {
+    struct clip_ctx * ctx_clip = NULL;
+    struct llama_context * ctx_llama = NULL;
+    struct llama_model * model = NULL;
+};
+
+static void print_usage(int, char ** argv) {
+    LOG("\n example usage:\n");
+    LOG("\n     %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
+}
+
+static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
+
+    // load and preprocess the image
+    llava_image_embed * embed = NULL;
+    auto prompt = params->prompt;
+    if (prompt_contains_image(prompt)) {
+        if (!params->image.empty()) {
+            LOG_INF("using base64 encoded image instead of command line image path\n");
+        }
+        embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
+        if (!embed) {
+            LOG_ERR("%s: can't load image from prompt\n", __func__);
+            return NULL;
+        }
+        params->prompt = remove_image_from_prompt(prompt);
+    } else {
+        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
+        if (!embed) {
+            fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
+            return NULL;
+        }
+    }
+
+    return embed;
+}
+
+static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
+    int n_past = 0;
+    int cur_pos_id = 0;
+
+    const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
+
+    std::string system_prompt, user_prompt;
+    size_t image_pos = prompt.find("<|vision_start|>");
+    if (image_pos != std::string::npos) {
+        // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
+        system_prompt = prompt.substr(0, image_pos);
+        user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length());
+        LOG_INF("system_prompt: %s\n", system_prompt.c_str());
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+        LOG_INF("user_prompt: %s\n", user_prompt.c_str());
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+    } else {
+        // llava-1.5 native mode
+        system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>";
+        user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n";
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+    }
+
+    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true);
+    if (image_embed != nullptr) {
+        auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip);
+        qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size);
+    }
+    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false);
+
+    // generate the response
+
+    LOG("\n");
+
+    struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
+    if (!smpl) {
+        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
+        exit(1);
+    }
+
+    std::string response = "";
+    for (int i = 0; i < max_tgt_len; i++) {
+        const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id);
+        response += tmp;
+        if (strcmp(tmp, "</s>") == 0) break;
+        if (strstr(tmp, "###")) break; // Yi-VL behavior
+        LOG("%s", tmp);
+        if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
+        if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
+        if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
+
+        fflush(stdout);
+    }
+
+    common_sampler_free(smpl);
+    LOG("\n");
+}
+
+static struct llama_model * llava_init(common_params * params) {
+    llama_backend_init();
+    llama_numa_init(params->numa);
+
+    llama_model_params model_params = common_model_params_to_llama(*params);
+
+    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    if (model == NULL) {
+        LOG_ERR("%s: unable to load model\n" , __func__);
+        return NULL;
+    }
+    return model;
+}
+
+static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
+    const char * clip_path = params->mmproj.c_str();
+
+    auto prompt = params->prompt;
+    if (prompt.empty()) {
+        prompt = "describe the image in detail.";
+    }
+
+    auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
+
+
+    llama_context_params ctx_params = common_context_params_to_llama(*params);
+    ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
+
+    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx_llama == NULL) {
+        LOG_ERR("%s: failed to create the llama_context\n" , __func__);
+        return NULL;
+    }
+
+    auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
+
+    ctx_llava->ctx_llama = ctx_llama;
+    ctx_llava->ctx_clip = ctx_clip;
+    ctx_llava->model = model;
+    return ctx_llava;
+}
+
+static void llava_free(struct llava_context * ctx_llava) {
+    if (ctx_llava->ctx_clip) {
+        clip_free(ctx_llava->ctx_clip);
+        ctx_llava->ctx_clip = NULL;
+    }
+
+    llama_free(ctx_llava->ctx_llama);
+    llama_free_model(ctx_llava->model);
+    llama_backend_free();
+}
+
+#ifndef NDEBUG
+
+static void debug_test_mrope_2d() {
+    // 1. Initialize backend
+    ggml_backend_t backend = NULL;
+    std::string backend_name = "";
+#ifdef GGML_USE_CUDA
+    fprintf(stderr, "%s: using CUDA backend\n", __func__);
+    backend = ggml_backend_cuda_init(0); // init device 0
+    backend_name = "cuda";
+    if (!backend) {
+        fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
+    }
+#endif
+    // if there aren't GPU Backends fallback to CPU backend
+    if (!backend) {
+        backend = ggml_backend_cpu_init();
+        backend_name = "cpu";
+    }
+
+    // Calculate the size needed to allocate
+    size_t ctx_size = 0;
+    ctx_size += 2 * ggml_tensor_overhead(); // tensors
+    // no need to allocate anything else!
+
+    // 2. Allocate `ggml_context` to store tensor data
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ ctx_size,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
+    };
+    struct ggml_context * ctx = ggml_init(params);
+
+    struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30);
+    ggml_set_name(inp_raw, "inp_raw");
+    ggml_set_input(inp_raw);
+
+    struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4);
+    ggml_set_name(pos, "pos");
+    ggml_set_input(pos);
+
+    std::vector<float> dummy_q;
+    dummy_q.resize(128 * 12 * 30);
+    std::fill(dummy_q.begin(), dummy_q.end(), 0.1);
+    // memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw));
+
+    std::vector<int> pos_id;
+    pos_id.resize(30 * 4);
+    for (int i = 0; i < 30; i ++) {
+        pos_id[i] = i;
+        pos_id[i + 30] = i + 10;
+        pos_id[i + 60] = i + 20;
+        pos_id[i + 90] = i + 30;
+    }
+    int sections[4] = {32, 32, 0, 0};
+
+    // 4. Allocate a `ggml_backend_buffer` to store all tensors
+    ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
+
+    // 5. Copy tensor data from main memory (RAM) to backend buffer
+    ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw));
+    ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos));
+
+    // 6. Create a `ggml_cgraph` for mul_mat operation
+    struct ggml_cgraph * gf = NULL;
+    struct ggml_context * ctx_cgraph = NULL;
+
+    // create a temporally context to build the graph
+    struct ggml_init_params params0 = {
+        /*.mem_size   =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
+    };
+    ctx_cgraph = ggml_init(params0);
+    gf = ggml_new_graph(ctx_cgraph);
+
+    struct ggml_tensor * result0 = ggml_rope_multi(
+        ctx_cgraph, inp_raw, pos, nullptr,
+        128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1,
+        0, 1, 32, 1);
+
+    // Add "result" tensor and all of its dependencies to the cgraph
+    ggml_build_forward_expand(gf, result0);
+
+    // 7. Create a `ggml_gallocr` for cgraph computation
+    ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
+    ggml_gallocr_alloc_graph(allocr, gf);
+
+    // 9. Run the computation
+    int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading
+    if (ggml_backend_is_cpu(backend)) {
+        ggml_backend_cpu_set_n_threads(backend, n_threads);
+    }
+    ggml_backend_graph_compute(backend, gf);
+
+    // 10. Retrieve results (output tensors)
+    // in this example, output tensor is always the last tensor in the graph
+    struct ggml_tensor * result = result0;
+    // struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1];
+    float * result_data = (float *)malloc(ggml_nbytes(result));
+    // because the tensor data is stored in device buffer, we need to copy it back to RAM
+    ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
+    const std::string bin_file = "mrope_2d_" + backend_name +".bin";
+    std::ofstream outFile(bin_file, std::ios::binary);
+
+    if (outFile.is_open()) {
+        outFile.write(reinterpret_cast<const char*>(result_data), ggml_nbytes(result));
+        outFile.close();
+        std::cout << "Data successfully written to " + bin_file << std::endl;
+    } else {
+        std::cerr << "Error opening file!" << std::endl;
+    }
+
+    free(result_data);
+    // 11. Free memory and exit
+    ggml_free(ctx_cgraph);
+    ggml_gallocr_free(allocr);
+    ggml_free(ctx);
+    ggml_backend_buffer_free(buffer);
+    ggml_backend_free(backend);
+}
+
+static void debug_dump_img_embed(struct llava_context * ctx_llava) {
+    int n_embd  = llama_n_embd(llama_get_model(ctx_llava->ctx_llama));
+    int ne = n_embd * 4;
+    float vals[56 * 56 * 3];
+    // float embd[ne];
+    std::vector<float> embd;
+    embd.resize(ne);
+
+    for (int i = 0; i < 56*56; i++)
+    {
+        for (int c = 0; c < 3; c++)
+            vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56);
+    }
+
+    clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data());
+
+    std::ofstream outFile("img_embed.bin", std::ios::binary);
+    if (outFile.is_open()) {
+        outFile.write(reinterpret_cast<const char*>(embd.data()), ne * sizeof(float));
+
+        outFile.close();
+        std::cout << "Data successfully written to mrope.bin" << std::endl;
+    } else {
+        std::cerr << "Error opening file!" << std::endl;
+    }
+}
+
+#endif
+
+
+int main(int argc, char ** argv) {
+    ggml_time_init();
+
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
+        return 1;
+    }
+
+    common_init();
+
+    if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
+        print_usage(argc, argv);
+        return 1;
+    }
+
+    auto * model = llava_init(&params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
+        return 1;
+    }
+
+    if (prompt_contains_image(params.prompt)) {
+        auto * ctx_llava = llava_init_context(&params, model);
+
+        auto * image_embed = load_image(ctx_llava, &params, "");
+
+        // process the prompt
+        process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+        llama_perf_context_print(ctx_llava->ctx_llama);
+        llava_image_embed_free(image_embed);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
+#ifndef NDEBUG
+    } else if (params.image[0].empty()) {
+        auto ctx_llava = llava_init_context(&params, model);
+
+        debug_test_mrope_2d();
+        debug_dump_img_embed(ctx_llava);
+
+        llama_perf_context_print(ctx_llava->ctx_llama);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
+#endif
+    } else {
+        for (auto & image : params.image) {
+            auto * ctx_llava = llava_init_context(&params, model);
+
+            auto * image_embed = load_image(ctx_llava, &params, image);
+            if (!image_embed) {
+                LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
+                return 1;
+            }
+
+            // process the prompt
+            process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+            llama_perf_context_print(ctx_llava->ctx_llama);
+            llava_image_embed_free(image_embed);
+            ctx_llava->model = NULL;
+            llava_free(ctx_llava);
+        }
+    }
+
+    llama_free_model(model);
+
+    return 0;
+}
@@ -177,16 +177,11 @@ Example usage: `--temp 0`

 -   `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
 -   `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
-   `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty.

 The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.

 The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).

-Use the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases.
-
-Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`
-
 ### DRY Repetition Penalty

 DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)).
@@ -54,8 +54,6 @@ As the models are currently fully loaded into memory, you will need adequate dis

 Several quantization methods are supported. They differ in the resulting model disk size and inference speed.

-The quantization formats `Q4_0_4_4`, `Q4_0_4_8` and `Q4_0_8_8` are block interleaved variants of the `Q4_0` format, providing a data layout that is better suited for specific implementations of optimized mulmat kernels. Since these formats differ only in data layout, they have the same quantized size as the `Q4_0` format.
-
 *(outdated)*

 | Model | Measure      |    F16 |   Q4_0 |   Q4_1 |   Q5_0 |   Q5_1 |   Q8_0 |
@@ -83,7 +81,7 @@ The quantization formats `Q4_0_4_4`, `Q4_0_4_8` and `Q4_0_8_8` are block interle
  - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
  - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
  - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
-  - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
+  - [#4996 - k-quants tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
  - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
  - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
  - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
@@ -48,9 +48,6 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
    { "Q5_K_M",   LLAMA_FTYPE_MOSTLY_Q5_K_M,   " 5.33G, +0.0569 ppl @ Llama-3-8B",  },
    { "Q6_K",     LLAMA_FTYPE_MOSTLY_Q6_K,     " 6.14G, +0.0217 ppl @ Llama-3-8B",  },
    { "Q8_0",     LLAMA_FTYPE_MOSTLY_Q8_0,     " 7.96G, +0.0026 ppl @ Llama-3-8B",  },
-    { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
-    { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
-    { "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
    { "F16",      LLAMA_FTYPE_MOSTLY_F16,      "14.00G, +0.0020 ppl @ Mistral-7B",  },
    { "BF16",     LLAMA_FTYPE_MOSTLY_BF16,     "14.00G, -0.0050 ppl @ Mistral-7B",  },
    { "F32",      LLAMA_FTYPE_ALL_F32,         "26.00G              @ 7B",          },
@@ -107,7 +107,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
        }

        float * out = output + batch.seq_id[i][0] * n_embd;
-        common_embd_normalize(embd, out, n_embd);
+        common_embd_normalize(embd, out, n_embd, 2);
    }
 }

@@ -143,7 +143,7 @@ int main(int argc, char ** argv) {
        std::vector<chunk> file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator);
        chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
    }
-    LOG_INF("Number of chunks: %ld\n", chunks.size());
+    LOG_INF("Number of chunks: %zu\n", chunks.size());

    llama_backend_init();
    llama_numa_init(params.numa);
@@ -1,5 +1,5 @@
 set(TARGET llama-run)
 add_executable(${TARGET} run.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
@@ -3,5 +3,47 @@
 The purpose of this example is to demonstrate a minimal usage of llama.cpp for running models.

 ```bash
-./llama-run Meta-Llama-3.1-8B-Instruct.gguf
-...
+llama-run granite-code
+```
+
+```bash
+llama-run -h
+Description:
+  Runs a llm
+
+Usage:
+  llama-run [options] model [prompt]
+
+Options:
+  -c, --context-size <value>
+      Context size (default: 2048)
+  -n, --ngl <value>
+      Number of GPU layers (default: 0)
+  -v, --verbose, --log-verbose
+      Set verbosity level to infinity (i.e. log all messages, useful for debugging)
+  -h, --help
+      Show help message
+
+Commands:
+  model
+      Model is a string with an optional prefix of
+      huggingface:// (hf://), ollama://, https:// or file://.
+      If no protocol is specified and a file exists in the specified
+      path, file:// is assumed, otherwise if a file does not exist in
+      the specified path, ollama:// is assumed. Models that are being
+      pulled are downloaded with .partial extension while being
+      downloaded and then renamed as the file without the .partial
+      extension when complete.
+
+Examples:
+  llama-run llama3
+  llama-run ollama://granite-code
+  llama-run ollama://smollm:135m
+  llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf
+  llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf
+  llama-run https://example.com/some-file1.gguf
+  llama-run some-file2.gguf
+  llama-run file://some-file3.gguf
+  llama-run --ngl 999 some-file4.gguf
+  llama-run --ngl 999 some-file5.gguf Hello World
+```
@@ -15,7 +15,7 @@ set(TARGET_SRCS
    httplib.h
 )
 set(PUBLIC_ASSETS
-    index.html
+    index.html.gz
    loading.html
 )

@@ -34,14 +34,6 @@ endforeach()
 add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)

-# clean up generated files in pre-build step
-foreach(asset ${PUBLIC_ASSETS})
-    set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
-    add_custom_command(TARGET ${TARGET} PRE_BUILD
-        COMMAND "${CMAKE_COMMAND}" -E remove -f "${output}"
-    )
-endforeach()
-
 target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})

 if (LLAMA_SERVER_SSL)
@@ -62,8 +62,8 @@ The project is under active development, and we are [looking for feedback and co
 | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
 | `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
 | `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
-| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
-| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
+| `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
+| `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: 0.1, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
@@ -104,7 +104,6 @@ The project is under active development, and we are [looking for feedback and co
 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
 | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
-| `--penalize-nl` | penalize newline tokens (default: false) |
 | `--temp N` | temperature (default: 0.8) |
 | `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
 | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
@@ -138,6 +137,7 @@ The project is under active development, and we are [looking for feedback and co
 | -------- | ----------- |
 | `--no-context-shift` | disables context shift on inifinite text generation (default: disabled)<br/>(env: LLAMA_ARG_NO_CONTEXT_SHIFT) |
 | `-sp, --special` | special tokens output enabled (default: false) |
+| `--no-warmup` | skip warming up the model with an empty run |
 | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
 | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified<br/>(env: LLAMA_ARG_POOLING) |
 | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
@@ -146,6 +146,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--host HOST` | ip address to listen (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
 | `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
 | `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
+| `--no-webui` | Disable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_NO_WEBUI) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
 | `--api-key KEY` | API key to use for authentication (default: none)<br/>(env: LLAMA_API_KEY) |
@@ -163,13 +164,13 @@ The project is under active development, and we are [looking for feedback and co
 | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>list of built-in templates:<br/>chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, exaone3, gemma, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, monarch, openchat, orion, phi3, rwkv-world, vicuna, vicuna-orca, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
-| `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16) |
-| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5) |
-| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9) |
-| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model) |
+| `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_DRAFT_MAX) |
+| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5)<br/>(env: LLAMA_ARG_DRAFT_MIN) |
+| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9)<br/>(env: LLAMA_ARG_DRAFT_P_MIN) |
+| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE_DRAFT) |
 | `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
-| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model |
-| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused) |
+| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
+| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |


 Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
@@ -302,23 +303,23 @@ mkdir llama-client
 cd llama-client
 ```

-Create a index.js file and put this inside:
+Create an index.js file and put this inside:

 ```javascript
-const prompt = `Building a website can be done in 10 simple steps:`;
+const prompt = "Building a website can be done in 10 simple steps:"

-async function Test() {
+async function test() {
    let response = await fetch("http://127.0.0.1:8080/completion", {
-        method: 'POST',
+        method: "POST",
        body: JSON.stringify({
            prompt,
-            n_predict: 512,
+            n_predict: 64,
        })
    })
    console.log((await response.json()).content)
 }

-Test()
+test()
 ```

 And run it:
@@ -380,7 +381,7 @@ Multiple prompts are also supported. In this case, the completion result will be
 `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
 By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.

-`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
+`stream`: Allows receiving each predicted token in real-time instead of waiting for the completion to finish (uses a different response format). To enable this, set to `true`.

 `stop`: Specify a JSON array of stopping strings.
 These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
@@ -391,8 +392,6 @@ These words will not be included in the completion, so make sure to add them to

 `repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.

-`penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
-
 `presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.

 `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
@@ -439,19 +438,22 @@ These words will not be included in the completion, so make sure to add them to

 `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true`

+`return_tokens`: Return the raw generated token ids in the `tokens` field. Otherwise `tokens` remains empty. Default: `false`
+
 `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values.

-    `timings_per_token`: Include prompt processing and text generation speed information in each response.  Default: `false`
+`timings_per_token`: Include prompt processing and text generation speed information in each response.  Default: `false`

 **Response format**

- Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion.
+- Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.

 - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:

 ```json
 {
-  "content": "<the token selected by the model>",
+  "content": "<the token generated by the model>",
+  "tokens": [ generated token ids if requested ],
  "probs": [
    {
      "prob": float,
@@ -469,13 +471,16 @@ These words will not be included in the completion, so make sure to add them to
 Notice that each `probs` is an array of length `n_probs`.

 - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
+- `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request.
 - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
 - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
 - `model`: The path to the model loaded with `-m`
 - `prompt`: The provided `prompt`
- `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token
- `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered
- `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided
+- `stop_type`: Indicating whether the completion has stopped. Possible values are:
+  - `none`: Generating (not stopped)
+  - `eos`: Stopped because it encountered the EOS token
+  - `limit`: Stopped because `n_predict` tokens were generated before stop words or EOS was encountered
+  - `word`: Stopped due to encountering a stopping word from `stop` JSON array provided
 - `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word)
 - `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second`
 - `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`)
@@ -616,14 +621,82 @@ This endpoint is public (no API key check). By default, it is read-only. To make

 ```json
 {
-  "default_generation_settings": { ... },
+  "default_generation_settings": {
+    "id": 0,
+    "id_task": -1,
+    "n_ctx": 1024,
+    "speculative": false,
+    "is_processing": false,
+    "params": {
+      "n_predict": -1,
+      "seed": 4294967295,
+      "temperature": 0.800000011920929,
+      "dynatemp_range": 0.0,
+      "dynatemp_exponent": 1.0,
+      "top_k": 40,
+      "top_p": 0.949999988079071,
+      "min_p": 0.05000000074505806,
+      "xtc_probability": 0.0,
+      "xtc_threshold": 0.10000000149011612,
+      "typical_p": 1.0,
+      "repeat_last_n": 64,
+      "repeat_penalty": 1.0,
+      "presence_penalty": 0.0,
+      "frequency_penalty": 0.0,
+      "dry_multiplier": 0.0,
+      "dry_base": 1.75,
+      "dry_allowed_length": 2,
+      "dry_penalty_last_n": -1,
+      "dry_sequence_breakers": [
+        "\n",
+        ":",
+        "\"",
+        "*"
+      ],
+      "mirostat": 0,
+      "mirostat_tau": 5.0,
+      "mirostat_eta": 0.10000000149011612,
+      "stop": [],
+      "max_tokens": -1,
+      "n_keep": 0,
+      "n_discard": 0,
+      "ignore_eos": false,
+      "stream": true,
+      "n_probs": 0,
+      "min_keep": 0,
+      "grammar": "",
+      "samplers": [
+        "dry",
+        "top_k",
+        "typ_p",
+        "top_p",
+        "min_p",
+        "xtc",
+        "temperature"
+      ],
+      "speculative.n_max": 16,
+      "speculative.n_min": 5,
+      "speculative.p_min": 0.8999999761581421,
+      "timings_per_token": false
+    },
+    "prompt": "",
+    "next_token": {
+      "has_next_token": true,
+      "has_new_line": false,
+      "n_remain": -1,
+      "n_decoded": 0,
+      "stopping_word": ""
+    }
+  },
  "total_slots": 1,
-  "chat_template": ""
+  "model_path": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
+  "chat_template": "..."
 }
 ```

 - `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
 - `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
+- `model_path` - the path to model file (same with `-m` argument)
 - `chat_template` - the model's original Jinja2 prompt template

 ### POST `/props`: Change server global properties.
@@ -690,6 +763,8 @@ curl http://localhost:8080/v1/chat/completions \

 ### POST `/v1/embeddings`: OpenAI-compatible embeddings API

+This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.
+
 *Options:*

 See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).
@@ -722,6 +797,46 @@ See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-r
  }'
  ```

+### POST `/embeddings`: non-OpenAI-compatible embeddings API
+
+This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidian norm.
+
+Note that the response format of this endpoint is different from `/v1/embeddings`.
+
+*Options:*
+
+Same as the `/v1/embeddings` endpoint.
+
+*Examples:*
+
+Same as the `/v1/embeddings` endpoint.
+
+**Response format**
+
+```json
+[
+  {
+    "index": 0,
+    "embedding": [
+      [ ... embeddings for token 0   ... ],
+      [ ... embeddings for token 1   ... ],
+      [ ... ]
+      [ ... embeddings for token N-1 ... ],
+    ]
+  },
+  ...
+  {
+    "index": P,
+    "embedding": [
+      [ ... embeddings for token 0   ... ],
+      [ ... embeddings for token 1   ... ],
+      [ ... ]
+      [ ... embeddings for token N-1 ... ],
+    ]
+  }
+]
+```
+
 ### GET `/slots`: Returns the current slots processing state

 > [!WARNING]
@@ -737,56 +852,73 @@ Example:

 ```json
 [
-    {
-        "dynatemp_exponent": 1.0,
-        "dynatemp_range": 0.0,
-        "frequency_penalty": 0.0,
-        "grammar": "",
-        "id": 0,
-        "ignore_eos": false,
-        "is_processing": false,
-        "logit_bias": [],
-        "min_p": 0.05000000074505806,
-        "mirostat": 0,
-        "mirostat_eta": 0.10000000149011612,
-        "mirostat_tau": 5.0,
-        "model": "llama-2-7b-32k-instruct.Q2_K.gguf",
-        "n_ctx": 2048,
-        "n_keep": 0,
-        "n_predict": 100000,
-        "n_probs": 0,
-        "next_token": {
-            "has_next_token": true,
-            "n_remain": -1,
-            "n_decoded": 0,
-            "stopped_eos": false,
-            "stopped_limit": false,
-            "stopped_word": false,
-            "stopping_word": ""
-        },
-        "penalize_nl": true,
-        "presence_penalty": 0.0,
-        "prompt": "Say hello to llama.cpp",
-        "repeat_last_n": 64,
-        "repeat_penalty": 1.100000023841858,
-        "samplers": [
-            "top_k",
-            "typical_p",
-            "top_p",
-            "min_p",
-            "temperature"
-        ],
-        "seed": 42,
-        "stop": [
-            "\n"
-        ],
-        "stream": false,
-        "task_id": 0,
-        "temperature": 0.0,
-        "top_k": 40,
-        "top_p": 0.949999988079071,
-        "typical_p": 1.0
+  {
+    "id": 0,
+    "id_task": -1,
+    "n_ctx": 1024,
+    "speculative": false,
+    "is_processing": false,
+    "params": {
+      "n_predict": -1,
+      "seed": 4294967295,
+      "temperature": 0.800000011920929,
+      "dynatemp_range": 0.0,
+      "dynatemp_exponent": 1.0,
+      "top_k": 40,
+      "top_p": 0.949999988079071,
+      "min_p": 0.05000000074505806,
+      "xtc_probability": 0.0,
+      "xtc_threshold": 0.10000000149011612,
+      "typical_p": 1.0,
+      "repeat_last_n": 64,
+      "repeat_penalty": 1.0,
+      "presence_penalty": 0.0,
+      "frequency_penalty": 0.0,
+      "dry_multiplier": 0.0,
+      "dry_base": 1.75,
+      "dry_allowed_length": 2,
+      "dry_penalty_last_n": -1,
+      "dry_sequence_breakers": [
+        "\n",
+        ":",
+        "\"",
+        "*"
+      ],
+      "mirostat": 0,
+      "mirostat_tau": 5.0,
+      "mirostat_eta": 0.10000000149011612,
+      "stop": [],
+      "max_tokens": -1,
+      "n_keep": 0,
+      "n_discard": 0,
+      "ignore_eos": false,
+      "stream": true,
+      "n_probs": 0,
+      "min_keep": 0,
+      "grammar": "",
+      "samplers": [
+        "dry",
+        "top_k",
+        "typ_p",
+        "top_p",
+        "min_p",
+        "xtc",
+        "temperature"
+      ],
+      "speculative.n_max": 16,
+      "speculative.n_min": 5,
+      "speculative.p_min": 0.8999999761581421,
+      "timings_per_token": false
+    },
+    "prompt": "",
+    "next_token": {
+      "has_next_token": true,
+      "has_new_line": false,
+      "n_remain": -1,
+      "n_decoded": 0,
+      "stopping_word": ""
    }
+  }
 ]
 ```

@@ -39,7 +39,6 @@
      temperature: 0.8, // adapt all following parameters to optimized min-p requierements. If for non-english, set to 0.6 or lower
      repeat_last_n: 0, // 0 = disable penalty, -1 = context size
      repeat_penalty: 1.0, // 1.0 = disabled
-      penalize_nl: false, // true only useful for infinite completion
      dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
      dry_base: 1.75,     // 0.0 = disabled
      dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
@@ -303,7 +303,6 @@
      temperature: 0.7,
      repeat_last_n: 256, // 0 = disable penalty, -1 = context size
      repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
      dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
      dry_base: 1.75,     // 0.0 = disabled
      dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
@@ -1006,7 +1005,6 @@
            ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
            ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
            ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
            ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
            ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
            ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
@@ -407,6 +407,9 @@ class SimpleChat {
                if (curLine.startsWith("data:")) {
                    curLine = curLine.substring(5);
                }
+                if (curLine.trim() === "[DONE]") {
+                    break;
+                }
                let curJson = JSON.parse(curLine);
                console.debug("DBUG:SC:PART:Json:", curJson);
                this.append_response(this.response_extract_stream(curJson, apiEP));
@@ -44,4 +44,10 @@ To run with stdout/stderr display in real time (verbose output, but useful for d
 DEBUG=1 ./tests.sh -s -v -x
 ```

+Hint: You can compile and run test in single command, useful for local developement:
+
+```shell
+cmake --build build -j --target llama-server && ./examples/server/tests/tests.sh
+```
+
 To see all available arguments, please refer to [pytest documentation](https://docs.pytest.org/en/stable/how-to/usage.html)
@@ -1,5 +1,9 @@
 #!/bin/bash

+# make sure we are in the right directory
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+cd $SCRIPT_DIR
+
 set -eu

 if [ $# -lt 1 ]
@@ -1,4 +1,5 @@
 import pytest
+import requests
 from utils import *

 server = ServerPreset.tinyllama2()
@@ -22,7 +23,12 @@ def test_server_props():
    server.start()
    res = server.make_request("GET", "/props")
    assert res.status_code == 200
+    assert ".gguf" in res.body["model_path"]
    assert res.body["total_slots"] == server.n_slots
+    default_val = res.body["default_generation_settings"]
+    assert server.n_ctx is not None and server.n_slots is not None
+    assert default_val["n_ctx"] == server.n_ctx / server.n_slots
+    assert default_val["params"]["seed"] == server.seed


 def test_server_models():
@@ -33,6 +39,31 @@ def test_server_models():
    assert len(res.body["data"]) == 1
    assert res.body["data"][0]["id"] == server.model_alias

+
+def test_server_slots():
+    global server
+
+    # without slots endpoint enabled, this should return error
+    server.server_slots = False
+    server.start()
+    res = server.make_request("GET", "/slots")
+    assert res.status_code == 501 # ERROR_TYPE_NOT_SUPPORTED
+    assert "error" in res.body
+    server.stop()
+
+    # with slots endpoint enabled, this should return slots info
+    server.server_slots = True
+    server.n_slots = 2
+    server.start()
+    res = server.make_request("GET", "/slots")
+    assert res.status_code == 200
+    assert len(res.body) == server.n_slots
+    assert server.n_ctx is not None and server.n_slots is not None
+    assert res.body[0]["n_ctx"] == server.n_ctx / server.n_slots
+    assert "params" in res.body[0]
+    assert res.body[0]["params"]["seed"] == server.seed
+
+
 def test_load_split_model():
    global server
    server.model_hf_repo = "ggml-org/models"
@@ -46,3 +77,20 @@ def test_load_split_model():
    })
    assert res.status_code == 200
    assert match_regex("(little|girl)+", res.body["content"])
+
+
+def test_no_webui():
+    global server
+    # default: webui enabled
+    server.start()
+    url = f"http://{server.server_host}:{server.server_port}"
+    res = requests.get(url)
+    assert res.status_code == 200
+    assert "<html>" in res.text
+    server.stop()
+
+    # with --no-webui
+    server.no_webui = True
+    server.start()
+    res = requests.get(url)
+    assert res.status_code == 404
@@ -12,13 +12,13 @@ def create_server():


@pytest.mark.parametrize(
-    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,truncated",
+    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
    [
-        ("llama-2", "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, False),
-        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, False),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
+        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
    ]
 )
-def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, truncated):
+def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
    global server
    server.start()
    res = server.make_request("POST", "/chat/completions", data={
@@ -30,29 +30,28 @@ def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_conte
        ],
    })
    assert res.status_code == 200
+    assert "cmpl" in res.body["id"] # make sure the completion id has the expected format
+    assert res.body["model"] == model if model is not None else server.model_alias
    assert res.body["usage"]["prompt_tokens"] == n_prompt
    assert res.body["usage"]["completion_tokens"] == n_predicted
    choice = res.body["choices"][0]
    assert "assistant" == choice["message"]["role"]
    assert match_regex(re_content, choice["message"]["content"])
-    if truncated:
-        assert choice["finish_reason"] == "length"
-    else:
-        assert choice["finish_reason"] == "stop"
+    assert choice["finish_reason"] == finish_reason


@pytest.mark.parametrize(
-    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,truncated",
+    "system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
    [
-        ("llama-2", "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, False),
-        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, False),
+        ("Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
+        ("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
    ]
 )
-def test_chat_completion_stream(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, truncated):
+def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
    global server
+    server.model_alias = None # try using DEFAULT_OAICOMPAT_MODEL
    server.start()
    res = server.make_stream_request("POST", "/chat/completions", data={
-        "model": model,
        "max_tokens": max_tokens,
        "messages": [
            {"role": "system", "content": system_prompt},
@@ -61,18 +60,19 @@ def test_chat_completion_stream(model, system_prompt, user_prompt, max_tokens, r
        "stream": True,
    })
    content = ""
+    last_cmpl_id = None
    for data in res:
        choice = data["choices"][0]
+        assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
+        if last_cmpl_id is None:
+            last_cmpl_id = data["id"]
+        assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream
        if choice["finish_reason"] in ["stop", "length"]:
            assert data["usage"]["prompt_tokens"] == n_prompt
            assert data["usage"]["completion_tokens"] == n_predicted
            assert "content" not in choice["delta"]
            assert match_regex(re_content, content)
-            # FIXME: not sure why this is incorrect in stream mode
-            # if truncated:
-            #   assert choice["finish_reason"] == "length"
-            # else:
-            #   assert choice["finish_reason"] == "stop"
+            assert choice["finish_reason"] == finish_reason
        else:
            assert choice["finish_reason"] is None
            content += choice["delta"]["content"]
@@ -93,7 +93,7 @@ def test_chat_completion_with_openai_library():
        temperature=0.8,
    )
    print(res)
-    assert res.choices[0].finish_reason == "stop"
+    assert res.choices[0].finish_reason == "length"
    assert res.choices[0].message.content is not None
    assert match_regex("(Suddenly)+", res.choices[0].message.content)

@@ -10,22 +10,29 @@ def create_server():
    global server
    server = ServerPreset.tinyllama2()

-@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [
-    ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False),
-    ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False),
+@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated,return_tokens", [
+    ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False, False),
+    ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False, True),
 ])
-def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool):
+def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool, return_tokens: bool):
    global server
    server.start()
    res = server.make_request("POST", "/completion", data={
        "n_predict": n_predict,
        "prompt": prompt,
+        "return_tokens": return_tokens,
    })
    assert res.status_code == 200
    assert res.body["timings"]["prompt_n"] == n_prompt
    assert res.body["timings"]["predicted_n"] == n_predicted
    assert res.body["truncated"] == truncated
+    assert type(res.body["has_new_line"]) == bool
    assert match_regex(re_content, res.body["content"])
+    if return_tokens:
+        assert len(res.body["tokens"]) > 0
+        assert all(type(tok) == int for tok in res.body["tokens"])
+    else:
+        assert res.body["tokens"] == []


@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [
@@ -42,15 +49,42 @@ def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_promp
    })
    content = ""
    for data in res:
+        assert "stop" in data and type(data["stop"]) == bool
        if data["stop"]:
            assert data["timings"]["prompt_n"] == n_prompt
            assert data["timings"]["predicted_n"] == n_predicted
            assert data["truncated"] == truncated
+            assert data["stop_type"] == "limit"
+            assert type(data["has_new_line"]) == bool
+            assert "generation_settings" in data
+            assert server.n_predict is not None
+            assert data["generation_settings"]["n_predict"] == min(n_predict, server.n_predict)
+            assert data["generation_settings"]["seed"] == server.seed
            assert match_regex(re_content, content)
        else:
+            assert len(data["tokens"]) > 0
+            assert all(type(tok) == int for tok in data["tokens"])
            content += data["content"]


+def test_completion_stream_vs_non_stream():
+    global server
+    server.start()
+    res_stream = server.make_stream_request("POST", "/completion", data={
+        "n_predict": 8,
+        "prompt": "I believe the meaning of life is",
+        "stream": True,
+    })
+    res_non_stream = server.make_request("POST", "/completion", data={
+        "n_predict": 8,
+        "prompt": "I believe the meaning of life is",
+    })
+    content_stream = ""
+    for data in res_stream:
+        content_stream += data["content"]
+    assert content_stream == res_non_stream.body["content"]
+
+
@pytest.mark.parametrize("n_slots", [1, 2])
 def test_consistent_result_same_seed(n_slots: int):
    global server
@@ -221,3 +255,24 @@ def test_completion_parallel_slots(n_slots: int, n_requests: int):
        assert len(res.body["content"]) > 10
        # FIXME: the result is not deterministic when using other slot than slot 0
        # assert match_regex(re_content, res.body["content"])
+
+
+def test_n_probs():
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "n_probs": 10,
+        "temperature": 0.0,
+        "n_predict": 5,
+    })
+    assert res.status_code == 200
+    assert "completion_probabilities" in res.body
+    assert len(res.body["completion_probabilities"]) == 5
+    for tok in res.body["completion_probabilities"]:
+        assert "probs" in tok
+        assert len(tok["probs"]) == 10
+        for prob in tok["probs"]:
+            assert "prob" in prob
+            assert "tok_str" in prob
+            assert 0.0 <= prob["prob"] <= 1.0
@@ -14,8 +14,9 @@ def create_server():

 def test_embedding_single():
    global server
+    server.pooling = 'last'
    server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
        "input": "I believe the meaning of life is",
    })
    assert res.status_code == 200
@@ -29,8 +30,9 @@ def test_embedding_single():

 def test_embedding_multiple():
    global server
+    server.pooling = 'last'
    server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
        "input": [
            "I believe the meaning of life is",
            "Write a joke about AI from a very long prompt which will not be truncated",
@@ -45,10 +47,69 @@ def test_embedding_multiple():
        assert len(d['embedding']) > 1


-def test_embedding_openai_library_single():
+@pytest.mark.parametrize(
+    "input,is_multi_prompt",
+    [
+        # single prompt
+        ("string", False),
+        ([12, 34, 56], False),
+        ([12, 34, "string", 56, 78], False),
+        # multiple prompts
+        (["string1", "string2"], True),
+        (["string1", [12, 34, 56]], True),
+        ([[12, 34, 56], [12, 34, 56]], True),
+        ([[12, 34, 56], [12, "string", 34, 56]], True),
+    ]
+)
+def test_embedding_mixed_input(input, is_multi_prompt: bool):
    global server
    server.start()
-    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    res = server.make_request("POST", "/v1/embeddings", data={"input": input})
+    assert res.status_code == 200
+    data = res.body['data']
+    if is_multi_prompt:
+        assert len(data) == len(input)
+        for d in data:
+            assert 'embedding' in d
+            assert len(d['embedding']) > 1
+    else:
+        assert 'embedding' in data[0]
+        assert len(data[0]['embedding']) > 1
+
+
+def test_embedding_pooling_none():
+    global server
+    server.pooling = 'none'
+    server.start()
+    res = server.make_request("POST", "/embeddings", data={
+        "input": "hello hello hello",
+    })
+    assert res.status_code == 200
+    assert 'embedding' in res.body[0]
+    assert len(res.body[0]['embedding']) == 5 # 3 text tokens + 2 special
+
+    # make sure embedding vector is not normalized
+    for x in res.body[0]['embedding']:
+        assert abs(sum([x ** 2 for x in x]) - 1) > EPSILON
+
+
+def test_embedding_pooling_none_oai():
+    global server
+    server.pooling = 'none'
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": "hello hello hello",
+    })
+
+    # /v1/embeddings does not support pooling type 'none'
+    assert res.status_code == 400
+
+
+def test_embedding_openai_library_single():
+    global server
+    server.pooling = 'last'
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
    res = client.embeddings.create(model="text-embedding-3-small", input="I believe the meaning of life is")
    assert len(res.data) == 1
    assert len(res.data[0].embedding) > 1
@@ -56,8 +117,9 @@ def test_embedding_openai_library_single():

 def test_embedding_openai_library_multiple():
    global server
+    server.pooling = 'last'
    server.start()
-    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
    res = client.embeddings.create(model="text-embedding-3-small", input=[
        "I believe the meaning of life is",
        "Write a joke about AI from a very long prompt which will not be truncated",
@@ -71,8 +133,9 @@ def test_embedding_openai_library_multiple():

 def test_embedding_error_prompt_too_long():
    global server
+    server.pooling = 'last'
    server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
        "input": "This is a test " * 512,
    })
    assert res.status_code != 200
@@ -80,8 +143,9 @@ def test_embedding_error_prompt_too_long():


 def test_same_prompt_give_same_result():
+    server.pooling = 'last'
    server.start()
-    res = server.make_request("POST", "/embeddings", data={
+    res = server.make_request("POST", "/v1/embeddings", data={
        "input": [
            "I believe the meaning of life is",
            "I believe the meaning of life is",
@@ -97,3 +161,33 @@ def test_same_prompt_give_same_result():
        vi = res.body['data'][i]['embedding']
        for x, y in zip(v0, vi):
            assert abs(x - y) < EPSILON
+
+
+@pytest.mark.parametrize(
+    "content,n_tokens",
+    [
+        ("I believe the meaning of life is", 9),
+        ("This is a test", 6),
+    ]
+)
+def test_embedding_usage_single(content, n_tokens):
+    global server
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={"input": content})
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == n_tokens
+
+
+def test_embedding_usage_multiple():
+    global server
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": [
+            "I believe the meaning of life is",
+            "I believe the meaning of life is",
+        ],
+    })
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == 2 * 9
@@ -13,28 +13,28 @@ def test_infill_without_input_extra():
    global server
    server.start()
    res = server.make_request("POST", "/infill", data={
-        "prompt": "Complete this",
-        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n    int n_threads = llama_",
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
        "input_suffix": "}\n",
    })
    assert res.status_code == 200
-    assert match_regex("(One|day|she|saw|big|scary|bird)+", res.body["content"])
+    assert match_regex("(Ann|small|shiny)+", res.body["content"])


 def test_infill_with_input_extra():
    global server
    server.start()
    res = server.make_request("POST", "/infill", data={
-        "prompt": "Complete this",
        "input_extra": [{
            "filename": "llama.h",
            "text": "LLAMA_API int32_t llama_n_threads();\n"
        }],
-        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n    int n_threads = llama_",
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
        "input_suffix": "}\n",
    })
    assert res.status_code == 200
-    assert match_regex("(cuts|Jimmy|mom|came|into|the|room)+", res.body["content"])
+    assert match_regex("(Dad|excited|park)+", res.body["content"])


@pytest.mark.parametrize("input_extra", [
@@ -48,10 +48,30 @@ def test_invalid_input_extra_req(input_extra):
    global server
    server.start()
    res = server.make_request("POST", "/infill", data={
-        "prompt": "Complete this",
        "input_extra": [input_extra],
-        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n    int n_threads = llama_",
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
        "input_suffix": "}\n",
    })
    assert res.status_code == 400
    assert "error" in res.body
+
+
+@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test")
+def test_with_qwen_model():
+    global server
+    server.model_file = None
+    server.model_hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-IQ3_XXS-GGUF"
+    server.model_hf_file = "qwen2.5-coder-1.5b-iq3_xxs-imat.gguf"
+    server.start(timeout_seconds=600)
+    res = server.make_request("POST", "/infill", data={
+        "input_extra": [{
+            "filename": "llama.h",
+            "text": "LLAMA_API int32_t llama_n_threads();\n"
+        }],
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
+        "input_suffix": "}\n",
+    })
+    assert res.status_code == 200
+    assert res.body["content"] == "n_threads();\n    printf(\"Number of threads: %d\\n\", n_threads);\n    return 0;\n"
@@ -53,3 +53,26 @@ def test_invalid_rerank_req(documents):
    })
    assert res.status_code == 400
    assert "error" in res.body
+
+
+@pytest.mark.parametrize(
+    "query,doc1,doc2,n_tokens",
+    [
+        ("Machine learning is", "A machine", "Learning is", 19),
+        ("Which city?", "Machine learning is ", "Paris, capitale de la", 26),
+    ]
+)
+def test_rerank_usage(query, doc1, doc2, n_tokens):
+    global server
+    server.start()
+
+    res = server.make_request("POST", "/rerank", data={
+        "query": query,
+        "documents": [
+            doc1,
+            doc2,
+        ]
+    })
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == n_tokens
@@ -64,6 +64,8 @@ class ServerProcess:
    server_embeddings: bool | None = False
    server_reranking: bool | None = False
    server_metrics: bool | None = False
+    server_slots: bool | None = False
+    pooling: str | None = None
    draft: int | None = None
    api_key: str | None = None
    response_format: str | None = None
@@ -71,6 +73,7 @@ class ServerProcess:
    disable_ctx_shift: int | None = False
    draft_min: int | None = None
    draft_max: int | None = None
+    no_webui: bool | None = None

    # session variables
    process: subprocess.Popen | None = None
@@ -91,7 +94,6 @@ class ServerProcess:
        else:
            server_path = "../../../build/bin/llama-server"
        server_args = [
-            "--slots",  # requires to get slot status via /slots endpoint
            "--host",
            self.server_host,
            "--port",
@@ -129,6 +131,10 @@ class ServerProcess:
            server_args.append("--reranking")
        if self.server_metrics:
            server_args.append("--metrics")
+        if self.server_slots:
+            server_args.append("--slots")
+        if self.pooling:
+            server_args.extend(["--pooling", self.pooling])
        if self.model_alias:
            server_args.extend(["--alias", self.model_alias])
        if self.n_ctx:
@@ -156,6 +162,8 @@ class ServerProcess:
            server_args.extend(["--draft-max", self.draft_max])
        if self.draft_min:
            server_args.extend(["--draft-min", self.draft_min])
+        if self.no_webui:
+            server_args.append("--no-webui")

        args = [str(arg) for arg in [server_path, *server_args]]
        print(f"bench: starting server with: {' '.join(args)}")
@@ -181,7 +189,7 @@ class ServerProcess:
        start_time = time.time()
        while time.time() - start_time < timeout_seconds:
            try:
-                response = self.make_request("GET", "/slots", headers={
+                response = self.make_request("GET", "/health", headers={
                    "Authorization": f"Bearer {self.api_key}" if self.api_key else None
                })
                if response.status_code == 200:
@@ -224,7 +232,7 @@ class ServerProcess:
        result.headers = dict(response.headers)
        result.status_code = response.status_code
        result.body = response.json() if parse_body else None
-        print("Response from server", result.body)
+        print("Response from server", json.dumps(result.body, indent=2))
        return result

    def make_stream_request(
@@ -245,7 +253,7 @@ class ServerProcess:
                break
            elif line.startswith('data: '):
                data = json.loads(line[6:])
-                print("Partial response from server", data)
+                print("Partial response from server", json.dumps(data, indent=2))
                yield data


@@ -369,3 +377,6 @@ def match_regex(regex: str, text: str) -> bool:
        ).search(text)
        is not None
    )
+
+def is_slow_test_allowed():
+    return os.environ.get("SLOW_TESTS") == "1" or os.environ.get("SLOW_TESTS") == "ON"
@@ -222,7 +222,6 @@
      temperature: 0.7,
      repeat_last_n: 256, // 0 = disable penalty, -1 = context size
      repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
      top_k: 40, // <= 0 to use vocab size
      top_p: 0.95, // 1.0 = disabled
      min_p: 0.05, // 0 = disabled
@@ -779,7 +778,6 @@
            ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
            ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
            ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
            ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
            ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
            ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
@@ -225,7 +225,6 @@
      temperature: 0.7,
      repeat_last_n: 256, // 0 = disable penalty, -1 = context size
      repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
      top_k: 40, // <= 0 to use vocab size
      top_p: 0.95, // 1.0 = disabled
      min_p: 0.05, // 0 = disabled
@@ -782,7 +781,6 @@
            ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
            ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
            ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
            ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
            ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
            ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
@@ -20,8 +20,9 @@
 #include <sstream>
 #include <string>
 #include <vector>
+#include <memory>

-#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo"

 using json = nlohmann::ordered_json;

@@ -40,17 +41,6 @@ using json = nlohmann::ordered_json;
 #define QUE_ERR(fmt, ...) LOG_ERR("que  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
 #define QUE_DBG(fmt, ...) LOG_DBG("que  %12.*s: " fmt, 12, __func__, __VA_ARGS__)

-// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
-enum error_type {
-    ERROR_TYPE_INVALID_REQUEST,
-    ERROR_TYPE_AUTHENTICATION,
-    ERROR_TYPE_SERVER,
-    ERROR_TYPE_NOT_FOUND,
-    ERROR_TYPE_PERMISSION,
-    ERROR_TYPE_UNAVAILABLE, // custom error
-    ERROR_TYPE_NOT_SUPPORTED, // custom error
-};
-
 template <typename T>
 static T json_value(const json & body, const std::string & key, const T & default_value) {
    // Fallback null to default value
@@ -148,6 +138,7 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_
 * and multiple prompts (multi-tasks):
 * - "prompt": ["string1", "string2"]
 * - "prompt": ["string1", [12, 34, 56]]
+ * - "prompt": [[12, 34, 56], [78, 90, 12]]
 * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]]
 */
 static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {
@@ -174,6 +165,9 @@ static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, con
    } else {
        throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts");
    }
+    if (result.empty()) {
+        throw std::runtime_error("\"prompt\" must not be empty");
+    }
    return result;
 }

@@ -337,12 +331,12 @@ static std::string llama_get_chat_template(const struct llama_model * model) {
    std::string template_key = "tokenizer.chat_template";
    // call with NULL buffer to get the total size of the string
    int32_t res = llama_model_meta_val_str(model, template_key.c_str(), NULL, 0);
-    if (res < 0) {
+    if (res < 2) {
        return "";
    } else {
-        std::vector<char> model_template(res, 0);
+        std::vector<char> model_template(res + 1, 0);
        llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
-        return std::string(model_template.data(), model_template.size());
+        return std::string(model_template.data(), model_template.size() - 1);
    }
 }

@@ -485,48 +479,11 @@ static std::string tokens_to_output_formatted_string(const llama_context * ctx,
    return out;
 }

-struct completion_token_output {
-    llama_token tok;
-    std::string text_to_send;
-
-    struct token_prob {
-        llama_token tok;
-        float prob;
-    };
-
-    std::vector<token_prob> probs;
-};
-
-// convert a vector of completion_token_output to json
-static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> & probs) {
-    json out = json::array();
-
-    for (const auto & prob : probs) {
-        json probs_for_token = json::array();
-
-        for (const auto & p : prob.probs) {
-            const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
-            probs_for_token.push_back(json {
-                {"tok_str", tok_str},
-                {"prob",    p.prob},
-            });
-        }
-
-        const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
-        out.push_back(json {
-            {"content", tok_str},
-            {"probs",   probs_for_token},
-        });
-    }
-
-    return out;
-}
-
 static bool server_sent_event(httplib::DataSink & sink, const char * event, const json & data) {
    const std::string str =
        std::string(event) + ": " +
        data.dump(-1, ' ', false, json::error_handler_t::replace) +
-        "\n\n"; // note: these newlines are important (not sure why though, if you know, add a comment to explain)
+        "\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row).

    LOG_DBG("data stream, to_send: %s", str.c_str());

@@ -543,8 +500,6 @@ static json oaicompat_completion_params_parse(
    const std::string & chat_template) {
    json llama_params;

-    llama_params["__oaicompat"] = true;
-
    // Apply chat template to the list of messages
    llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));

@@ -604,166 +559,9 @@ static json oaicompat_completion_params_parse(
    return llama_params;
 }

-static json format_final_response_oaicompat(const json & request, const json & result, const std::string & completion_id, bool streaming = false, bool verbose = false) {
-    bool stopped_word        = result.count("stopped_word") != 0;
-    bool stopped_eos         = json_value(result, "stopped_eos", false);
-    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
-    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
-    std::string content      = json_value(result, "content", std::string(""));
-
-    std::string finish_reason = "length";
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-
-    json choices =
-        streaming ? json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"delta", json::object()}}})
-                  : json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"message", json{{"content", content},
-                                                         {"role", "assistant"}}}}});
-
-    std::time_t t = std::time(0);
-
-    json res = json {
-        {"choices", choices},
-        {"created", t},
-        {"model",
-            json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
-        {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
-        {"usage", json {
-            {"completion_tokens", num_tokens_predicted},
-            {"prompt_tokens",     num_prompt_tokens},
-            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
-        }},
-        {"id", completion_id}
-    };
-
-    // extra fields for debugging purposes
-    if (verbose) {
-        res["__verbose"] = result;
-    }
-
-    if (result.contains("completion_probabilities")) {
-        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
-    }
-
-    if (result.contains("timings")) {
-        res.push_back({"timings", json_value(result, "timings", json::object())});
-    }
-
-    return res;
-}
-
-// return value is vector as there is one case where we might need to generate two responses
-static std::vector<json> format_partial_response_oaicompat(const json & result, const std::string & completion_id) {
-    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
-        return std::vector<json>({result});
-    }
-
-    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
-    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
-
-    bool stopped_word   = json_value(result, "stopped_word",  false);
-    bool stopped_eos    = json_value(result, "stopped_eos",   false);
-    bool stopped_limit  = json_value(result, "stopped_limit", false);
-    std::string content = json_value(result, "content",       std::string(""));
-
-    std::string finish_reason;
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-    if (stopped_limit) {
-        finish_reason = "length";
-    }
-
-    std::time_t t = std::time(0);
-
-    json choices;
-
-    if (!finish_reason.empty()) {
-        choices = json::array({json{{"finish_reason", finish_reason},
-                                    {"index", 0},
-                                    {"delta", json::object()}}});
-    } else {
-        if (first) {
-            if (content.empty()) {
-                choices = json::array({json{{"finish_reason", nullptr},
-                                            {"index", 0},
-                                            {"delta", json{{"role", "assistant"}}}}});
-            } else {
-                // We have to send this as two updates to conform to openai behavior
-                json initial_ret = json{{"choices", json::array({json{
-                                        {"finish_reason", nullptr},
-                                        {"index", 0},
-                                        {"delta", json{
-                                            {"role", "assistant"}
-                                        }}}})},
-                            {"created", t},
-                            {"id", completion_id},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                json second_ret = json{
-                            {"choices", json::array({json{{"finish_reason", nullptr},
-                                                            {"index", 0},
-                                                            {"delta", json{
-                                                            {"content", content}}}
-                                                            }})},
-                            {"created", t},
-                            {"id", completion_id},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                return std::vector<json>({initial_ret, second_ret});
-            }
-        } else {
-            // Some idiosyncrasy in task processing logic makes several trailing calls
-            // with empty content, we ignore these at the calee site.
-            if (content.empty()) {
-                return std::vector<json>({json::object()});
-            }
-
-            choices = json::array({json{
-                {"finish_reason", nullptr},
-                {"index", 0},
-                {"delta",
-                json{
-                    {"content", content},
-                }},
-            }});
-        }
-    }
-
-    json ret = json {
-        {"choices", choices},
-        {"created", t},
-        {"id",      completion_id},
-        {"model",   modelname},
-        {"object",  "chat.completion.chunk"}
-    };
-
-    if (result.contains("timings")) {
-        ret.push_back({"timings", json_value(result, "timings", json::object())});
-    }
-
-    if (!finish_reason.empty()) {
-        int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
-        int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
-        ret.push_back({"usage", json {
-            {"completion_tokens", num_tokens_predicted},
-            {"prompt_tokens",     num_prompt_tokens},
-            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
-        }});
-    }
-
-    return std::vector<json>({ret});
-}
-
 static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
    json data = json::array();
+    int32_t n_tokens = 0;
    int i = 0;
    for (const auto & elem : embeddings) {
        data.push_back(json{
@@ -771,14 +569,16 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
            {"index",     i++},
            {"object",    "embedding"}
        });
+
+        n_tokens += json_value(elem, "tokens_evaluated", 0);
    }

    json res = json {
        {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
        {"object", "list"},
-        {"usage", json { // TODO: fill
-            {"prompt_tokens", 0},
-            {"total_tokens", 0}
+        {"usage", json {
+            {"prompt_tokens", n_tokens},
+            {"total_tokens", n_tokens}
        }},
        {"data", data}
    };
@@ -788,20 +588,23 @@ static json format_embeddings_response_oaicompat(const json & request, const jso

 static json format_response_rerank(const json & request, const json & ranks) {
    json data = json::array();
+    int32_t n_tokens = 0;
    int i = 0;
    for (const auto & rank : ranks) {
        data.push_back(json{
            {"index",    i++},
            {"relevance_score", json_value(rank, "score", 0.0)},
        });
+
+        n_tokens += json_value(rank, "tokens_evaluated", 0);
    }

    json res = json {
        {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
        {"object", "list"},
-        {"usage", json { // TODO: fill
-            {"prompt_tokens", 0},
-            {"total_tokens", 0}
+        {"usage", json {
+            {"prompt_tokens", n_tokens},
+            {"total_tokens", n_tokens}
        }},
        {"results", data}
    };
@@ -854,42 +657,17 @@ static json format_detokenized_response(const std::string & content) {
    };
 }

-static json format_error_response(const std::string & message, const enum error_type type) {
-    std::string type_str;
-    int code = 500;
-    switch (type) {
-        case ERROR_TYPE_INVALID_REQUEST:
-            type_str = "invalid_request_error";
-            code = 400;
-            break;
-        case ERROR_TYPE_AUTHENTICATION:
-            type_str = "authentication_error";
-            code = 401;
-            break;
-        case ERROR_TYPE_NOT_FOUND:
-            type_str = "not_found_error";
-            code = 404;
-            break;
-        case ERROR_TYPE_SERVER:
-            type_str = "server_error";
-            code = 500;
-            break;
-        case ERROR_TYPE_PERMISSION:
-            type_str = "permission_error";
-            code = 403;
-            break;
-        case ERROR_TYPE_NOT_SUPPORTED:
-            type_str = "not_supported_error";
-            code = 501;
-            break;
-        case ERROR_TYPE_UNAVAILABLE:
-            type_str = "unavailable_error";
-            code = 503;
-            break;
+static json format_logit_bias(const std::vector<llama_logit_bias> & logit_bias) {
+    json data = json::array();
+    for (const auto & lb : logit_bias) {
+        data.push_back(json{
+            {"bias", lb.bias},
+            {"token", lb.token},
+        });
    }
-    return json {
-        {"code", code},
-        {"message", message},
-        {"type", type_str},
-    };
+    return data;
+}
+
+static std::string safe_json_to_str(json data) {
+    return data.dump(-1, ' ', false, json::error_handler_t::replace);
 }
@@ -15,7 +15,7 @@
      <!-- sidebar -->
      <div class="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
        <label for="toggle-drawer" aria-label="close sidebar" class="drawer-overlay"></label>
-        <div class="flex flex-col bg-base-200 min-h-full max-w-[calc(100vw-2em)] py-4 px-4">
+        <div class="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
          <div class="flex flex-row items-center justify-between mb-4 mt-4">
            <h2 class="font-bold ml-4">Conversations</h2>

@@ -120,51 +120,25 @@
            {{ messages.length === 0 ? 'Send a message to start' : '' }}
          </div>
          <div v-for="msg in messages" class="group">
-            <div :class="{
-              'chat': true,
-              'chat-start': msg.role !== 'user',
-              'chat-end': msg.role === 'user',
-            }">
-              <div :class="{
-                'chat-bubble markdown': true,
-                'chat-bubble-base-300': msg.role !== 'user',
-              }">
-                <!-- textarea for editing message -->
-                <template v-if="editingMsg && editingMsg.id === msg.id">
-                  <textarea
-                    class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
-                    v-model="msg.content"></textarea>
-                  <br/>
-                  <button class="btn btn-ghost mt-2 mr-2" @click="editingMsg = null">Cancel</button>
-                  <button class="btn mt-2" @click="editUserMsgAndRegenerate(msg)">Submit</button>
-                </template>
-                <!-- render message as markdown -->
-                <vue-markdown v-else :source="msg.content" />
-              </div>
-            </div>
-
-            <!-- actions for each message -->
-            <div :class="{'text-right': msg.role === 'user'}" class="mx-4 mt-2 mb-2">
-              <!-- user message -->
-              <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingMsg = msg" :disabled="isGenerating">
-                ✍️ Edit
-              </button>
-              <!-- assistant message -->
-              <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
-                🔄 Regenerate
-              </button>
-              <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg(msg)" :disabled="isGenerating">
-                📋 Copy
-              </button>
-            </div>
+            <message-bubble
+              :config="config"
+              :msg="msg"
+              :key="msg.id"
+              :is-generating="isGenerating"
+              :edit-user-msg-and-regenerate="editUserMsgAndRegenerate"
+              :regenerate-msg="regenerateMsg"></message-bubble>
          </div>

          <!-- pending (ongoing) assistant message -->
-          <div id="pending-msg" class="chat chat-start">
-            <div v-if="pendingMsg" class="chat-bubble markdown chat-bubble-base-300">
-              <span v-if="!pendingMsg.content" class="loading loading-dots loading-md"></span>
-              <vue-markdown v-else :source="pendingMsg.content" />
-            </div>
+          <div id="pending-msg" class="group">
+            <message-bubble
+              v-if="pendingMsg"
+              :config="config"
+              :msg="pendingMsg"
+              :key="pendingMsg.id"
+              :is-generating="isGenerating"
+              :edit-user-msg-and-regenerate="() => {}"
+              :regenerate-msg="() => {}"></message-bubble>
          </div>
        </div>

@@ -227,6 +201,14 @@
          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
            <summary class="collapse-title font-bold">Advanced config</summary>
            <div class="collapse-content">
+              <div class="flex flex-row items-center mb-2" v-if="isDev">
+                <!-- this button only shows in dev mode, used to import a demo conversation to test message rendering -->
+                <button class="btn" @click="debugImportDemoConv()">(debug) Import demo conversation</button>
+              </div>
+              <div class="flex flex-row items-center mb-2">
+                <input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
+                <span class="ml-4">Show tokens per second</span>
+              </div>
              <label class="form-control mb-2">
                <!-- Custom parameters input -->
                <div class="label inline">Custom JSON config (For more info, refer to <a class="underline" href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md" target="_blank" rel="noopener noreferrer">server documentation</a>)</div>
@@ -247,6 +229,66 @@

  </div>

+
+  <!-- Template to be used as message bubble -->
+  <template id="message-bubble">
+    <div :class="{
+      'chat': true,
+      'chat-start': msg.role !== 'user',
+      'chat-end': msg.role === 'user',
+    }">
+      <div :class="{
+        'chat-bubble markdown': true,
+        'chat-bubble-base-300': msg.role !== 'user',
+      }">
+        <!-- textarea for editing message -->
+        <template v-if="editingContent !== null">
+          <textarea
+            class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
+            v-model="editingContent"></textarea>
+          <br/>
+          <button class="btn btn-ghost mt-2 mr-2" @click="editingContent = null">Cancel</button>
+          <button class="btn mt-2" @click="editMsg()">Submit</button>
+        </template>
+        <template v-else>
+          <!-- show loading dots for pending message -->
+          <span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
+          <!-- render message as markdown -->
+          <vue-markdown v-else :source="msg.content"></vue-markdown>
+          <!-- render timings if enabled -->
+          <div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
+            <div tabindex="0" role="button" class="cursor-pointer font-semibold text-sm opacity-60">Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s</div>
+            <div class="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
+              <b>Prompt</b><br/>
+              - Tokens: {{ timings.prompt_n }}<br/>
+              - Time: {{ timings.prompt_ms }} ms<br/>
+              - Speed: {{ timings.prompt_per_second.toFixed(1) }} t/s<br/>
+              <b>Generation</b><br/>
+              - Tokens: {{ timings.predicted_n }}<br/>
+              - Time: {{ timings.predicted_ms }} ms<br/>
+              - Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s<br/>
+            </div>
+          </div>
+        </template>
+      </div>
+    </div>
+    <!-- actions for each message -->
+    <div :class="{'text-right': msg.role === 'user', 'opacity-0': isGenerating}" class="mx-4 mt-2 mb-2">
+      <!-- user message -->
+      <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingContent = msg.content" :disabled="isGenerating">
+        ✍️ Edit
+      </button>
+      <!-- assistant message -->
+      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
+        🔄 Regenerate
+      </button>
+      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg()" :disabled="isGenerating">
+        📋 Copy
+      </button>
+    </div>
+  </template>
+
+
  <!-- Template to be used by settings modal -->
  <template id="settings-modal-short-input">
    <label class="input input-bordered join-item grow flex items-center gap-2 mb-2">
@@ -8,15 +8,21 @@
      "name": "webui",
      "version": "0.0.0",
      "dependencies": {
+        "@sec-ant/readable-stream": "^0.6.0",
+        "@vscode/markdown-it-katex": "^1.1.1",
        "autoprefixer": "^10.4.20",
        "daisyui": "^4.12.14",
+        "highlight.js": "^11.10.0",
+        "katex": "^0.16.15",
        "markdown-it": "^14.1.0",
        "postcss": "^8.4.49",
        "tailwindcss": "^3.4.15",
+        "textlinestream": "^1.1.1",
        "vite-plugin-singlefile": "^2.0.3",
        "vue": "^3.5.13"
      },
      "devDependencies": {
+        "sass-embedded": "^1.83.0",
        "vite": "^5.4.10"
      }
    },
@@ -32,6 +38,13 @@
        "url": "https://github.com/sponsors/sindresorhus"
      }
    },
+    "node_modules/@bufbuild/protobuf": {
+      "version": "2.2.3",
+      "resolved": "https://registry.npmjs.org/@bufbuild/protobuf/-/protobuf-2.2.3.tgz",
+      "integrity": "sha512-tFQoXHJdkEOSwj5tRIZSPNUuXK3RaR7T1nUrPgbYX1pUbvqqaaZAsfo+NXBPsz5rZMSKVFrgK1WL8Q/MSLvprg==",
+      "devOptional": true,
+      "license": "(Apache-2.0 AND BSD-3-Clause)"
+    },
    "node_modules/@esbuild/aix-ppc64": {
      "version": "0.21.5",
      "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.21.5.tgz",
@@ -605,6 +618,21 @@
        "win32"
      ]
    },
+    "node_modules/@sec-ant/readable-stream": {
+      "version": "0.6.0",
+      "resolved": "https://registry.npmjs.org/@sec-ant/readable-stream/-/readable-stream-0.6.0.tgz",
+      "integrity": "sha512-uiBh8DrB5FN35gP6/o8JEhEQ7/ci1jUsOZO/VMUjyvTpjtV54VstOXVj1TvTj/wsT23pfX6butxxh3qufsW3+g==",
+      "license": "MIT"
+    },
+    "node_modules/@vscode/markdown-it-katex": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/@vscode/markdown-it-katex/-/markdown-it-katex-1.1.1.tgz",
+      "integrity": "sha512-3KTlbsRBPJQLE2YmLL7K6nunTlU+W9T5+FjfNdWuIUKgxSS6HWLQHaO3L4MkJi7z7MpIPpY+g4N+cWNBPE/MSA==",
+      "license": "MIT",
+      "dependencies": {
+        "katex": "^0.16.4"
+      }
+    },
    "node_modules/@vue/compiler-dom": {
      "version": "3.5.13",
      "resolved": "https://registry.npmjs.org/@vue/compiler-dom/-/compiler-dom-3.5.13.tgz",
@@ -1003,6 +1031,13 @@
        "browserslist": ">= 4.21.0"
      }
    },
+    "node_modules/buffer-builder": {
+      "version": "0.2.0",
+      "resolved": "https://registry.npmjs.org/buffer-builder/-/buffer-builder-0.2.0.tgz",
+      "integrity": "sha512-7VPMEPuYznPSoR21NE1zvd2Xna6c/CloiZCfcMXR1Jny6PjX0N4Nsa38zcBFo/FMK+BlA+FLKbJCQ0i2yxp+Xg==",
+      "devOptional": true,
+      "license": "MIT/X11"
+    },
    "node_modules/caniuse-lite": {
      "version": "1.0.30001684",
      "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001684.tgz",
@@ -1165,6 +1200,22 @@
        "node": ">=8.0"
      }
    },
+    "node_modules/colorjs.io": {
+      "version": "0.5.2",
+      "resolved": "https://registry.npmjs.org/colorjs.io/-/colorjs.io-0.5.2.tgz",
+      "integrity": "sha512-twmVoizEW7ylZSN32OgKdXRmo1qg+wT5/6C3xu5b9QsWzSFAhHLn2xd8ro0diCsKfCj1RdaTP/nrcW+vAoQPIw==",
+      "devOptional": true,
+      "license": "MIT"
+    },
+    "node_modules/commander": {
+      "version": "8.3.0",
+      "resolved": "https://registry.npmjs.org/commander/-/commander-8.3.0.tgz",
+      "integrity": "sha512-OkTL9umf+He2DZkUq8f8J9of7yL6RJKI24dVITBmNfZBmri9zYZQrKkuXiKhyfPSu8tUhnVBB1iKXevvnlR4Ww==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 12"
+      }
+    },
    "node_modules/css-selector-tokenizer": {
      "version": "0.8.0",
      "resolved": "https://registry.npmjs.org/css-selector-tokenizer/-/css-selector-tokenizer-0.8.0.tgz",
@@ -1472,6 +1523,31 @@
        "node": ">=10.13.0"
      }
    },
+    "node_modules/has-flag": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz",
+      "integrity": "sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==",
+      "devOptional": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/highlight.js": {
+      "version": "11.10.0",
+      "resolved": "https://registry.npmjs.org/highlight.js/-/highlight.js-11.10.0.tgz",
+      "integrity": "sha512-SYVnVFswQER+zu1laSya563s+F8VDGt7o35d4utbamowvUNLLMovFqwCLSocpZTz3MgaSRA1IbqRWZv97dtErQ==",
+      "engines": {
+        "node": ">=12.0.0"
+      }
+    },
+    "node_modules/immutable": {
+      "version": "5.0.3",
+      "resolved": "https://registry.npmjs.org/immutable/-/immutable-5.0.3.tgz",
+      "integrity": "sha512-P8IdPQHq3lA1xVeBRi5VPqUm5HDgKnx0Ru51wZz5mjxHr5n3RWhjIpOFU7ybkUxfB+5IToy+OLaHYDBIWsv+uw==",
+      "devOptional": true,
+      "license": "MIT"
+    },
    "node_modules/is-glob": {
      "version": "4.0.3",
      "resolved": "https://registry.npmjs.org/is-glob/-/is-glob-4.0.3.tgz",
@@ -1502,6 +1578,22 @@
        "jiti": "bin/jiti.js"
      }
    },
+    "node_modules/katex": {
+      "version": "0.16.15",
+      "resolved": "https://registry.npmjs.org/katex/-/katex-0.16.15.tgz",
+      "integrity": "sha512-yE9YJIEAk2aZ+FL/G8r+UGw0CTUzEA8ZFy6E+8tc3spHUKq3qBnzCkI1CQwGoI9atJhVyFPEypQsTY7mJ1Pi9w==",
+      "funding": [
+        "https://opencollective.com/katex",
+        "https://github.com/sponsors/katex"
+      ],
+      "license": "MIT",
+      "dependencies": {
+        "commander": "^8.3.0"
+      },
+      "bin": {
+        "katex": "cli.js"
+      }
+    },
    "node_modules/lilconfig": {
      "version": "2.1.0",
      "resolved": "https://registry.npmjs.org/lilconfig/-/lilconfig-2.1.0.tgz",
@@ -2021,6 +2113,381 @@
      "integrity": "sha512-AYnb1nQyY49te+VRAVgmzfcgjYS91mY5P0TKUDCLEM+gNnA+3T6rWITXRLYCpahpqSQbN5cE+gHpnPyXjHWxcw==",
      "license": "MIT"
    },
+    "node_modules/rxjs": {
+      "version": "7.8.1",
+      "resolved": "https://registry.npmjs.org/rxjs/-/rxjs-7.8.1.tgz",
+      "integrity": "sha512-AA3TVj+0A2iuIoQkWEK/tqFjBq2j+6PO6Y0zJcvzLAFhEFIO3HL0vls9hWLncZbAAbK0mar7oZ4V079I/qPMxg==",
+      "devOptional": true,
+      "license": "Apache-2.0",
+      "dependencies": {
+        "tslib": "^2.1.0"
+      }
+    },
+    "node_modules/sass-embedded": {
+      "version": "1.83.0",
+      "resolved": "https://registry.npmjs.org/sass-embedded/-/sass-embedded-1.83.0.tgz",
+      "integrity": "sha512-/8cYZeL39evUqe0o//193na51Q1VWZ61qhxioQvLJwOtWIrX+PgNhCyD8RSuTtmzc4+6+waFZf899bfp/MCUwA==",
+      "devOptional": true,
+      "license": "MIT",
+      "dependencies": {
+        "@bufbuild/protobuf": "^2.0.0",
+        "buffer-builder": "^0.2.0",
+        "colorjs.io": "^0.5.0",
+        "immutable": "^5.0.2",
+        "rxjs": "^7.4.0",
+        "supports-color": "^8.1.1",
+        "sync-child-process": "^1.0.2",
+        "varint": "^6.0.0"
+      },
+      "bin": {
+        "sass": "dist/bin/sass.js"
+      },
+      "engines": {
+        "node": ">=16.0.0"
+      },
+      "optionalDependencies": {
+        "sass-embedded-android-arm": "1.83.0",
+        "sass-embedded-android-arm64": "1.83.0",
+        "sass-embedded-android-ia32": "1.83.0",
+        "sass-embedded-android-riscv64": "1.83.0",
+        "sass-embedded-android-x64": "1.83.0",
+        "sass-embedded-darwin-arm64": "1.83.0",
+        "sass-embedded-darwin-x64": "1.83.0",
+        "sass-embedded-linux-arm": "1.83.0",
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+      "license": "MIT",
+      "optional": true,
+      "os": [
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+      ],
+      "engines": {
+        "node": ">=14.0.0"
+      }
+    },
+    "node_modules/sass-embedded-linux-musl-riscv64": {
+      "version": "1.83.0",
+      "resolved": "https://registry.npmjs.org/sass-embedded-linux-musl-riscv64/-/sass-embedded-linux-musl-riscv64-1.83.0.tgz",
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+      "optional": true,
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+    "node_modules/sass-embedded-linux-riscv64": {
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+        "riscv64"
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+      "license": "MIT",
+      "optional": true,
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+      "engines": {
+        "node": ">=14.0.0"
+      }
+    },
+    "node_modules/sass-embedded-linux-x64": {
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+      "resolved": "https://registry.npmjs.org/sass-embedded-linux-x64/-/sass-embedded-linux-x64-1.83.0.tgz",
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+      "cpu": [
+        "x64"
+      ],
+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "linux"
+      ],
+      "engines": {
+        "node": ">=14.0.0"
+      }
+    },
+    "node_modules/sass-embedded-win32-arm64": {
+      "version": "1.83.0",
+      "resolved": "https://registry.npmjs.org/sass-embedded-win32-arm64/-/sass-embedded-win32-arm64-1.83.0.tgz",
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+      "cpu": [
+        "arm64"
+      ],
+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "win32"
+      ],
+      "engines": {
+        "node": ">=14.0.0"
+      }
+    },
+    "node_modules/sass-embedded-win32-ia32": {
+      "version": "1.83.0",
+      "resolved": "https://registry.npmjs.org/sass-embedded-win32-ia32/-/sass-embedded-win32-ia32-1.83.0.tgz",
+      "integrity": "sha512-2PxNXJ8Pad4geVcTXY4rkyTr5AwbF8nfrCTDv0ulbTvPhzX2mMKEGcBZUXWn5BeHZTBc6whNMfS7d5fQXR9dDQ==",
+      "cpu": [
+        "ia32"
+      ],
+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "win32"
+      ],
+      "engines": {
+        "node": ">=14.0.0"
+      }
+    },
+    "node_modules/sass-embedded-win32-x64": {
+      "version": "1.83.0",
+      "resolved": "https://registry.npmjs.org/sass-embedded-win32-x64/-/sass-embedded-win32-x64-1.83.0.tgz",
+      "integrity": "sha512-muBXkFngM6eLTNqOV0FQi7Dv9s+YRQ42Yem26mosdan/GmJQc81deto6uDTgrYn+bzFNmiXcOdfm+0MkTWK3OQ==",
+      "cpu": [
+        "x64"
+      ],
+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "win32"
+      ],
+      "engines": {
+        "node": ">=14.0.0"
+      }
+    },
    "node_modules/sucrase": {
      "version": "3.35.0",
      "resolved": "https://registry.npmjs.org/sucrase/-/sucrase-3.35.0.tgz",
@@ -2640,6 +3107,45 @@
        "node": ">=8"
      }
    },
+    "node_modules/supports-color": {
+      "version": "8.1.1",
+      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-8.1.1.tgz",
+      "integrity": "sha512-MpUEN2OodtUzxvKQl72cUF7RQ5EiHsGvSsVG0ia9c5RbWGL2CI4C7EpPS8UTBIplnlzZiNuV56w+FuNxy3ty2Q==",
+      "devOptional": true,
+      "license": "MIT",
+      "dependencies": {
+        "has-flag": "^4.0.0"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/supports-color?sponsor=1"
+      }
+    },
+    "node_modules/sync-child-process": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/sync-child-process/-/sync-child-process-1.0.2.tgz",
+      "integrity": "sha512-8lD+t2KrrScJ/7KXCSyfhT3/hRq78rC0wBFqNJXv3mZyn6hW2ypM05JmlSvtqRbeq6jqA94oHbxAr2vYsJ8vDA==",
+      "devOptional": true,
+      "license": "MIT",
+      "dependencies": {
+        "sync-message-port": "^1.0.0"
+      },
+      "engines": {
+        "node": ">=16.0.0"
+      }
+    },
+    "node_modules/sync-message-port": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/sync-message-port/-/sync-message-port-1.1.3.tgz",
+      "integrity": "sha512-GTt8rSKje5FilG+wEdfCkOcLL7LWqpMlr2c3LRuKt/YXxcJ52aGSbGBAdI4L3aaqfrBt6y711El53ItyH1NWzg==",
+      "devOptional": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=16.0.0"
+      }
+    },
    "node_modules/tailwindcss": {
      "version": "3.4.15",
      "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-3.4.15.tgz",
@@ -2677,12 +3183,32 @@
        "node": ">=14.0.0"
      }
    },
+    "node_modules/textlinestream": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/textlinestream/-/textlinestream-1.1.1.tgz",
+      "integrity": "sha512-iBHbi7BQxrFmwZUQJsT0SjNzlLLsXhvW/kg7EyOMVMBIrlnj/qYofwo1LVLZi+3GbUEo96Iu2eqToI2+lZoAEQ==",
+      "license": "MIT"
+    },
+    "node_modules/tslib": {
+      "version": "2.8.1",
+      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz",
+      "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==",
+      "devOptional": true,
+      "license": "0BSD"
+    },
    "node_modules/uc.micro": {
      "version": "2.1.0",
      "resolved": "https://registry.npmjs.org/uc.micro/-/uc.micro-2.1.0.tgz",
      "integrity": "sha512-ARDJmphmdvUk6Glw7y9DQ2bFkKBHwQHLi2lsaH6PPmz/Ka9sFOBsBluozhDltWmnv9u/cF6Rt87znRTPV+yp/A==",
      "license": "MIT"
    },
+    "node_modules/varint": {
+      "version": "6.0.0",
+      "resolved": "https://registry.npmjs.org/varint/-/varint-6.0.0.tgz",
+      "integrity": "sha512-cXEIW6cfr15lFv563k4GuVuW/fiwjknytD37jIOLSdSWuOI6WnO/oKwmP2FQTU2l01LP8/M5TSAJpzUaGe3uWg==",
+      "devOptional": true,
+      "license": "MIT"
+    },
    "node_modules/vite": {
      "version": "5.4.11",
      "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.11.tgz",
@@ -6,17 +6,24 @@
  "scripts": {
    "dev": "vite",
    "build": "vite build",
-    "preview": "vite preview"
+    "preview": "vite preview",
+    "analyze": "ANALYZE=1 npx vite-bundle-visualizer"
  },
  "devDependencies": {
+    "sass-embedded": "^1.83.0",
    "vite": "^5.4.10"
  },
  "dependencies": {
+    "@sec-ant/readable-stream": "^0.6.0",
+    "@vscode/markdown-it-katex": "^1.1.1",
    "autoprefixer": "^10.4.20",
    "daisyui": "^4.12.14",
+    "highlight.js": "^11.10.0",
+    "katex": "^0.16.15",
    "markdown-it": "^14.1.0",
    "postcss": "^8.4.49",
    "tailwindcss": "^3.4.15",
+    "textlinestream": "^1.1.1",
    "vite-plugin-singlefile": "^2.0.3",
    "vue": "^3.5.13"
  }
@@ -0,0 +1,33 @@
+{
+  "demo": true,
+  "id": "conv-1734086746930",
+  "lastModified": 1734087548943,
+  "messages": [
+    {
+      "id": 1734086764521,
+      "role": "user",
+      "content": "this is a demo conversation, used in dev mode"
+    },
+    {
+      "id": 1734087548327,
+      "role": "assistant",
+      "content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$",
+      "timings": {
+        "prompt_n": 1,
+        "prompt_ms": 28.923,
+        "predicted_n": 25,
+        "predicted_ms": 573.016
+      }
+    },
+    {
+      "id": 1734087548328,
+      "role": "user",
+      "content": "this is a demo conversation, used in dev mode"
+    },
+    {
+      "id": 1734087548329,
+      "role": "assistant",
+      "content": "Code block:\n```js\nconsole.log('hello world')\n```\n```sh\nls -la /dev\n```"
+    }
+  ]
+}
@@ -1,225 +0,0 @@
-const paramDefaults = {
-  stream: true,
-  temperature: 0.2,
-};
-
-let generation_settings = null;
-
-export class CompletionError extends Error {
-  constructor(message, name, data) {
-    super(message);
-    this.name = name;
-  }
-};
-
-// Completes the prompt as a generator. Recommended for most use cases.
-//
-// Example:
-//
-//    import { llama } from '/completion.js'
-//
-//    const request = llama("Tell me a joke", {n_predict: 800})
-//    for await (const chunk of request) {
-//      document.write(chunk.data.content)
-//    }
-//
-export async function* llama(prompt, params = {}, config = {}) {
-  let controller = config.controller;
-  const api_url = config.api_url?.replace(/\/+$/, '') || "";
-
-  if (!controller) {
-    controller = new AbortController();
-  }
-
-  const completionParams = { ...paramDefaults, ...params, prompt };
-
-  const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, {
-    method: 'POST',
-    body: JSON.stringify(completionParams),
-    headers: {
-      'Connection': 'keep-alive',
-      'Content-Type': 'application/json',
-      'Accept': 'text/event-stream',
-      ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
-    },
-    signal: controller.signal,
-  });
-
-  const status = response.status;
-  if (status !== 200) {
-    try {
-      const body = await response.json();
-      if (body && body.error && body.error.message) {
-        throw new CompletionError(body.error.message, 'ServerError');
-      }
-    } catch (err) {
-      throw new CompletionError(err.message, 'ServerError');
-    }
-  }
-
-  const reader = response.body.getReader();
-  const decoder = new TextDecoder();
-
-  let content = "";
-  let leftover = ""; // Buffer for partially read lines
-
-  try {
-    let cont = true;
-
-    while (cont) {
-      const result = await reader.read();
-      if (result.done) {
-        break;
-      }
-
-      // Add any leftover data to the current chunk of data
-      const text = leftover + decoder.decode(result.value);
-
-      // Check if the last character is a line break
-      const endsWithLineBreak = text.endsWith('\n');
-
-      // Split the text into lines
-      let lines = text.split('\n');
-
-      // If the text doesn't end with a line break, then the last line is incomplete
-      // Store it in leftover to be added to the next chunk of data
-      if (!endsWithLineBreak) {
-        leftover = lines.pop();
-      } else {
-        leftover = ""; // Reset leftover if we have a line break at the end
-      }
-
-      // Parse all sse events and add them to result
-      const regex = /^(\S+):\s(.*)$/gm;
-      for (const line of lines) {
-        const match = regex.exec(line);
-        if (match) {
-          result[match[1]] = match[2];
-          if (result.data === '[DONE]') {
-            cont = false;
-            break;
-          }
-
-          // since we know this is llama.cpp, let's just decode the json in data
-          if (result.data) {
-            result.data = JSON.parse(result.data);
-            content += result.data.content;
-
-            // yield
-            yield result;
-
-            // if we got a stop token from server, we will break here
-            if (result.data.stop) {
-              if (result.data.generation_settings) {
-                generation_settings = result.data.generation_settings;
-              }
-              cont = false;
-              break;
-            }
-          }
-          if (result.error) {
-            try {
-              result.error = JSON.parse(result.error);
-              if (result.error.message.includes('slot unavailable')) {
-                // Throw an error to be caught by upstream callers
-                throw new Error('slot unavailable');
-              } else {
-                console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
-              }
-            } catch(e) {
-              console.error(`llama.cpp error ${result.error}`)
-            }
-          }
-        }
-      }
-    }
-  } catch (e) {
-    if (e.name !== 'AbortError') {
-      console.error("llama error: ", e);
-    }
-    throw e;
-  }
-  finally {
-    controller.abort();
-  }
-
-  return content;
-}
-
-// Call llama, return an event target that you can subscribe to
-//
-// Example:
-//
-//    import { llamaEventTarget } from '/completion.js'
-//
-//    const conn = llamaEventTarget(prompt)
-//    conn.addEventListener("message", (chunk) => {
-//      document.write(chunk.detail.content)
-//    })
-//
-export const llamaEventTarget = (prompt, params = {}, config = {}) => {
-  const eventTarget = new EventTarget();
-  (async () => {
-    let content = "";
-    for await (const chunk of llama(prompt, params, config)) {
-      if (chunk.data) {
-        content += chunk.data.content;
-        eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
-      }
-      if (chunk.data.generation_settings) {
-        eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
-      }
-      if (chunk.data.timings) {
-        eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
-      }
-    }
-    eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
-  })();
-  return eventTarget;
-}
-
-// Call llama, return a promise that resolves to the completed text. This does not support streaming
-//
-// Example:
-//
-//     llamaPromise(prompt).then((content) => {
-//       document.write(content)
-//     })
-//
-//     or
-//
-//     const content = await llamaPromise(prompt)
-//     document.write(content)
-//
-export const llamaPromise = (prompt, params = {}, config = {}) => {
-  return new Promise(async (resolve, reject) => {
-    let content = "";
-    try {
-      for await (const chunk of llama(prompt, params, config)) {
-        content += chunk.data.content;
-      }
-      resolve(content);
-    } catch (error) {
-      reject(error);
-    }
-  });
-};
-
-/**
- * (deprecated)
- */
-export const llamaComplete = async (params, controller, callback) => {
-  for await (const chunk of llama(params.prompt, params, { controller })) {
-    callback(chunk);
-  }
-}
-
-// Get the model info from the server. This is useful for getting the context window and so on.
-export const llamaModelInfo = async (config = {}) => {
-  if (!generation_settings) {
-    const api_url = config.api_url?.replace(/\/+$/, '') || "";
-    const props = await fetch(`${api_url}/props`).then(r => r.json());
-    generation_settings = props.default_generation_settings;
-  }
-  return generation_settings;
-}
@@ -0,0 +1,60 @@
+import hljs from 'highlight.js/lib/core';
+
+// only import commonly used languages to reduce bundle size
+
+import python from 'highlight.js/lib/languages/python';
+import javascript from 'highlight.js/lib/languages/javascript';
+import json from 'highlight.js/lib/languages/json';
+import bash from 'highlight.js/lib/languages/bash';
+import yaml from 'highlight.js/lib/languages/yaml';
+import markdown from 'highlight.js/lib/languages/markdown';
+import scss from 'highlight.js/lib/languages/scss';
+import xml from 'highlight.js/lib/languages/xml';
+import ruby from 'highlight.js/lib/languages/ruby';
+import go from 'highlight.js/lib/languages/go';
+import java from 'highlight.js/lib/languages/java';
+import rust from 'highlight.js/lib/languages/rust';
+import scala from 'highlight.js/lib/languages/scala';
+import cpp from 'highlight.js/lib/languages/cpp';
+import csharp from 'highlight.js/lib/languages/csharp';
+import swift from 'highlight.js/lib/languages/swift';
+import dart from 'highlight.js/lib/languages/dart';
+import elixir from 'highlight.js/lib/languages/elixir';
+import kotlin from 'highlight.js/lib/languages/kotlin';
+import lua from 'highlight.js/lib/languages/lua';
+import php from 'highlight.js/lib/languages/php';
+import latex from 'highlight.js/lib/languages/latex';
+
+hljs.registerLanguage('python', python);
+hljs.registerLanguage('javascript', javascript);
+hljs.registerLanguage('json', json);
+hljs.registerLanguage('yaml', yaml);
+hljs.registerLanguage('markdown', markdown);
+hljs.registerLanguage('xml', xml);
+hljs.registerLanguage('ruby', ruby);
+hljs.registerLanguage('go', go);
+hljs.registerLanguage('java', java);
+hljs.registerLanguage('rust', rust);
+hljs.registerLanguage('scala', scala);
+hljs.registerLanguage('csharp', csharp);
+hljs.registerLanguage('swift', swift);
+hljs.registerLanguage('dart', dart);
+hljs.registerLanguage('elixir', elixir);
+hljs.registerLanguage('kotlin', kotlin);
+hljs.registerLanguage('lua', lua);
+hljs.registerLanguage('php', php);
+hljs.registerLanguage('latex', latex);
+
+// reuse some languages to further reduce bundle size
+
+hljs.registerLanguage('shell', bash);
+hljs.registerLanguage('bash', bash);
+hljs.registerLanguage('sh', bash);
+
+hljs.registerLanguage('css', scss);
+hljs.registerLanguage('scss', scss);
+
+hljs.registerLanguage('c', cpp);
+hljs.registerLanguage('cpp', cpp);
+
+export default hljs;
@@ -0,0 +1,66 @@
+import katex from 'katex';
+
+// Adapted from https://github.com/SchneeHertz/markdown-it-katex-gpt
+// MIT license
+
+const defaultOptions = {
+  delimiters: [
+    { left: '\\[', right: '\\]', display: true },
+    { left: '\\(', right: '\\)', display: false },
+  ],
+};
+
+export function renderLatexHTML(content, display = false) {
+  return katex.renderToString(content, {
+    throwOnError: false,
+    output: 'mathml',
+    displayMode: display,
+  });
+}
+
+function escapedBracketRule(options) {
+  return (state, silent) => {
+    const max = state.posMax;
+    const start = state.pos;
+
+    for (const { left, right, display } of options.delimiters) {
+
+      // Check if it starts with the left delimiter
+      if (!state.src.slice(start).startsWith(left)) continue;
+
+      // Skip the length of the left delimiter
+      let pos = start + left.length;
+
+      // Find the matching right delimiter
+      while (pos < max) {
+        if (state.src.slice(pos).startsWith(right)) {
+          break;
+        }
+        pos++;
+      }
+
+      // No matching right delimiter found, skip to the next match
+      if (pos >= max) continue;
+
+      // If not in silent mode, convert LaTeX formula to MathML
+      if (!silent) {
+        const content = state.src.slice(start + left.length, pos);
+        try {
+          const renderedContent = renderLatexHTML(content, display);
+          const token = state.push('html_inline', '', 0);
+          token.content = renderedContent;
+        } catch (e) {
+          console.error(e);
+        }
+      }
+
+      // Update position, skip the length of the right delimiter
+      state.pos = pos + right.length;
+      return true;
+    }
+  }
+}
+
+export default function (md, options = defaultOptions) {
+  md.inline.ruler.after('text', 'escaped_bracket', escapedBracketRule(options));
+}
@@ -1,23 +1,42 @@
-import './styles.css';
+import './styles.scss';
 import { createApp, defineComponent, shallowRef, computed, h } from 'vue/dist/vue.esm-bundler.js';
-import { llama } from './completion.js';
 import MarkdownIt from 'markdown-it';
+import TextLineStream from 'textlinestream';
+
+// math formula rendering
+import 'katex/dist/katex.min.css';
+import markdownItKatexGpt from './katex-gpt';
+import markdownItKatexNormal from '@vscode/markdown-it-katex';
+
+// code highlighting
+import hljs from './highlight-config';
+import daisyuiThemes from 'daisyui/src/theming/themes';
+
+// ponyfill for missing ReadableStream asyncIterator on Safari
+import { asyncIterator } from "@sec-ant/readable-stream/ponyfill/asyncIterator";
+
+const isDev = import.meta.env.MODE === 'development';

 // utility functions
 const isString = (x) => !!x.toLowerCase;
-const isNumeric = (n) => !isString(n) && !isNaN(n);
+const isBoolean = (x) => x === true || x === false;
+const isNumeric = (n) => !isString(n) && !isNaN(n) && !isBoolean(n);
 const escapeAttr = (str) => str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
 const copyStr = (str) => navigator.clipboard.writeText(str);

 // constants
-const BASE_URL = localStorage.getItem('base') // for debugging
-  || (new URL('.', document.baseURI).href).toString(); // for production
+const BASE_URL = isDev
+  ? (localStorage.getItem('base') || 'https://localhost:8080') // for debugging
+  : (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
+console.log({ BASE_URL });
+
 const CONFIG_DEFAULT = {
  // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
  apiKey: '',
  systemMessage: 'You are a helpful assistant.',
+  showTokensPerSecond: false,
  // make sure these default values are in sync with `common.h`
-  samplers: 'dkypmxt',
+  samplers: 'edkypmxt',
  temperature: 0.8,
  dynatemp_range: 0.0,
  dynatemp_exponent: 1.0,
@@ -65,12 +84,39 @@ const CONFIG_INFO = {
 // config keys having numeric value (i.e. temperature, top_k, top_p, etc)
 const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT).filter(e => isNumeric(e[1])).map(e => e[0]);
 // list of themes supported by daisyui
-const THEMES = ['light', 'dark', 'cupcake', 'bumblebee', 'emerald', 'corporate', 'synthwave', 'retro', 'cyberpunk', 'valentine', 'halloween', 'garden', 'forest', 'aqua', 'lofi', 'pastel', 'fantasy', 'wireframe', 'black', 'luxury', 'dracula', 'cmyk', 'autumn', 'business', 'acid', 'lemonade', 'night', 'coffee', 'winter', 'dim', 'nord', 'sunset'];
+const THEMES = ['light', 'dark']
+  // make sure light & dark are always at the beginning
+  .concat(Object.keys(daisyuiThemes).filter(t => t !== 'light' && t !== 'dark'));

 // markdown support
 const VueMarkdown = defineComponent(
  (props) => {
-    const md = shallowRef(new MarkdownIt({ breaks: true }));
+    const md = shallowRef(new MarkdownIt({
+      breaks: true,
+      highlight: function (str, lang) { // Add highlight.js
+        if (lang && hljs.getLanguage(lang)) {
+          try {
+            return '<pre><code class="hljs">' +
+                   hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
+                   '</code></pre>';
+          } catch (__) {}
+        }
+        return '<pre><code class="hljs">' + md.value.utils.escapeHtml(str) + '</code></pre>';
+      }
+    }));
+    // support latex with double dollar sign and square brackets
+    md.value.use(markdownItKatexGpt, {
+      delimiters: [
+        { left: '\\[', right: '\\]', display: true },
+        { left: '\\(', right: '\\)', display: false },
+        { left: '$$', right: '$$', display: false },
+        // do not add single dollar sign here, other wise it will confused with dollar used for money symbol
+      ],
+      throwOnError: false,
+    });
+    // support latex with single dollar sign
+    md.value.use(markdownItKatexNormal, { throwOnError: false });
+    // add copy button to code blocks
    const origFenchRenderer = md.value.renderer.rules.fence;
    md.value.renderer.rules.fence = (tokens, idx, ...args) => {
      const content = tokens[idx].content;
@@ -101,6 +147,48 @@ const SettingsModalShortInput = defineComponent({
  },
 });

+// message bubble component
+const MessageBubble = defineComponent({
+  components: {
+    VueMarkdown
+  },
+  template: document.getElementById('message-bubble').innerHTML,
+  props: {
+    config: Object,
+    msg: Object,
+    isGenerating: Boolean,
+    editUserMsgAndRegenerate: Function,
+    regenerateMsg: Function,
+  },
+  data() {
+    return {
+      editingContent: null,
+    };
+  },
+  computed: {
+    timings() {
+      if (!this.msg.timings) return null;
+      return {
+        ...this.msg.timings,
+        prompt_per_second: this.msg.timings.prompt_n / (this.msg.timings.prompt_ms / 1000),
+        predicted_per_second: this.msg.timings.predicted_n / (this.msg.timings.predicted_ms / 1000),
+      };
+    }
+  },
+  methods: {
+    copyMsg() {
+      copyStr(this.msg.content);
+    },
+    editMsg() {
+      this.editUserMsgAndRegenerate({
+        ...this.msg,
+        content: this.editingContent,
+      });
+      this.editingContent = null;
+    },
+  },
+});
+
 // coversations is stored in localStorage
 // format: { [convId]: { id: string, lastModified: number, messages: [...] } }
 // convId is a string prefixed with 'conv-'
@@ -192,10 +280,29 @@ const chatScrollToBottom = (requiresNearBottom) => {
  }
 };

+// wrapper for SSE
+async function* sendSSEPostRequest(url, fetchOptions) {
+  const res = await fetch(url, fetchOptions);
+  const lines = res.body
+    .pipeThrough(new TextDecoderStream())
+    .pipeThrough(new TextLineStream());
+  for await (const line of asyncIterator(lines)) {
+    if (isDev) console.log({line});
+    if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
+      const data = JSON.parse(line.slice(5));
+      yield data;
+    } else if (line.startsWith('error:')) {
+      const data = JSON.parse(line.slice(6));
+      throw new Error(data.message || 'Unknown error');
+    }
+  }
+};
+
 const mainApp = createApp({
  components: {
    VueMarkdown,
    SettingsModalShortInput,
+    MessageBubble,
  },
  data() {
    return {
@@ -209,11 +316,11 @@ const mainApp = createApp({
      selectedTheme: StorageUtils.getTheme(),
      config: StorageUtils.getConfig(),
      showConfigDialog: false,
-      editingMsg: null,
      // const
      themes: THEMES,
      configDefault: {...CONFIG_DEFAULT},
      configInfo: {...CONFIG_INFO},
+      isDev,
    }
  },
  computed: {},
@@ -225,6 +332,16 @@ const mainApp = createApp({
      if (this.isGenerating) chatScrollToBottom(true);
    });
    resizeObserver.observe(pendingMsgElem);
+    this.setSelectedTheme(this.selectedTheme);
+  },
+  watch: {
+    viewingConvId: function(val, oldVal) {
+      if (val != oldVal) {
+        this.fetchMessages();
+        chatScrollToBottom();
+        this.hideSidebar();
+      }
+    }
  },
  methods: {
    hideSidebar() {
@@ -232,23 +349,17 @@ const mainApp = createApp({
    },
    setSelectedTheme(theme) {
      this.selectedTheme = theme;
+      document.body.setAttribute('data-theme', theme);
+      document.body.setAttribute('data-color-scheme', daisyuiThemes[theme]?.['color-scheme'] ?? 'auto');
      StorageUtils.setTheme(theme);
    },
    newConversation() {
      if (this.isGenerating) return;
      this.viewingConvId = StorageUtils.getNewConvId();
-      this.editingMsg = null;
-      this.fetchMessages();
-      chatScrollToBottom();
-      this.hideSidebar();
    },
    setViewingConv(convId) {
      if (this.isGenerating) return;
      this.viewingConvId = convId;
-      this.editingMsg = null;
-      this.fetchMessages();
-      chatScrollToBottom();
-      this.hideSidebar();
    },
    deleteConv(convId) {
      if (this.isGenerating) return;
@@ -256,7 +367,6 @@ const mainApp = createApp({
        StorageUtils.remove(convId);
        if (this.viewingConvId === convId) {
          this.viewingConvId = StorageUtils.getNewConvId();
-          this.editingMsg = null;
        }
        this.fetchConversation();
        this.fetchMessages();
@@ -291,7 +401,6 @@ const mainApp = createApp({
      this.fetchConversation();
      this.fetchMessages();
      this.inputMsg = '';
-      this.editingMsg = null;
      this.generateMessage(currConvId);
      chatScrollToBottom();
    },
@@ -299,7 +408,6 @@ const mainApp = createApp({
      if (this.isGenerating) return;
      this.pendingMsg = { id: Date.now()+1, role: 'assistant', content: null };
      this.isGenerating = true;
-      this.editingMsg = null;

      try {
        const abortController = new AbortController();
@@ -330,17 +438,21 @@ const mainApp = createApp({
          dry_allowed_length: this.config.dry_allowed_length,
          dry_penalty_last_n: this.config.dry_penalty_last_n,
          max_tokens: this.config.max_tokens,
+          timings_per_token: !!this.config.showTokensPerSecond,
          ...(this.config.custom.length ? JSON.parse(this.config.custom) : {}),
-          ...(this.config.apiKey ? { api_key: this.config.apiKey } : {}),
        };
-        const config = {
-          controller: abortController,
-          api_url: BASE_URL,
-          endpoint: '/chat/completions',
-        };
-        for await (const chunk of llama(prompt, params, config)) {
-          const stop = chunk.data.stop;
-          const addedContent = chunk.data.choices[0].delta.content;
+        const chunks = sendSSEPostRequest(`${BASE_URL}/v1/chat/completions`, {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+            ...(this.config.apiKey ? {'Authorization': `Bearer ${this.config.apiKey}`} : {})
+          },
+          body: JSON.stringify(params),
+          signal: abortController.signal,
+        });
+        for await (const chunk of chunks) {
+          const stop = chunk.stop;
+          const addedContent = chunk.choices[0].delta.content;
          const lastContent = this.pendingMsg.content || '';
          if (addedContent) {
            this.pendingMsg = {
@@ -349,6 +461,16 @@ const mainApp = createApp({
              content: lastContent + addedContent,
            };
          }
+          const timings = chunk.timings;
+          if (timings && this.config.showTokensPerSecond) {
+            // only extract what's really needed, to save some space
+            this.pendingMsg.timings = {
+              prompt_n: timings.prompt_n,
+              prompt_ms: timings.prompt_ms,
+              predicted_n: timings.predicted_n,
+              predicted_ms: timings.predicted_ms,
+            };
+          }
        }

        StorageUtils.appendMsg(currConvId, this.pendingMsg);
@@ -387,14 +509,10 @@ const mainApp = createApp({
      this.fetchMessages();
      this.generateMessage(currConvId);
    },
-    copyMsg(msg) {
-      copyStr(msg.content);
-    },
    editUserMsgAndRegenerate(msg) {
      if (this.isGenerating) return;
      const currConvId = this.viewingConvId;
      const newContent = msg.content;
-      this.editingMsg = null;
      StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
      StorageUtils.appendMsg(currConvId, {
        id: Date.now(),
@@ -441,6 +559,17 @@ const mainApp = createApp({
    fetchMessages() {
      this.messages = StorageUtils.getOneConversation(this.viewingConvId)?.messages ?? [];
    },
+
+    // debug functions
+    async debugImportDemoConv() {
+      const res = await fetch('/demo-conversation.json');
+      const demoConv = await res.json();
+      StorageUtils.remove(demoConv.id);
+      for (const msg of demoConv.messages) {
+        StorageUtils.appendMsg(demoConv.id, msg);
+      }
+      this.fetchConversation();
+    }
  },
 });
 mainApp.config.errorHandler = alert;
@@ -1,26 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-.markdown {
-  h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
-  pre {
-    @apply whitespace-pre-wrap rounded-lg p-2;
-    border: 1px solid currentColor;
-  }
-  /* TODO: fix markdown table */
-}
-
-.show-on-hover {
-  @apply md:opacity-0 md:group-hover:opacity-100;
-}
-.btn-mini {
-  @apply cursor-pointer hover:shadow-md;
-}
-.chat-screen { max-width: 900px; }
-
-.chat-bubble-base-300 {
-  --tw-bg-opacity: 1;
-  --tw-text-opacity: 1;
-  @apply bg-base-300 text-base-content;
-}
@@ -0,0 +1,48 @@
+@use "sass:meta";
+
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+.markdown {
+  h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
+  pre {
+    @apply whitespace-pre-wrap rounded-lg p-2;
+    border: 1px solid currentColor;
+  }
+  /* TODO: fix markdown table */
+}
+
+.show-on-hover {
+  @apply md:opacity-0 md:group-hover:opacity-100;
+}
+.btn-mini {
+  @apply cursor-pointer hover:shadow-md;
+}
+.chat-screen { max-width: 900px; }
+
+.chat-bubble-base-300 {
+  --tw-bg-opacity: 1;
+  --tw-text-opacity: 1;
+  @apply bg-base-300 text-base-content;
+}
+
+/* Highlight.js */
+[data-color-scheme='light'] {
+  @include meta.load-css('highlight.js/styles/stackoverflow-light');
+}
+[data-color-scheme='dark'] {
+  @include meta.load-css('highlight.js/styles/stackoverflow-dark');
+}
+[data-color-scheme='auto'] {
+  @media (prefers-color-scheme: light) {
+    @include meta.load-css('highlight.js/styles/stackoverflow-light');
+  }
+  @media (prefers-color-scheme: dark) {
+    @include meta.load-css('highlight.js/styles/stackoverflow-dark');
+  }
+}
+.hljs {
+  background: transparent !important;
+  padding: 0.5em !important;
+}
@@ -2,6 +2,9 @@
 import { viteSingleFile } from 'vite-plugin-singlefile';
 import path from 'path';
 import fs from 'fs';
+import zlib from 'zlib';
+
+const MAX_BUNDLE_SIZE = 1.5 * 1024 * 1024; // only increase when absolutely necessary

 const GUIDE_FOR_FRONTEND = `
 <!--
@@ -12,25 +15,45 @@ const GUIDE_FOR_FRONTEND = `
 -->
 `.trim();

-export default {
-  plugins: [
-    viteSingleFile(),
-    (function llamaCppPlugin() {
-      let config;
-      return {
-        name: 'llamacpp:build',
-        apply: 'build',
-        async configResolved(_config) {
-          config = _config;
-        },
-        writeBundle() {
-          const outputIndexHtml = path.join(config.build.outDir, 'index.html');
-          const content = fs.readFileSync(outputIndexHtml, 'utf-8');
+const BUILD_PLUGINS = [
+  viteSingleFile(),
+  (function llamaCppPlugin() {
+    let config;
+    return {
+      name: 'llamacpp:build',
+      apply: 'build',
+      async configResolved(_config) {
+        config = _config;
+      },
+      writeBundle() {
+        const outputIndexHtml = path.join(config.build.outDir, 'index.html');
+        const content = GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
+        const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), { level: 9 });

-          const targetOutputFile = path.join(config.build.outDir, '../../public/index.html');
-          fs.writeFileSync(targetOutputFile, GUIDE_FOR_FRONTEND + '\n' + content);
+        // because gzip header contains machine-specific info, we must remove these data from the header
+        // timestamp
+        compressed[0x4] = 0;
+        compressed[0x5] = 0;
+        compressed[0x6] = 0;
+        compressed[0x7] = 0;
+        // OS
+        compressed[0x9] = 0;
+
+        if (compressed.byteLength > MAX_BUNDLE_SIZE) {
+          throw new Error(
+            `Bundle size is too large (${Math.ceil(compressed.byteLength / 1024)} KB).\n` +
+            `Please reduce the size of the frontend or increase MAX_BUNDLE_SIZE in vite.config.js.\n`,
+          );
        }
+
+        const targetOutputFile = path.join(config.build.outDir, '../../public/index.html.gz');
+        fs.writeFileSync(targetOutputFile, compressed);
      }
-    })(),
-  ],
+    }
+  })(),
+];
+
+/** @type {import('vite').UserConfig} */
+export default {
+  plugins: process.env.ANALYZE ? [] : BUILD_PLUGINS,
 };
@@ -394,7 +394,7 @@ int main(int raw_argc, char ** raw_argv) {
    }

    if (show_token_count) {
-        printf("Total number of tokens: %ld\n", tokens.size());
+        printf("Total number of tokens: %zu\n", tokens.size());
    }
    // silence valgrind
    llama_free(ctx);
@@ -0,0 +1,5 @@
+set(TARGET llama-tts)
+add_executable(${TARGET} tts.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
@@ -0,0 +1,180 @@
+# convert the https://huggingface.co/novateur/WavTokenizer-large-speech-75token to HF format
+# the goal is to be able to reuse the convert_hf_to_gguf.py after that to create a GGUF file with the WavTokenizer decoder
+#
+# TODO: this script is LLM-generated and probably very inefficient and should be rewritten
+
+import torch
+import json
+import os
+import sys
+import re
+
+from safetensors.torch import save_file
+
+# default
+model_path = './model.pt';
+
+# read from CLI
+if len(sys.argv) > 1:
+    model_path = sys.argv[1]
+
+# get the directory of the input model
+path_dst = os.path.dirname(model_path)
+
+print(f"Loading model from {model_path}")
+
+model = torch.load(model_path, map_location='cpu')
+
+#print(model)
+
+# print all keys
+for key in model.keys():
+    print(key)
+    if key == 'hyper_parameters':
+        #print(model[key])
+        # dump as json pretty
+        print(json.dumps(model[key], indent=4))
+    #if key != 'state_dict' and key != 'optimizer_states':
+    #    print(model[key])
+
+# Check if the loaded model is a state_dict or a model instance
+if isinstance(model, torch.nn.Module):
+    state_dict = model.state_dict()
+else:
+    state_dict = model
+
+# Print the structure of the state_dict to understand its format
+print("State dictionary keys:")
+for key in state_dict.keys():
+    print(key)
+
+# Ensure the state_dict is flat and contains only torch.Tensor objects
+def flatten_state_dict(state_dict, parent_key='', sep='.'):
+    items = []
+    items_new = []
+
+    for k, v in state_dict.items():
+        new_key = f"{parent_key}{sep}{k}" if parent_key else k
+        if isinstance(v, torch.Tensor):
+            items.append((new_key, v))
+        elif isinstance(v, dict):
+            items.extend(flatten_state_dict(v, new_key, sep=sep).items())
+            return dict(items)
+
+    size_total_mb = 0
+
+    for key, value in list(items):
+        # keep only what we need for inference
+        if not key.startswith('state_dict.feature_extractor.encodec.quantizer.') and \
+           not key.startswith('state_dict.backbone.') and \
+           not key.startswith('state_dict.head.out'):
+               print('Skipping key: ', key)
+               continue
+
+        new_key = key
+
+        new_key = new_key.replace('state_dict.', '')
+        new_key = new_key.replace('pos_net', 'posnet')
+
+        # check if matches "backbone.posnet.%d.bias" or "backbone.posnet.%d.weight"
+        if new_key.startswith("backbone.posnet."):
+            match = re.match(r"backbone\.posnet\.(\d+)\.(bias|weight)", new_key)
+            if match:
+               new_key = f"backbone.posnet.{match.group(1)}.norm.{match.group(2)}"
+
+        # "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed" -> "backbone.embedding.weight"
+        if new_key == "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed":
+            new_key = "backbone.embedding.weight"
+
+        # these are the only rows used
+        # ref: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/wav_tokenizer/audio_codec.py#L100
+        if new_key.endswith("norm.scale.weight"):
+            new_key = new_key.replace("norm.scale.weight", "norm.weight")
+            value = value[0]
+
+        if new_key.endswith("norm.shift.weight"):
+            new_key = new_key.replace("norm.shift.weight", "norm.bias")
+            value = value[0]
+
+        if new_key.endswith("gamma"):
+            new_key = new_key.replace("gamma", "gamma.weight")
+
+        # convert from 1D [768] to 2D [768, 1] so that ggml_add can broadcast the bias
+        if (new_key.endswith("norm.weight") or new_key.endswith("norm1.weight") or new_key.endswith("norm2.weight") or new_key.endswith(".bias")) and (new_key.startswith("backbone.posnet") or new_key.startswith("backbone.embed.bias")):
+            value = value.unsqueeze(1)
+
+        if new_key.endswith("dwconv.bias"):
+            value = value.unsqueeze(1)
+
+        size_mb = value.element_size() * value.nelement() / (1024 * 1024)
+        print(f"{size_mb:8.2f} MB - {new_key}: {value.shape}")
+
+        size_total_mb += size_mb
+
+        #print(key, '->', new_key, ': ', value)
+        #print(key, '->', new_key)
+
+        items_new.append((new_key, value))
+
+    print(f"Total size: {size_total_mb:8.2f} MB")
+
+    return dict(items_new)
+
+flattened_state_dict = flatten_state_dict(state_dict)
+
+
+# Convert the model to the safetensors format
+output_path = path_dst + '/model.safetensors'
+save_file(flattened_state_dict, output_path)
+
+print(f"Model has been successfully converted and saved to {output_path}")
+
+# Calculate the total size of the .safetensors file
+total_size = os.path.getsize(output_path)
+
+# Create the weight map
+weight_map = {
+    "model.safetensors": ["*"]  # Assuming all weights are in one file
+}
+
+# Create metadata for the index.json file
+metadata = {
+    "total_size": total_size,
+    "weight_map": weight_map
+}
+
+# Save the metadata to index.json
+index_path = path_dst + '/index.json'
+with open(index_path, 'w') as f:
+    json.dump(metadata, f, indent=4)
+
+print(f"Metadata has been saved to {index_path}")
+
+config = {
+    "architectures": [
+        "WavTokenizerDec"
+    ],
+    "hidden_size": 1282,
+    "n_embd_features": 512,
+    "n_ff": 2304,
+    "vocab_size": 4096,
+    "n_head": 1,
+    "layer_norm_epsilon": 1e-6,
+    "group_norm_epsilon": 1e-6,
+    "group_norm_groups": 32,
+    "max_position_embeddings": 8192, # ?
+    "n_layer": 12,
+    "posnet": {
+        "n_embd": 768,
+        "n_layer": 6
+    },
+    "convnext": {
+        "n_embd": 768,
+        "n_layer": 12
+    },
+}
+
+with open(path_dst + '/config.json', 'w') as f:
+    json.dump(config, f, indent=4)
+
+print(f"Config has been saved to {path_dst + 'config.json'}")
@@ -0,0 +1,175 @@
+import sys
+#import json
+#import struct
+import requests
+import re
+
+def process_text(text: str):
+    text = re.sub(r'\d+(\.\d+)?', lambda x: x.group(), text.lower()) # TODO this needs to be fixed
+    text = re.sub(r'[-_/,\.\\]', ' ', text)
+    text = re.sub(r'[^a-z\s]', '', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text.split()
+
+# usage:
+# python tts-outetts.py http://server-llm:port http://server-dec:port "text"
+
+if len(sys.argv) <= 3:
+    print("usage: python tts-outetts.py http://server-llm:port http://server-dec:port \"text\"")
+    exit(1)
+
+host_llm = sys.argv[1]
+host_dec = sys.argv[2]
+text = sys.argv[3]
+
+prefix = """<|im_start|>
+<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>"""
+
+words = process_text(text)
+words = "<|text_sep|>".join([i.strip() for i in words])
+words += "<|text_end|>\n"
+
+# voice data
+# TODO: load from json
+#suffix = """<|audio_start|>
+#the<|t_0.08|><|code_start|><|257|><|740|><|636|><|913|><|788|><|1703|><|code_end|>
+#overall<|t_0.36|><|code_start|><|127|><|201|><|191|><|774|><|700|><|532|><|1056|><|557|><|798|><|298|><|1741|><|747|><|1662|><|1617|><|1702|><|1527|><|368|><|1588|><|1049|><|1008|><|1625|><|747|><|1576|><|728|><|1019|><|1696|><|1765|><|code_end|>
+#package<|t_0.56|><|code_start|><|935|><|584|><|1319|><|627|><|1016|><|1491|><|1344|><|1117|><|1526|><|1040|><|239|><|1435|><|951|><|498|><|723|><|1180|><|535|><|789|><|1649|><|1637|><|78|><|465|><|1668|><|901|><|595|><|1675|><|117|><|1009|><|1667|><|320|><|840|><|79|><|507|><|1762|><|1508|><|1228|><|1768|><|802|><|1450|><|1457|><|232|><|639|><|code_end|>
+#from<|t_0.19|><|code_start|><|604|><|782|><|1682|><|872|><|1532|><|1600|><|1036|><|1761|><|647|><|1554|><|1371|><|653|><|1595|><|950|><|code_end|>
+#just<|t_0.25|><|code_start|><|1782|><|1670|><|317|><|786|><|1748|><|631|><|599|><|1155|><|1364|><|1524|><|36|><|1591|><|889|><|1535|><|541|><|440|><|1532|><|50|><|870|><|code_end|>
+#two<|t_0.24|><|code_start|><|1681|><|1510|><|673|><|799|><|805|><|1342|><|330|><|519|><|62|><|640|><|1138|><|565|><|1552|><|1497|><|1552|><|572|><|1715|><|1732|><|code_end|>
+#people<|t_0.39|><|code_start|><|593|><|274|><|136|><|740|><|691|><|633|><|1484|><|1061|><|1138|><|1485|><|344|><|428|><|397|><|1562|><|645|><|917|><|1035|><|1449|><|1669|><|487|><|442|><|1484|><|1329|><|1832|><|1704|><|600|><|761|><|653|><|269|><|code_end|>
+#is<|t_0.16|><|code_start|><|566|><|583|><|1755|><|646|><|1337|><|709|><|802|><|1008|><|485|><|1583|><|652|><|10|><|code_end|>
+#pretty<|t_0.32|><|code_start|><|1818|><|1747|><|692|><|733|><|1010|><|534|><|406|><|1697|><|1053|><|1521|><|1355|><|1274|><|816|><|1398|><|211|><|1218|><|817|><|1472|><|1703|><|686|><|13|><|822|><|445|><|1068|><|code_end|>
+#remarkable<|t_0.68|><|code_start|><|230|><|1048|><|1705|><|355|><|706|><|1149|><|1535|><|1787|><|1356|><|1396|><|835|><|1583|><|486|><|1249|><|286|><|937|><|1076|><|1150|><|614|><|42|><|1058|><|705|><|681|><|798|><|934|><|490|><|514|><|1399|><|572|><|1446|><|1703|><|1346|><|1040|><|1426|><|1304|><|664|><|171|><|1530|><|625|><|64|><|1708|><|1830|><|1030|><|443|><|1509|><|1063|><|1605|><|1785|><|721|><|1440|><|923|><|code_end|>
+#sure<|t_0.36|><|code_start|><|792|><|1780|><|923|><|1640|><|265|><|261|><|1525|><|567|><|1491|><|1250|><|1730|><|362|><|919|><|1766|><|543|><|1|><|333|><|113|><|970|><|252|><|1606|><|133|><|302|><|1810|><|1046|><|1190|><|1675|><|code_end|>
+#i<|t_0.08|><|code_start|><|123|><|439|><|1074|><|705|><|1799|><|637|><|code_end|>
+#have<|t_0.16|><|code_start|><|1509|><|599|><|518|><|1170|><|552|><|1029|><|1267|><|864|><|419|><|143|><|1061|><|0|><|code_end|>
+#some<|t_0.16|><|code_start|><|619|><|400|><|1270|><|62|><|1370|><|1832|><|917|><|1661|><|167|><|269|><|1366|><|1508|><|code_end|>
+#critiques<|t_0.60|><|code_start|><|559|><|584|><|1163|><|1129|><|1313|><|1728|><|721|><|1146|><|1093|><|577|><|928|><|27|><|630|><|1080|><|1346|><|1337|><|320|><|1382|><|1175|><|1682|><|1556|><|990|><|1683|><|860|><|1721|><|110|><|786|><|376|><|1085|><|756|><|1523|><|234|><|1334|><|1506|><|1578|><|659|><|612|><|1108|><|1466|><|1647|><|308|><|1470|><|746|><|556|><|1061|><|code_end|>
+#about<|t_0.29|><|code_start|><|26|><|1649|><|545|><|1367|><|1263|><|1728|><|450|><|859|><|1434|><|497|><|1220|><|1285|><|179|><|755|><|1154|><|779|><|179|><|1229|><|1213|><|922|><|1774|><|1408|><|code_end|>
+#some<|t_0.23|><|code_start|><|986|><|28|><|1649|><|778|><|858|><|1519|><|1|><|18|><|26|><|1042|><|1174|><|1309|><|1499|><|1712|><|1692|><|1516|><|1574|><|code_end|>
+#of<|t_0.07|><|code_start|><|197|><|716|><|1039|><|1662|><|64|><|code_end|>
+#the<|t_0.08|><|code_start|><|1811|><|1568|><|569|><|886|><|1025|><|1374|><|code_end|>
+#gameplay<|t_0.48|><|code_start|><|1269|><|1092|><|933|><|1362|><|1762|><|1700|><|1675|><|215|><|781|><|1086|><|461|><|838|><|1022|><|759|><|649|><|1416|><|1004|><|551|><|909|><|787|><|343|><|830|><|1391|><|1040|><|1622|><|1779|><|1360|><|1231|><|1187|><|1317|><|76|><|997|><|989|><|978|><|737|><|189|><|code_end|>
+#aspects<|t_0.56|><|code_start|><|1423|><|797|><|1316|><|1222|><|147|><|719|><|1347|><|386|><|1390|><|1558|><|154|><|440|><|634|><|592|><|1097|><|1718|><|712|><|763|><|1118|><|1721|><|1311|><|868|><|580|><|362|><|1435|><|868|><|247|><|221|><|886|><|1145|><|1274|><|1284|><|457|><|1043|><|1459|><|1818|><|62|><|599|><|1035|><|62|><|1649|><|778|><|code_end|>
+#but<|t_0.20|><|code_start|><|780|><|1825|><|1681|><|1007|><|861|><|710|><|702|><|939|><|1669|><|1491|><|613|><|1739|><|823|><|1469|><|648|><|code_end|>
+#its<|t_0.09|><|code_start|><|92|><|688|><|1623|><|962|><|1670|><|527|><|599|><|code_end|>
+#still<|t_0.27|><|code_start|><|636|><|10|><|1217|><|344|><|713|><|957|><|823|><|154|><|1649|><|1286|><|508|><|214|><|1760|><|1250|><|456|><|1352|><|1368|><|921|><|615|><|5|><|code_end|>
+#really<|t_0.36|><|code_start|><|55|><|420|><|1008|><|1659|><|27|><|644|><|1266|><|617|><|761|><|1712|><|109|><|1465|><|1587|><|503|><|1541|><|619|><|197|><|1019|><|817|><|269|><|377|><|362|><|1381|><|507|><|1488|><|4|><|1695|><|code_end|>
+#enjoyable<|t_0.49|><|code_start|><|678|><|501|><|864|><|319|><|288|><|1472|><|1341|><|686|><|562|><|1463|><|619|><|1563|><|471|><|911|><|730|><|1811|><|1006|><|520|><|861|><|1274|><|125|><|1431|><|638|><|621|><|153|><|876|><|1770|><|437|><|987|><|1653|><|1109|><|898|><|1285|><|80|><|593|><|1709|><|843|><|code_end|>
+#and<|t_0.15|><|code_start|><|1285|><|987|><|303|><|1037|><|730|><|1164|><|502|><|120|><|1737|><|1655|><|1318|><|code_end|>
+#it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><|code_end|>
+#looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|>
+#lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>"""
+
+# TODO: tokenization is slow for some reason - here is pre-tokenized input
+suffix = [ 151667, 198, 1782, 155780, 151669, 151929, 152412, 152308, 152585, 152460, 153375, 151670, 198, 74455,
+          155808, 151669, 151799, 151873, 151863, 152446, 152372, 152204, 152728, 152229, 152470, 151970, 153413,
+          152419, 153334, 153289, 153374, 153199, 152040, 153260, 152721, 152680, 153297, 152419, 153248, 152400,
+          152691, 153368, 153437, 151670, 198, 1722, 155828, 151669, 152607, 152256, 152991, 152299, 152688, 153163,
+          153016, 152789, 153198, 152712, 151911, 153107, 152623, 152170, 152395, 152852, 152207, 152461, 153321,
+          153309, 151750, 152137, 153340, 152573, 152267, 153347, 151789, 152681, 153339, 151992, 152512, 151751,
+          152179, 153434, 153180, 152900, 153440, 152474, 153122, 153129, 151904, 152311, 151670, 198, 1499, 155791,
+          151669, 152276, 152454, 153354, 152544, 153204, 153272, 152708, 153433, 152319, 153226, 153043, 152325,
+          153267, 152622, 151670, 198, 4250, 155797, 151669, 153454, 153342, 151989, 152458, 153420, 152303, 152271,
+          152827, 153036, 153196, 151708, 153263, 152561, 153207, 152213, 152112, 153204, 151722, 152542, 151670, 198,
+          19789, 155796, 151669, 153353, 153182, 152345, 152471, 152477, 153014, 152002, 152191, 151734, 152312, 152810,
+          152237, 153224, 153169, 153224, 152244, 153387, 153404, 151670, 198, 16069, 155811, 151669, 152265, 151946,
+          151808, 152412, 152363, 152305, 153156, 152733, 152810, 153157, 152016, 152100, 152069, 153234, 152317,
+          152589, 152707, 153121, 153341, 152159, 152114, 153156, 153001, 153504, 153376, 152272, 152433, 152325,
+          151941, 151670, 198, 285, 155788, 151669, 152238, 152255, 153427, 152318, 153009, 152381, 152474, 152680,
+          152157, 153255, 152324, 151682, 151670, 198, 32955, 155804, 151669, 153490, 153419, 152364, 152405, 152682,
+          152206, 152078, 153369, 152725, 153193, 153027, 152946, 152488, 153070, 151883, 152890, 152489, 153144,
+          153375, 152358, 151685, 152494, 152117, 152740, 151670, 198, 37448, 480, 155840, 151669, 151902, 152720,
+          153377, 152027, 152378, 152821, 153207, 153459, 153028, 153068, 152507, 153255, 152158, 152921, 151958,
+          152609, 152748, 152822, 152286, 151714, 152730, 152377, 152353, 152470, 152606, 152162, 152186, 153071,
+          152244, 153118, 153375, 153018, 152712, 153098, 152976, 152336, 151843, 153202, 152297, 151736, 153380,
+          153502, 152702, 152115, 153181, 152735, 153277, 153457, 152393, 153112, 152595, 151670, 198, 19098, 155808,
+          151669, 152464, 153452, 152595, 153312, 151937, 151933, 153197, 152239, 153163, 152922, 153402, 152034,
+          152591, 153438, 152215, 151673, 152005, 151785, 152642, 151924, 153278, 151805, 151974, 153482, 152718,
+          152862, 153347, 151670, 198, 72, 155780, 151669, 151795, 152111, 152746, 152377, 153471, 152309, 151670, 198,
+          19016, 155788, 151669, 153181, 152271, 152190, 152842, 152224, 152701, 152939, 152536, 152091, 151815, 152733,
+          151672, 151670, 198, 14689, 155788, 151669, 152291, 152072, 152942, 151734, 153042, 153504, 152589, 153333,
+          151839, 151941, 153038, 153180, 151670, 198, 36996, 8303, 155832, 151669, 152231, 152256, 152835, 152801,
+          152985, 153400, 152393, 152818, 152765, 152249, 152600, 151699, 152302, 152752, 153018, 153009, 151992,
+          153054, 152847, 153354, 153228, 152662, 153355, 152532, 153393, 151782, 152458, 152048, 152757, 152428,
+          153195, 151906, 153006, 153178, 153250, 152331, 152284, 152780, 153138, 153319, 151980, 153142, 152418,
+          152228, 152733, 151670, 198, 9096, 155801, 151669, 151698, 153321, 152217, 153039, 152935, 153400, 152122,
+          152531, 153106, 152169, 152892, 152957, 151851, 152427, 152826, 152451, 151851, 152901, 152885, 152594,
+          153446, 153080, 151670, 198, 14689, 155795, 151669, 152658, 151700, 153321, 152450, 152530, 153191, 151673,
+          151690, 151698, 152714, 152846, 152981, 153171, 153384, 153364, 153188, 153246, 151670, 198, 1055, 155779,
+          151669, 151869, 152388, 152711, 153334, 151736, 151670, 198, 1782, 155780, 151669, 153483, 153240, 152241,
+          152558, 152697, 153046, 151670, 198, 5804, 1363, 155820, 151669, 152941, 152764, 152605, 153034, 153434,
+          153372, 153347, 151887, 152453, 152758, 152133, 152510, 152694, 152431, 152321, 153088, 152676, 152223,
+          152581, 152459, 152015, 152502, 153063, 152712, 153294, 153451, 153032, 152903, 152859, 152989, 151748,
+          152669, 152661, 152650, 152409, 151861, 151670, 198, 300, 7973, 155828, 151669, 153095, 152469, 152988,
+          152894, 151819, 152391, 153019, 152058, 153062, 153230, 151826, 152112, 152306, 152264, 152769, 153390,
+          152384, 152435, 152790, 153393, 152983, 152540, 152252, 152034, 153107, 152540, 151919, 151893, 152558,
+          152817, 152946, 152956, 152129, 152715, 153131, 153490, 151734, 152271, 152707, 151734, 153321, 152450,
+          151670, 198, 8088, 155792, 151669, 152452, 153497, 153353, 152679, 152533, 152382, 152374, 152611, 153341,
+          153163, 152285, 153411, 152495, 153141, 152320, 151670, 198, 1199, 155781, 151669, 151764, 152360, 153295,
+          152634, 153342, 152199, 152271, 151670, 198, 43366, 155799, 151669, 152308, 151682, 152889, 152016, 152385,
+          152629, 152495, 151826, 153321, 152958, 152180, 151886, 153432, 152922, 152128, 153024, 153040, 152593,
+          152287, 151677, 151670, 198, 53660, 155808, 151669, 151727, 152092, 152680, 153331, 151699, 152316, 152938,
+          152289, 152433, 153384, 151781, 153137, 153259, 152175, 153213, 152291, 151869, 152691, 152489, 151941,
+          152049, 152034, 153053, 152179, 153160, 151676, 153367, 151670, 198, 268, 4123, 480, 155821, 151669, 152350,
+          152173, 152536, 151991, 151960, 153144, 153013, 152358, 152234, 153135, 152291, 153235, 152143, 152583,
+          152402, 153483, 152678, 152192, 152533, 152946, 151797, 153103, 152310, 152293, 151825, 152548, 153442,
+          152109, 152659, 153325, 152781, 152570, 152957, 151752, 152265, 153381, 152515, 151670, 198, 437, 155787,
+          151669, 152957, 152659, 151975, 152709, 152402, 152836, 152174, 151792, 153409, 153327, 152990, 151670, 198,
+          275, 155781, 151669, 152520, 153038, 152067, 153273, 153185, 152265, 152974, 151670, 198, 94273, 155799,
+          151669, 152953, 152938, 153427, 152244, 151920, 153423, 152929, 152367, 153052, 152129, 152331, 152257,
+          152987, 152777, 153448, 152408, 151696, 152408, 152326, 152699, 151670, 198, 385, 16239, 155828, 151669,
+          152306, 152268, 153438, 153228, 152978, 152957, 153153, 153393, 152795, 152110, 152918, 152923, 152467,
+          152331, 153053, 153330, 151889, 153444, 152234, 152624, 151779, 152801, 152784, 152139, 152222, 152751,
+          152512, 153287, 153141, 153052, 151840, 152589, 152508, 153499, 152109, 152255, 151739, 152267, 152759,
+          153318, 153165, 153349, 151670, ]
+
+response = requests.post(
+    host_llm + "/completion",
+    json={
+        "prompt": [prefix + words, *suffix],
+        "n_predict": 1024,
+        "cache_prompt": True,
+        "return_tokens": True,
+        "samplers": ["top_k"],
+        "top_k": 16,
+        "seed": 1003,
+    }
+)
+
+response_json = response.json()
+
+#print(json.dumps(response_json, indent=4))
+#print(json.dumps(response_json["prompt"], indent=4).replace("\\n", "\n"))
+#print(json.dumps(response_json["timings"], indent=4))
+#print(json.dumps(response_json["tokens"], indent=4))
+
+codes = response_json["tokens"]
+
+codes = [t - 151672 for t in codes if t >= 151672 and t <= 155772]
+
+response = requests.post(
+    host_dec + "/embeddings",
+    json={
+        "input": [*codes],
+    }
+)
+
+response_json = response.json()
+
+#print(json.dumps(response_json, indent=4))
+
+# spectrogram
+embd = response_json[0]["embedding"]
+
+n_codes = len(embd)
+n_embd = len(embd[0])
+
+print('spectrogram generated: n_codes: %d, n_embd: %d' % (n_codes, n_embd))
+
+# post-process the spectrogram to convert to audio
+# TODO: see the tts.cpp:embd_to_audio() and implement it in Python
+print('converting to audio ...')
+print('TODO: see the tts.cpp:embd_to_audio() and implement it in Python')
@@ -0,0 +1,932 @@
+#include "arg.h"
+#include "common.h"
+#include "sampling.h"
+#include "log.h"
+#include "llama.h"
+
+#define _USE_MATH_DEFINES // For M_PI on MSVC
+
+#include <algorithm>
+#include <cmath>
+#include <cstdio>
+#include <fstream>
+#include <map>
+#include <regex>
+#include <string>
+#include <thread>
+#include <vector>
+
+//
+// Terminal utils
+//
+
+#define SQR(X)    ((X) * (X))
+#define UNCUBE(x) x < 48 ? 0 : x < 115 ? 1 : (x - 35) / 40
+
+/**
+ * Quantizes 24-bit RGB to xterm256 code range [16,256).
+ */
+static int rgb2xterm256(int r, int g, int b) {
+    unsigned char cube[] = {0, 0137, 0207, 0257, 0327, 0377};
+    int av, ir, ig, ib, il, qr, qg, qb, ql;
+    av = r * .299 + g * .587 + b * .114 + .5;
+    ql = (il = av > 238 ? 23 : (av - 3) / 10) * 10 + 8;
+    qr = cube[(ir = UNCUBE(r))];
+    qg = cube[(ig = UNCUBE(g))];
+    qb = cube[(ib = UNCUBE(b))];
+    if (SQR(qr - r) + SQR(qg - g) + SQR(qb - b) <=
+        SQR(ql - r) + SQR(ql - g) + SQR(ql - b))
+        return ir * 36 + ig * 6 + ib + 020;
+    return il + 0350;
+}
+
+static std::string set_xterm256_foreground(int r, int g, int b) {
+    int x = rgb2xterm256(r, g, b);
+    std::ostringstream oss;
+    oss << "\033[38;5;" << x << "m";
+    return oss.str();
+}
+
+const std::vector<std::string> k_colors = {
+    set_xterm256_foreground(220,   5,  12),
+    set_xterm256_foreground(232,  96,  28),
+    set_xterm256_foreground(241, 147,  45),
+    set_xterm256_foreground(246, 193,  65),
+    set_xterm256_foreground(247, 240,  86),
+    set_xterm256_foreground(144, 201, 135),
+    set_xterm256_foreground( 78, 178, 101),
+};
+
+static void print_usage(int, char ** argv) {
+    LOG("\nexample usage:\n");
+    LOG("\n    %s -m model.gguf -p \"Hello!\"\n", argv[0]);
+    LOG("\n");
+}
+
+struct wav_header {
+    char riff[4] = {'R', 'I', 'F', 'F'};
+    uint32_t chunk_size;
+    char wave[4] = {'W', 'A', 'V', 'E'};
+    char fmt[4] = {'f', 'm', 't', ' '};
+    uint32_t fmt_chunk_size = 16;
+    uint16_t audio_format = 1; // PCM
+    uint16_t num_channels = 1; // Mono
+    uint32_t sample_rate;
+    uint32_t byte_rate;
+    uint16_t block_align;
+    uint16_t bits_per_sample = 16;
+    char data[4] = {'d', 'a', 't', 'a'};
+    uint32_t data_size;
+};
+
+static void save_wav16(const std::string & fname, const std::vector<float> & data, int sample_rate) {
+    std::ofstream file(fname, std::ios::binary);
+    if (!file) {
+        LOG_ERR("%s: Failed to open file '%s' for writing", __func__, fname.c_str());
+        return;
+    }
+
+    wav_header header;
+    header.sample_rate = sample_rate;
+    header.byte_rate = header.sample_rate * header.num_channels * (header.bits_per_sample / 8);
+    header.block_align = header.num_channels * (header.bits_per_sample / 8);
+    header.data_size = data.size() * (header.bits_per_sample / 8);
+    header.chunk_size = 36 + header.data_size;
+
+    file.write(reinterpret_cast<const char*>(&header), sizeof(header));
+
+    for (const auto & sample : data) {
+        int16_t pcm_sample = static_cast<int16_t>(std::clamp(sample * 32767.0, -32768.0, 32767.0));
+        file.write(reinterpret_cast<const char*>(&pcm_sample), sizeof(pcm_sample));
+    }
+
+    file.close();
+}
+
+static void fill_hann_window(int length, bool periodic, float * output) {
+    int offset = -1;
+    if (periodic) {
+        offset = 0;
+    }
+    for (int i = 0; i < length; i++) {
+        output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
+    }
+}
+
+// very poor-man fft
+static void twiddle(float * real, float * imag, int k, int N) {
+    float angle = 2 * M_PI * k / N;
+    *real = cos(angle);
+    *imag = sin(angle);
+}
+
+static void irfft(int n, const float * inp_cplx, float * out_real) {
+    int N = n / 2 + 1;
+
+    std::vector<float> real_input(N);
+    std::vector<float> imag_input(N);
+    for (int i = 0; i < N; ++i) {
+        real_input[i] = inp_cplx[2 * i];
+        imag_input[i] = inp_cplx[2 * i + 1];
+    }
+
+    std::vector<float> real_output(n);
+    std::vector<float> imag_output(n);
+
+    for (int k = 0; k < n; ++k) {
+        real_output[k] = 0.0f;
+        imag_output[k] = 0.0f;
+        for (int m = 0; m < N; ++m) {
+            float twiddle_real;
+            float twiddle_imag;
+
+            twiddle(&twiddle_real, &twiddle_imag, k * m, n);
+
+            real_output[k] += real_input[m] * twiddle_real - imag_input[m] * twiddle_imag;
+            imag_output[k] += real_input[m] * twiddle_imag + imag_input[m] * twiddle_real;
+        }
+    }
+
+    for (int i = 0; i < n; ++i) {
+        out_real[i] = real_output[i] / N;
+    }
+}
+
+//
+//  y = torch.nn.functional.fold(
+//       data, output_size=(1, output_size), kernel_size=(1, self.win_length), stride=(1, self.hop_length),
+//  )[:, 0, 0, pad:-pad]
+//
+// data.shape =  torch.Size([1, 1280, 261])
+// output_size =  84480
+// win_length =  1280
+// hop_length =  320
+// pad =  480
+//
+static void fold(const std::vector<float> & data, int64_t n_out, int64_t n_win, int64_t n_hop, int64_t n_pad, std::vector<float> & output) {
+    int64_t output_height = n_out;
+    int64_t kernel_w = n_win;
+    int64_t stride_w = n_hop;
+    int64_t width    = n_out;
+
+    output.resize(width, 0.0f);
+
+    int64_t col_idx = 0;
+    for (int64_t w_col = 0; w_col < width; ++w_col) {
+        int64_t start = w_col * stride_w - n_pad;
+        int64_t end   = start + kernel_w;
+
+        for (int64_t w_im = start; w_im < end; ++w_im) {
+            if (w_im >= 0 && w_im < output_height && col_idx < (int64_t) data.size()) {
+                output[w_im] += data[col_idx];
+            }
+            col_idx++;
+        }
+    }
+
+    output.resize(n_out - 2 * n_pad);
+}
+
+// TODO: not optimized at all
+static std::vector<float> embd_to_audio(
+        const float * embd,
+        const int n_codes,
+        const int n_embd,
+        const int n_thread) {
+    const int n_fft = 1280;
+    const int n_hop = 320;
+    const int n_win = 1280;
+    const int n_pad = (n_win - n_hop)/2;
+    const int n_out = (n_codes - 1)*n_hop + n_win;
+
+    std::vector<float> hann(n_fft);
+
+    fill_hann_window(hann.size(), true, hann.data());
+
+    int n_spec = n_embd*n_codes;
+
+    std::vector<float> E (n_spec);
+    std::vector<float> S (n_spec);
+    std::vector<float> ST(n_spec);
+
+    for (int l = 0; l < n_codes; ++l) {
+        for (int k = 0; k < n_embd; ++k) {
+            E[k*n_codes + l] = embd[l*n_embd + k];
+        }
+    }
+
+    for (int k = 0; k < n_embd/2; ++k) {
+        for (int l = 0; l < n_codes; ++l) {
+            float mag = E[(k           )*n_codes + l];
+            float phi = E[(k + n_embd/2)*n_codes + l];
+
+            mag = exp(mag);
+
+            if (mag > 1e2) {
+                mag = 1e2;
+            }
+            S[2*(k*n_codes + l) + 0] = mag*cosf(phi);
+            S[2*(k*n_codes + l) + 1] = mag*sinf(phi);
+        }
+    }
+
+    for (int l = 0; l < n_codes; ++l) {
+        for (int k = 0; k < n_embd/2; ++k) {
+            ST[l*n_embd + 2*k + 0] = S[2*(k*n_codes + l) + 0];
+            ST[l*n_embd + 2*k + 1] = S[2*(k*n_codes + l) + 1];
+        }
+    }
+
+    std::vector<float> res  (n_codes*n_fft);
+    std::vector<float> hann2(n_codes*n_fft);
+
+    std::vector<std::thread> workers(n_thread);
+    for (int i = 0; i < n_thread; ++i) {
+        workers[i] = std::thread([&, i]() {
+            for (int l = i; l < n_codes; l += n_thread) {
+                irfft(n_fft, ST.data() + l*n_embd, res.data() + l*n_fft);
+                for (int j = 0; j < n_fft; ++j) {
+                    res  [l*n_fft + j] *= hann[j];
+                    hann2[l*n_fft + j]  = hann[j] * hann[j];
+                }
+            }
+        });
+    }
+    for (int i = 0; i < n_thread; ++i) {
+        workers[i].join();
+    }
+
+    std::vector<float> audio;
+    std::vector<float> env;
+
+    fold(res,   n_out, n_win, n_hop, n_pad, audio);
+    fold(hann2, n_out, n_win, n_hop, n_pad, env); // TODO: can be done once
+
+    for (size_t i = 0; i < audio.size(); ++i) {
+        audio[i] /= env[i];
+    }
+
+    return audio;
+}
+
+static const std::map<int, std::string> ones = {
+    {0, "zero"}, {1, "one"}, {2, "two"}, {3, "three"}, {4, "four"},
+    {5, "five"}, {6, "six"}, {7, "seven"}, {8, "eight"}, {9, "nine"},
+    {10, "ten"}, {11, "eleven"}, {12, "twelve"}, {13, "thirteen"}, {14, "fourteen"},
+    {15, "fifteen"}, {16, "sixteen"}, {17, "seventeen"}, {18, "eighteen"}, {19, "nineteen"}
+};
+
+static const std::map<int, std::string> tens = {
+    {2, "twenty"}, {3, "thirty"}, {4, "forty"}, {5, "fifty"},
+    {6, "sixty"}, {7, "seventy"}, {8, "eighty"}, {9, "ninety"}
+};
+
+// Convert a number less than 1000 to words
+static std::string convert_less_than_thousand(int num) {
+    std::string result;
+
+    if (num >= 100) {
+        result += ones.at(num / 100) + " hundred ";
+        num %= 100;
+    }
+
+    if (num >= 20) {
+        result += tens.at(num / 10);
+        if (num % 10 > 0) {
+            result += "-" + ones.at(num % 10);
+        }
+    } else if (num > 0) {
+        result += ones.at(num);
+    }
+
+    return result;
+}
+
+static std::string number_to_words(const std::string & number_str) {
+    try {
+        size_t decimal_pos = number_str.find('.');
+        std::string integer_part = number_str.substr(0, decimal_pos);
+
+        int int_number = std::stoi(integer_part);
+        std::string result;
+
+        if (int_number == 0) {
+            result = "zero";
+        } else {
+            if (int_number >= 1000000000) {
+                int billions = int_number / 1000000000;
+                result += convert_less_than_thousand(billions) + " billion ";
+                int_number %= 1000000000;
+            }
+
+            if (int_number >= 1000000) {
+                int millions = int_number / 1000000;
+                result += convert_less_than_thousand(millions) + " million ";
+                int_number %= 1000000;
+            }
+
+            if (int_number >= 1000) {
+                int thousands = int_number / 1000;
+                result += convert_less_than_thousand(thousands) + " thousand ";
+                int_number %= 1000;
+            }
+
+            if (int_number > 0) {
+                result += convert_less_than_thousand(int_number);
+            }
+        }
+
+        // Handle decimal part
+        if (decimal_pos != std::string::npos) {
+            result += " point";
+            std::string decimal_part = number_str.substr(decimal_pos + 1);
+            for (char digit : decimal_part) {
+                result += " " + ones.at(digit - '0');
+            }
+        }
+
+        return result;
+    } catch (const std::exception& e) {
+        // Skip if fails
+        return " ";
+    }
+}
+
+static std::string replace_numbers_with_words(const std::string & input_text) {
+    std::regex number_pattern(R"(\d+(\.\d+)?)");
+    std::string result;
+    auto it = std::sregex_iterator(input_text.begin(), input_text.end(), number_pattern);
+    auto end = std::sregex_iterator();
+
+    size_t last_pos = 0;
+    for (std::sregex_iterator i = it; i != end; ++i) {
+        const std::smatch& match = *i;
+        result.append(input_text, last_pos, match.position() - last_pos);
+        result.append(number_to_words(match.str()));
+        last_pos = match.position() + match.length();
+    }
+    result.append(input_text, last_pos);
+
+    return result;
+}
+
+// Based on: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/version/v1/prompt_processor.py#L39
+static std::string process_text(const std::string & text) {
+
+    // For now I skipped text romanization as I am unsure how to handle
+    // uroman and MeCab implementations in C++
+    // maybe something like https://github.com/anyascii/anyascii/ could work.
+    // currently only English would be supported in this function
+
+    std::string processed_text = replace_numbers_with_words(text);
+
+    std::transform(processed_text.begin(), processed_text.end(),
+                  processed_text.begin(), ::tolower);
+
+    std::regex special_chars(R"([-_/,\.\\])");
+    processed_text = std::regex_replace(processed_text, special_chars, " ");
+
+    std::regex non_alpha(R"([^a-z\s])");
+    processed_text = std::regex_replace(processed_text, non_alpha, "");
+
+    std::regex multiple_spaces(R"(\s+)");
+    processed_text = std::regex_replace(processed_text, multiple_spaces, " ");
+
+    processed_text = std::regex_replace(processed_text, std::regex(R"(^\s+|\s+$)"), "");
+
+    /*
+        Replace spaces with the separator token same as in line 365
+
+        for (auto & c : prompt_user) {
+        if (c == ' ') {
+            prompt_clean += "<|text_sep|>";
+    */
+    processed_text = std::regex_replace(processed_text, std::regex(R"(\s)"), "<|text_sep|>");
+
+    return processed_text;
+}
+
+static void prompt_add(llama_tokens & prompt, llama_token token) {
+    prompt.push_back(token);
+}
+
+static void prompt_add(llama_tokens & prompt, const llama_tokens & tokens) {
+    prompt.insert(prompt.end(), tokens.begin(), tokens.end());
+}
+
+static void prompt_add(llama_tokens & prompt, const llama_model * model, const std::string & txt, bool add_special, bool parse_special) {
+    auto tmp = common_tokenize(model, txt, add_special, parse_special);
+    prompt_add(prompt, tmp);
+}
+
+static void prompt_init(llama_tokens & prompt, const llama_model * model) {
+    prompt.clear();
+
+    prompt_add(prompt, model, "<|im_start|>\n", true, true);
+}
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    params.prompt = "";
+
+    params.n_predict = 4096;
+    params.n_batch   = 8192;
+    params.n_ctx     = 8192;
+
+    params.sampling.top_k = 4;
+    params.sampling.samplers = { COMMON_SAMPLER_TYPE_TOP_K, };
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_TTS, print_usage)) {
+        return 1;
+    }
+
+    const int n_parallel = params.n_parallel;
+    const int n_predict  = params.n_predict;
+
+    common_init();
+
+    // init LLM
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model_ttc = NULL; // text-to-codes
+    llama_model * model_cts = NULL; // codes-to-speech
+
+    llama_context * ctx_ttc = NULL;
+    llama_context * ctx_cts = NULL;
+
+    common_init_result llama_init_ttc = common_init_from_params(params);
+    model_ttc = llama_init_ttc.model;
+    ctx_ttc = llama_init_ttc.context;
+
+    // TODO: refactor in a common struct
+    params.model     = params.vocoder.model;
+    params.model_url = params.vocoder.model_url;
+    params.hf_repo   = params.vocoder.hf_repo;
+    params.hf_file   = params.vocoder.hf_file;
+
+    params.embedding = true;
+
+    common_init_result llama_init_cts = common_init_from_params(params);
+    model_cts = llama_init_cts.model;
+    ctx_cts = llama_init_cts.context;
+
+    std::vector<common_sampler *> smpl(n_parallel);
+    for (int i = 0; i < n_parallel; ++i) {
+        params.sampling.no_perf = (i != 0);
+        params.sampling.seed = params.sampling.seed + 1;
+
+        smpl[i] = common_sampler_init(model_ttc, params.sampling);
+    }
+
+    LOG_INF("sampler seed: %u\n",     common_sampler_get_seed(smpl[0]));
+    LOG_INF("sampler params: \n%s\n", params.sampling.print().c_str());
+    LOG_INF("sampler chain: %s\n",    common_sampler_print(smpl[0]).c_str());
+
+    LOG_INF("%s: loading done\n", __func__);
+
+    const auto t_main_start = ggml_time_us();
+
+    std::vector<llama_token> codes;
+
+    // process prompt and generate voice codes
+    {
+        LOG_INF("%s: constructing prompt ..\n", __func__);
+
+        std::vector<llama_token> prompt_inp;
+
+        prompt_init(prompt_inp, model_ttc);
+
+        prompt_add(prompt_inp, model_ttc, "<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>", false, true);
+
+        // convert the input text into the necessary format expected by OuteTTS
+        {
+            std::string prompt_clean = process_text(params.prompt);
+
+            LOG_INF("%s: prompt: '%s'\n", __func__, prompt_clean.c_str());
+
+            prompt_add(prompt_inp, model_ttc, prompt_clean, false, true);
+        }
+
+        prompt_add(prompt_inp, model_ttc, "<|text_end|>\n", false, true);
+
+        // disabled to save time on tokenizing each time
+        // TODO: load voices from the json files
+#if 0
+        const std::string voice_data = R"(<|audio_start|>
+the<|t_0.08|><|code_start|><|257|><|740|><|636|><|913|><|788|><|1703|><|code_end|>
+overall<|t_0.36|><|code_start|><|127|><|201|><|191|><|774|><|700|><|532|><|1056|><|557|><|798|><|298|><|1741|><|747|><|1662|><|1617|><|1702|><|1527|><|368|><|1588|><|1049|><|1008|><|1625|><|747|><|1576|><|728|><|1019|><|1696|><|1765|><|code_end|>
+package<|t_0.56|><|code_start|><|935|><|584|><|1319|><|627|><|1016|><|1491|><|1344|><|1117|><|1526|><|1040|><|239|><|1435|><|951|><|498|><|723|><|1180|><|535|><|789|><|1649|><|1637|><|78|><|465|><|1668|><|901|><|595|><|1675|><|117|><|1009|><|1667|><|320|><|840|><|79|><|507|><|1762|><|1508|><|1228|><|1768|><|802|><|1450|><|1457|><|232|><|639|><|code_end|>
+from<|t_0.19|><|code_start|><|604|><|782|><|1682|><|872|><|1532|><|1600|><|1036|><|1761|><|647|><|1554|><|1371|><|653|><|1595|><|950|><|code_end|>
+just<|t_0.25|><|code_start|><|1782|><|1670|><|317|><|786|><|1748|><|631|><|599|><|1155|><|1364|><|1524|><|36|><|1591|><|889|><|1535|><|541|><|440|><|1532|><|50|><|870|><|code_end|>
+two<|t_0.24|><|code_start|><|1681|><|1510|><|673|><|799|><|805|><|1342|><|330|><|519|><|62|><|640|><|1138|><|565|><|1552|><|1497|><|1552|><|572|><|1715|><|1732|><|code_end|>
+people<|t_0.39|><|code_start|><|593|><|274|><|136|><|740|><|691|><|633|><|1484|><|1061|><|1138|><|1485|><|344|><|428|><|397|><|1562|><|645|><|917|><|1035|><|1449|><|1669|><|487|><|442|><|1484|><|1329|><|1832|><|1704|><|600|><|761|><|653|><|269|><|code_end|>
+is<|t_0.16|><|code_start|><|566|><|583|><|1755|><|646|><|1337|><|709|><|802|><|1008|><|485|><|1583|><|652|><|10|><|code_end|>
+pretty<|t_0.32|><|code_start|><|1818|><|1747|><|692|><|733|><|1010|><|534|><|406|><|1697|><|1053|><|1521|><|1355|><|1274|><|816|><|1398|><|211|><|1218|><|817|><|1472|><|1703|><|686|><|13|><|822|><|445|><|1068|><|code_end|>
+remarkable<|t_0.68|><|code_start|><|230|><|1048|><|1705|><|355|><|706|><|1149|><|1535|><|1787|><|1356|><|1396|><|835|><|1583|><|486|><|1249|><|286|><|937|><|1076|><|1150|><|614|><|42|><|1058|><|705|><|681|><|798|><|934|><|490|><|514|><|1399|><|572|><|1446|><|1703|><|1346|><|1040|><|1426|><|1304|><|664|><|171|><|1530|><|625|><|64|><|1708|><|1830|><|1030|><|443|><|1509|><|1063|><|1605|><|1785|><|721|><|1440|><|923|><|code_end|>
+sure<|t_0.36|><|code_start|><|792|><|1780|><|923|><|1640|><|265|><|261|><|1525|><|567|><|1491|><|1250|><|1730|><|362|><|919|><|1766|><|543|><|1|><|333|><|113|><|970|><|252|><|1606|><|133|><|302|><|1810|><|1046|><|1190|><|1675|><|code_end|>
+i<|t_0.08|><|code_start|><|123|><|439|><|1074|><|705|><|1799|><|637|><|code_end|>
+have<|t_0.16|><|code_start|><|1509|><|599|><|518|><|1170|><|552|><|1029|><|1267|><|864|><|419|><|143|><|1061|><|0|><|code_end|>
+some<|t_0.16|><|code_start|><|619|><|400|><|1270|><|62|><|1370|><|1832|><|917|><|1661|><|167|><|269|><|1366|><|1508|><|code_end|>
+critiques<|t_0.60|><|code_start|><|559|><|584|><|1163|><|1129|><|1313|><|1728|><|721|><|1146|><|1093|><|577|><|928|><|27|><|630|><|1080|><|1346|><|1337|><|320|><|1382|><|1175|><|1682|><|1556|><|990|><|1683|><|860|><|1721|><|110|><|786|><|376|><|1085|><|756|><|1523|><|234|><|1334|><|1506|><|1578|><|659|><|612|><|1108|><|1466|><|1647|><|308|><|1470|><|746|><|556|><|1061|><|code_end|>
+about<|t_0.29|><|code_start|><|26|><|1649|><|545|><|1367|><|1263|><|1728|><|450|><|859|><|1434|><|497|><|1220|><|1285|><|179|><|755|><|1154|><|779|><|179|><|1229|><|1213|><|922|><|1774|><|1408|><|code_end|>
+some<|t_0.23|><|code_start|><|986|><|28|><|1649|><|778|><|858|><|1519|><|1|><|18|><|26|><|1042|><|1174|><|1309|><|1499|><|1712|><|1692|><|1516|><|1574|><|code_end|>
+of<|t_0.07|><|code_start|><|197|><|716|><|1039|><|1662|><|64|><|code_end|>
+the<|t_0.08|><|code_start|><|1811|><|1568|><|569|><|886|><|1025|><|1374|><|code_end|>
+gameplay<|t_0.48|><|code_start|><|1269|><|1092|><|933|><|1362|><|1762|><|1700|><|1675|><|215|><|781|><|1086|><|461|><|838|><|1022|><|759|><|649|><|1416|><|1004|><|551|><|909|><|787|><|343|><|830|><|1391|><|1040|><|1622|><|1779|><|1360|><|1231|><|1187|><|1317|><|76|><|997|><|989|><|978|><|737|><|189|><|code_end|>
+aspects<|t_0.56|><|code_start|><|1423|><|797|><|1316|><|1222|><|147|><|719|><|1347|><|386|><|1390|><|1558|><|154|><|440|><|634|><|592|><|1097|><|1718|><|712|><|763|><|1118|><|1721|><|1311|><|868|><|580|><|362|><|1435|><|868|><|247|><|221|><|886|><|1145|><|1274|><|1284|><|457|><|1043|><|1459|><|1818|><|62|><|599|><|1035|><|62|><|1649|><|778|><|code_end|>
+but<|t_0.20|><|code_start|><|780|><|1825|><|1681|><|1007|><|861|><|710|><|702|><|939|><|1669|><|1491|><|613|><|1739|><|823|><|1469|><|648|><|code_end|>
+its<|t_0.09|><|code_start|><|92|><|688|><|1623|><|962|><|1670|><|527|><|599|><|code_end|>
+still<|t_0.27|><|code_start|><|636|><|10|><|1217|><|344|><|713|><|957|><|823|><|154|><|1649|><|1286|><|508|><|214|><|1760|><|1250|><|456|><|1352|><|1368|><|921|><|615|><|5|><|code_end|>
+really<|t_0.36|><|code_start|><|55|><|420|><|1008|><|1659|><|27|><|644|><|1266|><|617|><|761|><|1712|><|109|><|1465|><|1587|><|503|><|1541|><|619|><|197|><|1019|><|817|><|269|><|377|><|362|><|1381|><|507|><|1488|><|4|><|1695|><|code_end|>
+enjoyable<|t_0.49|><|code_start|><|678|><|501|><|864|><|319|><|288|><|1472|><|1341|><|686|><|562|><|1463|><|619|><|1563|><|471|><|911|><|730|><|1811|><|1006|><|520|><|861|><|1274|><|125|><|1431|><|638|><|621|><|153|><|876|><|1770|><|437|><|987|><|1653|><|1109|><|898|><|1285|><|80|><|593|><|1709|><|843|><|code_end|>
+and<|t_0.15|><|code_start|><|1285|><|987|><|303|><|1037|><|730|><|1164|><|502|><|120|><|1737|><|1655|><|1318|><|code_end|>
+it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><|code_end|>
+looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|>
+lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>)";
+
+        auto tmp = common_tokenize(model_ttc, voice_data, false, true);
+        printf("\n\n");
+        for (int i = 0; i < tmp.size(); ++i) {
+            printf("%d, ", tmp[i]);
+        }
+        printf("\n\n");
+#else
+        prompt_add(prompt_inp, llama_tokens {
+            151667, 198, 1782, 155780, 151669, 151929, 152412, 152308, 152585,
+            152460, 153375, 151670, 198, 74455, 155808, 151669, 151799,
+            151873, 151863, 152446, 152372, 152204, 152728, 152229, 152470,
+            151970, 153413, 152419, 153334, 153289, 153374, 153199, 152040,
+            153260, 152721, 152680, 153297, 152419, 153248, 152400, 152691,
+            153368, 153437, 151670, 198, 1722, 155828, 151669, 152607,
+            152256, 152991, 152299, 152688, 153163, 153016, 152789, 153198,
+            152712, 151911, 153107, 152623, 152170, 152395, 152852, 152207,
+            152461, 153321, 153309, 151750, 152137, 153340, 152573, 152267,
+            153347, 151789, 152681, 153339, 151992, 152512, 151751, 152179,
+            153434, 153180, 152900, 153440, 152474, 153122, 153129, 151904,
+            152311, 151670, 198, 1499, 155791, 151669, 152276, 152454,
+            153354, 152544, 153204, 153272, 152708, 153433, 152319, 153226,
+            153043, 152325, 153267, 152622, 151670, 198, 4250, 155797,
+            151669, 153454, 153342, 151989, 152458, 153420, 152303, 152271,
+            152827, 153036, 153196, 151708, 153263, 152561, 153207, 152213,
+            152112, 153204, 151722, 152542, 151670, 198, 19789, 155796,
+            151669, 153353, 153182, 152345, 152471, 152477, 153014, 152002,
+            152191, 151734, 152312, 152810, 152237, 153224, 153169, 153224,
+            152244, 153387, 153404, 151670, 198, 16069, 155811, 151669,
+            152265, 151946, 151808, 152412, 152363, 152305, 153156, 152733,
+            152810, 153157, 152016, 152100, 152069, 153234, 152317, 152589,
+            152707, 153121, 153341, 152159, 152114, 153156, 153001, 153504,
+            153376, 152272, 152433, 152325, 151941, 151670, 198, 285,
+            155788, 151669, 152238, 152255, 153427, 152318, 153009, 152381,
+            152474, 152680, 152157, 153255, 152324, 151682, 151670, 198,
+            32955, 155804, 151669, 153490, 153419, 152364, 152405, 152682,
+            152206, 152078, 153369, 152725, 153193, 153027, 152946, 152488,
+            153070, 151883, 152890, 152489, 153144, 153375, 152358, 151685,
+            152494, 152117, 152740, 151670, 198, 37448, 480, 155840, 151669,
+            151902, 152720, 153377, 152027, 152378, 152821, 153207, 153459,
+            153028, 153068, 152507, 153255, 152158, 152921, 151958, 152609,
+            152748, 152822, 152286, 151714, 152730, 152377, 152353, 152470,
+            152606, 152162, 152186, 153071, 152244, 153118, 153375, 153018,
+            152712, 153098, 152976, 152336, 151843, 153202, 152297, 151736,
+            153380, 153502, 152702, 152115, 153181, 152735, 153277, 153457,
+            152393, 153112, 152595, 151670, 198, 19098, 155808, 151669,
+            152464, 153452, 152595, 153312, 151937, 151933, 153197, 152239,
+            153163, 152922, 153402, 152034, 152591, 153438, 152215, 151673,
+            152005, 151785, 152642, 151924, 153278, 151805, 151974, 153482,
+            152718, 152862, 153347, 151670, 198, 72, 155780, 151669, 151795,
+            152111, 152746, 152377, 153471, 152309, 151670, 198, 19016,
+            155788, 151669, 153181, 152271, 152190, 152842, 152224, 152701,
+            152939, 152536, 152091, 151815, 152733, 151672, 151670, 198,
+            14689, 155788, 151669, 152291, 152072, 152942, 151734, 153042,
+            153504, 152589, 153333, 151839, 151941, 153038, 153180, 151670,
+            198, 36996, 8303, 155832, 151669, 152231, 152256, 152835,
+            152801, 152985, 153400, 152393, 152818, 152765, 152249, 152600,
+            151699, 152302, 152752, 153018, 153009, 151992, 153054, 152847,
+            153354, 153228, 152662, 153355, 152532, 153393, 151782, 152458,
+            152048, 152757, 152428, 153195, 151906, 153006, 153178, 153250,
+            152331, 152284, 152780, 153138, 153319, 151980, 153142, 152418,
+            152228, 152733, 151670, 198, 9096, 155801, 151669, 151698,
+            153321, 152217, 153039, 152935, 153400, 152122, 152531, 153106,
+            152169, 152892, 152957, 151851, 152427, 152826, 152451, 151851,
+            152901, 152885, 152594, 153446, 153080, 151670, 198, 14689,
+            155795, 151669, 152658, 151700, 153321, 152450, 152530, 153191,
+            151673, 151690, 151698, 152714, 152846, 152981, 153171, 153384,
+            153364, 153188, 153246, 151670, 198, 1055, 155779, 151669,
+            151869, 152388, 152711, 153334, 151736, 151670, 198, 1782,
+            155780, 151669, 153483, 153240, 152241, 152558, 152697, 153046,
+            151670, 198, 5804, 1363, 155820, 151669, 152941, 152764, 152605,
+            153034, 153434, 153372, 153347, 151887, 152453, 152758, 152133,
+            152510, 152694, 152431, 152321, 153088, 152676, 152223, 152581,
+            152459, 152015, 152502, 153063, 152712, 153294, 153451, 153032,
+            152903, 152859, 152989, 151748, 152669, 152661, 152650, 152409,
+            151861, 151670, 198, 300, 7973, 155828, 151669, 153095, 152469,
+            152988, 152894, 151819, 152391, 153019, 152058, 153062, 153230,
+            151826, 152112, 152306, 152264, 152769, 153390, 152384, 152435,
+            152790, 153393, 152983, 152540, 152252, 152034, 153107, 152540,
+            151919, 151893, 152558, 152817, 152946, 152956, 152129, 152715,
+            153131, 153490, 151734, 152271, 152707, 151734, 153321, 152450,
+            151670, 198, 8088, 155792, 151669, 152452, 153497, 153353,
+            152679, 152533, 152382, 152374, 152611, 153341, 153163, 152285,
+            153411, 152495, 153141, 152320, 151670, 198, 1199, 155781,
+            151669, 151764, 152360, 153295, 152634, 153342, 152199, 152271,
+            151670, 198, 43366, 155799, 151669, 152308, 151682, 152889,
+            152016, 152385, 152629, 152495, 151826, 153321, 152958, 152180,
+            151886, 153432, 152922, 152128, 153024, 153040, 152593, 152287,
+            151677, 151670, 198, 53660, 155808, 151669, 151727, 152092,
+            152680, 153331, 151699, 152316, 152938, 152289, 152433, 153384,
+            151781, 153137, 153259, 152175, 153213, 152291, 151869, 152691,
+            152489, 151941, 152049, 152034, 153053, 152179, 153160, 151676,
+            153367, 151670, 198, 268, 4123, 480, 155821, 151669, 152350,
+            152173, 152536, 151991, 151960, 153144, 153013, 152358, 152234,
+            153135, 152291, 153235, 152143, 152583, 152402, 153483, 152678,
+            152192, 152533, 152946, 151797, 153103, 152310, 152293, 151825,
+            152548, 153442, 152109, 152659, 153325, 152781, 152570, 152957,
+            151752, 152265, 153381, 152515, 151670, 198, 437, 155787,
+            151669, 152957, 152659, 151975, 152709, 152402, 152836, 152174,
+            151792, 153409, 153327, 152990, 151670, 198, 275, 155781,
+            151669, 152520, 153038, 152067, 153273, 153185, 152265, 152974,
+            151670, 198, 94273, 155799, 151669, 152953, 152938, 153427,
+            152244, 151920, 153423, 152929, 152367, 153052, 152129, 152331,
+            152257, 152987, 152777, 153448, 152408, 151696, 152408, 152326,
+            152699, 151670, 198, 385, 16239, 155828, 151669, 152306, 152268,
+            153438, 153228, 152978, 152957, 153153, 153393, 152795, 152110,
+            152918, 152923, 152467, 152331, 153053, 153330, 151889, 153444,
+            152234, 152624, 151779, 152801, 152784, 152139, 152222, 152751,
+            152512, 153287, 153141, 153052, 151840, 152589, 152508, 153499,
+            152109, 152255, 151739, 152267, 152759, 153318, 153165, 153349,
+            151670,});
+#endif
+
+        // print the prompt token-by-token
+
+        LOG("\n");
+
+        for (auto id : prompt_inp) {
+            LOG("%s", common_token_to_piece(ctx_ttc, id).c_str());
+        }
+
+        LOG_INF("%s: prompt size: %d\n", __func__, (int) prompt_inp.size());
+
+        LOG("\n");
+
+        // create a llama_batch
+        // we use this object to submit token data for decoding
+        llama_batch batch = llama_batch_init(std::max(prompt_inp.size(), (size_t) n_parallel), 0, n_parallel);
+
+        std::vector<llama_seq_id> seq_ids(n_parallel, 0);
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            seq_ids[i] = i;
+        }
+
+        // evaluate the initial prompt
+        for (size_t i = 0; i < prompt_inp.size(); ++i) {
+            common_batch_add(batch, prompt_inp[i], i, seq_ids, false);
+        }
+        GGML_ASSERT(batch.n_tokens == (int) prompt_inp.size());
+
+        // llama_decode will output logits only for the last token of the prompt
+        batch.logits[batch.n_tokens - 1] = true;
+
+        if (llama_decode(ctx_ttc, batch) != 0) {
+            LOG_ERR("%s: llama_decode() failed\n", __func__);
+            return 1;
+        }
+
+        if (n_parallel > 1) {
+            LOG_INF("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
+        }
+
+        llama_synchronize(ctx_ttc);
+
+        LOG_INF("%s: time for prompt: %.3f ms\n\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
+
+        const auto t_dec_start = ggml_time_us();
+
+        // main loop
+
+        // remember the batch index of the last token for each parallel sequence
+        // we need this to determine which logits to sample from
+        std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
+
+        int n_past   = batch.n_tokens;
+        int n_decode = 0;
+
+        while (n_decode <= n_predict) {
+            // prepare the next batch
+            common_batch_clear(batch);
+
+            // sample the next token for each parallel sequence / stream
+            for (int32_t i = 0; i < n_parallel; ++i) {
+                if (i_batch[i] < 0) {
+                    // the stream has already finished
+                    continue;
+                }
+
+                const llama_token new_token_id = common_sampler_sample(smpl[i], ctx_ttc, i_batch[i]);
+
+                common_sampler_accept(smpl[i], new_token_id, true);
+
+                codes.push_back(new_token_id);
+
+                const auto * cands = common_sampler_get_candidates(smpl[i]);
+
+                // is it an end of generation? -> mark the stream as finished
+                if (llama_token_is_eog(model_ttc, new_token_id) || n_decode == n_predict) {
+                    std::string reason;
+                    if (llama_token_is_eog(model_ttc, new_token_id)) {
+                        reason = "eos";
+                    } else {
+                        reason = "n_predict";
+                    }
+
+                    i_batch[i] = -1;
+
+                    LOG("\n");
+                    if (n_parallel > 1) {
+                        LOG_CNT("\n");
+                        LOG_INF("%s: stream %d finished at n_past = %d, reason = '%s'\n", __func__, i, n_past, reason.c_str());
+                    }
+
+                    continue;
+                }
+
+                {
+                    const float p = cands->data[cands->selected].p;
+
+                    const int col = std::max(0, std::min((int) k_colors.size() - 1, (int) ((3*p)*float(k_colors.size()))));
+
+                    LOG_CNT("%s%d%s", k_colors[col].c_str(), i, "\033[0m");
+                    //LOG_CNT("%d", i);
+                }
+
+                i_batch[i] = batch.n_tokens;
+
+                // push this new token for next evaluation
+                common_batch_add(batch, new_token_id, n_past, { i }, true);
+            }
+
+            // all streams are finished
+            if (batch.n_tokens == 0) {
+                break;
+            }
+
+            n_decode += 1;
+            n_past += 1;
+
+            // evaluate the current batch with the transformer model
+            if (llama_decode(ctx_ttc, batch)) {
+                LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
+                return 1;
+            }
+        }
+
+        llama_batch_free(batch);
+
+        LOG("\n");
+        LOG_INF("%s: time for decoder:       %.3f ms\n", __func__, (ggml_time_us() - t_dec_start) / 1000.0f);
+    }
+
+    common_perf_print(ctx_ttc, smpl[0]);
+
+    //std::vector<llama_token> codes = {198, 88225, 155856, 151669, 152205,
+    //    153064, 152537, 153421, 153209, 152524, 151689, 152993, 152438, 152695,
+    //    153091, 152945, 152829, 152534, 152934, 153020, 151997, 152263, 153010,
+    //    153146, 152399, 153208, 152496, 151793, 152848, 152263, 152571, 153286,
+    //    152227, 153300, 152934, 152263, 153208, 152263, 152965, 152430, 152296,
+    //    153146, 152920, 152376, 152556, 153363, 151775, 152044, 152972, 152690,
+    //    153379, 152368, 152233, 153422, 152490, 151996, 152022, 151694, 152061,
+    //    153238, 152539, 153356, 152640, 153021, 153123, 151962, 153094, 151670,
+    //    198, 20339, 13189, 155824, 151669, 152070, 152007, 152910, 151683,
+    //    152000, 152373, 152760, 152046, 151735, 152334, 152394, 153073, 152908,
+    //    151856, 151953, 153247, 153293, 151903, 153480, 153168, 152478, 153359,
+    //    153429, 151905, 151678, 152567, 152411, 152165, 152556, 153075, 153424,
+    //    151993, 152999, 153078, 152151, 152088, 153389, 152484, 151874, 151670,
+    //    198, 285, 155784, 151669, 152226, 152126, 152638, 153215, 151729,
+    //    152959, 153479, 153059, 151838, 151670, 198, 1782, 155783, 151669,
+    //    153288, 153055, 153314, 152497, 152962, 152741, 152076, 153253, 151670,
+    //    198, 471, 16488, 155825, 151669, 152060, 152916, 151893, 153469, 152501,
+    //    152080, 152743, 151932, 153161, 152096, 152761, 152698, 153401, 153242,
+    //    153336, 152441, 152838, 153467, 152706, 153496, 153310, 152422, 153360,
+    //    153115, 152763, 151998, 152373, 153450, 152554, 151968, 153323, 152055,
+    //    152468, 153111, 153358, 152813, 152010, 151770, 152823, 152960, 151670,
+    //    198, 22627, 155823, 151669, 152814, 152366, 153484, 152931, 153441,
+    //    152164, 152877, 152915, 153463, 151692, 152911, 152747, 152776, 151831,
+    //    153449, 151882, 152975, 152031, 152513, 153150, 152448, 152667, 153133,
+    //    153189, 152619, 153466, 152054, 152106, 153119, 152277, 152439, 153109,
+    //    152997, 152141, 153154, 153256, 153311, 151922, 151670, 198, 1055,
+    //    155781, 151669, 152633, 151850, 153060, 153270, 152560, 153348, 152729,
+    //    151670, 198, 25312, 155803, 151669, 152521, 153403, 152561, 153337,
+    //    153383, 152199, 153493, 153326, 151830, 152254, 152248, 152349, 152153,
+    //    153007, 151823, 153037, 152575, 152457, 152406, 152592, 153116, 153365,
+    //    153456, 151670, 198, 88225, 155817, 151669, 153271, 151925, 152218,
+    //    152418, 152253, 153140, 151903, 153151, 152626, 152338, 152647, 153464,
+    //    152785, 152768, 151711, 152037, 152033, 151804, 152216, 151701, 151855,
+    //    152348, 152995, 152955, 152905, 152342, 152340, 153391, 153453, 152418,
+    //    153415, 151990, 153083, 152884, 151670, 198, 151668, 198, 151645};
+
+    {
+        const std::string inp_txt = common_detokenize(ctx_ttc, codes, true);
+
+        LOG("\n");
+        LOG_INF("codes: '%s'\n", inp_txt.c_str());
+        LOG_INF("%s: codes size: %d\n", __func__, (int) codes.size());
+    }
+
+    // remove all non-audio tokens (i.e. < 151672 || > 155772)
+    codes.erase(std::remove_if(codes.begin(), codes.end(), [](llama_token t) { return t < 151672 || t > 155772; }), codes.end());
+
+    {
+        const std::string inp_txt = common_detokenize(ctx_ttc, codes, true);
+        LOG_INF("codes audio: '%s'\n", inp_txt.c_str());
+        LOG_INF("%s: codes audio size: %d\n", __func__, (int) codes.size());
+    }
+
+    for (auto & token : codes) {
+        token -= 151672;
+    }
+
+    const auto t_voc_start = ggml_time_us();
+
+    const int n_codes = codes.size();
+
+    llama_batch batch = llama_batch_init(n_codes, 0, 1);
+
+    for (size_t i = 0; i < codes.size(); ++i) {
+        common_batch_add(batch, codes[i], i, { 0 }, true); // TODO: all logits?
+    }
+    GGML_ASSERT(batch.n_tokens == n_codes);
+
+    if (llama_decode(ctx_cts, batch) != 0) {
+        LOG_ERR("%s: llama_decode() failed\n", __func__);
+        return 1;
+    }
+
+    llama_synchronize(ctx_cts);
+
+    LOG_INF("%s: time for vocoder:      %.3f ms\n", __func__, (ggml_time_us() - t_voc_start) / 1000.0f);
+
+    const auto t_spec_start = ggml_time_us();
+
+#if 1
+    // spectral operations
+    const int n_embd = llama_n_embd(model_cts);
+    const float * embd = llama_get_embeddings(ctx_cts);
+
+    auto audio = embd_to_audio(embd, n_codes, n_embd, params.cpuparams.n_threads);
+
+#else
+    // read the spectrogram from a file for debugging purposes
+    std::vector<float> audio;
+    {
+        std::ifstream fin("out.bin", std::ios::binary);
+        if (!fin) {
+            LOG_ERR("%s: failed to open file '%s'\n", __func__, "out.bin");
+            return 1;
+        }
+
+        std::vector<float> embd;
+
+        int n_codes;
+        int n_embd;
+
+        fin.read(reinterpret_cast<char *>(&n_codes), sizeof(int));
+        fin.read(reinterpret_cast<char *>(&n_embd), sizeof(int));
+
+        embd.resize(n_codes * n_embd);
+        fin.read(reinterpret_cast<char *>(embd.data()), n_codes * n_embd * sizeof(float));
+        fin.close();
+
+        LOG_INF("%s: n_codes: %d, n_embd: %d\n", __func__, n_codes, n_embd);
+
+        audio = embd_to_audio(embd.data(), n_codes, n_embd, params.cpuparams.n_threads);
+    }
+#endif
+
+    const std::string fname = "output.wav";
+
+    const int n_sr = 24000; // sampling rate
+
+    // zero out first 0.25 seconds
+    for (int i = 0; i < 24000/4; ++i) {
+        audio[i] = 0.0f;
+    }
+
+    LOG_INF("%s: time for spectral ops: %.3f ms\n", __func__, (ggml_time_us() - t_spec_start) / 1000.0f);
+    LOG_INF("%s: total time:            %.3f ms\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
+
+    save_wav16(fname, audio, n_sr);
+
+    LOG_INF("%s: audio written to file '%s'\n", __func__, fname.c_str());
+
+    llama_free(ctx_ttc);
+    llama_free_model(model_ttc);
+
+    llama_free(ctx_cts);
+    llama_free_model(model_cts);
+
+    llama_backend_free();
+
+    return 0;
+}
@@ -32,6 +32,13 @@ else()
    endif()
 endif()

+# remove the lib prefix on win32 mingw
+if (WIN32)
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+    set(CMAKE_SHARED_LIBRARY_PREFIX "")
+    set(CMAKE_SHARED_MODULE_PREFIX  "")
+endif()
+
 option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
 option(GGML_BACKEND_DL   "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)

@@ -67,10 +74,10 @@ if (NOT GGML_CUDA_GRAPHS_DEFAULT)
 endif()

 # general
-option(GGML_STATIC "ggml: static link libraries"         OFF)
-option(GGML_NATIVE "ggml: enable -march=native flag"     ${GGML_NATIVE_DEFAULT})
-option(GGML_LTO    "ggml: enable link time optimization" OFF)
-option(GGML_CCACHE "ggml: use ccache if available"       ON)
+option(GGML_STATIC "ggml: static link libraries"                     OFF)
+option(GGML_NATIVE "ggml: optimize the build for the current system" ${GGML_NATIVE_DEFAULT})
+option(GGML_LTO    "ggml: enable link time optimization"             OFF)
+option(GGML_CCACHE "ggml: use ccache if available"                   ON)

 # debug
 option(GGML_ALL_WARNINGS           "ggml: enable all compiler warnings"                   ON)
@@ -113,8 +120,9 @@ endif()
 option(GGML_LASX             "ggml: enable lasx"             ON)
 option(GGML_LSX              "ggml: enable lsx"              ON)
 option(GGML_RVV              "ggml: enable rvv"              ON)
-option(GGML_SVE              "ggml: enable SVE"              OFF)
+
 option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
+set(GGML_CPU_ARM_ARCH "" CACHE STRING "ggml: CPU architecture for ARM")


 if (WIN32)
@@ -172,6 +180,11 @@ set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
 set   (GGML_SYCL_DEVICE_ARCH "" CACHE STRING
                                            "ggml: sycl device architecture")

+option(GGML_OPENCL                          "ggml: use OpenCL"                                OFF)
+option(GGML_OPENCL_PROFILING                "ggml: use OpenCL profiling (increases overhead)" OFF)
+option(GGML_OPENCL_EMBED_KERNELS            "ggml: embed kernels"                             ON)
+option(GGML_OPENCL_USE_ADRENO_KERNELS       "ggml: use optimized kernels for Adreno"          ON)
+
 # extra artifacts
 option(GGML_BUILD_TESTS    "ggml: build tests"    ${GGML_STANDALONE})
 option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
@@ -228,6 +228,7 @@ extern "C" {
    GGML_API void               ggml_backend_unload(ggml_backend_reg_t reg);
    // Load all known backends from dynamic libraries
    GGML_API void               ggml_backend_load_all(void);
+    GGML_API void               ggml_backend_load_all_from_path(const char * dir_path);

    //
    // Backend scheduler
@@ -103,24 +103,14 @@ extern "C" {

    // Internal types and functions exposed for tests and benchmarks

-    typedef void (*ggml_from_float_to_mat_t)
-                                     (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs);
    typedef void (*ggml_vec_dot_t)  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
                                       const void * GGML_RESTRICT y, size_t by, int nrc);
-    typedef void (*ggml_gemv_t)     (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
-                                       const void * GGML_RESTRICT y, int nr, int nc);
-    typedef void (*ggml_gemm_t)     (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
-                                       const void * GGML_RESTRICT y, int nr, int nc);

    struct ggml_type_traits_cpu {
        ggml_from_float_t        from_float;
-        ggml_from_float_to_mat_t from_float_to_mat;
        ggml_vec_dot_t           vec_dot;
        enum ggml_type           vec_dot_type;
        int64_t                  nrows; // number of rows to process simultaneously
-        int64_t                  ncols; // number of columns to process simultaneously
-        ggml_gemv_t              gemv;
-        ggml_gemm_t              gemm;
    };

    GGML_BACKEND_API const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type);
@@ -140,13 +130,6 @@ extern "C" {

    GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);

-#ifdef GGML_USE_CPU_HBM
-    GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
-#endif
-
-    GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void);
-    GGML_BACKEND_API bool ggml_backend_cpu_buft_is_aarch64(ggml_backend_buffer_type_t buft);
-
 #ifdef __cplusplus
 }
 #endif
@@ -0,0 +1,26 @@
+#ifndef GGML_OPENCL_H
+#define GGML_OPENCL_H
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+//
+// backend API
+//
+GGML_BACKEND_API ggml_backend_t ggml_backend_opencl_init(void);
+GGML_BACKEND_API bool ggml_backend_is_opencl(ggml_backend_t backend);
+
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_opencl_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
+
+#endif // GGML_OPENCL_H
@@ -237,7 +237,9 @@
 #define GGML_EXIT_SUCCESS 0
 #define GGML_EXIT_ABORTED 1

-#define GGML_ROPE_TYPE_NEOX 2
+#define GGML_ROPE_TYPE_NEOX   2
+#define GGML_ROPE_TYPE_MROPE  8
+#define GGML_ROPE_TYPE_VISION 24

 #define GGUF_MAGIC "GGUF"

@@ -384,15 +386,15 @@ extern "C" {
        GGML_TYPE_F64     = 28,
        GGML_TYPE_IQ1_M   = 29,
        GGML_TYPE_BF16    = 30,
-        GGML_TYPE_Q4_0_4_4 = 31,
-        GGML_TYPE_Q4_0_4_8 = 32,
-        GGML_TYPE_Q4_0_8_8 = 33,
+        // GGML_TYPE_Q4_0_4_4 = 31, support has been removed from gguf files
+        // GGML_TYPE_Q4_0_4_8 = 32,
+        // GGML_TYPE_Q4_0_8_8 = 33,
        GGML_TYPE_TQ1_0   = 34,
        GGML_TYPE_TQ2_0   = 35,
-        GGML_TYPE_IQ4_NL_4_4 = 36,
+        // GGML_TYPE_IQ4_NL_4_4 = 36,
        // GGML_TYPE_IQ4_NL_4_8 = 37,
        // GGML_TYPE_IQ4_NL_8_8 = 38,
-        GGML_TYPE_COUNT,
+        GGML_TYPE_COUNT   = 39,
    };

    // precision
@@ -433,9 +435,6 @@ extern "C" {
        GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
        GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
        GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0_4_4 = 25, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0_4_8 = 26, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0_8_8 = 27, // except 1d tensors
    };

    // available tensor operations:
@@ -1446,6 +1445,22 @@ extern "C" {
            float                 beta_fast,
            float                 beta_slow);

+    GGML_API struct ggml_tensor * ggml_rope_multi(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[4],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
    // in-place, returns view(a)
    GGML_API struct ggml_tensor * ggml_rope_ext_inplace(
            struct ggml_context * ctx,
@@ -1549,17 +1564,6 @@ extern "C" {
        int                   d1, // dilation dimension 1
        bool                  is_2D);

-    GGML_API struct ggml_tensor * ggml_conv_depthwise_2d(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,  // convolution kernel
-            struct ggml_tensor  * b,  // data
-            int                  s0,  // stride dimension 0
-            int                  s1,  // stride dimension 1
-            int                  p0,  // padding dimension 0
-            int                  p1,  // padding dimension 1
-            int                  d0,  // dilation dimension 0
-            int                  d1); // dilation dimension 1
-
    GGML_API struct ggml_tensor * ggml_conv_1d(
            struct ggml_context * ctx,
            struct ggml_tensor  * a,   // convolution kernel
@@ -1577,6 +1581,23 @@ extern "C" {
            int                   s,  // stride
            int                   d); // dilation

+    // depthwise
+    // TODO: this is very likely wrong for some cases! - needs more testing
+    GGML_API struct ggml_tensor * ggml_conv_1d_dw(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride
+            int                   p0,  // padding
+            int                   d0); // dilation
+
+    GGML_API struct ggml_tensor * ggml_conv_1d_dw_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride
+            int                   d0); // dilation
+
    GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
            struct ggml_context * ctx,
            struct ggml_tensor  * a,   // convolution kernel
@@ -1596,7 +1617,6 @@ extern "C" {
            int                   d0,  // dilation dimension 0
            int                   d1); // dilation dimension 1

-
    // kernel size is a->ne[0] x a->ne[1]
    // stride is equal to kernel size
    // padding is zero
@@ -1623,6 +1643,18 @@ extern "C" {
            struct ggml_tensor  * a,
            struct ggml_tensor  * b);

+    // depthwise
+    GGML_API struct ggml_tensor * ggml_conv_2d_dw(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // convolution kernel
+            struct ggml_tensor  * b,  // data
+            int                  s0,  // stride dimension 0
+            int                  s1,  // stride dimension 1
+            int                  p0,  // padding dimension 0
+            int                  p1,  // padding dimension 1
+            int                  d0,  // dilation dimension 0
+            int                  d1); // dilation dimension 1
+
    GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
            struct ggml_context * ctx,
            struct ggml_tensor  * a,
@@ -2205,11 +2237,19 @@ extern "C" {
    GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
    GGML_API void   gguf_get_meta_data(const struct gguf_context * ctx, void * data);

-#ifdef  __cplusplus
-// restrict not standard in C++
-#define GGML_RESTRICT
+#ifdef __cplusplus
+    // restrict not standard in C++
+#    if defined(__GNUC__)
+#        define GGML_RESTRICT __restrict__
+#    elif defined(__clang__)
+#        define GGML_RESTRICT __restrict
+#    elif defined(_MSC_VER)
+#        define GGML_RESTRICT __restrict
+#    else
+#        define GGML_RESTRICT
+#    endif
 #else
-#define GGML_RESTRICT restrict
+#    define GGML_RESTRICT restrict
 #endif
    typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
    typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t k);
@@ -194,11 +194,6 @@ endif()

 if (WIN32)
    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
-
-    if (BUILD_SHARED_LIBS)
-        # TODO: should not use this
-        set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
-    endif()
 endif()

 # ggml
@@ -220,9 +215,7 @@ add_library(ggml-base
            ggml-threading.cpp
            ggml-threading.h
            ggml-quants.c
-            ggml-quants.h
-            ggml-aarch64.c
-            ggml-aarch64.h)
+            ggml-quants.h)

 target_include_directories(ggml-base PRIVATE .)

@@ -315,6 +308,7 @@ ggml_add_backend(MUSA)
 ggml_add_backend(RPC)
 ggml_add_backend(SYCL)
 ggml_add_backend(Vulkan)
+ggml_add_backend(OpenCL)

 foreach (target ggml-base ggml)
    target_include_directories(${target} PUBLIC    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)
@@ -1,129 +0,0 @@
-#define GGML_COMMON_DECL_C
-#include "ggml-common.h"
-
-#include "ggml-aarch64.h"
-#include "ggml-impl.h"
-#include "ggml-quants.h"
-#include <assert.h>
-
-#define UNUSED GGML_UNUSED
-
-static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) {
-    block_q4_0x4 out;
-
-    for (int i = 0; i < 4; i++) {
-        out.d[i] = in[i].d;
-    }
-
-    const int end = QK4_0 * 2 / blck_size_interleave;
-
-    if (blck_size_interleave == 8) {
-        const uint64_t xor_mask = 0x8888888888888888ULL;
-        for (int i = 0; i < end; ++i) {
-            int src_id = i % 4;
-            int src_offset = (i / 4) * blck_size_interleave;
-            int dst_offset = i * blck_size_interleave;
-
-            uint64_t elems;
-            // Using memcpy to avoid unaligned memory accesses
-            memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
-            elems ^= xor_mask;
-            memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
-        }
-    } else if (blck_size_interleave == 4) {
-        const uint32_t xor_mask = 0x88888888;
-        for (int i = 0; i < end; ++i) {
-            int src_id = i % 4;
-            int src_offset = (i / 4) * blck_size_interleave;
-            int dst_offset = i * blck_size_interleave;
-
-            uint32_t elems;
-            memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint32_t));
-            elems ^= xor_mask;
-            memcpy(&out.qs[dst_offset], &elems, sizeof(uint32_t));
-        }
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    return out;
-}
-
-// interleave 8 block_q4_0s in blocks of blck_size_interleave
-// returns an interleaved block_q4_0x8
-// in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks
-// first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave
-static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave) {
-    block_q4_0x8 out;
-
-    for (int i = 0; i < 8; i++) {
-        out.d[i] = in[i].d;
-    }
-
-    const int end = QK4_0 * 4 / blck_size_interleave;
-    const uint64_t xor_mask = 0x8888888888888888ULL;
-
-    for (int i = 0; i < end; ++i) {
-        int src_id = i % 8;
-        int src_offset = (i / 8) * blck_size_interleave;
-        int dst_offset = i * blck_size_interleave;
-
-        uint64_t elems;
-        memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
-        elems ^= xor_mask;
-        memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
-    }
-
-    return out;
-}
-
-static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int blck_size_interleave) {
-    assert(n_per_row % QK4_0 == 0);
-    const int nb = n_per_row / QK4_0;
-
-    void * out_ptr = NULL;
-    if (nrows_interleaved == 8) {
-        out_ptr = (block_q4_0x8 *) dst;
-    }
-    else if (nrows_interleaved == 4) {
-        out_ptr = (block_q4_0x4 *) dst;
-    }
-    assert(nrows_interleaved <= 8);
-    block_q4_0 dst_tmp[8];
-
-    for (int b = 0; b < (nrow * n_per_row); b += nrows_interleaved * n_per_row) {
-
-        for (int64_t x = 0; x < nb; x++) {
-
-            for (int i  = 0; i < nrows_interleaved; i++ ) {
-                quantize_row_q4_0_ref(src + b + i * n_per_row + x * QK4_0, (block_q4_0 *) dst_tmp + i, QK4_0);
-            }
-
-            if (nrows_interleaved == 8) {
-                *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave);
-                out_ptr = (block_q4_0x8 *) out_ptr + 1;
-            }
-            else if (nrows_interleaved == 4) {
-                *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave);
-                out_ptr = (block_q4_0x4 *) out_ptr + 1;
-            }
-        }
-    }
-
-    return ((nrow * n_per_row) / QK4_0 * sizeof(block_q4_0));
-}
-
-size_t quantize_q4_0_4x4(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
-    UNUSED(quant_weights);
-    return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 4);
-}
-
-size_t quantize_q4_0_4x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
-    UNUSED(quant_weights);
-    return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 8);
-}
-
-size_t quantize_q4_0_8x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
-    UNUSED(quant_weights);
-    return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8);
-}
@@ -1,19 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-
-// GGML internal header
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
-size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
-size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
-size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
-
-#ifdef __cplusplus
-}
-#endif
-
@@ -534,7 +534,6 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
        size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node);
        hn->buffer_id = buffer_id;
        hn->offset = offset;
-        return;
    }
 }

@@ -46,6 +46,10 @@
 #include "ggml-vulkan.h"
 #endif

+#ifdef GGML_USE_OPENCL
+#include "ggml-opencl.h"
+#endif
+
 #ifdef GGML_USE_BLAS
 #include "ggml-blas.h"
 #endif
@@ -146,6 +150,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_VULKAN
        register_backend(ggml_backend_vk_reg());
 #endif
+#ifdef GGML_USE_OPENCL
+        register_backend(ggml_backend_opencl_reg());
+#endif
 #ifdef GGML_USE_CANN
        register_backend(ggml_backend_cann_reg());
 #endif
@@ -449,11 +456,21 @@ static std::string backend_filename_suffix() {
 #endif
 }

-static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent) {
+static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, const char * user_search_path) {
    // enumerate all the files that match [lib]ggml-name-*.[so|dll] in the search paths
     // TODO: search system paths
-    std::vector<std::string> search_paths = { "./", get_executable_path() };
    std::string file_prefix = backend_filename_prefix() + name + "-";
+    std::vector<std::string> search_paths;
+    if (user_search_path == nullptr) {
+        search_paths.push_back("./");
+        search_paths.push_back(get_executable_path());
+    } else {
+#if defined(_WIN32)
+        search_paths.push_back(std::string(user_search_path) + "\\");
+#else
+        search_paths.push_back(std::string(user_search_path) + "/");
+#endif
+    }

    int best_score = 0;
    std::string best_path;
@@ -463,7 +480,8 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent)
        if (!fs::exists(search_path)) {
            continue;
        }
-        for (const auto & entry : fs::directory_iterator(search_path)) {
+        fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
+        for (const auto & entry : dir_it) {
            if (entry.is_regular_file()) {
                std::string filename = entry.path().filename().string();
                std::string ext = entry.path().extension().string();
@@ -509,21 +527,26 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent)
 }

 void ggml_backend_load_all() {
+    ggml_backend_load_all_from_path(nullptr);
+}
+
+void ggml_backend_load_all_from_path(const char * dir_path) {
 #ifdef NDEBUG
    bool silent = true;
 #else
    bool silent = false;
 #endif

-    ggml_backend_load_best("blas", silent);
-    ggml_backend_load_best("cann", silent);
-    ggml_backend_load_best("cuda", silent);
-    ggml_backend_load_best("hip", silent);
-    ggml_backend_load_best("kompute", silent);
-    ggml_backend_load_best("metal", silent);
-    ggml_backend_load_best("rpc", silent);
-    ggml_backend_load_best("sycl", silent);
-    ggml_backend_load_best("vulkan", silent);
-    ggml_backend_load_best("musa", silent);
-    ggml_backend_load_best("cpu", silent);
+    ggml_backend_load_best("blas", silent, dir_path);
+    ggml_backend_load_best("cann", silent, dir_path);
+    ggml_backend_load_best("cuda", silent, dir_path);
+    ggml_backend_load_best("hip", silent, dir_path);
+    ggml_backend_load_best("kompute", silent, dir_path);
+    ggml_backend_load_best("metal", silent, dir_path);
+    ggml_backend_load_best("rpc", silent, dir_path);
+    ggml_backend_load_best("sycl", silent, dir_path);
+    ggml_backend_load_best("vulkan", silent, dir_path);
+    ggml_backend_load_best("opencl", silent, dir_path);
+    ggml_backend_load_best("musa", silent, dir_path);
+    ggml_backend_load_best("cpu", silent, dir_path);
 }
@@ -1747,6 +1747,15 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
            if (*ext_factor != 0) {
                return false;
            }
+
+            const int mode = ((const int32_t *) op->op_params)[2];
+            if (mode & GGML_ROPE_TYPE_MROPE) {
+                return false;
+            }
+            if (mode & GGML_ROPE_TYPE_VISION) {
+                return false;
+            }
+
            return true;
        }
        case GGML_OP_UPSCALE: {
@@ -2089,7 +2098,7 @@ static void * ggml_backend_cann_reg_get_proc_address(ggml_backend_reg_t reg, con
 static const ggml_backend_reg_i ggml_backend_cann_reg_interface = {
    /* .get_name          = */ ggml_backend_cann_reg_get_name,
    /* .get_device_count  = */ ggml_backend_cann_reg_get_device_count,
-    /* .get_device_get    = */ ggml_backend_cann_reg_get_device,
+    /* .get_device        = */ ggml_backend_cann_reg_get_device,
    /* .get_proc_address  = */ ggml_backend_cann_reg_get_proc_address,
 };

@@ -6,7 +6,20 @@
 typedef uint16_t ggml_half;
 typedef uint32_t ggml_half2;

-#define GGML_COMMON_AGGR
+#define GGML_COMMON_AGGR_U
+#define GGML_COMMON_AGGR_S
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_CPP)
+#include <cstdint>
+
+typedef uint16_t ggml_half;
+typedef uint32_t ggml_half2;
+
+// std-c++ allow anonymous unions but some compiler warn on it
+#define GGML_COMMON_AGGR_U data
+// std-c++ do not allow it.
+#define GGML_COMMON_AGGR_S data

 #define GGML_COMMON_DECL
 #elif defined(GGML_COMMON_DECL_METAL)
@@ -15,7 +28,8 @@ typedef uint32_t ggml_half2;
 typedef half  ggml_half;
 typedef half2 ggml_half2;

-#define GGML_COMMON_AGGR
+#define GGML_COMMON_AGGR_U
+#define GGML_COMMON_AGGR_S

 #define GGML_COMMON_DECL
 #elif defined(GGML_COMMON_DECL_CUDA)
@@ -29,7 +43,8 @@ typedef half2 ggml_half2;
 typedef half  ggml_half;
 typedef half2 ggml_half2;

-#define GGML_COMMON_AGGR data
+#define GGML_COMMON_AGGR_U
+#define GGML_COMMON_AGGR_S data

 #define GGML_COMMON_DECL
 #elif defined(GGML_COMMON_DECL_HIP)
@@ -39,7 +54,8 @@ typedef half2 ggml_half2;
 typedef half  ggml_half;
 typedef half2 ggml_half2;

-#define GGML_COMMON_AGGR data
+#define GGML_COMMON_AGGR_U
+#define GGML_COMMON_AGGR_S data

 #define GGML_COMMON_DECL
 #elif defined(GGML_COMMON_DECL_SYCL)
@@ -49,7 +65,8 @@ typedef half2 ggml_half2;
 typedef sycl::half  ggml_half;
 typedef sycl::half2 ggml_half2;

-#define GGML_COMMON_AGGR data
+#define GGML_COMMON_AGGR_U
+#define GGML_COMMON_AGGR_S data

 #define GGML_COMMON_DECL
 #endif
@@ -154,9 +171,9 @@ typedef struct {
        struct {
            ggml_half d; // delta
            ggml_half m; // min
-        } GGML_COMMON_AGGR;
+        } GGML_COMMON_AGGR_S;
        ggml_half2 dm;
-    };
+    } GGML_COMMON_AGGR_U;
    uint8_t qs[QK4_1 / 2]; // nibbles / quants
 } block_q4_1;
 static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_half) + QK4_1 / 2, "wrong q4_1 block size/padding");
@@ -175,9 +192,9 @@ typedef struct {
        struct {
            ggml_half d; // delta
            ggml_half m; // min
-        } GGML_COMMON_AGGR;
+        } GGML_COMMON_AGGR_S;
        ggml_half2 dm;
-    };
+    } GGML_COMMON_AGGR_U;
    uint8_t qh[4];         // 5-th bit of quants
    uint8_t qs[QK5_1 / 2]; // nibbles / quants
 } block_q5_1;
@@ -196,37 +213,13 @@ typedef struct {
        struct {
            ggml_half d; // delta
            ggml_half s; // d * sum(qs[i])
-        } GGML_COMMON_AGGR;
+        } GGML_COMMON_AGGR_S;
        ggml_half2 ds;
-    };
+    } GGML_COMMON_AGGR_U;
    int8_t qs[QK8_1]; // quants
 } block_q8_1;
 static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_half) + QK8_1, "wrong q8_1 block size/padding");

-typedef struct {
-    ggml_half d[4];        // deltas for 4 q4_0 blocks
-    uint8_t qs[QK4_0 * 2]; // nibbles / quants for 4 q4_0 blocks
-} block_q4_0x4;
-static_assert(sizeof(block_q4_0x4) == 4 * sizeof(ggml_half) + QK4_0 * 2, "wrong q4_0x4 block size/padding");
-
-typedef struct {
-    ggml_half d[8];        // deltas for 8 q4_0 blocks
-    uint8_t qs[QK4_0 * 4]; // nibbles / quants for 8 q4_0 blocks
-} block_q4_0x8;
-static_assert(sizeof(block_q4_0x8) == 8 * sizeof(ggml_half) + QK4_0 * 4, "wrong q4_0x8 block size/padding");
-
-typedef struct {
-    ggml_half d[4];        // deltas for 4 q8_0 blocks
-    int8_t qs[QK8_0 * 4];  // quants for 4 q8_0 blocks
-} block_q8_0x4;
-static_assert(sizeof(block_q8_0x4) == 4 * sizeof(ggml_half) + QK8_0 * 4, "wrong q8_0x4 block size/padding");
-
-typedef struct {
-    ggml_half d[8];        // deltas for 8 q8_0 blocks
-    int8_t qs[QK8_0 * 8];  // quants for 8 q8_0 blocks
-} block_q8_0x8;
-static_assert(sizeof(block_q8_0x8) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong q8_0x8 block size/padding");
-
 //
 // Ternary quantization
 //
@@ -261,9 +254,9 @@ typedef struct {
        struct {
            ggml_half d;    // super-block scale for quantized scales
            ggml_half dmin; // super-block scale for quantized mins
-        } GGML_COMMON_AGGR;
+        } GGML_COMMON_AGGR_S;
        ggml_half2 dm;
-    };
+    } GGML_COMMON_AGGR_U;
 } block_q2_K;
 static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");

@@ -288,9 +281,9 @@ typedef struct {
        struct {
            ggml_half d;    // super-block scale for quantized scales
            ggml_half dmin; // super-block scale for quantized mins
-        } GGML_COMMON_AGGR;
+        } GGML_COMMON_AGGR_S;
        ggml_half2 dm;
-    };
+    } GGML_COMMON_AGGR_U;
    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
    uint8_t qs[QK_K/2];           // 4--bit quants
 } block_q4_K;
@@ -305,9 +298,9 @@ typedef struct {
        struct {
            ggml_half d;    // super-block scale for quantized scales
            ggml_half dmin; // super-block scale for quantized mins
-        } GGML_COMMON_AGGR;
+        } GGML_COMMON_AGGR_S;
        ggml_half2 dm;
-    };
+    } GGML_COMMON_AGGR_U;
    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
    uint8_t qh[QK_K/8];           // quants, high bit
    uint8_t qs[QK_K/2];           // quants, low 4 bits
@@ -418,12 +411,6 @@ typedef struct {
 } block_iq4_xs;
 static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");

-typedef struct {
-    ggml_half d[4];        // deltas for 4 iq4_nl blocks
-    uint8_t qs[QK4_NL * 2];// nibbles / quants for 4 iq4_nl blocks
-} block_iq4_nlx4;
-static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wrong iq4_nlx4 block size/padding");
-
 #endif // GGML_COMMON_DECL
 #endif // GGML_COMMON_DECL

@@ -437,6 +424,13 @@ static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wro
 #define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = {
 #define GGML_TABLE_END() };

+#define GGML_COMMON_IMPL
+#elif defined(GGML_COMMON_IMPL_CPP)
+#include <cstdint>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = {
+#define GGML_TABLE_END() };
+
 #define GGML_COMMON_IMPL
 #elif defined(GGML_COMMON_IMPL_METAL)
 #include <metal_stdlib>
@@ -479,7 +473,7 @@ GGML_TABLE_BEGIN(uint8_t, ksigns_iq2xs, 128)
    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
 GGML_TABLE_END()

-//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+//#if __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A // lowest compute capability for integer intrinsics
 GGML_TABLE_BEGIN(uint64_t, ksigns64, 128)
    0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff,
    0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff,
@@ -10,10 +10,14 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
    list (APPEND GGML_CPU_SOURCES
        ggml-cpu/ggml-cpu.c
        ggml-cpu/ggml-cpu.cpp
-        ggml-cpu/ggml-cpu-aarch64.c
+        ggml-cpu/ggml-cpu-aarch64.cpp
        ggml-cpu/ggml-cpu-aarch64.h
+        ggml-cpu/ggml-cpu-hbm.cpp
+        ggml-cpu/ggml-cpu-hbm.h
        ggml-cpu/ggml-cpu-quants.c
        ggml-cpu/ggml-cpu-quants.h
+        ggml-cpu/ggml-cpu-traits.cpp
+        ggml-cpu/ggml-cpu-traits.h
        ggml-cpu/amx/amx.cpp
        ggml-cpu/amx/amx.h
        ggml-cpu/amx/mmq.cpp
@@ -70,112 +74,77 @@ function(ggml_add_cpu_backend_variant_impl tag_name)

    if (CMAKE_OSX_ARCHITECTURES      STREQUAL "arm64" OR
        CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
-        (NOT CMAKE_OSX_ARCHITECTURES      AND
-        NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+        (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
            CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))

        message(STATUS "ARM detected")

-        if (MSVC)
-            list(APPEND ARCH_DEFINITIONS __aarch64__) # MSVC defines _M_ARM64 instead
-            list(APPEND ARCH_DEFINITIONS __ARM_NEON)
-            list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FMA)
-
-            set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS})
-            string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2")
-
-            check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
-            if (GGML_COMPILER_SUPPORT_DOTPROD)
-                list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD)
-
-                message(STATUS "ARM feature DOTPROD enabled")
-            endif ()
-
-            check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8)
-
-            if (GGML_COMPILER_SUPPORT_MATMUL_INT8)
-                list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8)
-
-                message(STATUS "ARM feature MATMUL_INT8 enabled")
-            endif ()
-
-            check_cxx_source_compiles("#include <arm_neon.h>\nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
-            if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
-                list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-
-                message(STATUS "ARM feature FP16_VECTOR_ARITHMETIC enabled")
-            endif ()
-
-            set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV})
-        elseif (APPLE)
-            if (GGML_NATIVE)
-                set(USER_PROVIDED_MARCH FALSE)
-                foreach(flag_var IN ITEMS CMAKE_C_FLAGS CMAKE_CXX_FLAGS CMAKE_REQUIRED_FLAGS)
-                    if ("${${flag_var}}" MATCHES "-march=[a-zA-Z0-9+._-]+")
-                        set(USER_PROVIDED_MARCH TRUE)
-                        break()
-                    endif()
-                endforeach()
-
-                if (NOT USER_PROVIDED_MARCH)
-                    set(MARCH_FLAGS "-march=armv8.2a")
-
-                    check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
-                    if (GGML_COMPILER_SUPPORT_DOTPROD)
-                        set(MARCH_FLAGS "${MARCH_FLAGS}+dotprod")
-                        list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD)
-
-                        message(STATUS "ARM feature DOTPROD enabled")
-                    endif ()
-
-                    set(TEST_I8MM_FLAGS "-march=armv8.2a+i8mm")
-
-                    set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
-                    set(CMAKE_REQUIRED_FLAGS     "${CMAKE_REQUIRED_FLAGS} ${TEST_I8MM_FLAGS}")
-
-                    check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8)
-                    if (GGML_COMPILER_SUPPORT_MATMUL_INT8)
-                        set(MARCH_FLAGS "${MARCH_FLAGS}+i8mm")
-                        list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8)
-
-                        message(STATUS "ARM feature MATMUL_INT8 enabled")
-                    endif ()
-
-                    set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
-
-                    list(APPEND ARCH_FLAGS "${MARCH_FLAGS}")
-                endif ()
-            endif ()
+        if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang")
+            message(FATAL_ERROR "MSVC is not supported for ARM, use clang")
        else()
            check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)
            if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
                list(APPEND ARCH_FLAGS -mfp16-format=ieee)
            endif()
-            if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
-                # Raspberry Pi 1, Zero
-                list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access)
-            endif()
-            if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
-                if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android")
-                    # Android armeabi-v7a
-                    list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations)
-                else()
-                    # Raspberry Pi 2
-                    list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
+
+            if (GGML_NATIVE)
+                list(APPEND ARCH_FLAGS -mcpu=native)
+
+                set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
+
+                # -mcpu=native does not always enable all the features in some compilers,
+                # so we check for them manually and enable them if available
+
+                include(CheckCXXSourceRuns)
+
+                set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+dotprod")
+                check_cxx_source_runs(
+                    "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }"
+                    GGML_COMPILER_SUPPORT_DOTPROD)
+                if (GGML_COMPILER_SUPPORT_DOTPROD)
+                    set(ARCH_FLAGS "${ARCH_FLAGS}+dotprod")
+                endif()
+
+                set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+i8mm")
+                check_cxx_source_runs(
+                    "#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }"
+                    GGML_COMPILER_SUPPORT_I8MM)
+                if (GGML_COMPILER_SUPPORT_I8MM)
+                    set(ARCH_FLAGS "${ARCH_FLAGS}+i8mm")
+                endif()
+
+                set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
+
+            else()
+                if (GGML_CPU_ARM_ARCH)
+                    list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH})
                endif()
            endif()
-            if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
-                # Android arm64-v8a
-                # Raspberry Pi 3, 4, Zero 2 (32-bit)
-                list(APPEND ARCH_FLAGS -mno-unaligned-access)
-            endif()
-            if (GGML_SVE)
-                list(APPEND ARCH_FLAGS -march=armv8.6-a+sve)
+
+            # show enabled features
+            execute_process(
+                COMMAND ${CMAKE_C_COMPILER} ${ARCH_FLAGS} -dM -E -
+                INPUT_FILE "/dev/null"
+                OUTPUT_VARIABLE ARM_FEATURE
+                RESULT_VARIABLE ARM_FEATURE_RESULT
+            )
+            if (ARM_FEATURE_RESULT)
+                message(FATAL_ERROR "Failed to get ARM features")
+            else()
+                foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC)
+                    string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos)
+                    if (NOT ${feature_pos} EQUAL -1)
+                        message(STATUS "ARM feature ${feature} enabled")
+                    endif()
+                endforeach()
            endif()
        endif()
    elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
            (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
-            CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$"))
+            CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
+
+        message(STATUS "x86 detected")
+
        if (MSVC)
            # instruction set detection for MSVC only
            if (GGML_NATIVE)
@@ -5,6 +5,7 @@
 #include "ggml-backend.h"
 #include "ggml-impl.h"
 #include "ggml-cpu.h"
+#include "ggml-cpu-traits.h"

 #if defined(__gnu_linux__)
 #include <sys/syscall.h>
@@ -17,31 +18,65 @@

 #if defined(__AMX_INT8__) && defined(__AVX512VNNI__)

+// AMX type_trais
+namespace ggml::cpu::amx {
+class tensor_traits : public ggml::cpu::tensor_traits {
+    bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
+        size = ggml_backend_amx_desired_wsize(op);
+        return true;
+    }
+
+    bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) override {
+        if (op->op == GGML_OP_MUL_MAT) {
+            ggml_backend_amx_mul_mat(params, op);
+            return true;
+        }
+        return false;
+    }
+};
+
+static ggml::cpu::tensor_traits * get_tensor_traits(ggml_backend_buffer_t, struct ggml_tensor *) {
+    static tensor_traits traits;
+    return &traits;
+}
+}  // namespace ggml::cpu::amx
+
 // AMX buffer interface
 static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    free(buffer->context);
 }

 static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) {
-    return (void *)(buffer->context);
+    return (void *) (buffer->context);
 }

-static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
-    memset((char *)tensor->data + offset, value, size);
+static void ggml_backend_amx_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+    tensor->extra = (void *) ggml::cpu::amx::get_tensor_traits(buffer, tensor);

    GGML_UNUSED(buffer);
 }

-static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
+                                                  uint8_t value, size_t offset, size_t size) {
+    memset((char *) tensor->data + offset, value, size);
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
+                                               const void * data, size_t offset, size_t size) {
    if (qtype_has_amx_kernels(tensor->type)) {
+        GGML_LOG_DEBUG("%s: amx repack tensor %s of type %s\n", __func__, tensor->name, ggml_type_name(tensor->type));
        ggml_backend_amx_convert_weight(tensor, data, offset, size);
    } else {
-        memcpy((char *)tensor->data + offset, data, size);
+        memcpy((char *) tensor->data + offset, data, size);
    }

    GGML_UNUSED(buffer);
 }

+/*
+// need to figure what we need to do with buffer->extra.
 static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    GGML_ASSERT(!qtype_has_amx_kernels(tensor->type));
    memcpy(data, (const char *)tensor->data + offset, size);
@@ -62,6 +97,7 @@ static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con

    GGML_UNUSED(buffer);
 }
+*/

 static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
    memset(buffer->context, value, buffer->size);
@@ -70,13 +106,13 @@ static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t
 static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = {
    /* .free_buffer     = */ ggml_backend_amx_buffer_free_buffer,
    /* .get_base        = */ ggml_backend_amx_buffer_get_base,
-    /* .init_tensor     = */ NULL, // no initialization required
+    /* .init_tensor     = */ ggml_backend_amx_buffer_init_tensor,
    /* .memset_tensor   = */ ggml_backend_amx_buffer_memset_tensor,
    /* .set_tensor      = */ ggml_backend_amx_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_amx_buffer_get_tensor,
-    /* .cpy_tensor      = */ ggml_backend_amx_buffer_cpy_tensor,
+    /* .get_tensor      = */ nullptr,
+    /* .cpy_tensor      = */ nullptr,
    /* .clear           = */ ggml_backend_amx_buffer_clear,
-    /* .reset           = */ NULL,
+    /* .reset           = */ nullptr,
 };

 static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
@@ -86,7 +122,7 @@ static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_ty
 }

 static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    void * data = aligned_alloc(TENSOR_ALIGNMENT, size);
+    void * data = ggml_aligned_malloc(size);
    if (data == NULL) {
        fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
        return NULL;
@@ -101,18 +137,48 @@ static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_typ
    GGML_UNUSED(buft);
 }

-static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) {
+namespace ggml::cpu::amx {
+class extra_buffer_type : ggml::cpu::extra_buffer_type {
+    bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
+        // handle only 2d gemm for now
+        auto is_contiguous_2d = [](const struct ggml_tensor * t) {
+            return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1;
+        };
+
+        if (op->op == GGML_OP_MUL_MAT && is_contiguous_2d(op->src[0]) &&  // src0 must be contiguous
+            is_contiguous_2d(op->src[1]) &&                               // src1 must be contiguous
+            op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_amx_buffer_type() &&
+            op->ne[0] % (TILE_N * 2) == 0 &&                              // out_features is 32x
+            (qtype_has_amx_kernels(op->src[0]->type) || (op->src[0]->type == GGML_TYPE_F16))) {
+            // src1 must be host buffer
+            if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
+                return false;
+            }
+            // src1 must be float32
+            if (op->src[1]->type == GGML_TYPE_F32) {
+                return true;
+            }
+        }
+        return false;
+    }
+
+    ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override {
+        if (op->op == GGML_OP_MUL_MAT && op->src[0]->buffer &&
+            op->src[0]->buffer->buft == ggml_backend_amx_buffer_type()) {
+            return (ggml::cpu::tensor_traits *) op->src[0]->extra;
+        }
+
+        return nullptr;
+    }
+};
+}  // namespace ggml::cpu::amx
+
+static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
    return ggml_backend_amx_get_alloc_size(tensor);

    GGML_UNUSED(buft);
 }

-static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
-    return false;
-
-    GGML_UNUSED(buft);
-}
-
 #define ARCH_GET_XCOMP_PERM     0x1022
 #define ARCH_REQ_XCOMP_PERM     0x1023
 #define XFEATURE_XTILECFG       17
@@ -129,68 +195,26 @@ static bool ggml_amx_init() {
    return true;
 #endif
 }
+
 ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() {
    static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = {
        /* .iface = */ {
-            /* .get_name         = */ ggml_backend_amx_buffer_type_get_name,
-            /* .alloc_buffer     = */ ggml_backend_amx_buffer_type_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_amx_buffer_type_get_alignment,
-            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-            /* .get_alloc_size   = */ ggml_backend_amx_buffer_type_get_alloc_size,
-            /* .is_host          = */ ggml_backend_amx_buffer_type_is_host,
-        },
+                        /* .get_name         = */ ggml_backend_amx_buffer_type_get_name,
+                        /* .alloc_buffer     = */ ggml_backend_amx_buffer_type_alloc_buffer,
+                        /* .get_alignment    = */ ggml_backend_amx_buffer_type_get_alignment,
+                        /* .get_max_size     = */ nullptr,  // defaults to SIZE_MAX
+                        /* .get_alloc_size   = */ ggml_backend_amx_buffer_type_get_alloc_size,
+                        /* .is_host          = */ nullptr,
+                        },
        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
-        /* .context = */ NULL,
+        /* .context = */ new ggml::cpu::amx::extra_buffer_type(),
    };

    if (!ggml_amx_init()) {
-        return NULL;
+        return nullptr;
    }

    return &ggml_backend_buffer_type_amx;
 }

-bool ggml_backend_amx_buft_is_amx(ggml_backend_buffer_type_t buft) {
-    return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name;
-}
-
-bool ggml_backend_amx_device_supports_op(const struct ggml_tensor * op) {
-    // handle only 2d gemm for now
-    auto is_contiguous_2d = [](const struct ggml_tensor * t) {
-        return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1;
-    };
-
-    switch (op->op) {
-        case GGML_OP_NONE:
-        case GGML_OP_RESHAPE:
-        case GGML_OP_VIEW:
-        case GGML_OP_PERMUTE:
-        case GGML_OP_TRANSPOSE:
-            return true;
-
-        case GGML_OP_MUL_MAT: {
-            const struct ggml_tensor * src0 = op->src[0];
-            const struct ggml_tensor * src1 = op->src[1];
-
-            const enum ggml_type type = src0->type;
-            const int64_t ne0 = op->ne[0];
-
-            // amx kernels enables for Q4_0, Q4_1, Q8_0, F16
-            // Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256
-            bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16);
-
-            bool can_use_amx =
-                is_contiguous_2d(src0) &&       // src0 must be contiguous
-                is_contiguous_2d(src1) &&       // src1 must be contiguous
-                src1->type == GGML_TYPE_F32 &&  // src1 must be float32
-                has_amx_kernels &&              // with amx kernel impls
-                ne0 % (TILE_N * 2) == 0;        // out_features is 32x
-
-            return can_use_amx;
-        }
-        default:
-            return false;
-    }
-}
-
-#endif // defined(__AMX_INT8__) && defined(__AVX512VNNI__)
+#endif  // defined(__AMX_INT8__) && defined(__AVX512VNNI__)
@@ -1,20 +1,8 @@
 #include "ggml-backend.h"
 #include "ggml-cpu-impl.h"

-#ifdef __cplusplus
-extern "C" {
-#endif
+// GGML internal header

 #if defined(__AMX_INT8__) && defined(__AVX512VNNI__)
-
 ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void);
-bool ggml_backend_amx_buft_is_amx(ggml_backend_buffer_type_t buft);
-bool ggml_backend_amx_device_supports_op(const struct ggml_tensor * op);
-void ggml_backend_amx_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst);
-
-#endif
-
-#ifdef __cplusplus
-}
 #endif
@@ -7,7 +7,7 @@
 #include <memory>
 #include <type_traits>

-#if defined(_OPENMP)
+#if defined(GGML_USE_OPENMP)
 #include <omp.h>
 #endif

@@ -56,11 +56,11 @@ inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) {
 }

 template <typename func_t>
-inline void parallel_for(int nth, int n, const func_t& f) {
-#if defined(_OPENMP)
-#pragma omp parallel num_threads(nth)
+inline void parallel_for(int n, const func_t& f) {
+#if defined(GGML_USE_OPENMP)
+#pragma omp parallel
 {
-    //int nth = omp_get_num_threads();
+    int nth = omp_get_num_threads();
    int ith = omp_get_thread_num();
    int tbegin, tend;
    balance211(n, nth, ith, tbegin, tend);
@@ -68,8 +68,6 @@ inline void parallel_for(int nth, int n, const func_t& f) {
 }
 #else
    f(0, n);
-
-    GGML_UNUSED(nth);
 #endif
 }

@@ -91,10 +89,3 @@ inline bool qtype_has_amx_kernels(const enum ggml_type type) {
        (type == GGML_TYPE_Q6_K) ||
        (type == GGML_TYPE_IQ4_XS);
 }
-
-// ggml backend context
-struct ggml_backend_amx_context {
-    int n_threads = GGML_DEFAULT_N_THREADS;
-    std::unique_ptr<char[]> work_data;
-    size_t work_size = 0;
-};
@@ -18,10 +18,6 @@
 #include <unistd.h>
 #endif

-#if defined(_OPENMP)
-#include <omp.h>
-#endif
-
 #if (defined(_WIN32) || defined(_WIN64))
 #define RESTRICT __restrict
 #else
@@ -1382,13 +1378,13 @@ struct tinygemm_kernel_avx<float, ggml_fp16_t, float, BLOCK_M, BLOCK_N, BLOCK_K>
 #define PACKED_INDEX(n, k, KB, tile_size) (n * KB + k) * tile_size

 template<typename TB, int BLOCK_K>
-void convert_B_packed_format(void * RESTRICT packed_B, const TB * RESTRICT B, int N, int K, int n_threads) {
+void convert_B_packed_format(void * RESTRICT packed_B, const TB * RESTRICT B, int N, int K) {
    const int NB = N / TILE_N;
    const int KB = K / BLOCK_K;
    const int TILE_SIZE = get_tile_size<TB>();

    // parallel on NB should be enough
-    parallel_for(n_threads, NB, [&](int begin, int end) {
+    parallel_for(NB, [&](int begin, int end) {
        for (int n = begin; n < end; ++n) {
            for (int k = 0; k < KB; ++k) {
                int n0 = n * TILE_N;
@@ -2334,15 +2330,8 @@ void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * d
    const int K = tensor->ne[0]; // ne0: in_features
    const int N = tensor->ne[1]; // ne1: out_features

-#if defined(_OPENMP)
-    // the buffer ctx is not initialized when .set_tensor is called
-    int n_threads = omp_get_num_threads();
-#else
-    int n_threads = 1;
-#endif
-
    GGML_DISPATCH_QTYPES(TYPE, [&] {
-        convert_B_packed_format<type, blck_size>((void *)((char *)tensor->data + offset), (const type *)data, N, K, n_threads);
+        convert_B_packed_format<type, blck_size>((void *)((char *)tensor->data + offset), (const type *)data, N, K);
    });
 }

@@ -1,16 +1,10 @@
 #pragma once
 #include "common.h"

-#ifdef __cplusplus
-extern "C" {
-#endif
+size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst);

 size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor);

 void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);

 void ggml_backend_amx_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-
-#ifdef __cplusplus
-}
-#endif
@@ -1,20 +1,57 @@
-#define GGML_COMMON_IMPL_C
+#define GGML_COMMON_IMPL_CPP
+#define GGML_COMMON_DECL_CPP
 #include "ggml-common.h"
+#include "ggml-backend-impl.h"

 #include "ggml-quants.h"
 #include "ggml-impl.h"
 #include "ggml-cpu.h"
-#include "ggml-cpu/ggml-cpu-impl.h"
+#include "ggml-cpu-impl.h"
+#include "ggml-cpu-traits.h"

-#include <math.h>
-#include <string.h>
-#include <assert.h>
-#include <float.h>
-#include <stdlib.h> // for qsort
-#include <stdio.h>  // for GGML_ASSERT
+#include <cmath>
+#include <cstring>
+#include <cassert>
+#include <cfloat>
+#include <cstdlib> // for qsort
+#include <cstdio>  // for GGML_ASSERT

 #include "ggml-cpu-aarch64.h"

+// TODO: move to include file?
+template <int K> constexpr int QK_0() {
+    if constexpr (K == 4) {
+        return QK4_0;
+    }
+    if constexpr (K == 8) {
+        return QK8_0;
+    }
+    return -1;
+}
+
+template <int K, int N> struct block {
+    ggml_half d[N];                         // deltas for N qK_0 blocks
+    int8_t    qs[(QK_0<K>() * N * K) / 8];  // quants for N qK_0 blocks
+};
+
+// control size
+static_assert(sizeof(block<4, 4>) == 4 * sizeof(ggml_half) + QK8_0 * 2, "wrong block<4,4> size/padding");
+static_assert(sizeof(block<4, 8>) == 8 * sizeof(ggml_half) + QK8_0 * 4, "wrong block<4,8> size/padding");
+static_assert(sizeof(block<8, 4>) == 4 * sizeof(ggml_half) + QK8_0 * 4, "wrong block<8,4> size/padding");
+static_assert(sizeof(block<8, 8>) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong block<8,8> size/padding");
+
+using block_q4_0x4 = block<4, 4>;
+using block_q4_0x8 = block<4, 8>;
+using block_q8_0x4 = block<8, 4>;
+using block_q8_0x8 = block<8, 8>;
+
+struct block_iq4_nlx4 {
+    ggml_half d[4];            // deltas for 4 iq4_nl blocks
+    uint8_t   qs[QK4_NL * 2];  // nibbles / quants for 4 iq4_nl blocks
+};
+
+static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wrong iq4_nlx4 block size/padding");
+
 #if defined(__GNUC__)
 #pragma GCC diagnostic ignored "-Woverlength-strings"
 #elif defined(_MSC_VER)
@@ -185,12 +222,12 @@ static inline __m256i mul_sum_i8_pairs_int32x8(const __m256i x, const __m256i y)

 static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};

-static void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k) {
+static void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
    assert(QK8_0 == 32);
    assert(k % QK8_0 == 0);
    const int nb = k / QK8_0;

-    block_q8_0x4 * restrict y = (block_q8_0x4 *) vy;
+    block_q8_0x4 * GGML_RESTRICT y = (block_q8_0x4 *) vy;

 #if defined(__ARM_NEON)
    float32x4_t srcv[4][8];
@@ -279,12 +316,12 @@ static void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int6
 #endif
 }

-static void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k) {
+static void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
    assert(QK8_0 == 32);
    assert(k % QK8_0 == 0);
    const int nb = k / QK8_0;

-    block_q8_0x4 * restrict y = (block_q8_0x4 *) vy;
+    block_q8_0x4 * GGML_RESTRICT y = (block_q8_0x4 *) vy;

 #if defined(__ARM_NEON)
    float32x4_t srcv[4][8];
@@ -494,7 +531,7 @@ static void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int6
 #endif
 }

-void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) {
+static void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) {
    assert(nrow == 4);
    UNUSED(nrow);
    if (blck_size_interleave == 4) {
@@ -506,7 +543,7 @@ void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nro
    }
 }

-void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+static void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
@@ -591,7 +628,7 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void *
    }
 }

-void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+static void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
@@ -701,7 +738,7 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void *
    }
 }

-void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+static void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
@@ -974,7 +1011,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
    }
 }

-void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+static void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
@@ -1070,7 +1107,7 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void
    }
 }

-void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+static void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
@@ -1586,7 +1623,7 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void *
    }
 }

-void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+static void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
@@ -2040,7 +2077,7 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void *
    }
 }

-void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+static void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 8;
@@ -2560,31 +2597,31 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
                    const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31)

                    // Shuffle pattern one - right side input
-                    const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3)
-                    const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3)
+                    const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3)
+                    const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3)

-                    const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11)
-                    const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11)
+                    const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11)
+                    const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11)

-                    const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19)
-                    const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19)
+                    const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19)
+                    const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19)

-                    const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27)
-                    const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27)
+                    const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27)
+                    const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27)

                    // Shuffle pattern two - right side input

-                    const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7)
-                    const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7)
+                    const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7)
+                    const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7)

-                    const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15)
-                    const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15)
+                    const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15)
+                    const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15)

-                    const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23)
-                    const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23)
+                    const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23)
+                    const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23)

-                    const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31)
-                    const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31)
+                    const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31)
+                    const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31)

                    // Scale values - Load the weight scale values of two block_q4_0x8
                    const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d);
@@ -2618,31 +2655,31 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *

                        // Shuffle pattern one - left side input

-                        const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
-                        const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
+                        const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
+                        const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)

-                        const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
-                        const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
+                        const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
+                        const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)

-                        const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
-                        const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
+                        const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
+                        const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)

-                        const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
-                        const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
+                        const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
+                        const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)

                        // Shuffle pattern two - left side input

-                        const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
-                        const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
+                        const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
+                        const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)

-                        const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
-                        const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
+                        const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
+                        const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)

-                        const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
-                        const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
+                        const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
+                        const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)

-                        const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
-                        const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
+                        const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
+                        const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)

                        // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
                        // Resembles MMLAs into 2x2 matrices in ARM Version
@@ -2671,10 +2708,10 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *


                        // Straighten out to make 4 row vectors
-                        __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78));
-                        __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01);
-                        __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78));
-                        __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11);
+                        __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, (_MM_PERM_ENUM)78));
+                        __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, (_MM_PERM_ENUM)78), iacc_mat_01);
+                        __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, (_MM_PERM_ENUM)78));
+                        __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, (_MM_PERM_ENUM)78), iacc_mat_11);

                        // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
                        const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68);
@@ -2753,31 +2790,31 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
                    const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31)

                    // Shuffle pattern one - right side input
-                    const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3)
-                    const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3)
+                    const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3)
+                    const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3)

-                    const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11)
-                    const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11)
+                    const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11)
+                    const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11)

-                    const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19)
-                    const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19)
+                    const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19)
+                    const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19)

-                    const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27)
-                    const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27)
+                    const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27)
+                    const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27)

                    // Shuffle pattern two - right side input

-                    const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7)
-                    const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7)
+                    const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7)
+                    const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7)

-                    const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15)
-                    const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15)
+                    const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15)
+                    const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15)

-                    const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23)
-                    const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23)
+                    const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23)
+                    const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23)

-                    const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31)
-                    const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31)
+                    const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31)
+                    const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31)


                    // Scale values - Load the weight scale values of two block_q4_0x8
@@ -2809,31 +2846,31 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *

                    // Shuffle pattern one - left side input

-                    const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
-                    const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
+                    const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
+                    const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)

-                    const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
-                    const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
+                    const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
+                    const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)

-                    const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
-                    const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
+                    const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
+                    const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)

-                    const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
-                    const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
+                    const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
+                    const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)

                    // Shuffle pattern two - left side input

-                    const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
-                    const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
+                    const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
+                    const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)

-                    const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
-                    const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
+                    const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
+                    const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)

-                    const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
-                    const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
+                    const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
+                    const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)

-                    const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
-                    const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
+                    const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
+                    const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)

                    // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
                    // Resembles MMLAs into 2x2 matrices in ARM Version
@@ -2862,10 +2899,10 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *


                    // Straighten out to make 4 row vectors
-                    __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78));
-                    __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01);
-                    __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78));
-                    __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11);
+                    __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, (_MM_PERM_ENUM)78));
+                    __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, (_MM_PERM_ENUM)78), iacc_mat_01);
+                    __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, (_MM_PERM_ENUM)78));
+                    __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, (_MM_PERM_ENUM)78), iacc_mat_11);

                    // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
                    const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68);
@@ -3460,7 +3497,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
    }
 }

-void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+static void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
    const int nb = n / qk;
    const int ncols_interleaved = 4;
@@ -3571,7 +3608,6 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void
    }
 }

-// FIXME: this code is duplicated from ggml-aarch64.c
 static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) {
    block_q4_0x4 out;

@@ -3641,20 +3677,20 @@ static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_in
    return out;
 }

-static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * restrict data, size_t data_size) {
+static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
    GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
+    constexpr int nrows_interleaved = 4;

    block_q4_0x4 * dst = (block_q4_0x4 *)t->data;
    const block_q4_0 * src = (const block_q4_0 *)data;
    block_q4_0 dst_tmp[4];
-    int nrow = t->ne[1]; // Number of rows
-    int nrows_interleaved = 4;
+    int nrow = ggml_nrows(t);
    int nblocks = t->ne[0] / QK4_0;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));

-    if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
        return -1;
    }

@@ -3672,20 +3708,20 @@ static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block
    GGML_UNUSED(data_size);
 }

-static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block, const void * restrict data, size_t data_size) {
+static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
    GGML_ASSERT(interleave_block == 8);
+    constexpr int nrows_interleaved = 8;

    block_q4_0x8 * dst = (block_q4_0x8*)t->data;
    const block_q4_0 * src = (const block_q4_0*) data;
    block_q4_0 dst_tmp[8];
-    int nrow = t->ne[1]; // Number of rows
-    int nrows_interleaved = 8;
+    int nrow = ggml_nrows(t);
    int nblocks = t->ne[0] / QK4_0;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));

-    if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
        return -1;
    }

@@ -3712,16 +3748,18 @@ static block_iq4_nlx4 make_block_iq4_nlx4(block_iq4_nl * in, unsigned int blck_s

    const int end = QK4_NL * 2 / blck_size_interleave;

-    if (blck_size_interleave == 8) {
-        for (int i = 0; i < end; ++i) {
-            int src_id = i % 4;
-            int src_offset = (i / 4) * blck_size_interleave;
-            int dst_offset = i * blck_size_interleave;
+    // TODO: this branch seems wrong
+    //if (blck_size_interleave == 8) {
+    //    for (int i = 0; i < end; ++i) {
+    //        int src_id = i % 4;
+    //        int src_offset = (i / 4) * blck_size_interleave;
+    //        int dst_offset = i * blck_size_interleave;

-            // Using memcpy to avoid unaligned memory accesses
-            memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t));
-        }
-    } else if (blck_size_interleave == 4) {
+    //        // Using memcpy to avoid unaligned memory accesses
+    //        memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t));
+    //    }
+    //} else
+    if (blck_size_interleave == 4) {
        for (int i = 0; i < end; ++i) {
            int src_id = i % 4;
            int src_offset = (i / 4) * blck_size_interleave;
@@ -3736,20 +3774,21 @@ static block_iq4_nlx4 make_block_iq4_nlx4(block_iq4_nl * in, unsigned int blck_s
    return out;
 }

-static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_block, const void * restrict data, size_t data_size) {
+static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
    GGML_ASSERT(t->type == GGML_TYPE_IQ4_NL);
-    GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
+    //GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
+    GGML_ASSERT(interleave_block == 4);

    block_iq4_nlx4 * dst = (block_iq4_nlx4 *)t->data;
    const block_iq4_nl * src = (const block_iq4_nl *)data;
    block_iq4_nl dst_tmp[4];
-    int nrow = t->ne[1]; // Number of rows
+    int nrow = ggml_nrows(t);
    int nrows_interleaved = 4;
    int nblocks = t->ne[0] / QK4_0;

    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl));

-    if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
        return -1;
    }

@@ -3767,57 +3806,457 @@ static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_b
    GGML_UNUSED(data_size);
 }

-// Prepare for optimized kernels if applicable
-void ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_type, const void * restrict data, size_t data_size) {
-    if (cur->type == repack_type) {
-        memcpy(cur->data, data, data_size);
-        return;
-    }
+namespace ggml::cpu::aarch64 {
+// repack
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
+int repack(struct ggml_tensor *, const void *, size_t);

-    if (cur->type == GGML_TYPE_Q4_0) {
-        switch (repack_type) {
-            case GGML_TYPE_Q4_0_8_8:
-                repack_q4_0_to_q4_0_8_bl(cur, 8, data, data_size);
-                break;
-            case GGML_TYPE_Q4_0_4_8:
-                repack_q4_0_to_q4_0_4_bl(cur, 8, data, data_size);
-                break;
-            case GGML_TYPE_Q4_0_4_4:
-                repack_q4_0_to_q4_0_4_bl(cur, 4, data, data_size);
-                break;
-            default:
-                GGML_ABORT("Unsupported type");
-        }
-    } else if (cur->type == GGML_TYPE_IQ4_NL) {
-        switch (repack_type) {
-            case GGML_TYPE_IQ4_NL_4_4:
-                repack_iq4_nl_to_iq4_nl_4_bl(cur, 4, data, data_size);
-                break;
-            default:
-                GGML_ABORT("Unsupported type");
-        }
-    } else {
-        GGML_ABORT("Unsupported type");
-    }
+// TODO: generalise.
+template <> int repack<block_q4_0, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q4_0_to_q4_0_4_bl(t, 4, data, data_size);
 }

-enum ggml_type ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur) {
+template <> int repack<block_q4_0, 8, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q4_0_to_q4_0_4_bl(t, 8, data, data_size);
+}
+
+template <> int repack<block_q4_0, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q4_0_to_q4_0_8_bl(t, 8, data, data_size);
+}
+
+template <> int repack<block_iq4_nl, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_iq4_nl_to_iq4_nl_4_bl(t, 4, data, data_size);
+}
+
+// TODO: needs to be revisited
+//template <> int repack<block_iq4_nl, 8, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
+//    return repack_iq4_nl_to_iq4_nl_4_bl(t, 8, data, data_size);
+//}
+
+// gemv
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
+void gemv(int, float *, size_t, const void *, const void *, int, int);
+
+template <> void gemv<block_q4_0, 4, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_q4_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+template <> void gemv<block_q4_0, 8, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_q4_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+template <> void gemv<block_q4_0, 8, 8>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+template <>
+void gemv<block_iq4_nl, 4, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+// gemm
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
+void gemm(int, float *, size_t, const void *, const void *, int, int);
+
+template <> void gemm<block_q4_0, 4, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_q4_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+template <> void gemm<block_q4_0, 8, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_q4_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+template <> void gemm<block_q4_0, 8, 8>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+template <>
+void gemm<block_iq4_nl, 4, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+class tensor_traits_base : public ggml::cpu::tensor_traits {
+  public:
+    virtual int repack(struct ggml_tensor * t, const void * data, size_t data_size) = 0;
+};
+
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> class tensor_traits : public tensor_traits_base {
+
+    bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
+        // not realy a GGML_TYPE_Q8_0 but same size.
+        switch (op->op) {
+        case GGML_OP_MUL_MAT:
+            size = ggml_row_size(GGML_TYPE_Q8_0, ggml_nelements(op->src[1]));
+            return true;
+        case GGML_OP_MUL_MAT_ID:
+            size = ggml_row_size(GGML_TYPE_Q8_0, ggml_nelements(op->src[1]));
+            size = GGML_PAD(size, sizeof(int64_t));  // + padding for next bloc.
+            size += sizeof(int64_t) * (1+op->src[0]->ne[2]) * op->src[1]->ne[2];
+            return true;
+        default:
+            // GGML_ABORT("fatal error");
+            break;
+        }
+        return false;
+    }
+
+    bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) override {
+        switch (op->op) {
+        case GGML_OP_MUL_MAT:
+            forward_mul_mat(params, op);
+            return true;
+        case GGML_OP_MUL_MAT_ID:
+            forward_mul_mat_id(params, op);
+            return true;
+        default:
+            // GGML_ABORT("fatal error");
+            break;
+        }
+        return false;
+    }
+
+    void forward_mul_mat(ggml_compute_params * params, ggml_tensor * op) {
+        const ggml_tensor * src0 = op->src[0];
+        const ggml_tensor * src1 = op->src[1];
+        ggml_tensor *       dst  = op;
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        const int ith = params->ith;
+        const int nth = params->nth;
+
+        GGML_ASSERT(ne0 == ne01);
+        GGML_ASSERT(ne1 == ne11);
+        GGML_ASSERT(ne2 == ne12);
+        GGML_ASSERT(ne3 == ne13);
+
+        // dst cannot be transposed or permuted
+        GGML_ASSERT(nb0 == sizeof(float));
+        GGML_ASSERT(nb0 <= nb1);
+        GGML_ASSERT(nb1 <= nb2);
+        GGML_ASSERT(nb2 <= nb3);
+
+        GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+        GGML_ASSERT(ggml_n_dims(op->src[0]) == 2);
+        // GGML_ASSERT(ggml_n_dims(op->src[1]) == 2);
+
+        char *       wdata = static_cast<char *>(params->wdata);
+        const size_t nbw1  = ggml_row_size(GGML_TYPE_Q8_0, ne10);
+
+        assert(params->wsize >= nbw1 * ne11);
+
+        const ggml_from_float_t from_float = ggml_get_type_traits_cpu(GGML_TYPE_Q8_0)->from_float;
+
+        int64_t i11_processed = 0;
+        for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
+            quantize_mat_q8_0((float *) ((char *) src1->data + i11 * nb11), (void *) (wdata + i11 * nbw1), 4, ne10,
+                              INTER_SIZE);
+        }
+        i11_processed = ne11 - ne11 % 4;
+        for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
+            from_float((float *) ((char *) src1->data + i11 * nb11), (void *) (wdata + i11 * nbw1), ne10);
+        }
+
+        ggml_barrier(params->threadpool);
+
+        const void * src1_wdata      = params->wdata;
+        const size_t src1_col_stride = ggml_row_size(GGML_TYPE_Q8_0, ne10);
+        int64_t      src0_start      = (ith * ne01) / nth;
+        int64_t      src0_end        = ((ith + 1) * ne01) / nth;
+        src0_start = (src0_start % NB_COLS) ? src0_start + NB_COLS - (src0_start % NB_COLS) : src0_start;
+        src0_end   = (src0_end % NB_COLS) ? src0_end + NB_COLS - (src0_end % NB_COLS) : src0_end;
+        if (src0_start >= src0_end) {
+            return;
+        }
+
+        // If there are more than three rows in src1, use gemm; otherwise, use gemv.
+        if (ne11 > 3) {
+            gemm<BLOC_TYPE, INTER_SIZE, NB_COLS>(ne00, (float *) ((char *) dst->data) + src0_start, ne01,
+                                                 (const char *) src0->data + src0_start * nb01,
+                                                 (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
+        }
+        for (int iter = ne11 - ne11 % 4; iter < ne11; iter++) {
+            gemv<BLOC_TYPE, INTER_SIZE, NB_COLS>(ne00, (float *) ((char *) dst->data + (iter * nb1)) + src0_start, ne01,
+                                                 (const char *) src0->data + src0_start * nb01,
+                                                 (const char *) src1_wdata + (src1_col_stride * iter), 1,
+                                                 src0_end - src0_start);
+        }
+    }
+
+    void forward_mul_mat_id(ggml_compute_params * params, ggml_tensor * op) {
+        const ggml_tensor * src0 = op->src[0];
+        const ggml_tensor * src1 = op->src[1];
+        const ggml_tensor * ids  = op->src[2];
+        ggml_tensor *       dst  = op;
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        const int ith = params->ith;
+        const int nth = params->nth;
+
+        const ggml_from_float_t from_float = ggml_get_type_traits_cpu(GGML_TYPE_Q8_0)->from_float;
+
+        // we don't support permuted src0 or src1
+        GGML_ASSERT(nb00 == ggml_type_size(src0->type));
+        GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+
+        // dst cannot be transposed or permuted
+        GGML_ASSERT(nb0 == sizeof(float));
+        GGML_ASSERT(nb0 <= nb1);
+        GGML_ASSERT(nb1 <= nb2);
+        GGML_ASSERT(nb2 <= nb3);
+
+        GGML_ASSERT(ne03 == 1);
+        GGML_ASSERT(ne13 == 1);
+        GGML_ASSERT(ne3  == 1);
+
+        GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+        // row groups
+        const int n_ids = ids->ne[0]; // n_expert_used
+        const int n_as  = ne02;       // n_expert
+
+        const size_t nbw1 = ggml_row_size(GGML_TYPE_Q8_0, ne10);
+        const size_t nbw2 = nbw1*ne11;
+        const size_t nbw3 = nbw2*ne12;
+
+        struct mmid_row_mapping {
+            int32_t i1;
+            int32_t i2;
+        };
+
+        GGML_ASSERT(params->wsize >= (GGML_PAD(nbw3, sizeof(int64_t)) + n_as * sizeof(int64_t) +
+                                      n_as * ne12 * sizeof(mmid_row_mapping)));
+
+        auto                      wdata             = (char *) params->wdata;
+        auto                      wdata_src1_end    = (char *) wdata + GGML_PAD(nbw3, sizeof(int64_t));
+        int64_t *                 matrix_row_counts = (int64_t *) (wdata_src1_end);                      // [n_as]
+        struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) (matrix_row_counts + n_as);  // [n_as][ne12]
+
+        // src1: float32 => block_q8_0
+        for (int64_t i12 = 0; i12 < ne12; ++i12) {
+            for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
+                from_float((float *)((char *) src1->data + i12 * nb12 + i11 * nb11),
+                           (void *)               (wdata + i12 * nbw2 + i11 * nbw1),
+                           ne10);
+            }
+        }
+
+#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) * ne12 + (i1)]
+
+        if (ith == 0) {
+            // initialize matrix_row_counts
+            memset(matrix_row_counts, 0, n_as * sizeof(int64_t));
+
+            // group rows by src0 matrix
+            for (int32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
+                for (int32_t id = 0; id < n_ids; ++id) {
+                    const int32_t i02 =
+                        *(const int32_t *) ((const char *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]);
+
+                    GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+                    MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = { id, iid1 };
+                    matrix_row_counts[i02] += 1;
+                }
+            }
+        }
+
+        ggml_barrier(params->threadpool);
+
+        // compute each matrix multiplication in sequence
+        for (int cur_a = 0; cur_a < n_as; ++cur_a) {
+            const int64_t cne1 = matrix_row_counts[cur_a];
+
+            if (cne1 == 0) {
+                continue;
+            }
+
+            auto src0_cur = (const char *) src0->data + cur_a*nb02;
+
+            //const int64_t nr0 = ne01; // src0 rows
+            const int64_t nr1 = cne1; // src1 rows
+
+            int64_t src0_cur_start = (ith * ne01) / nth;
+            int64_t src0_cur_end   = ((ith + 1) * ne01) / nth;
+            src0_cur_start =
+                (src0_cur_start % NB_COLS) ? src0_cur_start + NB_COLS - (src0_cur_start % NB_COLS) : src0_cur_start;
+            src0_cur_end = (src0_cur_end % NB_COLS) ? src0_cur_end + NB_COLS - (src0_cur_end % NB_COLS) : src0_cur_end;
+
+            if (src0_cur_start >= src0_cur_end) return;
+
+            for (int ir1 = 0; ir1 < nr1; ir1++) {
+                struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1);
+                const int id       = row_mapping.i1; // selected expert index
+
+                const int64_t  i11 = id % ne11;
+                const int64_t  i12 = row_mapping.i2; // row index in src1
+
+                const int64_t  i1 = id;  // selected expert index
+                const int64_t  i2 = i12; // row
+
+                auto src1_col = (const char *) wdata + (i11 * nbw1 + i12 * nbw2);
+
+                gemv<BLOC_TYPE, INTER_SIZE, NB_COLS>(
+                        ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start,
+                        ne01,                    src0_cur + src0_cur_start * nb01,
+                        src1_col, 1, src0_cur_end - src0_cur_start);
+            }
+        }
+#undef MMID_MATRIX_ROW
+    }
+
+    int repack(struct ggml_tensor * t, const void * data, size_t data_size) override {
+        GGML_LOG_DEBUG("%s: repack tensor %s with %s_%dx%d\n", __func__, t->name, ggml_type_name(t->type),
+                       (int) NB_COLS, (int) INTER_SIZE);
+        return ggml::cpu::aarch64::repack<BLOC_TYPE, INTER_SIZE, NB_COLS>(t, data, data_size);
+    }
+};
+
+// instance for Q4
+static const tensor_traits<block_q4_0, 4, 4> q4_0_4x4_q8_0;
+static const tensor_traits<block_q4_0, 8, 4> q4_0_4x8_q8_0;
+static const tensor_traits<block_q4_0, 8, 8> q4_0_8x8_q8_0;
+
+// instance for IQ4
+static const tensor_traits<block_iq4_nl, 4, 4> iq4_nl_4x4_q8_0;
+
+}  // namespace ggml::cpu::aarch64
+
+static const ggml::cpu::tensor_traits * ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur) {
    if (cur->type == GGML_TYPE_Q4_0) {
-        // TODO: enable for AVX2 - currently disabled due to bad gemv performance
-        if (/* ggml_cpu_has_avx2() || */ (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) {
-            return GGML_TYPE_Q4_0_8_8;
+        if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) {
+            if (cur->ne[1] % 8 == 0) {
+                return &ggml::cpu::aarch64::q4_0_8x8_q8_0;
+            }
        }
        if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
-            return GGML_TYPE_Q4_0_4_8;
+            if (cur->ne[1] % 4 == 0) {
+                return &ggml::cpu::aarch64::q4_0_4x8_q8_0;
+            }
        }
        if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
-            return GGML_TYPE_Q4_0_4_4;
+            if (cur->ne[1] % 4 == 0) {
+                return &ggml::cpu::aarch64::q4_0_4x4_q8_0;
+            }
        }
    } else if (cur->type == GGML_TYPE_IQ4_NL) {
        if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
-            return GGML_TYPE_IQ4_NL_4_4;
+            if (cur->ne[1] % 4 == 0) {
+                return &ggml::cpu::aarch64::iq4_nl_4x4_q8_0;
+            }
        }
    }

-    return cur->type;
+    return nullptr;
+}
+
+static void ggml_backend_cpu_aarch64_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+    tensor->extra = (void *) const_cast<ggml::cpu::tensor_traits *>(ggml_aarch64_get_optimal_repack_type(tensor));
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_cpu_aarch64_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
+                                                       const void * data, size_t offset, size_t size) {
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+
+    auto tensor_traits = (ggml::cpu::aarch64::tensor_traits_base *) tensor->extra;
+    auto OK            = tensor_traits->repack(tensor, data, size);
+
+    GGML_ASSERT(OK == 0);
+    GGML_UNUSED(buffer);
+}
+
+static const char * ggml_backend_cpu_aarch64_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    return "CPU_AARCH64";
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_t ggml_backend_cpu_aarch64_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+
+    if (buffer == nullptr) {
+        return nullptr;
+    }
+
+    buffer->buft              = buft;
+    buffer->iface.init_tensor = ggml_backend_cpu_aarch64_buffer_init_tensor;
+    buffer->iface.set_tensor  = ggml_backend_cpu_aarch64_buffer_set_tensor;
+    return buffer;
+}
+
+static size_t ggml_backend_cpu_aarch64_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return TENSOR_ALIGNMENT;
+
+    GGML_UNUSED(buft);
+}
+
+namespace ggml::cpu::aarch64 {
+class extra_buffer_type : ggml::cpu::extra_buffer_type {
+    bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
+        if (    op->op == GGML_OP_MUL_MAT &&
+                op->src[0]->buffer &&
+                (ggml_n_dims(op->src[0]) == 2) &&
+                op->src[0]->buffer->buft == ggml_backend_cpu_aarch64_buffer_type() &&
+                ggml_aarch64_get_optimal_repack_type(op->src[0])
+                ) {
+            if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
+                return false;
+            }
+            if (op->src[1]->type == GGML_TYPE_F32) {
+                return true;
+            }
+            //if (op->src[1]->type == GGML_TYPE_Q8_0) {
+            //    return true;
+            //}
+            // may be possible if Q8_0 packed...
+        } else if (op->op == GGML_OP_MUL_MAT_ID
+                && op->src[0]->buffer
+                && (ggml_n_dims(op->src[0]) == 3)
+                && op->src[0]->buffer->buft == ggml_backend_cpu_aarch64_buffer_type()
+                && ggml_aarch64_get_optimal_repack_type(op->src[0])
+                ) {
+            if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
+                return false;
+            }
+            if (op->src[1]->type == GGML_TYPE_F32) {
+                return true;
+            }
+            //if (op->src[1]->type == GGML_TYPE_Q8_0) {
+            //    return true;
+            //}
+        }
+        return false;
+    }
+
+    ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override {
+        if (op->op == GGML_OP_MUL_MAT || op->op == GGML_OP_MUL_MAT_ID) {
+            if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_aarch64_buffer_type()) {
+                return (ggml::cpu::tensor_traits *) op->src[0]->extra;
+            }
+        }
+        return nullptr;
+    }
+};
+}  // namespace ggml::cpu::aarch64
+
+ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void) {
+    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_aarch64 = {
+        /* .iface    = */ {
+                           /* .get_name         = */ ggml_backend_cpu_aarch64_buffer_type_get_name,
+                           /* .alloc_buffer     = */ ggml_backend_cpu_aarch64_buffer_type_alloc_buffer,
+                           /* .get_alignment    = */ ggml_backend_cpu_aarch64_buffer_type_get_alignment,
+                           /* .get_max_size     = */ nullptr,  // defaults to SIZE_MAX
+                           /* .get_alloc_size   = */ nullptr,  // defaults to ggml_nbytes
+                           /* .is_host          = */ nullptr,
+                           },
+        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
+        /* .context = */ new ggml::cpu::aarch64::extra_buffer_type(),
+    };
+
+    return &ggml_backend_cpu_buffer_type_aarch64;
 }
@@ -1,32 +1,8 @@
 #pragma once

+#include "ggml-cpu-traits.h"
 #include "ggml.h"

 // GGML internal header

-#ifdef __cplusplus
-extern "C" {
-#endif
-
-// Quantization
-void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t blck_size_interleave);
-
-// GEMV
-void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-
-// GEMM
-void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-
-void           ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_type, const void * data, size_t data_size);
-enum ggml_type ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur);
-
-#ifdef __cplusplus
-}
-#endif
-
+ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void);
--- a/Show More
+++ b/Show More