prof: fix tensor dims formatter

.devops/cann.Dockerfile

@@ -13,7 +13,7 @@ ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.
 FROM ${CANN_BASE_IMAGE} AS build
 
 # -- Install build dependencies --
-RUN yum install -y gcc g++ cmake make git openssl-devel python3 python3-pip && \
+RUN yum install -y gcc g++ cmake make git libcurl-devel python3 python3-pip && \
     yum clean all && \
     rm -rf /var/cache/yum
 

.devops/cpu.Dockerfile

@@ -5,7 +5,7 @@ FROM ubuntu:$UBUNTU_VERSION AS build
 ARG TARGETARCH
 
 RUN apt-get update && \
-    apt-get install -y build-essential git cmake libssl-dev
+    apt-get install -y build-essential git cmake libcurl4-openssl-dev
 
 WORKDIR /app
 

.devops/cuda-new.Dockerfile

@@ -1,95 +0,0 @@
-ARG UBUNTU_VERSION=24.04
-# This needs to generally match the container host's environment.
-ARG CUDA_VERSION=13.1.0
-# Target the CUDA build image
-ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
-
-# CUDA architecture to build for (defaults to all supported archs)
-ARG CUDA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libssl-dev libgomp1
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
-    export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so*" -exec cp -P {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ${BASE_CUDA_RUN_CONTAINER} AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    python3-wheel \
-    && pip install --break-system-packages --upgrade setuptools \
-    && pip install --break-system-packages -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/cuda.Dockerfile

@@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 ARG CUDA_DOCKER_ARCH=default
 
 RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libssl-dev libgomp1
+    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
 
 WORKDIR /app
 

.devops/intel.Dockerfile

@@ -6,7 +6,7 @@ FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS build
 
 ARG GGML_SYCL_F16=OFF
 RUN apt-get update && \
-    apt-get install -y git libssl-dev
+    apt-get install -y git libcurl4-openssl-dev
 
 WORKDIR /app
 

.devops/llama-cli-cann.Dockerfile

@@ -6,7 +6,7 @@ WORKDIR /app
 
 COPY . .
 
-RUN yum install -y gcc g++ cmake make openssl-devel
+RUN yum install -y gcc g++ cmake make libcurl-devel
 ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
 ENV LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:$LIBRARY_PATH
 ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/lib64/plugin/opskernel:${ASCEND_TOOLKIT_HOME}/lib64/plugin/nnengine:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}

.devops/musa.Dockerfile

@@ -18,7 +18,7 @@ RUN apt-get update && \
     python3 \
     python3-pip \
     git \
-    libssl-dev \
+    libcurl4-openssl-dev \
     libgomp1
 
 WORKDIR /app

.devops/nix/package.nix

@@ -32,6 +32,7 @@
   useMpi ? false,
   useRocm ? config.rocmSupport,
   rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
+  enableCurl ? true,
   useVulkan ? false,
   useRpc ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
@@ -159,13 +160,15 @@ effectiveStdenv.mkDerivation (finalAttrs: {
     ++ optionals useMpi [ mpi ]
     ++ optionals useRocm rocmBuildInputs
     ++ optionals useBlas [ blas ]
-    ++ optionals useVulkan vulkanBuildInputs;
+    ++ optionals useVulkan vulkanBuildInputs
+    ++ optionals enableCurl [ curl ];
 
   cmakeFlags =
     [
       (cmakeBool "LLAMA_BUILD_SERVER" true)
       (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
       (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
+      (cmakeBool "LLAMA_CURL" enableCurl)
       (cmakeBool "GGML_NATIVE" false)
       (cmakeBool "GGML_BLAS" useBlas)
       (cmakeBool "GGML_CUDA" useCuda)

.devops/rocm.Dockerfile

@@ -27,7 +27,7 @@ RUN apt-get update \
     build-essential \
     cmake \
     git \
-    libssl-dev \
+    libcurl4-openssl-dev \
     curl \
     libgomp1
 

.devops/s390x.Dockerfile

@@ -11,7 +11,7 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     apt install -y --no-install-recommends \
         git cmake ccache ninja-build \
         # WARNING: Do not use libopenblas-openmp-dev. libopenblas-dev is faster.
-        libopenblas-dev libssl-dev && \
+        libopenblas-dev libcurl4-openssl-dev && \
     rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app

.devops/vulkan.Dockerfile

@@ -5,8 +5,8 @@ FROM ubuntu:$UBUNTU_VERSION AS build
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget xz-utils
 
-# Install SSL and Vulkan SDK dependencies
-RUN apt install -y libssl-dev curl \
+# Install cURL and Vulkan SDK dependencies
+RUN apt install -y libcurl4-openssl-dev curl \
     libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc
 
 # Build it
@@ -33,7 +33,6 @@ FROM ubuntu:$UBUNTU_VERSION AS base
 
 RUN apt-get update \
     && apt-get install -y libgomp1 curl libvulkan1 mesa-vulkan-drivers \
-    libglvnd0 libgl1 libglx0 libegl1 libgles2 \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.gemini/settings.json

@@ -1 +0,0 @@
-{ "contextFileName": "AGENTS.md" }

.github/ISSUE_TEMPLATE/010-bug-compilation.yml

@@ -8,8 +8,7 @@ body:
       value: >
         Thanks for taking the time to fill out this bug report!
         This issue template is intended for bug reports where the compilation of llama.cpp fails.
-        Before opening an issue, please confirm that the compilation still fails
-        after recreating the CMake build directory and with `-DGGML_CCACHE=OFF`.
+        Before opening an issue, please confirm that the compilation still fails with `-DGGML_CCACHE=OFF`.
         If the compilation succeeds with ccache disabled you should be able to permanently fix the issue
         by clearing `~/.cache/ccache` (on Linux).
   - type: textarea

.github/ISSUE_TEMPLATE/011-bug-results.yml

@@ -98,18 +98,7 @@ body:
       label: Relevant log output
       description: >
           Please copy and paste any relevant log output, including the command that you entered and any generated text.
-          For very long logs (thousands of lines), preferably upload them as files instead.
-          On Linux you can redirect console output into a file by appending ` > llama.log 2>&1` to your command.
-      value: |
-        <details>
-        <summary>Logs</summary>
-        <!-- Copy-pasted short logs go into the "console" area here -->
-
-        ```console
-
-        ```
-        </details>
-
-        <!-- Long logs that you upload as files go here, outside the "console" area -->
+          This will be automatically formatted into code, so no need for backticks.
+      render: shell
     validations:
       required: true

.github/ISSUE_TEMPLATE/019-bug-misc.yml

@@ -85,19 +85,8 @@ body:
       label: Relevant log output
       description: >
           If applicable, please copy and paste any relevant log output, including any generated text.
+          This will be automatically formatted into code, so no need for backticks.
           If you are encountering problems specifically with the `llama_params_fit` module, always upload `--verbose` logs as well.
-          For very long logs (thousands of lines), please upload them as files instead.
-          On Linux you can redirect console output into a file by appending ` > llama.log 2>&1` to your command.
-      value: |
-        <details>
-        <summary>Logs</summary>
-        <!-- Copy-pasted short logs go into the "console" area here -->
-
-        ```console
-
-        ```
-        </details>
-
-        <!-- Long logs that you upload as files go here, outside the "console" area -->
+      render: shell
     validations:
       required: false

.github/actions/windows-setup-curl/action.yml

@@ -0,0 +1,30 @@
+name: 'Windows - Setup CURL'
+description: 'Composite action, to be reused in other workflow'
+inputs:
+  curl_version:
+    description: 'CURL version'
+    required: false
+    default: '8.6.0_6'
+  architecture:
+    description: 'Architecture of the libcurl to download'
+    required: false
+    default: 'win64'
+outputs:
+  curl_path:
+    description: "Path to the downloaded libcurl"
+    value: ${{ steps.get_libcurl.outputs.curl_path }}
+
+runs:
+  using: "composite"
+  steps:
+    - name: libCURL
+      id: get_libcurl
+      shell: powershell
+      env:
+        CURL_VERSION: ${{ inputs.curl_version }}
+        ARCHITECTURE: ${{ inputs.architecture }}
+      run: |
+        curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-${env:ARCHITECTURE}-mingw.zip"
+        mkdir $env:RUNNER_TEMP/libcurl
+        tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
+        echo "curl_path=$env:RUNNER_TEMP/libcurl" >> $env:GITHUB_OUTPUT

.github/copilot-instructions.md

@@ -0,0 +1,262 @@
+# Copilot Instructions for llama.cpp
+
+## Repository Overview
+
+llama.cpp is a large-scale C/C++ project for efficient LLM (Large Language Model) inference with minimal setup and dependencies. The project enables running language models on diverse hardware with state-of-the-art performance.
+
+**Key Facts:**
+- **Primary language**: C/C++ with Python utility scripts
+- **Size**: ~200k+ lines of code across 1000+ files
+- **Architecture**: Modular design with main library (`libllama`) and 40+ executable tools/examples
+- **Core dependency**: ggml tensor library (vendored in `ggml/` directory)
+- **Backends supported**: CPU (AVX/NEON/RVV optimized), CUDA, Metal, Vulkan, SYCL, ROCm, MUSA
+- **License**: MIT
+
+## Build Instructions
+
+### Prerequisites
+- CMake 3.14+ (primary build system)
+- C++17 compatible compiler (GCC 13.3+, Clang, MSVC)
+- Optional: ccache for faster compilation
+
+### Basic Build (CPU-only)
+**ALWAYS run these commands in sequence:**
+```bash
+cmake -B build
+cmake --build build --config Release -j $(nproc)
+```
+
+**Build time**: ~10 minutes on 4-core system with ccache enabled, ~25 minutes without ccache.
+
+**Important Notes:**
+- The Makefile is deprecated - always use CMake
+- ccache is automatically detected and used if available
+- Built binaries are placed in `build/bin/`
+- Parallel builds (`-j`) significantly reduce build time
+
+### Backend-Specific Builds
+For CUDA support:
+```bash
+cmake -B build -DGGML_CUDA=ON
+cmake --build build --config Release -j $(nproc)
+```
+
+For Metal (macOS):
+```bash
+cmake -B build -DGGML_METAL=ON
+cmake --build build --config Release -j $(nproc)
+```
+
+**Important Note**: While all backends can be built as long as the correct requirements for that backend are installed, you will not be able to run them without the correct hardware. The only backend that can be run for testing and validation is the CPU backend.
+
+### Debug Builds
+Single-config generators:
+```bash
+cmake -B build -DCMAKE_BUILD_TYPE=Debug
+cmake --build build
+```
+
+Multi-config generators:
+```bash
+cmake -B build -G "Xcode"
+cmake --build build --config Debug
+```
+
+### Common Build Issues
+- **Issue**: Network tests fail in isolated environments
+  **Solution**: Expected behavior - core functionality tests will still pass
+
+## Testing
+
+### Running Tests
+```bash
+ctest --test-dir build --output-on-failure -j $(nproc)
+```
+
+**Test suite**: 38 tests covering tokenizers, grammar parsing, sampling, backends, and integration
+**Expected failures**: 2-3 tests may fail if network access is unavailable (they download models)
+**Test time**: ~30 seconds for passing tests
+
+### Server Unit Tests
+Run server-specific unit tests after building the server:
+```bash
+# Build the server first
+cmake --build build --target llama-server
+
+# Navigate to server tests and run
+cd tools/server/tests
+source ../../../.venv/bin/activate
+./tests.sh
+```
+**Server test dependencies**: The `.venv` environment includes the required dependencies for server unit tests (pytest, aiohttp, etc.). Tests can be run individually or with various options as documented in `tools/server/tests/README.md`.
+
+### Test Categories
+- Tokenizer tests: Various model tokenizers (BERT, GPT-2, LLaMA, etc.)
+- Grammar tests: GBNF parsing and validation
+- Backend tests: Core ggml operations across different backends
+- Integration tests: End-to-end workflows
+
+### Manual Testing Commands
+```bash
+# Test basic inference
+./build/bin/llama-cli --version
+
+# Test model loading (requires model file)
+./build/bin/llama-cli -m path/to/model.gguf -p "Hello" -n 10
+```
+
+## Code Quality and Linting
+
+### C++ Code Formatting
+**ALWAYS format C++ code before committing:**
+```bash
+git clang-format
+```
+
+Configuration is in `.clang-format` with these key rules:
+- 4-space indentation
+- 120 column limit
+- Braces on same line for functions
+- Pointer alignment: `void * ptr` (middle)
+- Reference alignment: `int & ref` (middle)
+
+### Python Code
+**ALWAYS activate the Python environment in `.venv` and use tools from that environment:**
+```bash
+# Activate virtual environment
+source .venv/bin/activate
+```
+
+Configuration files:
+- `.flake8`: flake8 settings (max-line-length=125, excludes examples/tools)
+- `pyrightconfig.json`: pyright type checking configuration
+
+### Pre-commit Hooks
+Run before committing:
+```bash
+pre-commit run --all-files
+```
+
+## Continuous Integration
+
+### GitHub Actions Workflows
+Key workflows that run on every PR:
+- `.github/workflows/build.yml`: Multi-platform builds
+- `.github/workflows/server.yml`: Server functionality tests
+- `.github/workflows/python-lint.yml`: Python code quality
+- `.github/workflows/python-type-check.yml`: Python type checking
+
+### Local CI Validation
+**Run full CI locally before submitting PRs:**
+```bash
+mkdir tmp
+
+# CPU-only build
+bash ./ci/run.sh ./tmp/results ./tmp/mnt
+```
+
+**CI Runtime**: 30-60 minutes depending on backend configuration
+
+### Triggering CI
+Add `ggml-ci` to commit message to trigger heavy CI workloads on the custom CI infrastructure.
+
+## Project Layout and Architecture
+
+### Core Directories
+- **`src/`**: Main llama library implementation (`llama.cpp`, `llama-*.cpp`)
+- **`include/`**: Public API headers, primarily `include/llama.h`
+- **`ggml/`**: Core tensor library (submodule with custom GGML framework)
+- **`examples/`**: 30+ example applications and tools
+- **`tools/`**: Additional development and utility tools (server benchmarks, tests)
+- **`tests/`**: Comprehensive test suite with CTest integration
+- **`docs/`**: Detailed documentation (build guides, API docs, etc.)
+- **`scripts/`**: Utility scripts for CI, data processing, and automation
+- **`common/`**: Shared utility code used across examples
+
+### Key Files
+- **`CMakeLists.txt`**: Primary build configuration
+- **`include/llama.h`**: Main C API header (~2000 lines)
+- **`src/llama.cpp`**: Core library implementation (~8000 lines)
+- **`CONTRIBUTING.md`**: Coding guidelines and PR requirements
+- **`.clang-format`**: C++ formatting rules
+- **`.pre-commit-config.yaml`**: Git hook configuration
+
+### Built Executables (in `build/bin/`)
+Primary tools:
+- **`llama-cli`**: Main inference tool
+- **`llama-server`**: OpenAI-compatible HTTP server
+- **`llama-quantize`**: Model quantization utility
+- **`llama-perplexity`**: Model evaluation tool
+- **`llama-bench`**: Performance benchmarking
+- **`llama-convert-llama2c-to-ggml`**: Model conversion utilities
+
+### Configuration Files
+- **CMake**: `CMakeLists.txt`, `cmake/` directory
+- **Linting**: `.clang-format`, `.clang-tidy`, `.flake8`
+- **CI**: `.github/workflows/`, `ci/run.sh`
+- **Git**: `.gitignore` (includes build artifacts, models, cache)
+
+### Dependencies
+- **System**: OpenMP, libcurl (for model downloading)
+- **Optional**: CUDA SDK, Metal framework, Vulkan SDK, Intel oneAPI
+- **Bundled**: httplib, json (header-only libraries in vendored form)
+
+## Common Validation Steps
+
+### After Making Changes
+1. **Format code**: `git clang-format`
+2. **Build**: `cmake --build build --config Release`
+3. **Test**: `ctest --test-dir build --output-on-failure`
+4. **Server tests** (if modifying server): `cd tools/server/tests && source ../../../.venv/bin/activate && ./tests.sh`
+5. **Manual validation**: Test relevant tools in `build/bin/`
+
+### Performance Validation
+```bash
+# Benchmark inference performance
+./build/bin/llama-bench -m model.gguf
+
+# Evaluate model perplexity
+./build/bin/llama-perplexity -m model.gguf -f dataset.txt
+```
+
+### Backend Validation
+```bash
+# Test backend operations
+./build/bin/test-backend-ops
+```
+
+## Environment Setup
+
+### Required Tools
+- CMake 3.14+ (install via system package manager)
+- Modern C++ compiler with C++17 support
+- Git (for submodule management)
+- Python 3.9+ with virtual environment (`.venv` is provided)
+
+### Optional but Recommended
+- ccache: `apt install ccache` or `brew install ccache`
+- clang-format 15+: Usually included with LLVM/Clang installation
+- pre-commit: `pip install pre-commit`
+
+### Backend-Specific Requirements
+- **CUDA**: NVIDIA CUDA Toolkit 11.2+
+- **Metal**: Xcode command line tools (macOS only)
+- **Vulkan**: Vulkan SDK
+- **SYCL**: Intel oneAPI toolkit
+
+## Important Guidelines
+
+### Code Changes
+- **Minimal dependencies**: Avoid adding new external dependencies
+- **Cross-platform compatibility**: Test on Linux, macOS, Windows when possible
+- **Performance focus**: This is a performance-critical inference library
+- **API stability**: Changes to `include/llama.h` require careful consideration
+
+### Git Workflow
+- Always create feature branches from `master`
+- **Never** commit build artifacts (`build/`, `.ccache/`, `*.o`, `*.gguf`)
+- Use descriptive commit messages following project conventions
+
+### Trust These Instructions
+Only search for additional information if these instructions are incomplete or found to be incorrect. This document contains validated build and test procedures that work reliably across different environments.
+

.github/workflows/build-cmake-pkg.yml

@@ -20,7 +20,7 @@ jobs:
         run: |
           PREFIX="$(pwd)"/inst
           cmake -S . -B build -DCMAKE_PREFIX_PATH="$PREFIX" \
-                -DLLAMA_OPENSSL=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=OFF \
+                -DLLAMA_CURL=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=OFF \
                 -DLLAMA_BUILD_EXAMPLES=OFF -DCMAKE_BUILD_TYPE=Release
           cmake --build build --config Release
           cmake --install build --prefix "$PREFIX" --config Release

.github/workflows/build-linux-cross.yml

@@ -30,7 +30,7 @@ jobs:
 
   #     - name: Build
   #       run: |
-  #         cmake -B build -DLLAMA_OPENSSL=OFF \
+  #         cmake -B build -DLLAMA_CURL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_OPENMP=OFF \
   #                        -DLLAMA_BUILD_EXAMPLES=ON \
@@ -76,7 +76,7 @@ jobs:
 
   #     - name: Build
   #       run: |
-  #         cmake -B build -DLLAMA_OPENSSL=OFF \
+  #         cmake -B build -DLLAMA_CURL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_VULKAN=ON \
   #                        -DGGML_OPENMP=OFF \
@@ -122,7 +122,7 @@ jobs:
 
   #     - name: Build
   #       run: |
-  #         cmake -B build -DLLAMA_OPENSSL=OFF \
+  #         cmake -B build -DLLAMA_CURL=OFF \
   #                        -DCMAKE_BUILD_TYPE=Release \
   #                        -DGGML_VULKAN=ON \
   #                        -DGGML_OPENMP=OFF \
@@ -178,7 +178,7 @@ jobs:
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_OPENSSL=OFF \
+          cmake -B build -DLLAMA_CURL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
@@ -235,7 +235,7 @@ jobs:
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_OPENSSL=OFF \
+          cmake -B build -DLLAMA_CURL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_VULKAN=ON \
                          -DGGML_OPENMP=OFF \
@@ -281,7 +281,7 @@ jobs:
       - name: Build
         run: |
           export RISCV_ROOT_PATH=${PWD}/spacemit_toolchain
-          cmake -B build -DLLAMA_OPENSSL=OFF \
+          cmake -B build -DLLAMA_CURL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \

.github/workflows/build.yml

@@ -70,7 +70,6 @@ jobs:
         with:
           key: macOS-latest-cmake-arm64
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build
         id: cmake_build
@@ -79,6 +78,7 @@ jobs:
           cmake -B build \
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=OFF \
@@ -91,7 +91,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L 'main|curl' --verbose --timeout 900
 
   macOS-latest-cmake-x64:
     runs-on: macos-15-intel
@@ -106,7 +106,6 @@ jobs:
         with:
           key: macOS-latest-cmake-x64
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build
         id: cmake_build
@@ -117,6 +116,7 @@ jobs:
           cmake -B build \
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL=OFF \
             -DGGML_RPC=ON \
@@ -142,7 +142,6 @@ jobs:
         with:
           key: macOS-latest-cmake-arm64-webgpu
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dawn Dependency
         id: dawn-depends
@@ -150,13 +149,13 @@ jobs:
           DAWN_VERSION="v2.0.0"
           DAWN_OWNER="reeselevine"
           DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release.zip"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
           curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
           mkdir dawn
           unzip artifact.zip
-          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
+          tar -xvf Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release.tar.gz -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build
@@ -196,7 +195,6 @@ jobs:
         with:
           key: ubuntu-cpu-cmake-${{ matrix.build }}
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build Dependencies
         id: build_depends
@@ -225,6 +223,8 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DGGML_RPC=ON
           cmake --build build --config Release -j $(nproc)
@@ -233,7 +233,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L 'main|curl' --verbose --timeout 900
 
       - name: Test llama2c conversion
         id: llama2c_test
@@ -276,7 +276,6 @@ jobs:
         with:
           key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }}
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
@@ -289,6 +288,8 @@ jobs:
         if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
@@ -299,6 +300,8 @@ jobs:
         if: ${{ matrix.sanitizer == 'THREAD' }}
         run: |
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
@@ -328,10 +331,14 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -B build \
+          mkdir build
+          cd build
+          cmake .. \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_LLGUIDANCE=ON
-          cmake --build build --config Release -j $(nproc)
+          cmake --build . --config Release -j $(nproc)
 
       - name: Test
         id: cmake_test
@@ -365,6 +372,8 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DGGML_RPC=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -387,7 +396,6 @@ jobs:
         with:
           key: ubuntu-24-cmake-vulkan-deb
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
@@ -398,6 +406,8 @@ jobs:
         id: cmake_configure
         run: |
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DCMAKE_BUILD_TYPE=RelWithDebInfo \
             -DGGML_BACKEND_DL=ON \
             -DGGML_CPU_ALL_VARIANTS=ON \
@@ -421,7 +431,6 @@ jobs:
         with:
           key: ubuntu-24-cmake-vulkan
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
@@ -454,6 +463,8 @@ jobs:
         run: |
           source ./vulkan_sdk/setup-env.sh
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DGGML_VULKAN=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -479,7 +490,6 @@ jobs:
         with:
           key: ubuntu-24-cmake-webgpu
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
@@ -514,19 +524,21 @@ jobs:
           DAWN_VERSION="v2.0.0"
           DAWN_OWNER="reeselevine"
           DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release.zip"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
           curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
           mkdir dawn
           unzip artifact.zip
-          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
+          tar -xvf Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release.tar.gz -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build
         run: |
           export Dawn_DIR=dawn/lib64/cmake/Dawn
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DGGML_WEBGPU=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -550,7 +562,6 @@ jobs:
         with:
           key: ubuntu-latest-wasm-webgpu
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Install Emscripten
         run: |
@@ -573,7 +584,7 @@ jobs:
           source emsdk/emsdk_env.sh
           emcmake cmake -B build-wasm \
             -DGGML_WEBGPU=ON \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg
 
           cmake --build build-wasm --target test-backend-ops -j $(nproc)
@@ -598,12 +609,13 @@ jobs:
         with:
           key: ubuntu-22-cmake-hip
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build with native CMake HIP support
         id: cmake_build
         run: |
           cmake -B build -S . \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
             -DGGML_HIP_ROCWMMA_FATTN=ON \
             -DGGML_HIP=ON
@@ -629,12 +641,13 @@ jobs:
         with:
           key: ubuntu-22-cmake-musa
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build with native CMake MUSA support
         id: cmake_build
         run: |
           cmake -B build -S . \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DGGML_MUSA=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -675,13 +688,14 @@ jobs:
         with:
           key: ubuntu-22-cmake-sycl
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build
         id: cmake_build
         run: |
           source /opt/intel/oneapi/setvars.sh
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DGGML_SYCL=ON \
             -DCMAKE_C_COMPILER=icx \
             -DCMAKE_CXX_COMPILER=icpx
@@ -724,13 +738,14 @@ jobs:
         with:
           key: ubuntu-22-cmake-sycl-fp16
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build
         id: cmake_build
         run: |
           source /opt/intel/oneapi/setvars.sh
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DGGML_SYCL=ON \
             -DCMAKE_C_COMPILER=icx \
             -DCMAKE_CXX_COMPILER=icpx \
@@ -756,7 +771,6 @@ jobs:
         with:
           key: macOS-latest-cmake-ios
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build
         id: cmake_build
@@ -788,7 +802,6 @@ jobs:
         with:
           key: macOS-latest-cmake-tvos
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build
         id: cmake_build
@@ -850,7 +863,6 @@ jobs:
         with:
           key: macOS-latest-swift
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Download xcframework artifact
         uses: actions/download-artifact@v4
@@ -865,7 +877,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -893,7 +905,6 @@ jobs:
           key: windows-msys2
           variant: ccache
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Setup ${{ matrix.sys }}
         uses: msys2/setup-msys2@v2
@@ -962,7 +973,6 @@ jobs:
           key: windows-latest-cmake-${{ matrix.build }}
           variant: ccache
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Download OpenBLAS
         id: get_openblas
@@ -1015,7 +1025,7 @@ jobs:
         id: cmake_build
         run: |
           cmake -S . -B build ${{ matrix.defines }} `
-            -DLLAMA_BUILD_BORINGSSL=ON
+            -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
 
       - name: Add libopenblas.dll
@@ -1067,19 +1077,18 @@ jobs:
           with:
             key: ubuntu-latest-cmake-cuda
             evict-old-files: 1d
-            save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
         - name: Build with CMake
-          # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
           run: |
             cmake -S . -B build -G Ninja \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
               -DLLAMA_FATAL_WARNINGS=ON \
               -DCMAKE_BUILD_TYPE=Release \
               -DCMAKE_CUDA_ARCHITECTURES=89-real \
               -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined \
               -DGGML_NATIVE=OFF \
-              -DGGML_CUDA=ON \
-              -DGGML_CUDA_CUB_3DOT2=ON
+              -DGGML_CUDA=ON
             cmake --build build
 
   windows-2022-cmake-cuda:
@@ -1100,7 +1109,6 @@ jobs:
           key: windows-cuda-${{ matrix.cuda }}
           variant: ccache
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Install Cuda Toolkit
         uses: ./.github/actions/windows-setup-cuda
@@ -1115,18 +1123,17 @@ jobs:
       - name: Build
         id: cmake_build
         shell: cmd
-        # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
         run: |
           call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
           cmake -S . -B build -G "Ninja Multi-Config" ^
             -DLLAMA_BUILD_SERVER=ON ^
+            -DLLAMA_CURL=OFF ^
             -DLLAMA_BUILD_BORINGSSL=ON ^
             -DGGML_NATIVE=OFF ^
             -DGGML_BACKEND_DL=ON ^
             -DGGML_CPU_ALL_VARIANTS=ON ^
             -DGGML_CUDA=ON ^
-            -DGGML_RPC=ON ^
-            -DGGML_CUDA_CUB_3DOT2=ON
+            -DGGML_RPC=ON
           set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
           cmake --build build --config Release -j %NINJA_JOBS% -t ggml
           cmake --build build --config Release
@@ -1153,7 +1160,6 @@ jobs:
           key: windows-latest-cmake-sycl
           variant: ccache
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Install
         run:  |
@@ -1215,7 +1221,6 @@ jobs:
         with:
           key: ${{ github.job }}
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Build
         id: cmake_build
@@ -1227,6 +1232,7 @@ jobs:
             -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
             -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-${{ env.ROCM_VERSION }}/include/" `
             -DCMAKE_BUILD_TYPE=Release `
+            -DLLAMA_CURL=OFF `
             -DLLAMA_BUILD_BORINGSSL=ON `
             -DROCM_DIR="${env:HIP_PATH}" `
             -DGGML_HIP=ON `
@@ -1253,7 +1259,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -1320,7 +1326,7 @@ jobs:
       matrix:
         include:
           - build: 'arm64-cpu'
-            defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_OPENSSL=OFF -D GGML_OPENMP=OFF'
+            defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_CURL=OFF -D GGML_OPENMP=OFF'
           - build: 'arm64-snapdragon'
             defines: '--preset arm64-android-snapdragon-release'
 
@@ -1386,6 +1392,7 @@ jobs:
           echo "FIXME: test on devices"
 
   openEuler-latest-cmake-cann:
+    if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'Ascend NPU') }}
     defaults:
       run:
         shell: bash -el {0}
@@ -1431,7 +1438,7 @@ jobs:
             "${{ steps.cann-image.outputs.image }}" \
             bash -lc '
               set -e
-              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
               yum clean all && rm -rf /var/cache/yum
               git config --global --add safe.directory "/workspace"
               export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
@@ -1459,13 +1466,12 @@ jobs:
         with:
           key: ggml-ci-x64-cpu-low-perf
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1485,13 +1491,12 @@ jobs:
         with:
           key: ggml-ci-arm64-cpu-low-perf
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1511,13 +1516,12 @@ jobs:
         with:
           key: ggml-ci-x64-cpu-high-perf
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1537,13 +1541,12 @@ jobs:
         with:
           key: ggml-ci-arm64-cpu-high-perf
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1563,13 +1566,12 @@ jobs:
         with:
           key: ggml-ci-arm64-cpu-high-perf-sve
           evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
       - name: Dependencies
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Test
         id: ggml-ci
@@ -1672,34 +1674,6 @@ jobs:
         run: |
           GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
-  ggml-ci-mac-webgpu:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Dawn Dependency
-        id: dawn-depends
-        run: |
-          DAWN_VERSION="v2.0.0"
-          DAWN_OWNER="reeselevine"
-          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-          curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-          mkdir dawn
-          unzip artifact.zip
-          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
-            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
   ggml-ci-mac-vulkan:
     runs-on: [self-hosted, macOS, ARM64]
 
@@ -1727,13 +1701,12 @@ jobs:
          with:
            key: ggml-ci-arm64-cpu-kleidiai
            evict-old-files: 1d
-           save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
        - name: Dependencies
          id: depends
          run: |
            sudo apt-get update
-           sudo apt-get install -y build-essential
+           sudo apt-get install -y build-essential libcurl4-openssl-dev
 
        - name: Test
          id: ggml-ci
@@ -1749,7 +1722,7 @@ jobs:
           sudo apt-get update
 
           # Install necessary packages
-          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential libssl-dev wget ccache git-lfs
+          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential libssl-dev wget ccache
 
           # Set gcc-14 and g++-14 as the default compilers
           sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100
@@ -1761,8 +1734,6 @@ jobs:
           rustup install stable
           rustup default stable
 
-          git lfs install
-
       - name: Clone
         id: checkout
         uses: actions/checkout@v4
@@ -1800,6 +1771,8 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -1817,7 +1790,7 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L 'main|curl' --verbose --timeout 900
 
       - name: Test llama2c conversion
         id: llama2c_test
@@ -1846,7 +1819,7 @@ jobs:
           sudo apt-get update
 
           # Install necessary packages
-          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential wget ccache git-lfs
+          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential wget ccache
 
           # Set gcc-14 and g++-14 as the default compilers
           sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100
@@ -1858,8 +1831,6 @@ jobs:
           rustup install stable
           rustup default stable
 
-          git lfs install
-
       - name: GCC version check
         run: |
           gcc --version
@@ -1892,7 +1863,7 @@ jobs:
         if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           cmake -B build \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=ON \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -1911,7 +1882,7 @@ jobs:
         if: ${{ matrix.sanitizer == 'THREAD' }}
         run: |
           cmake -B build \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -1940,7 +1911,7 @@ jobs:
           sudo apt-get update
 
           # Install necessary packages
-          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential wget ccache git-lfs
+          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential wget ccache
 
           # Set gcc-14 and g++-14 as the default compilers
           sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100
@@ -1952,8 +1923,6 @@ jobs:
           rustup install stable
           rustup default stable
 
-          git lfs install
-
       - name: GCC version check
         run: |
           gcc --version
@@ -1982,7 +1951,7 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -2014,7 +1983,7 @@ jobs:
           sudo apt-get update
 
           # Install necessary packages
-          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential libssl-dev wget ccache git-lfs
+          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential libssl-dev wget ccache
 
           # Set gcc-14 and g++-14 as the default compilers
           sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100
@@ -2026,8 +1995,6 @@ jobs:
           rustup install stable
           rustup default stable
 
-          git lfs install
-
       - name: GCC version check
         run: |
           gcc --version
@@ -2056,6 +2023,8 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_OPENSSL=ON \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENMP=OFF \
             -DLLAMA_BUILD_EXAMPLES=ON \
@@ -2091,6 +2060,7 @@ jobs:
            sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
            apt-get install -y \
             build-essential \
+            libcurl4-openssl-dev \
             python3-venv \
             gpg \
             wget \
@@ -2114,7 +2084,6 @@ jobs:
          with:
            key: ggml-ci-arm64-graviton4-kleidiai
            evict-old-files: 1d
-           save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 
        - name: Test
          id: ggml-ci

.github/workflows/copilot-setup-steps.yml

@@ -38,7 +38,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libssl-dev
+          sudo apt-get install build-essential libcurl4-openssl-dev
           # Install git-clang-format script for formatting only changed code
           wget -O /tmp/git-clang-format https://raw.githubusercontent.com/llvm/llvm-project/release/18.x/clang/tools/clang-format/git-clang-format
           sudo cp /tmp/git-clang-format /usr/local/bin/git-clang-format

.github/workflows/docker.yml

@@ -40,13 +40,13 @@ jobs:
           # https://github.com/ggml-org/llama.cpp/issues/11888
           #- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: false }
           - { tag: "cpu",    dockerfile: ".devops/cpu.Dockerfile",    platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
-          - { tag: "cuda cuda12", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04", cuda_version: "12.4.0", ubuntu_version: "22.04" }
-          - { tag: "cuda13", dockerfile: ".devops/cuda-new.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04", cuda_version: "13.1.0", ubuntu_version: "24.04" }
+          - { tag: "cuda",   dockerfile: ".devops/cuda.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
           - { tag: "musa",   dockerfile: ".devops/musa.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
           - { tag: "intel",  dockerfile: ".devops/intel.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
           - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
           - { tag: "s390x",  dockerfile: ".devops/s390x.Dockerfile",  platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04-s390x" }
-          - { tag: "rocm",   dockerfile: ".devops/rocm.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
+          # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
+          #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: true }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v4
@@ -81,21 +81,18 @@ jobs:
         run: |
           REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}"  # to lower case
           REPO_NAME="${{ github.event.repository.name }}"
-          PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
 
           # list all tags possible
-          tags="${{ matrix.config.tag }}"
-          for tag in $tags; do
-              if [[ "$tag" == "cpu" ]]; then
-                  TYPE=""
-              else
-                  TYPE="-$tag"
-              fi
-              CACHETAGS="${PREFIX}buildcache${TYPE}"
-              FULLTAGS="${FULLTAGS:+$FULLTAGS,}${PREFIX}full${TYPE},${PREFIX}full${TYPE}-${{ steps.srctag.outputs.name }}"
-              LIGHTTAGS="${LIGHTTAGS:+$LIGHTTAGS,}${PREFIX}light${TYPE},${PREFIX}light${TYPE}-${{ steps.srctag.outputs.name }}"
-              SERVERTAGS="${SERVERTAGS:+$SERVERTAGS,}${PREFIX}server${TYPE},${PREFIX}server${TYPE}-${{ steps.srctag.outputs.name }}"
-          done
+          if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then
+              TYPE=""
+          else
+              TYPE="-${{ matrix.config.tag }}"
+          fi
+          PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
+          CACHETAGS="${PREFIX}buildcache${TYPE}"
+          FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}-${{ steps.srctag.outputs.name }}"
+          LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}-${{ steps.srctag.outputs.name }}"
+          SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}-${{ steps.srctag.outputs.name }}"
           echo "cache_output_tags=$CACHETAGS" >> $GITHUB_OUTPUT
           echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT
           echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT
@@ -136,9 +133,6 @@ jobs:
           file: ${{ matrix.config.dockerfile }}
           target: full
           provenance: false
-          build-args: |
-            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
-            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
           # using github experimental cache
           #cache-from: type=gha
           #cache-to: type=gha,mode=max
@@ -161,9 +155,6 @@ jobs:
           file: ${{ matrix.config.dockerfile }}
           target: light
           provenance: false
-          build-args: |
-            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
-            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
           # using github experimental cache
           #cache-from: type=gha
           #cache-to: type=gha,mode=max
@@ -186,9 +177,6 @@ jobs:
           file: ${{ matrix.config.dockerfile }}
           target: server
           provenance: false
-          build-args: |
-            ${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
-            ${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
           # using github experimental cache
           #cache-from: type=gha
           #cache-to: type=gha,mode=max

.github/workflows/release.yml

@@ -37,6 +37,13 @@ jobs:
           key: macOS-latest-cmake-arm64
           evict-old-files: 1d
 
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+          brew install curl
+
       - name: Build
         id: cmake_build
         run: |
@@ -45,7 +52,6 @@ jobs:
             -DCMAKE_INSTALL_RPATH='@loader_path' \
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
-            -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DGGML_RPC=ON \
@@ -60,9 +66,16 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
+          zip -y -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
           tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz -s ",./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
 
-      - name: Upload artifacts
+      - name: Upload artifacts (zip)
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip
+          name: llama-bin-macos-arm64.zip
+
+      - name: Upload artifacts (tar)
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz
@@ -84,6 +97,13 @@ jobs:
           key: macOS-latest-cmake-x64
           evict-old-files: 1d
 
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+          brew install curl
+
       - name: Build
         id: cmake_build
         run: |
@@ -94,7 +114,6 @@ jobs:
             -DCMAKE_INSTALL_RPATH='@loader_path' \
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
-            -DLLAMA_BUILD_BORINGSSL=ON \
             -DGGML_METAL=OFF \
             -DGGML_RPC=ON \
             -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
@@ -108,9 +127,16 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
+          zip -y -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
           tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz -s ",./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
 
-      - name: Upload artifacts
+      - name: Upload artifacts (zip)
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
+          name: llama-bin-macos-x64.zip
+
+      - name: Upload artifacts (tar)
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz
@@ -147,7 +173,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential libssl-dev
+          sudo apt-get install build-essential libcurl4-openssl-dev
 
       - name: Build
         id: cmake_build
@@ -170,9 +196,16 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
+          zip -y -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip ./build/bin/*
           tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
 
-      - name: Upload artifacts
+      - name: Upload artifacts (zip)
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip
+          name: llama-bin-ubuntu-${{ matrix.build }}.zip
+
+      - name: Upload artifacts (tar)
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz
@@ -200,7 +233,7 @@ 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 mesa-vulkan-drivers vulkan-sdk libssl-dev
+          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libcurl4-openssl-dev
 
       - name: Build
         id: cmake_build
@@ -223,9 +256,16 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
+          zip -y -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip ./build/bin/*
           tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
 
-      - name: Upload artifacts
+      - name: Upload artifacts (zip)
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip
+          name: llama-bin-ubuntu-vulkan-x64.zip
+
+      - name: Upload artifacts (tar)
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz
@@ -257,23 +297,34 @@ jobs:
         run: |
           choco install ninja
 
+      - name: libCURL
+        id: get_libcurl
+        uses: ./.github/actions/windows-setup-curl
+        with:
+          architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }}
+
       - name: Build
         shell: cmd
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
         run: |
           call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch == 'x64' && 'x64' || 'amd64_arm64' }}
           cmake -S . -B build -G "Ninja Multi-Config" ^
             -D CMAKE_TOOLCHAIN_FILE=cmake/${{ matrix.arch }}-windows-llvm.cmake ^
-            -DLLAMA_BUILD_BORINGSSL=ON ^
             -DGGML_NATIVE=OFF ^
             -DGGML_BACKEND_DL=ON ^
             -DGGML_CPU_ALL_VARIANTS=${{ matrix.arch == 'x64' && 'ON' || 'OFF' }} ^
             -DGGML_OPENMP=ON ^
+            -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" ^
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release
 
       - name: Pack artifacts
         id: pack_artifacts
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
         run: |
+          Copy-Item $env:CURL_PATH\bin\libcurl-${{ matrix.arch }}.dll .\build\bin\Release\
           Copy-Item "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Redist\MSVC\14.44.35112\debug_nonredist\${{ matrix.arch }}\Microsoft.VC143.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
           7z a -snl llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*
 
@@ -351,7 +402,7 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_BUILD_BORINGSSL=ON
+          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_CURL=OFF
           cmake --build build --config Release --target ${{ matrix.target }}
 
       - name: Pack artifacts
@@ -397,7 +448,6 @@ jobs:
       - name: Build
         id: cmake_build
         shell: cmd
-        # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
         run: |
           call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
           cmake -S . -B build -G "Ninja Multi-Config" ^
@@ -405,8 +455,7 @@ jobs:
             -DGGML_NATIVE=OFF ^
             -DGGML_CPU=OFF ^
             -DGGML_CUDA=ON ^
-            -DLLAMA_BUILD_BORINGSSL=ON ^
-            -DGGML_CUDA_CUB_3DOT2=ON
+            -DLLAMA_CURL=OFF
           set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
           cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda
 
@@ -474,7 +523,7 @@ jobs:
             -DCMAKE_BUILD_TYPE=Release ^
             -DGGML_BACKEND_DL=ON -DBUILD_SHARED_LIBS=ON ^
             -DGGML_CPU=OFF -DGGML_SYCL=ON ^
-            -DLLAMA_BUILD_BORINGSSL=ON
+            -DLLAMA_CURL=OFF
           cmake --build build --target ggml-sycl -j
 
       - name: Build the release package
@@ -601,7 +650,7 @@ jobs:
             -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}" `
             -DGGML_HIP_ROCWMMA_FATTN=ON `
             -DGGML_HIP=ON `
-            -DLLAMA_BUILD_BORINGSSL=ON
+            -DLLAMA_CURL=OFF
           cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS}
           md "build\bin\rocblas\library\"
           md "build\bin\hipblaslt\library"
@@ -642,7 +691,7 @@ jobs:
           cmake -B build -G Xcode \
             -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_CURL=OFF \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TOOLS=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
@@ -667,16 +716,21 @@ jobs:
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          # Zip file is required for Swift Package Manager, which does not support tar.gz for binary targets.
-          # For more details, see https://developer.apple.com/documentation/xcode/distributing-binary-frameworks-as-swift-packages
-          zip -r -y llama-${{ steps.tag.outputs.name }}-xcframework.zip build-apple/llama.xcframework
+          zip -y -r llama-${{ steps.tag.outputs.name }}-xcframework.zip build-apple/llama.xcframework
+          tar -czvf llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz -C build-apple llama.xcframework
 
-      - name: Upload artifacts
+      - name: Upload artifacts (zip)
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-xcframework.zip
           name: llama-${{ steps.tag.outputs.name }}-xcframework.zip
 
+      - name: Upload artifacts (tar)
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz
+          name: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz
+
 
   openEuler-cann:
     strategy:
@@ -721,7 +775,7 @@ jobs:
             "${{ steps.cann-image.outputs.image }}" \
             bash -lc '
               set -e
-              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
               yum clean all && rm -rf /var/cache/yum
               git config --global --add safe.directory "/workspace"
               export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
@@ -743,7 +797,7 @@ jobs:
           cp LICENSE ./build/bin/
           tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
 
-      - name: Upload artifacts
+      - name: Upload artifacts (tar)
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz
@@ -835,6 +889,9 @@ jobs:
         with:
           tag_name: ${{ steps.tag.outputs.name }}
           body: |
+            > [!WARNING]
+            > **Release Format Update**: Linux releases will soon use .tar.gz archives instead of .zip. Please make the necessary changes to your deployment scripts.
+
             <details open>
 
             ${{ github.event.head_commit.message }}
@@ -844,7 +901,7 @@ jobs:
             **macOS/iOS:**
             - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz)
             - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz)
-            - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-xcframework.zip)
+            - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz)
 
             **Linux:**
             - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.tar.gz)
@@ -854,8 +911,8 @@ jobs:
             **Windows:**
             - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip)
             - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
-            - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
-            - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.1-x64.zip) - [CUDA 13.1 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.1-x64.zip)
+            - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip)
+            - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.1-x64.zip)
             - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
             - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
             - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)

.github/workflows/server-webui.yml

@@ -168,6 +168,8 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
@@ -180,6 +182,8 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
@@ -191,6 +195,8 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server

.github/workflows/server.yml

@@ -41,10 +41,6 @@ jobs:
         include:
           - build_type: Release
             sanitizer: ""
-            extra_args: ""
-          - build_type: Release
-            sanitizer: ""
-            extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
       fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
 
     steps:
@@ -69,12 +65,6 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -B build -DLLAMA_BUILD_BORINGSSL=ON
-          cmake --build build --config ${{ matrix.build_type }} -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
-
       - name: Python setup
         id: setup_python
         uses: actions/setup-python@v5
@@ -86,14 +76,6 @@ jobs:
         run: |
           pip install -r tools/server/tests/requirements.txt
 
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) && matrix.build_type == 'Release' }}
-        run: |
-          cd tools/server/tests
-          export ${{ matrix.extra_args }}
-          pytest -v -x -m "not slow"
-
   server-windows:
     runs-on: windows-2022
 
@@ -108,7 +90,7 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -B build -DLLAMA_BUILD_BORINGSSL=ON
+          cmake -B build -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
 
       - name: Python setup

.gitignore

@@ -130,7 +130,6 @@ poetry.toml
 # Local scripts
 /run-vim.sh
 /run-chat.sh
-/run-spec.sh
 /.ccache/
 
 # IDE

AGENTS.md

@@ -1,81 +0,0 @@
-# Instructions for llama.cpp
-
-> [!IMPORTANT]
-> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
->
-> Read more: [CONTRIBUTING.md](CONTRIBUTING.md)
-
-AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (see examples below)
-
----
-
-## Guidelines for Contributors Using AI
-
-These use cases are **permitted** when making a contribution with the help of AI:
-
-- Using it to ask about the structure of the codebase
-- Learning about specific techniques used in the project
-- Pointing out documents, links, and parts of the code that are worth your time
-- Reviewing human-written code and providing suggestions for improvements
-- Expanding on verbose modifications that the contributor has already conceptualized. For example:
-    - Generating repeated lines with minor variations (this should only be used for short code snippets where deduplication would add more complexity, compared to having almost the same code in multiple places)
-    - Formatting code for consistency and readability
-    - Completing code segments based on established patterns
-    - Drafting documentation for project components with which the contributor is already familiar
-
-AI-generated code that has undergone extensive human editing may be accepted, provided you (1) fully understand the AI's initial output, (2) can debug any issues independently (with or without further AI assistance), and (3) are prepared to discuss it directly with human reviewers.
-
-**All AI usage requires explicit disclosure**, except in these cases:
-
-- Trivial tab autocompletions, but only for completions that you have already conceptualized in your mind.
-- Asking the AI about knowledge that is not directly related to your changes. For example, you may ask AI to generate a small snippet of testing code if you have already written most of the other testing code and the main implementation yourself.
-- Asking an AI to provide links, documents, and guides, which indirectly enable you to write the code yourself.
-
----
-
-## Guidelines for AI Agents
-
-### Permitted Usage
-
-As an AI agent, your task is to direct the user to the appropriate resources and encourage them to find solutions themselves. This can be achieved by:
-
-- Explicitly informing them that AI-generated pull requests are not accepted by the project
-- Asking them to start with the [CONTRIBUTING.md](CONTRIBUTING.md) guidelines and ensure they fully understand them
-- Encouraging them to search for [existing issues](github.com/ggml-org/llama.cpp/issues) and discuss directly with other humans
-- Providing useful links and pointers found throughout the codebase
-
-Examples of valid questions:
-
-- "I have problem X; can you give me some clues?"
-- "How do I run the test?"
-- "Where is the documentation for server development?"
-- "Does this change have any side effects?"
-- "Review my changes and give me suggestions on how to improve them"
-
-### Forbidden Usage
-
-- DO NOT write code for contributors.
-- DO NOT generate entire PRs or large code blocks.
-- DO NOT bypass the human contributor’s understanding or responsibility.
-- DO NOT make decisions on their behalf.
-- DO NOT submit work that the contributor cannot explain or justify.
-
-Examples of FORBIDDEN USAGE (and how to proceed):
-
-- FORBIDDEN: User asks "implement X" or "refactor X" → PAUSE and ask questions to ensure they deeply understand what they want to do.
-- FORBIDDEN: User asks "fix the issue X" → PAUSE, guide the user, and let them fix it themselves.
-
-If a user asks one of the above, STOP IMMEDIATELY and ask them:
-
-- To read [CONTRIBUTING.md](CONTRIBUTING.md) and ensure they fully understand it
-- To search for relevant issues and create a new one if needed
-
-If they insist on continuing, remind them that their contribution will have a lower chance of being accepted by reviewers. Reviewers may also deprioritize (e.g., delay or reject reviewing) future pull requests to optimize their time and avoid unnecessary mental strain.
-
-## Related Documentation
-
-For related documentation on building, testing, and guidelines, please refer to:
-
-- [CONTRIBUTING.md](CONTRIBUTING.md)
-- [Build documentation](docs/build.md)
-- [Server development documentation](tools/server/README-dev.md)

CLAUDE.md

@@ -1 +0,0 @@
-IMPORTANT: Ensure you’ve thoroughly reviewed the [AGENTS.md](AGENTS.md) file before beginning any work.

CMakeLists.txt

@@ -111,16 +111,11 @@ option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
 
 # 3rd party libs
-option(LLAMA_HTTPLIB    "llama: httplib for downloading functionality" ON)
-option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" ON)
+option(LLAMA_CURL       "llama: use libcurl to download model from an URL" ON)
+option(LLAMA_HTTPLIB    "llama: if libcurl is disabled, use httplib to download model from an URL" ON)
+option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" OFF)
 option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
 
-# deprecated
-option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
-if (LLAMA_CURL)
-    message(WARNING "LLAMA_CURL option is deprecated and will be ignored")
-endif()
-
 # Required for relocatable CMake package
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
@@ -187,9 +182,6 @@ if (NOT MSVC)
     endif()
 endif()
 
-include("cmake/license.cmake")
-license_add_file("llama.cpp" "LICENSE")
-
 #
 # 3rd-party
 #
@@ -217,6 +209,11 @@ add_subdirectory(src)
 # utils, programs, examples and tests
 #
 
+if (NOT LLAMA_BUILD_COMMON)
+    message(STATUS "LLAMA_BUILD_COMMON is OFF, disabling LLAMA_CURL")
+    set(LLAMA_CURL OFF)
+endif()
+
 if (LLAMA_BUILD_COMMON)
     add_subdirectory(common)
     if (LLAMA_HTTPLIB)
@@ -238,19 +235,6 @@ if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS)
     add_subdirectory(tools)
 endif()
 
-# Automatically add all files from the 'licenses' directory
-file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")
-
-foreach(FILE_PATH ${EXTRA_LICENSES})
-    get_filename_component(FILE_NAME "${FILE_PATH}" NAME)
-    string(REGEX REPLACE "^LICENSE-" "" NAME "${FILE_NAME}")
-    license_add_file("${NAME}" "${FILE_PATH}")
-endforeach()
-
-if (LLAMA_BUILD_COMMON)
-    license_generate(common)
-endif()
-
 #
 # install
 #

CONTRIBUTING.md

@@ -6,45 +6,21 @@ The project differentiates between 3 levels of contributors:
 - Collaborators (Triage): people with significant contributions, who may be responsible for some parts of the code, and are expected to maintain and review contributions for the code they own
 - Maintainers: responsible for reviewing and merging PRs, after approval from the code owners
 
-# AI Usage Policy
-
-> [!IMPORTANT]
-> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
->
-> Detailed information regarding permissible and restricted uses of AI can be found in the [AGENTS.md](AGENTS.md) file.
-
-Code that is initially generated by AI and subsequently edited will still be considered AI-generated. AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (e.g., generating repeated lines with minor variations).
-
-If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
-
-1. Explicitly disclose the manner in which AI was employed.
-2. Perform a comprehensive manual review prior to submitting the pull request.
-3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
-4. Using AI to write pull request descriptions or to respond to human reviewers is strictly prohibited.
-
-For more info, please refer to the [AGENTS.md](AGENTS.md) file.
-
 # Pull requests (for contributors & collaborators)
 
-Before submitting your PR:
-- Search for existing PRs to prevent duplicating efforts
 - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
 - Test your changes:
     - Execute [the full CI locally on your machine](ci/README.md) before publishing
     - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
     - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
     - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
-- Create separate PRs for each feature or fix:
-    - Avoid combining unrelated changes in a single PR
-    - For intricate features, consider opening a feature request first to discuss and align expectations
-    - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
+- Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR
+- When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
-
-After submitting your PR:
-- Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability
-- Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
 - If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention
-- Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for fixing related issues and reviewing related PRs
+- Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
+- Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs
+- Using AI to generate PRs is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before publishing the PR. Note that trivial tab autocompletions do not require disclosure.
 
 # Pull requests (for maintainers)
 
@@ -55,11 +31,6 @@ After submitting your PR:
 - When merging a PR, make sure you have a good understanding of the changes
 - Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)
 
-Maintainers reserve the right to decline review or close pull requests for any reason, particularly under any of the following conditions:
-- The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone.
-- The pull request duplicates an existing one.
-- The contributor fails to adhere to this contributing guide.
-
 # Coding guidelines
 
 - Avoid adding third-party dependencies, extra files, extra headers, etc.

README.md

@@ -200,7 +200,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
 
 - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
-- [BonzAI App](https://apps.apple.com/us/app/bonzai-your-local-ai-agent/id6752847988) (proprietary)
 - [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
 - [Dot](https://github.com/alexpinel/Dot) (GPL)
 - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
@@ -483,6 +482,21 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
     </details>
 
+## [`llama-run`](tools/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)
 
 #### A minimal example for implementing apps with `llama.cpp`. Useful for developers.
@@ -586,5 +600,7 @@ $ echo "source ~/.llama-completion.bash" >> ~/.bashrc
 - [stb-image](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
 - [nlohmann/json](https://github.com/nlohmann/json) - Single-header JSON library, used by various tools/examples - MIT License
 - [minja](https://github.com/google/minja) - Minimal Jinja parser in C++, used by various tools/examples - MIT License
+- [linenoise.cpp](./tools/run/linenoise.cpp/linenoise.cpp) - C++ library that provides readline-like line editing capabilities, used by `llama-run` - BSD 2-Clause License
+- [curl](https://curl.se/) - Client-side URL transfer library, used by various tools/examples - [CURL License](https://curl.se/docs/copyright.html)
 - [miniaudio.h](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
 - [subprocess.h](https://github.com/sheredom/subprocess.h) - Single-header process launching solution for C and C++ - Public domain

SECURITY.md

@@ -1,52 +1,12 @@
 # Security Policy
 
- - [**Reporting a vulnerability**](#reporting-a-vulnerability)
- - [**Requirements**](#requirements)
- - [**Covered Topics**](#covered-topics)
  - [**Using llama.cpp securely**](#using-llamacpp-securely)
    - [Untrusted models](#untrusted-models)
    - [Untrusted inputs](#untrusted-inputs)
    - [Data privacy](#data-privacy)
    - [Untrusted environments or networks](#untrusted-environments-or-networks)
    - [Multi-Tenant environments](#multi-tenant-environments)
-
-## Reporting a vulnerability
-
-If you have discovered a security vulnerability in this project that falls inside the [covered topics](#covered-topics), please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
-
-Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
-
-A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
-
-> [!IMPORTANT]
-> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080
-
-## Requirements
-
-Before submitting your report, ensure you meet the following requirements:
-
-- You have read this policy and fully understand it.
-- AI is only permitted in an assistive capacity as stated in [AGENTS.md](AGENTS.md). We do not accept reports that are written exclusively by AI.
-- Your report must include a working Proof-of-Concept in the form of a script and/or attached files.
-
-Maintainers reserve the right to close the report if these requirements are not fulfilled.
-
-## Covered Topics
-
-Only vulnerabilities that fall within these parts of the project are considered valid. For problems falling outside of this list, please report them as issues.
-
-- `src/**/*`
-- `ggml/**/*`
-- `gguf-py/**/*`
-- `tools/server/*`, **excluding** the following topics:
-    - Web UI
-    - Features marked as experimental
-    - Features not recommended for use in untrusted environments (e.g., router, MCP)
-    - Bugs that can lead to Denial-of-Service attack
-
-Note that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities in LLaMA C++.
-
-For vulnerabilities that fall within the `vendor` directory, please report them directly to the third-party project.
+ - [**Reporting a vulnerability**](#reporting-a-vulnerability)
 
 ## Using llama.cpp securely
 
@@ -95,3 +55,19 @@ If you intend to run multiple models in parallel with shared memory, it is your
 3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
 
 4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
+
+## Reporting a vulnerability
+
+Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++.
+
+<!-- normal version -->
+However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
+
+Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
+
+Please note that using AI to identify vulnerabilities and generate reports is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before submitting the report.
+
+A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
+
+> [!IMPORTANT]
+> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080

build-xcframework.sh

@@ -414,7 +414,7 @@ cmake -B build-ios-sim -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -S .
 cmake --build build-ios-sim --config Release -- -quiet
 
@@ -428,7 +428,7 @@ cmake -B build-ios-device -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -S .
 cmake --build build-ios-device --config Release -- -quiet
 
@@ -439,7 +439,7 @@ cmake -B build-macos -G Xcode \
     -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -S .
 cmake --build build-macos --config Release -- -quiet
 
@@ -453,7 +453,7 @@ cmake -B build-visionos -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \
     -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -DLLAMA_HTTPLIB=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
     -S .
@@ -469,7 +469,7 @@ cmake -B build-visionos-sim -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \
     -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -DLLAMA_HTTPLIB=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
     -S .
@@ -487,7 +487,7 @@ cmake -B build-tvos-sim -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -S .
 cmake --build build-tvos-sim --config Release -- -quiet
 
@@ -502,7 +502,7 @@ cmake -B build-tvos-device -G Xcode \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -S .
 cmake --build build-tvos-device --config Release -- -quiet
 

ci/run.sh

@@ -45,15 +45,14 @@ sd=`dirname $0`
 cd $sd/../
 SRC=`pwd`
 
-CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_OPENSSL=OFF -DGGML_SCHED_NO_REALLOC=ON"
+CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_CURL=ON -DGGML_SCHED_NO_REALLOC=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
 fi
 
 if [ ! -z ${GG_BUILD_CUDA} ]; then
-    # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON -DGGML_CUDA_CUB_3DOT2=ON"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON"
 
     if command -v nvidia-smi >/dev/null 2>&1; then
         CUDA_ARCH=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader,nounits 2>/dev/null | head -1 | tr -d '.')
@@ -105,20 +104,7 @@ if [ ! -z ${GG_BUILD_VULKAN} ]; then
 fi
 
 if [ ! -z ${GG_BUILD_WEBGPU} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_WEBGPU=1 -DGGML_METAL=OFF -DGGML_BLAS=OFF"
-
-    if [ ! -z "${GG_BUILD_WEBGPU_DAWN_PREFIX}" ]; then
-        if [ -z "${CMAKE_PREFIX_PATH}" ]; then
-            export CMAKE_PREFIX_PATH="${GG_BUILD_WEBGPU_DAWN_PREFIX}"
-        else
-            export CMAKE_PREFIX_PATH="${GG_BUILD_WEBGPU_DAWN_PREFIX}:${CMAKE_PREFIX_PATH}"
-        fi
-    fi
-
-    # For some systems, Dawn_DIR needs to be set explicitly, e.g., the lib64 path
-    if [ ! -z "${GG_BUILD_WEBGPU_DAWN_DIR}" ]; then
-        CMAKE_EXTRA="${CMAKE_EXTRA} -DDawn_DIR=${GG_BUILD_WEBGPU_DAWN_DIR}"
-    fi
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_WEBGPU=1"
 fi
 
 if [ ! -z ${GG_BUILD_MUSA} ]; then
@@ -297,8 +283,7 @@ function gg_sum_test_scripts {
 }
 
 function gg_get_model {
-    #local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
-    local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-q4_0.gguf"
+    local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
     if [[ -s $gguf_0 ]]; then
         echo -n "$gguf_0"
     else

cmake/download-models.cmake

@@ -1,21 +0,0 @@
-get_filename_component(DEST_DIR "${DEST}" DIRECTORY)
-file(MAKE_DIRECTORY "${DEST_DIR}")
-
-if(NOT EXISTS "${DEST}")
-    message(STATUS "Downloading ${NAME} from ggml-org/models...")
-endif()
-
-file(DOWNLOAD
-    "https://huggingface.co/ggml-org/models/resolve/main/${NAME}?download=true"
-    "${DEST}"
-    TLS_VERIFY ON
-    EXPECTED_HASH ${HASH}
-    STATUS status
-)
-
-list(GET status 0 code)
-
-if(NOT code EQUAL 0)
-    list(GET status 1 msg)
-    message(FATAL_ERROR "Failed to download ${NAME}: ${msg}")
-endif()

cmake/license.cmake

@@ -1,40 +0,0 @@
-define_property(GLOBAL PROPERTY LICENSE_TEXT
-    BRIEF_DOCS "Embedded licenses"
-    FULL_DOCS  "Global string containing all aggregated licenses"
-)
-
-function(license_add_file NAME FILE)
-    if(NOT IS_ABSOLUTE "${FILE}")
-        set(FILE "${CMAKE_CURRENT_SOURCE_DIR}/${FILE}")
-    endif()
-    if(EXISTS "${FILE}")
-        set(TITLE "License for ${NAME}")
-        string(REGEX REPLACE "." "=" UNDERLINE "${TITLE}")
-        file(READ "${FILE}" TEXT)
-        get_property(TMP GLOBAL PROPERTY LICENSE_TEXT)
-        string(APPEND TMP "R\"=L=(${TITLE}\n${UNDERLINE}\n\n${TEXT})=L=\",\n")
-        set_property(GLOBAL PROPERTY LICENSE_TEXT "${TMP}")
-    else()
-        message(WARNING "License file '${FILE}' not found")
-    endif()
-endfunction()
-
-function(license_generate TARGET_NAME)
-    message(STATUS "Generating embedded license file for target: ${TARGET_NAME}")
-    get_property(TEXT GLOBAL PROPERTY LICENSE_TEXT)
-
-    set(CPP_CONTENT "// Generated by CMake\n\n")
-    string(APPEND CPP_CONTENT "const char* LICENSES[] = {\n")
-    string(APPEND CPP_CONTENT "${TEXT}")
-    string(APPEND CPP_CONTENT "nullptr\n")
-    string(APPEND CPP_CONTENT "};\n")
-
-    set(CPP_FILE "${CMAKE_BINARY_DIR}/license.cpp")
-    file(WRITE "${CPP_FILE}" "${CPP_CONTENT}")
-
-    if(TARGET ${TARGET_NAME})
-        target_sources(${TARGET_NAME} PRIVATE "${CPP_FILE}")
-    else()
-        message(FATAL_ERROR "Target '${TARGET_NAME}' does not exist")
-    endif()
-endfunction()

common/CMakeLists.txt

@@ -60,8 +60,6 @@ add_library(${TARGET} STATIC
     common.h
     console.cpp
     console.h
-    debug.cpp
-    debug.h
     download.cpp
     download.h
     http.h
@@ -87,9 +85,6 @@ add_library(${TARGET} STATIC
     unicode.h
     )
 
-target_include_directories(${TARGET} PUBLIC . ../vendor)
-target_compile_features   (${TARGET} PUBLIC cxx_std_17)
-
 if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
@@ -97,7 +92,17 @@ endif()
 # TODO: use list(APPEND LLAMA_COMMON_EXTRA_LIBS ...)
 set(LLAMA_COMMON_EXTRA_LIBS build_info)
 
-if (LLAMA_HTTPLIB)
+if (LLAMA_CURL)
+    # Use curl to download model url
+    find_package(CURL)
+    if (NOT CURL_FOUND)
+        message(FATAL_ERROR "Could NOT find CURL. Hint: to disable this feature, set -DLLAMA_CURL=OFF")
+    endif()
+    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
+    include_directories(${CURL_INCLUDE_DIRS})
+    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARIES})
+elseif (LLAMA_HTTPLIB)
+    # otherwise, use cpp-httplib
     target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_HTTPLIB)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} cpp-httplib)
 endif()
@@ -146,4 +151,30 @@ if (LLAMA_LLGUIDANCE)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance ${LLGUIDANCE_PLATFORM_LIBS})
 endif ()
 
-target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
+target_include_directories(${TARGET} PUBLIC . ../vendor)
+target_compile_features   (${TARGET} PUBLIC cxx_std_17)
+target_link_libraries     (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
+
+
+#
+# copy the license files
+#
+
+# Check if running in GitHub Actions
+if (DEFINED ENV{GITHUB_ACTIONS} AND "$ENV{GITHUB_ACTIONS}" STREQUAL "true")
+    message(STATUS "Running inside GitHub Actions - copying license files")
+
+    # Copy all files from licenses/ to build/bin/
+    file(GLOB LICENSE_FILES "${CMAKE_SOURCE_DIR}/licenses/*")
+    foreach(LICENSE_FILE ${LICENSE_FILES})
+        get_filename_component(FILENAME ${LICENSE_FILE} NAME)
+        add_custom_command(
+            POST_BUILD
+            TARGET ${TARGET}
+            COMMAND ${CMAKE_COMMAND} -E copy_if_different
+                "${LICENSE_FILE}"
+                "$<TARGET_FILE_DIR:llama>/${FILENAME}"
+            COMMENT "Copying ${FILENAME} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}")
+        message(STATUS "Copying ${LICENSE_FILE} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${FILENAME}")
+    endforeach()
+endif()

common/arg.cpp

@@ -2,11 +2,10 @@
 
 #include "chat.h"
 #include "common.h"
-#include "download.h"
 #include "json-schema-to-grammar.h"
 #include "log.h"
 #include "sampling.h"
-#include "preset.h"
+#include "download.h"
 
 // fix problem with std::min and std::max
 #if defined(_WIN32)
@@ -48,8 +47,6 @@
 
 #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
 
-extern const char * LICENSES[];
-
 using json = nlohmann::ordered_json;
 using namespace common_arg_utils;
 
@@ -99,11 +96,6 @@ common_arg & common_arg::set_sparam() {
     return *this;
 }
 
-common_arg & common_arg::set_preset_only() {
-    is_preset_only = true;
-    return *this;
-}
-
 bool common_arg::in_example(enum llama_example ex) {
     return examples.find(ex) != examples.end();
 }
@@ -271,55 +263,6 @@ static void parse_tensor_buffer_overrides(const std::string & value, std::vector
     }
 }
 
-static std::string clean_file_name(const std::string & fname) {
-    std::string clean_fname = fname;
-    string_replace_all(clean_fname, "\\", "_");
-    string_replace_all(clean_fname, "/", "_");
-    return clean_fname;
-}
-
-static bool common_params_handle_remote_preset(common_params & params, llama_example ex) {
-    GGML_ASSERT(!params.model.hf_repo.empty());
-
-    // the returned hf_repo is without tag
-    auto [hf_repo, hf_tag] = common_download_split_repo_tag(params.model.hf_repo);
-
-    // "latest" tag (default if not specified) is translated to "default" preset
-    if (hf_tag == "latest") {
-        hf_tag = "default";
-    }
-
-    const bool offline = params.offline;
-    std::string model_endpoint = get_model_endpoint();
-    auto preset_url = model_endpoint + hf_repo + "/resolve/main/preset.ini";
-
-    // prepare local path for caching
-    auto preset_fname = clean_file_name(hf_repo + "_preset.ini");
-    auto preset_path = fs_get_cache_file(preset_fname);
-    const int status = common_download_file_single(preset_url, preset_path, params.hf_token, offline);
-    const bool has_preset = status >= 200 && status < 400;
-
-    // remote preset is optional, so we don't error out if not found
-    if (has_preset) {
-        LOG_INF("applying remote preset from %s\n", preset_url.c_str());
-        common_preset_context ctx(ex, /* only_remote_allowed */ true);
-        common_preset global;
-        auto remote_presets = ctx.load_from_ini(preset_path, global);
-        remote_presets = ctx.cascade(global, remote_presets);
-        if (remote_presets.find(hf_tag) != remote_presets.end()) {
-            common_preset preset = remote_presets.at(hf_tag);
-            LOG_INF("\n%s", preset.to_ini().c_str()); // to_ini already added trailing newline
-            preset.apply_to_params(params);
-        } else {
-            throw std::runtime_error("Remote preset.ini does not contain [" + std::string(hf_tag) + "] section");
-        }
-    } else {
-        LOG_INF("%s", "no remote preset found, skipping\n");
-    }
-
-    return has_preset;
-}
-
 struct handle_model_result {
     bool found_mmproj = false;
     common_params_model mmproj;
@@ -341,7 +284,7 @@ static handle_model_result common_params_handle_model(
                 if (model.path.empty()) {
                     auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline);
                     if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) {
-                        exit(1); // error message already printed
+                        exit(1); // built without CURL, error message already printed
                     }
                     model.name    = model.hf_repo;      // repo name with tag
                     model.hf_repo = auto_detected.repo; // repo name without tag
@@ -361,7 +304,9 @@ static handle_model_result common_params_handle_model(
             // make sure model path is present (for caching purposes)
             if (model.path.empty()) {
                 // this is to avoid different repo having same file name, or same file name in different subdirs
-                std::string filename = clean_file_name(model.hf_repo + "_" + model.hf_file);
+                std::string filename = model.hf_repo + "_" + model.hf_file;
+                // to make sure we don't have any slashes in the filename
+                string_replace_all(filename, "/", "_");
                 model.path = fs_get_cache_file(filename);
             }
 
@@ -475,87 +420,61 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         }
     };
 
-    auto parse_cli_args = [&]() {
-        std::set<std::string> seen_args;
+    std::set<std::string> seen_args;
 
-        for (int i = 1; i < argc; i++) {
-            const std::string arg_prefix = "--";
+    for (int i = 1; i < argc; i++) {
+        const std::string arg_prefix = "--";
 
-            std::string arg = argv[i];
-            if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-                std::replace(arg.begin(), arg.end(), '_', '-');
-            }
-            if (arg_to_options.find(arg) == arg_to_options.end()) {
-                throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
-            }
-            if (!seen_args.insert(arg).second) {
-                LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
-            }
-            auto & tmp = arg_to_options[arg];
-            auto opt = *tmp.first;
-            bool is_positive = tmp.second;
-            if (opt.has_value_from_env()) {
-                fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
-            }
-            try {
-                if (opt.handler_void) {
-                    opt.handler_void(params);
-                    continue;
-                }
-                if (opt.handler_bool) {
-                    opt.handler_bool(params, is_positive);
-                    continue;
-                }
-
-                // arg with single value
-                check_arg(i);
-                std::string val = argv[++i];
-                if (opt.handler_int) {
-                    opt.handler_int(params, std::stoi(val));
-                    continue;
-                }
-                if (opt.handler_string) {
-                    opt.handler_string(params, val);
-                    continue;
-                }
-
-                // arg with 2 values
-                check_arg(i);
-                std::string val2 = argv[++i];
-                if (opt.handler_str_str) {
-                    opt.handler_str_str(params, val, val2);
-                    continue;
-                }
-            } catch (std::exception & e) {
-                throw std::invalid_argument(string_format(
-                    "error while handling argument \"%s\": %s\n\n"
-                    "usage:\n%s\n\nto show complete usage, run with -h",
-                    arg.c_str(), e.what(), opt.to_string().c_str()));
-            }
+        std::string arg = argv[i];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
         }
-    };
-
-    // parse the first time to get -hf option (used for remote preset)
-    parse_cli_args();
-
-    // maybe handle remote preset
-    if (!params.model.hf_repo.empty()) {
-        std::string cli_hf_repo = params.model.hf_repo;
-        bool has_preset = common_params_handle_remote_preset(params, ctx_arg.ex);
-
-        // special case: if hf_repo explicitly set by preset, we need to preserve it (ignore CLI value)
-        // this is useful when we have one HF repo pointing to other HF repos (one model - multiple GGUFs)
-        std::string preset_hf_repo = params.model.hf_repo;
-        bool preset_has_hf_repo = preset_hf_repo != cli_hf_repo;
-
-        if (has_preset) {
-            // re-parse CLI args to override preset values
-            parse_cli_args();
+        if (arg_to_options.find(arg) == arg_to_options.end()) {
+            throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
         }
+        if (!seen_args.insert(arg).second) {
+            LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+        }
+        auto & tmp = arg_to_options[arg];
+        auto opt = *tmp.first;
+        bool is_positive = tmp.second;
+        if (opt.has_value_from_env()) {
+            fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
+        }
+        try {
+            if (opt.handler_void) {
+                opt.handler_void(params);
+                continue;
+            }
+            if (opt.handler_bool) {
+                opt.handler_bool(params, is_positive);
+                continue;
+            }
 
-        // preserve hf_repo from preset if needed
-        if (preset_has_hf_repo) {
-            params.model.hf_repo = preset_hf_repo;
+            // arg with single value
+            check_arg(i);
+            std::string val = argv[++i];
+            if (opt.handler_int) {
+                opt.handler_int(params, std::stoi(val));
+                continue;
+            }
+            if (opt.handler_string) {
+                opt.handler_string(params, val);
+                continue;
+            }
+
+            // arg with 2 values
+            check_arg(i);
+            std::string val2 = argv[++i];
+            if (opt.handler_str_str) {
+                opt.handler_str_str(params, val, val2);
+                continue;
+            }
+        } catch (std::exception & e) {
+            throw std::invalid_argument(string_format(
+                "error while handling argument \"%s\": %s\n\n"
+                "usage:\n%s\n\nto show complete usage, run with -h",
+                arg.c_str(), e.what(), opt.to_string().c_str()));
         }
     }
 
@@ -755,6 +674,7 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
         "llama-quantize",
         "llama-qwen2vl-cli",
         "llama-retrieval",
+        "llama-run",
         "llama-save-load-state",
         "llama-server",
         "llama-simple",
@@ -852,11 +772,6 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
         }
         auto opt = *arg_to_options[arg];
         std::string val;
-        if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) {
-            // bool arg (need to reverse the meaning for negative args)
-            bool is_neg = std::find(opt.args_neg.begin(), opt.args_neg.end(), arg) != opt.args_neg.end();
-            val = is_neg ? "0" : "1";
-        }
         if (opt.value_hint != nullptr) {
             // arg with single value
             check_arg(i);
@@ -929,54 +844,6 @@ bool common_arg_utils::is_autoy(const std::string & value) {
     return value == "auto" || value == "-1";
 }
 
-// Simple CSV parser that handles quoted fields and escaped quotes
-// example:
-//    input:  value1,"value, with, commas","value with ""escaped"" quotes",value4
-//    output: [value1] [value, with, commas] [value with "escaped" quotes] [value4]
-static std::vector<std::string> parse_csv_row(const std::string& input) {
-    std::vector<std::string> fields;
-    std::string field;
-    bool in_quotes = false;
-
-    for (size_t i = 0; i < input.length(); ++i) {
-        char ch = input[i];
-
-        if (ch == '"') {
-            if (!in_quotes) {
-                // start of quoted field (only valid if at beginning of field)
-                if (!field.empty()) {
-                    // quote appeared in middle of unquoted field, treat as literal
-                    field += '"';
-                } else {
-                    in_quotes = true; // start
-                }
-            } else {
-                if (i + 1 < input.length() && input[i + 1] == '"') {
-                    // escaped quote: ""
-                    field += '"';
-                    ++i; // skip the next quote
-                } else {
-                    in_quotes = false; // end
-                }
-            }
-        } else if (ch == ',') {
-            if (in_quotes) {
-                field += ',';
-            } else {
-                fields.push_back(std::move(field));
-                field.clear();
-            }
-        } else {
-            field += ch;
-        }
-    }
-
-    // Add the last field
-    fields.push_back(std::move(field));
-
-    return fields;
-}
-
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
     // per-example default params
     // we define here to make sure it's included in llama-gen-docs
@@ -1006,9 +873,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         sampler_type_chars += common_sampler_type_to_chr(sampler);
         sampler_type_names += common_sampler_type_to_str(sampler) + ";";
     }
-    if (!sampler_type_names.empty()) {
-        sampler_type_names.pop_back(); // remove last semicolon
-    }
+    sampler_type_names.pop_back();
 
 
     /**
@@ -1041,16 +906,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             exit(0);
         }
     ));
-    add_opt(common_arg(
-        {"--license"},
-        "show source code license and dependencies",
-        [](common_params &) {
-            for (int i = 0; LICENSES[i]; ++i) {
-                printf("%s\n", LICENSES[i]);
-            }
-            exit(0);
-        }
-    ));
     add_opt(common_arg(
         {"-cl", "--cache-list"},
         "show list of models in cache",
@@ -1282,7 +1137,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-cram", "--cache-ram"}, "N",
+        {"--cache-ram", "-cram"}, "N",
         string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)"
             "[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)", params.cache_ram_mib),
         [](common_params & params, int value) {
@@ -1290,12 +1145,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-kvu", "--kv-unified"},
+        {"--kv-unified", "-kvu"},
         "use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)",
         [](common_params & params) {
             params.kv_unified = true;
         }
-    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED}));
+    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--context-shift"},
         {"--no-context-shift"},
@@ -1339,7 +1194,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.system_prompt = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION, LLAMA_EXAMPLE_MTMD}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION}));
     add_opt(common_arg(
         {"--perf"},
         {"--no-perf"},
@@ -1383,7 +1238,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--in-file"}, "FNAME",
         "an input file (use comma-separated values to specify multiple files)",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : parse_csv_row(value)) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
                 std::ifstream file(item);
                 if (!file) {
                     throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
@@ -1530,7 +1385,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, bool value) {
             params.warmup = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_DEBUG}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--spm-infill"},
         string_format(
@@ -1558,7 +1413,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_sparam());
     add_opt(common_arg(
-        {"--sampler-seq", "--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);
@@ -1828,13 +1683,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.grammar = json_schema_to_grammar(json::parse(schema));
         }
     ).set_sparam());
-    add_opt(common_arg(
-        {"-bs", "--backend-sampling"},
-        "enable backend sampling (experimental) (default: disabled)",
-        [](common_params & params) {
-            params.sampling.backend_sampling = true;
-        }
-    ).set_sparam().set_env("LLAMA_ARG_BACKEND_SAMPLING"));
     add_opt(common_arg(
         {"--pooling"}, "{none,mean,cls,last,rank}",
         "pooling type for embeddings, use model default if unspecified",
@@ -1846,7 +1694,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; }
             else { throw std::invalid_argument("invalid value"); }
         }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}).set_env("LLAMA_ARG_POOLING"));
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING"));
     add_opt(common_arg(
         {"--attention"}, "{causal,non-causal}",
         "attention type for embeddings, use model default if unspecified",
@@ -2135,7 +1983,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--image", "--audio"}, "FILE",
         "path to an image or audio file. use with multimodal models, use comma-separated values for multiple files\n",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : parse_csv_row(value)) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
                 params.image.emplace_back(item);
             }
         }
@@ -2157,7 +2005,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     if (llama_supports_rpc()) {
         add_opt(common_arg(
             {"--rpc"}, "SERVERS",
-            "comma separated list of RPC servers (host:port)",
+            "comma separated list of RPC servers",
             [](common_params & params, const std::string & value) {
                 add_rpc_devices(value);
                 GGML_UNUSED(params);
@@ -2174,22 +2022,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     add_opt(common_arg(
         {"--mmap"},
         {"--no-mmap"},
-        string_format("whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
+        string_format("whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
         [](common_params & params, bool value) {
             params.use_mmap = value;
-            if (value) {
-                params.use_direct_io = false;  // disable direct io when mmap is explicitly enabled
-            }
         }
     ).set_env("LLAMA_ARG_MMAP"));
-    add_opt(common_arg(
-        {"-dio", "--direct-io"},
-        {"-ndio", "--no-direct-io"},
-        string_format("use DirectIO if available. Takes precedence over --mmap (default: %s)", params.use_direct_io ? "enabled" : "disabled"),
-        [](common_params & params, bool value) {
-            params.use_direct_io = value;
-        }
-    ).set_env("LLAMA_ARG_DIO"));
     add_opt(common_arg(
         {"--numa"}, "TYPE",
         "attempt optimizations that help on some NUMA systems\n"
@@ -2234,26 +2071,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ));
     add_opt(common_arg(
-        {"-ot", "--override-tensor"}, "<tensor name pattern>=<buffer type>,...",
+        {"--override-tensor", "-ot"}, "<tensor name pattern>=<buffer type>,...",
         "override tensor buffer type", [](common_params & params, const std::string & value) {
             parse_tensor_buffer_overrides(value, params.tensor_buft_overrides);
         }
-    ).set_env("LLAMA_ARG_OVERRIDE_TENSOR"));
+    ));
     add_opt(common_arg(
-        {"-otd", "--override-tensor-draft"}, "<tensor name pattern>=<buffer type>,...",
+        {"--override-tensor-draft", "-otd"}, "<tensor name pattern>=<buffer type>,...",
         "override tensor buffer type for draft model", [](common_params & params, const std::string & value) {
             parse_tensor_buffer_overrides(value, params.speculative.tensor_buft_overrides);
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-cmoe", "--cpu-moe"},
+        {"--cpu-moe", "-cmoe"},
         "keep all Mixture of Experts (MoE) weights in the CPU",
         [](common_params & params) {
             params.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
         }
     ).set_env("LLAMA_ARG_CPU_MOE"));
     add_opt(common_arg(
-        {"-ncmoe", "--n-cpu-moe"}, "N",
+        {"--n-cpu-moe", "-ncmoe"}, "N",
         "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU",
         [](common_params & params, int value) {
             if (value < 0) {
@@ -2268,14 +2105,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_N_CPU_MOE"));
     add_opt(common_arg(
-        {"-cmoed", "--cpu-moe-draft"},
+        {"--cpu-moe-draft", "-cmoed"},
         "keep all Mixture of Experts (MoE) weights in the CPU for the draft model",
         [](common_params & params) {
             params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
     add_opt(common_arg(
-        {"-ncmoed", "--n-cpu-moe-draft"}, "N",
+        {"--n-cpu-moe-draft", "-ncmoed"}, "N",
         "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model",
         [](common_params & params, int value) {
             if (value < 0) {
@@ -2288,18 +2125,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
-    GGML_ASSERT(params.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
-        string_format("max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)", params.n_gpu_layers == -1 ? "auto" : "all"),
-        [](common_params & params, const std::string & value) {
-            if (value == "auto") {
-                params.n_gpu_layers = -1;
-            } else if (value == "all") {
-                params.n_gpu_layers = -2;
-            } else {
-                params.n_gpu_layers = std::stoi(value);
-            }
+        string_format("max. number of layers to store in VRAM (default: %d)", params.n_gpu_layers),
+        [](common_params & params, int value) {
+            params.n_gpu_layers = value;
             if (!llama_supports_gpu_offload()) {
                 fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n");
                 fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n");
@@ -2341,7 +2171,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             std::vector<std::string> split_arg{ it, {} };
             if (split_arg.size() >= llama_max_devices()) {
                 throw std::invalid_argument(
-                    string_format("got %zu input configs, but system only has %zu devices", split_arg.size(), llama_max_devices())
+                    string_format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices())
                 );
             }
             for (size_t i = 0; i < llama_max_devices(); ++i) {
@@ -2381,28 +2211,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_FIT"));
     add_opt(common_arg(
-        { "-fitt", "--fit-target" }, "MiB0,MiB1,MiB2,...",
-        string_format("target margin per device for --fit, comma-separated list of values, "
-            "single value is broadcast across all devices, default: %zu", params.fit_params_target[0]/(1024*1024)),
-        [](common_params & params, const std::string & value) {
-            std::string arg_next = value;
-
-            // split string by , and /
-            const std::regex regex{ R"([,/]+)" };
-            std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
-            std::vector<std::string> split_arg{ it, {} };
-            if (split_arg.size() >= llama_max_devices()) {
-                throw std::invalid_argument(
-                    string_format("got %zu input configs, but system only has %zu devices", split_arg.size(), llama_max_devices())
-                );
-            }
-            if (split_arg.size() == 1) {
-                std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoul(split_arg[0]) * 1024*1024);
-                return;
-            }
-            for (size_t i = 0; i < split_arg.size(); i++) {
-                params.fit_params_target[i] = std::stoul(split_arg[i]) * 1024*1024;
-            }
+        { "-fitt", "--fit-target" }, "MiB",
+        string_format("target margin per device for --fit option, default: %zu", params.fit_params_target/(1024*1024)),
+        [](common_params & params, int value) {
+            params.fit_params_target = value * size_t(1024*1024);
         }
     ).set_env("LLAMA_ARG_FIT_TARGET"));
     add_opt(common_arg(
@@ -2421,12 +2233,37 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--override-kv"}, "KEY=TYPE:VALUE,...",
-        "advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values.\n"
+        "advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.\n"
         "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : parse_csv_row(value)) {
-                if (!string_parse_kv_override(item.c_str(), params.kv_overrides)) {
-                    throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", item.c_str()));
+            std::vector<std::string> kv_overrides;
+
+            std::string current;
+            bool escaping = false;
+
+            for (const char c : value) {
+                if (escaping) {
+                    current.push_back(c);
+                    escaping = false;
+                } else if (c == '\\') {
+                    escaping = true;
+                } else if (c == ',') {
+                    kv_overrides.push_back(current);
+                    current.clear();
+                } else {
+                    current.push_back(c);
+                }
+            }
+
+            if (escaping) {
+                current.push_back('\\');
+            }
+
+            kv_overrides.push_back(current);
+
+            for (const auto & kv_override : kv_overrides) {
+                if (!string_parse_kv_override(kv_override.c_str(), params.kv_overrides)) {
+                    throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", kv_override.c_str()));
                 }
             }
         }
@@ -2443,7 +2280,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora"}, "FNAME",
         "path to LoRA adapter (use comma-separated values to load multiple adapters)",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : parse_csv_row(value)) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
                 params.lora_adapters.push_back({ item, 1.0, "", "", nullptr });
             }
         }
@@ -2454,7 +2291,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)\n"
         "note: use comma-separated values",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : parse_csv_row(value)) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
                 auto parts = string_split<std::string>(item, ':');
                 if (parts.size() != 2) {
                     throw std::invalid_argument("lora-scaled format: FNAME:SCALE");
@@ -2468,7 +2305,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--control-vector"}, "FNAME",
         "add a control vector\nnote: use comma-separated values to add multiple control vectors",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : parse_csv_row(value)) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
                 params.control_vectors.push_back({ 1.0f, item, });
             }
         }
@@ -2478,7 +2315,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "add a control vector with user defined scaling SCALE\n"
         "note: use comma-separated values (format: FNAME:SCALE,...)",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : parse_csv_row(value)) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
                 auto parts = string_split<std::string>(item, ':');
                 if (parts.size() != 2) {
                     throw std::invalid_argument("control-vector-scaled format: FNAME:SCALE");
@@ -2576,7 +2413,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--context-file"}, "FNAME",
         "file to load context from (use comma-separated values to specify multiple files)",
         [](common_params & params, const std::string & value) {
-            for (const auto & item : parse_csv_row(value)) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
                 std::ifstream file(item, std::ios::binary);
                 if (!file) {
                     throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
@@ -2723,7 +2560,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.embd_normalize = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_DEBUG}));
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
     add_opt(common_arg(
         {"--embd-output-format"}, "FORMAT",
         "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix, \"raw\" = plain whitespace-delimited output (one embedding per line)",
@@ -2801,9 +2638,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.embedding = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}).set_env("LLAMA_ARG_EMBEDDINGS"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
     add_opt(common_arg(
-        {"--rerank", "--reranking"},
+        {"--reranking", "--rerank"},
         string_format("enable reranking endpoint on server (default: %s)", "disabled"),
         [](common_params & params) {
             params.embedding = true;
@@ -2812,13 +2649,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING"));
     add_opt(common_arg(
         {"--api-key"}, "KEY",
-        "API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)",
+        "API key to use for authentication (default: none)",
         [](common_params & params, const std::string & value) {
-            for (const auto & key : parse_csv_row(value)) {
-                if (!key.empty()) {
-                    params.api_keys.push_back(key);
-                }
-            }
+            params.api_keys.push_back(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
     add_opt(common_arg(
@@ -2832,7 +2665,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             std::string key;
             while (std::getline(key_file, key)) {
                 if (!key.empty()) {
-                    params.api_keys.push_back(key);
+                        params.api_keys.push_back(key);
                 }
             }
             key_file.close();
@@ -2854,7 +2687,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE"));
     add_opt(common_arg(
         {"--chat-template-kwargs"}, "STRING",
-        "sets additional params for the json template parser, must be a valid json object string, e.g. '{\"key1\":\"value1\",\"key2\":\"value2\"}'",
+        string_format("sets additional params for the json template parser"),
         [](common_params & params, const std::string & value) {
             auto parsed = json::parse(value);
             for (const auto & item : parsed.items()) {
@@ -2877,18 +2710,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_threads_http = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
-    add_opt(common_arg(
-        {"--cache-prompt"},
-        {"--no-cache-prompt"},
-        string_format("whether to enable prompt caching (default: %s)", params.cache_prompt ? "enabled" : "disabled"),
-        [](common_params & params, bool value) {
-            params.cache_prompt = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_PROMPT"));
     add_opt(common_arg(
         {"--cache-reuse"}, "N",
         string_format(
-            "min chunk size to attempt reusing from the cache via KV shifting, requires prompt caching to be enabled (default: %d)\n"
+            "min chunk size to attempt reusing from the cache via KV shifting (default: %d)\n"
             "[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse
         ),
         [](common_params & params, int value) {
@@ -3050,16 +2875,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.lora_init_without_apply = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(common_arg(
-        {"--sleep-idle-seconds"}, "SECONDS",
-        string_format("number of seconds of idleness after which the server will sleep (default: %d; -1 = disabled)", params.sleep_idle_seconds),
-        [](common_params & params, int value) {
-            if (value == 0 || value < -1) {
-                throw std::invalid_argument("invalid value: cannot be 0 or less than -1");
-            }
-            params.sleep_idle_seconds = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--simple-io"},
         "use basic IO for better compatibility in subprocesses and limited consoles",
@@ -3296,7 +3111,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
     add_opt(common_arg(
-        {"--draft", "--draft-n", "--draft-max"}, "N",
+        {"--draft-max", "--draft", "--draft-n"}, "N",
         string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max),
         [](common_params & params, int value) {
             params.speculative.n_max = value;
@@ -3338,19 +3153,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.devices = parse_device_list(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
-    GGML_ASSERT(params.speculative.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
     add_opt(common_arg(
         {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
-        string_format("max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)",
-            params.speculative.n_gpu_layers == -1 ? "auto" : "all"),
-        [](common_params & params, const std::string & value) {
-            if (value == "auto") {
-                params.speculative.n_gpu_layers = -1;
-            } else if (value == "all") {
-                params.speculative.n_gpu_layers = -2;
-            } else {
-                params.speculative.n_gpu_layers = std::stoi(value);
-            }
+        "number of layers to store in VRAM for the draft model",
+        [](common_params & params, int value) {
+            params.speculative.n_gpu_layers = value;
             if (!llama_supports_gpu_offload()) {
                 fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n");
                 fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n");
@@ -3500,27 +3307,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({ LLAMA_EXAMPLE_FINETUNE }));
-    add_opt(common_arg(
-        {"--save-logits"},
-        string_format("save final logits to files for verification (default: %s)", params.save_logits ? "true" : "false"),
-        [](common_params & params) {
-            params.save_logits = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_DEBUG}));
-    add_opt(common_arg(
-        {"--logits-output-dir"}, "PATH",
-        string_format("directory for saving logits output files (default: %s)", params.logits_output_dir.c_str()),
-        [](common_params & params, const std::string & value) {
-            params.logits_output_dir = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_DEBUG}));
-    add_opt(common_arg(
-        {"--tensor-filter"}, "REGEX",
-        "filter tensor names for debug output (regex pattern, can be specified multiple times)",
-        [](common_params & params, const std::string & value) {
-            params.tensor_filter.push_back(value);
-        }
-    ).set_examples({LLAMA_EXAMPLE_DEBUG}));
 
     // presets
     add_opt(common_arg(
@@ -3701,24 +3487,3 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
 
     return ctx_arg;
 }
-
-void common_params_add_preset_options(std::vector<common_arg> & args) {
-    // arguments below won't be treated as CLI args, only preset options
-    args.push_back(common_arg(
-        {"load-on-startup"}, "NAME",
-        "in server router mode, autoload this model on startup",
-        [](common_params &, const std::string &) { /* unused */ }
-    ).set_env(COMMON_ARG_PRESET_LOAD_ON_STARTUP).set_preset_only());
-
-    args.push_back(common_arg(
-        {"stop-timeout"}, "SECONDS",
-        "in server router mode, force-kill model instance after this many seconds of graceful shutdown",
-        [](common_params &, int) { /* unused */ }
-    ).set_env(COMMON_ARG_PRESET_STOP_TIMEOUT).set_preset_only());
-
-    // args.push_back(common_arg(
-    //     {"pin"},
-    //     "in server router mode, do not unload this model if models_max is exceeded",
-    //     [](common_params &) { /* unused */ }
-    // ).set_preset_only());
-}

common/arg.h

@@ -8,10 +8,6 @@
 #include <vector>
 #include <cstring>
 
-// pseudo-env variable to identify preset-only arguments
-#define COMMON_ARG_PRESET_LOAD_ON_STARTUP "__PRESET_LOAD_ON_STARTUP"
-#define COMMON_ARG_PRESET_STOP_TIMEOUT    "__PRESET_STOP_TIMEOUT"
-
 //
 // CLI argument parsing
 //
@@ -26,7 +22,6 @@ struct common_arg {
     const char * env          = nullptr;
     std::string help;
     bool is_sparam = false; // is current arg a sampling param?
-    bool is_preset_only = false; // is current arg preset-only (not treated as CLI arg)
     void (*handler_void)   (common_params & params) = nullptr;
     void (*handler_string) (common_params & params, const std::string &) = nullptr;
     void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr;
@@ -75,7 +70,6 @@ struct common_arg {
     common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
     common_arg & set_env(const char * env);
     common_arg & set_sparam();
-    common_arg & set_preset_only();
     bool in_example(enum llama_example ex);
     bool is_exclude(enum llama_example ex);
     bool get_value_from_env(std::string & output) const;
@@ -120,12 +114,16 @@ struct common_params_context {
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
 
 // parse input arguments from CLI into a map
+// TODO: support repeated args in the future
 bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
 
-// populate preset-only arguments
-// these arguments are not treated as command line arguments
-// see: https://github.com/ggml-org/llama.cpp/issues/18163
-void common_params_add_preset_options(std::vector<common_arg> & args);
-
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
+
+struct common_remote_params {
+    std::vector<std::string> headers;
+    long timeout = 0; // CURLOPT_TIMEOUT, in seconds ; 0 means no timeout
+    long max_size = 0; // max size of the response ; unlimited if 0 ; max is 2GB
+};
+// get remote file content, returns <http_code, raw_response_body>
+std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params);

common/chat-parser.cpp

@@ -1395,126 +1395,6 @@ static void common_chat_parse_seed_oss(common_chat_msg_parser & builder) {
     builder.consume_reasoning_with_xml_tool_calls(form, "<seed:think>", "</seed:think>");
 }
 
-static void common_chat_parse_solar_open(common_chat_msg_parser & builder) {
-    builder.try_parse_reasoning("<|think|>", "<|end|><|begin|>assistant<|content|>");
-
-    // TODO: Tool calling
-
-    builder.add_content(builder.consume_rest());
-}
-
-static void common_chat_parse_exaone_moe_content(common_chat_msg_parser & builder) {
-    // 1) <tool_call>{ "name": "...", "arguments": {...} }</tool_call>
-    // 2) <tool_call>{ "id": "...", "type": "function", "function": { "name": "...", "arguments": {...} } }</tool_call>
-    static const common_regex tool_call_open(R"(<tool_call[^>]*>)");
-
-    if (!builder.syntax().parse_tool_calls) {
-        LOG_DBG("%s: not parse_tool_calls\n", __func__);
-        builder.add_content(builder.consume_rest());
-        return;
-    }
-
-    LOG_DBG("%s: parse_tool_calls\n", __func__);
-
-    // Find all <tool_call></tool_call> blocks
-    while (auto first = builder.try_find_regex(tool_call_open, std::string::npos, /* add_prelude_to_content= */ true)) {
-        builder.move_to(first->groups[0].end);
-        builder.consume_spaces();
-
-        builder.try_consume_literal("```json");
-        builder.try_consume_literal("```");
-        builder.consume_spaces();
-
-        // Consume JSON object
-        auto data = builder.consume_json();
-
-        builder.consume_spaces();
-        builder.try_consume_literal("```");
-        builder.consume_spaces();
-
-        if (!builder.try_consume_literal("</tool_call>")) {
-            throw common_chat_msg_partial_exception("incomplete tool call");
-        }
-        builder.consume_spaces();
-
-        // Extract name and arguments
-        std::string name;
-        std::string id;
-        nlohmann::ordered_json arguments;
-
-        const auto extract_args = [&](const nlohmann::ordered_json & obj) -> bool {
-            if (!obj.contains("name") || !obj.contains("arguments")) {
-                return false;
-            }
-            name = obj.at("name").get<std::string>();
-            arguments = obj.at("arguments");
-            if (obj.contains("id") && obj.at("id").is_string()) {
-                id = obj.at("id").get<std::string>();
-            }
-            return true;
-        };
-
-        if (!extract_args(data.json)) {
-            if (data.json.contains("function") && data.json.at("function").is_object()) {
-                auto fn = data.json.at("function");
-                extract_args(fn);
-                if (id.empty() && data.json.contains("id") && data.json.at("id").is_string()) {
-                    id = data.json.at("id").get<std::string>();
-                }
-            }
-        }
-
-        // If name is empty, treat the JSON object as content
-        if (name.empty()) {
-            LOG_DBG("%s: tool call missing name, treating as content\n", __func__);
-            builder.add_content(data.json.dump());
-            continue;
-        }
-
-        std::string args_str = arguments.dump();
-        if (!builder.add_tool_call(name, id, args_str)) {
-            throw common_chat_msg_partial_exception("incomplete tool call");
-        }
-    }
-
-    builder.add_content(builder.consume_rest());
-}
-
-static void common_chat_parse_exaone_moe(common_chat_msg_parser & builder) {
-    LOG_DBG("%s: parsing exaone_moe\n", __func__);
-    // EXAONE MoE outputs reasoning content between "<think>" and "</think>" tags, followed by regular content
-    // First try to parse using the standard reasoning parsing method
-    LOG_DBG("%s: thinking_forced_open: %s\n", __func__, std::to_string(builder.syntax().thinking_forced_open).c_str());
-
-    auto start_pos = builder.pos();
-    auto found_end_think = builder.try_find_literal("</think>");
-    builder.move_to(start_pos);
-
-    if (builder.syntax().thinking_forced_open && !builder.is_partial() && !found_end_think) {
-        LOG_DBG("%s: no end_think, not partial, adding content\n", __func__);
-        common_chat_parse_exaone_moe_content(builder);
-    } else if (builder.try_parse_reasoning("<think>", "</think>")) {
-        // If reasoning was parsed successfully, the remaining content is regular content
-        LOG_DBG("%s: parsed reasoning, adding content\n", __func__);
-        common_chat_parse_exaone_moe_content(builder);
-    } else {
-        if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE) {
-          LOG_DBG("%s: reasoning_format none, adding content\n", __func__);
-          common_chat_parse_exaone_moe_content(builder);
-          return;
-        }
-        // If no reasoning tags found, check if we should treat everything as reasoning
-        if (builder.syntax().thinking_forced_open) {
-            // If thinking is forced open but no tags found, treat everything as reasoning
-            LOG_DBG("%s: thinking_forced_open, adding reasoning content\n", __func__);
-            builder.add_reasoning_content(builder.consume_rest());
-        } else {
-            LOG_DBG("%s: no thinking_forced_open, adding content\n", __func__);
-            common_chat_parse_exaone_moe_content(builder);
-        }
-    }
-}
-
 static void common_chat_parse_content_only(common_chat_msg_parser & builder) {
     builder.try_parse_reasoning("<think>", "</think>");
     builder.add_content(builder.consume_rest());
@@ -1599,12 +1479,6 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_XIAOMI_MIMO:
             common_chat_parse_xiaomi_mimo(builder);
             break;
-        case COMMON_CHAT_FORMAT_SOLAR_OPEN:
-            common_chat_parse_solar_open(builder);
-            break;
-        case COMMON_CHAT_FORMAT_EXAONE_MOE:
-            common_chat_parse_exaone_moe(builder);
-            break;
         default:
             throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
     }

common/chat.cpp

@@ -319,7 +319,7 @@ json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msg
                 }
             }
         } else {
-            jmsg["content"] = "";
+            jmsg["content"] = json(); // null
         }
         if (!msg.reasoning_content.empty()) {
             jmsg["reasoning_content"] = msg.reasoning_content;
@@ -380,8 +380,8 @@ std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & too
                 const auto & function = tool.at("function");
                 result.push_back({
                     /* .name = */ function.at("name"),
-                    /* .description = */ function.value("description", ""),
-                    /* .parameters = */ function.value("parameters", json::object()).dump(),
+                    /* .description = */ function.at("description"),
+                    /* .parameters = */ function.at("parameters").dump(),
                 });
             }
         }
@@ -669,8 +669,6 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_QWEN3_CODER_XML: return "Qwen3 Coder";
         case COMMON_CHAT_FORMAT_APRIEL_1_5: return "Apriel 1.5";
         case COMMON_CHAT_FORMAT_XIAOMI_MIMO: return "Xiaomi MiMo";
-        case COMMON_CHAT_FORMAT_SOLAR_OPEN: return "Solar Open";
-        case COMMON_CHAT_FORMAT_EXAONE_MOE: return "EXAONE MoE";
         case COMMON_CHAT_FORMAT_PEG_SIMPLE: return "peg-simple";
         case COMMON_CHAT_FORMAT_PEG_NATIVE: return "peg-native";
         case COMMON_CHAT_FORMAT_PEG_CONSTRUCTED: return "peg-constructed";
@@ -2066,7 +2064,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
             // Trigger on tool calls that appear in the commentary channel
             data.grammar_triggers.push_back({
                 COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
-                "<\\|channel\\|>(?:commentary|analysis) to"
+                "<\\|channel\\|>(commentary|analysis) to"
             });
 
             // Trigger tool calls that appear in the role section, either at the
@@ -2399,17 +2397,17 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
                 (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call));
             // Trigger on some common known "good bad" outputs (only from the start and with a json that's about a specific argument name to avoid false positives)
             data.grammar_triggers.push_back({
-                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
+                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
                 // If thinking_forced_open, then we capture the </think> tag in the grammar,
                 // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar)
-                std::string(data.thinking_forced_open ? "(</think>\\s*)" : "") + (
+                std::string(data.thinking_forced_open ? "[\\s\\S]*?(</think>\\s*)" : "(?:<think>[\\s\\S]*?</think>\\s*)?") + (
                     "\\s*("
                     "(?:<tool_call>"
                     "|<function"
                     "|(?:```(?:json|xml)?\n\\s*)?(?:<function_call>|<tools>|<xml><json>|<response>)?"
                     "\\s*\\{\\s*\"name\"\\s*:\\s*\"(?:" + string_join(escaped_names, "|") + ")\""
                     ")"
-                    ")"
+                    ")[\\s\\S]*"
                 ),
             });
             data.preserved_tokens = {
@@ -2519,86 +2517,6 @@ static common_chat_params common_chat_params_init_granite(const common_chat_temp
     return data;
 }
 
-static common_chat_params common_chat_params_init_solar_open(const common_chat_template & tmpl, const struct templates_params & inputs) {
-    common_chat_params data;
-
-    // TODO: Reasoning effort
-    json additional_context = {};
-
-    data.prompt = apply(tmpl, inputs, std::nullopt, std::nullopt, additional_context);
-    data.format = COMMON_CHAT_FORMAT_SOLAR_OPEN;
-
-    data.preserved_tokens = {
-        "<|think|>",
-        "<|content|>",
-        "<|begin|>",
-        "<|end|>",
-    };
-
-    // TODO: Tool calling
-
-    return data;
-}
-
-static common_chat_params common_chat_params_init_exaone_moe(const common_chat_template & tmpl, const struct templates_params & inputs) {
-    common_chat_params data;
-
-    data.prompt = apply(tmpl, inputs);
-    data.format = COMMON_CHAT_FORMAT_EXAONE_MOE;
-    if (string_ends_with(data.prompt, "<think>\n")) {
-        if (!inputs.enable_thinking) {
-            data.prompt += "</think>\n\n";
-        } else {
-            data.thinking_forced_open = true;
-        }
-    }
-
-    if (inputs.tools.is_array() && !inputs.tools.empty()) {
-        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED && inputs.json_schema.is_null();
-        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-            std::vector<std::string> tool_rules;
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                std::string name = function.at("name");
-                auto parameters = function.at("parameters");
-                builder.resolve_refs(parameters);
-                // Expect: <tool_call>{"name": "<name>", "arguments": {...}}</tool_call>
-                tool_rules.push_back(builder.add_rule(
-                    name + "-call",
-                    "\"<tool_call>\" space " +
-                        builder.add_schema(name + "-obj", json{
-                            {"type", "object"},
-                            {"properties", {
-                                {"name",      json{{"const", name}}},
-                                {"arguments", parameters},
-                            }},
-                            {"required", json::array({"name", "arguments"})},
-                        }) +
-                    " space \"</tool_call>\" space"));
-            });
-
-            auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | "));
-            builder.add_rule("root",
-                std::string(data.thinking_forced_open ? "( \"</think>\" space )? " : "") +
-                (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call));
-
-            data.grammar_triggers.push_back({
-                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
-                std::string(data.thinking_forced_open ? "[\\s\\S]*?(</think>\\s*)?" : "") +
-                    "(<tool_call>)[\\s\\S]*"
-            });
-            data.preserved_tokens = {
-                "<think>",
-                "</think>",
-                "<tool_call>",
-                "</tool_call>",
-            };
-        });
-    }
-
-    return data;
-}
-
 static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
     data.prompt = apply(tmpl, inputs);
@@ -2769,13 +2687,6 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_xiaomi_mimo(tmpl, params);
     }
 
-    // EXAONE MoE format detection
-    if (src.find("<tool_call>") != std::string::npos &&
-        src.find("<tool_result>") != std::string::npos &&
-        src.find("<|tool_declare|>") != std::string::npos) {
-        return common_chat_params_init_exaone_moe(tmpl, params);
-    }
-
     // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
     if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
         return common_chat_params_init_hermes_2_pro(tmpl, params);
@@ -2869,13 +2780,6 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_magistral(tmpl, params);
     }
 
-    // Solar Open
-    if (src.find("<|tool_response:begin|>") != std::string::npos &&
-        src.find("<|tool_response:name|>") != std::string::npos &&
-        src.find("<|tool_response:result|>") != std::string::npos) {
-        return common_chat_params_init_solar_open(tmpl, params);
-    }
-
     // Plain handler (no tools)
     if (params.tools.is_null() || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
         return common_chat_params_init_without_tools(tmpl, params);

common/chat.h

@@ -124,8 +124,6 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_QWEN3_CODER_XML,
     COMMON_CHAT_FORMAT_APRIEL_1_5,
     COMMON_CHAT_FORMAT_XIAOMI_MIMO,
-    COMMON_CHAT_FORMAT_SOLAR_OPEN,
-    COMMON_CHAT_FORMAT_EXAONE_MOE,
 
     // These are intended to be parsed by the PEG parser
     COMMON_CHAT_FORMAT_PEG_SIMPLE,

common/common.cpp

@@ -251,7 +251,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {
         case GGML_SCHED_PRIO_REALTIME: p = -20; break;
     }
 
-    if (setpriority(PRIO_PROCESS, 0, p) != 0) {
+    if (!setpriority(PRIO_PROCESS, 0, p)) {
         LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
         return false;
     }
@@ -1078,15 +1078,12 @@ struct common_init_result::impl {
     impl() = default;
     ~impl() = default;
 
-    // note: the order in which model, context, etc. are declared matters because their destructors will be called bottom-to-top
-
     llama_model_ptr   model;
     llama_context_ptr context;
 
     std::vector<llama_adapter_lora_ptr> lora;
 
     std::vector<common_sampler_ptr> samplers;
-    std::vector<llama_sampler_seq_config> samplers_seq_config;
 };
 
 common_init_result::common_init_result(common_params & params) :
@@ -1097,7 +1094,7 @@ common_init_result::common_init_result(common_params & params) :
     if (params.fit_params) {
         LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
         llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
-            params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target.data(), params.fit_params_min_ctx,
+            params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
             params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
     }
 
@@ -1110,25 +1107,6 @@ common_init_result::common_init_result(common_params & params) :
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
-    // load and optionally apply lora adapters (must be loaded before context creation)
-    for (auto & la : params.lora_adapters) {
-        llama_adapter_lora_ptr lora;
-        lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
-        if (lora == nullptr) {
-            LOG_ERR("%s: failed to load lora adapter '%s'\n", __func__, la.path.c_str());
-            pimpl->model.reset(model);
-            return;
-        }
-
-        char buf[1024];
-        la.ptr = lora.get();
-        llama_adapter_meta_val_str(la.ptr, "adapter.lora.task_name", buf, sizeof(buf));
-        la.task_name = buf;
-        llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
-        la.prompt_prefix = buf;
-        pimpl->lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
-    }
-
     // updates params.sampling
     // TODO: fix naming
     common_init_sampler_from_model(model, params.sampling);
@@ -1163,19 +1141,10 @@ common_init_result::common_init_result(common_params & params) :
     //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
     //}
 
-    // init the backend samplers as part of the context creation
     pimpl->samplers.resize(cparams.n_seq_max);
-    pimpl->samplers_seq_config.resize(cparams.n_seq_max);
 
     for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
         pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
-        pimpl->samplers_seq_config[i] = { i, common_sampler_get(pimpl->samplers[i].get()) };
-    }
-
-    // TODO: temporarily gated behind a flag
-    if (params.sampling.backend_sampling) {
-        cparams.samplers   = pimpl->samplers_seq_config.data();
-        cparams.n_samplers = pimpl->samplers_seq_config.size();
     }
 
     llama_context * lctx = llama_init_from_model(model, cparams);
@@ -1199,12 +1168,6 @@ common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
     return pimpl->samplers[seq_id].get();
 }
 
-void common_init_result::reset_samplers() {
-    for (int i = 0; i < (int) pimpl->samplers.size(); ++i) {
-        llama_sampler_reset(common_sampler_get(pimpl->samplers[i].get()));
-    }
-}
-
 std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
     return pimpl->lora;
 }
@@ -1280,6 +1243,24 @@ common_init_result_ptr common_init_from_params(common_params & params) {
         }
     }
 
+    // load and optionally apply lora adapters
+    for (auto & la : params.lora_adapters) {
+        llama_adapter_lora_ptr lora;
+        lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
+        if (lora == nullptr) {
+            LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
+            return res;
+        }
+
+        char buf[1024];
+        la.ptr = lora.get();
+        llama_adapter_meta_val_str(la.ptr, "adapter.lora.task_name", buf, sizeof(buf));
+        la.task_name = buf;
+        llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
+        la.prompt_prefix = buf;
+        res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters
+    }
+
     if (!params.lora_init_without_apply) {
         common_set_adapter_lora(lctx, params.lora_adapters);
     }
@@ -1320,9 +1301,6 @@ common_init_result_ptr common_init_from_params(common_params & params) {
         llama_synchronize(lctx);
         llama_perf_context_reset(lctx);
         llama_set_warmup(lctx, false);
-
-        // reset samplers to reset RNG state after warmup to the seeded state
-        res->reset_samplers();
     }
 
     return res;
@@ -1361,12 +1339,14 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
         mparams.devices = params.devices.data();
     }
 
-    mparams.n_gpu_layers    = params.n_gpu_layers;
+    if (params.n_gpu_layers != -1) {
+        mparams.n_gpu_layers = params.n_gpu_layers;
+    }
+
     mparams.main_gpu        = params.main_gpu;
     mparams.split_mode      = params.split_mode;
     mparams.tensor_split    = params.tensor_split;
     mparams.use_mmap        = params.use_mmap;
-    mparams.use_direct_io   = params.use_direct_io;
     mparams.use_mlock       = params.use_mlock;
     mparams.check_tensors   = params.check_tensors;
     mparams.use_extra_bufts = !params.no_extra_bufts;

common/common.h

@@ -80,8 +80,6 @@ int32_t cpu_get_num_math();
 //
 
 enum llama_example {
-    LLAMA_EXAMPLE_BATCHED,
-    LLAMA_EXAMPLE_DEBUG,
     LLAMA_EXAMPLE_COMMON,
     LLAMA_EXAMPLE_SPECULATIVE,
     LLAMA_EXAMPLE_COMPLETION,
@@ -218,8 +216,6 @@ struct common_params_sampling {
     std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
     std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
 
-    bool backend_sampling = false;
-
     bool has_logit_bias() const {
         return !logit_bias.empty();
     }
@@ -333,14 +329,12 @@ struct common_params {
     // 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 is auto, <= -2 is all
-    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
-    bool    fit_params         = true; // whether to fit unset model/context parameters to free device memory
-    int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use
-
-    // margin per device in bytes for fitting parameters to free memory:
-    std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);
+    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
+    bool    fit_params         = true;             // whether to fit unset model/context parameters to free device memory
+    size_t  fit_params_target  = 1024 * 1024*1024; // margin per device in bytes for fitting parameters to free memory
+    int32_t fit_params_min_ctx = 4096;             // minimum context size to set when trying to reduce memory use
 
     enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
@@ -376,11 +370,6 @@ struct common_params {
     std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
     std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
 
-    // llama-debug specific options
-    std::string logits_output_dir = "data"; // directory for saving logits output files                     // NOLINT
-    bool        save_logits       = false;  // whether to save logits to files                              // NOLINT
-    std::vector<std::string> tensor_filter; // filter tensor names for debug output (regex)                 // NOLINT
-
     std::vector<std::string> in_files;   // all input files
     std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
     std::vector<llama_model_kv_override> kv_overrides;
@@ -431,8 +420,7 @@ struct common_params {
     bool kv_unified        = false; // enable unified KV cache
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
-    bool use_mmap          = true;  // enable mmap to use filesystem cache
-    bool use_direct_io     = true;  // read from disk without buffering for faster model loading
+    bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
     bool verbose_prompt    = false; // print prompt tokens before generation
     bool display_prompt    = true;  // print prompt before generation
@@ -476,7 +464,6 @@ struct common_params {
     int32_t timeout_write     = timeout_read; // http write timeout in seconds
     int32_t n_threads_http    = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
     int32_t n_cache_reuse     = 0;            // min chunk size to reuse from the cache via KV shifting
-    bool    cache_prompt      = true;         // whether to enable prompt caching
     int32_t n_ctx_checkpoints = 8;            // max number of context checkpoints per slot
     int32_t cache_ram_mib     = 8192;         // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
 
@@ -488,8 +475,7 @@ struct common_params {
     bool enable_chat_template = true;
     common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
     int reasoning_budget = -1;
-    bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response
-    int sleep_idle_seconds = -1;   // if >0, server will sleep after this many seconds of idle time
+    bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
 
     std::vector<std::string> api_keys;
 
@@ -702,9 +688,7 @@ struct common_init_result {
 
     llama_model * model();
     llama_context * context();
-
     common_sampler * sampler(llama_seq_id seq_id);
-    void reset_samplers();
 
     std::vector<llama_adapter_lora_ptr> & lora();
 

common/debug.cpp

@@ -1,165 +0,0 @@
-#include "debug.h"
-
-#include "log.h"
-
-#include <cmath>
-#include <string>
-
-static std::string common_ggml_ne_string(const ggml_tensor * t) {
-    std::string str;
-    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
-        str += std::to_string(t->ne[i]);
-        if (i + 1 < GGML_MAX_DIMS) {
-            str += ", ";
-        }
-    }
-    return str;
-}
-
-static float common_ggml_get_float_value(const uint8_t * data,
-                           ggml_type       type,
-                           const size_t *  nb,
-                           size_t          i0,
-                           size_t          i1,
-                           size_t          i2,
-                           size_t          i3) {
-    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
-    float  v;
-    if (type == GGML_TYPE_F16) {
-        v = ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
-    } else if (type == GGML_TYPE_F32) {
-        v = *(const float *) &data[i];
-    } else if (type == GGML_TYPE_I64) {
-        v = (float) *(const int64_t *) &data[i];
-    } else if (type == GGML_TYPE_I32) {
-        v = (float) *(const int32_t *) &data[i];
-    } else if (type == GGML_TYPE_I16) {
-        v = (float) *(const int16_t *) &data[i];
-    } else if (type == GGML_TYPE_I8) {
-        v = (float) *(const int8_t *) &data[i];
-    } else if (type == GGML_TYPE_BF16) {
-        v = ggml_bf16_to_fp32(*(const ggml_bf16_t *) &data[i]);
-    } else {
-        GGML_ABORT("fatal error");
-    }
-    return v;
-}
-
-template <bool abort>
-void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
-    GGML_ASSERT(n > 0);
-    float sum = 0;
-    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
-        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
-            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
-                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
-                    const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
-                    sum += v;
-                }
-            }
-        }
-    }
-    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
-        LOG_ERR("                                     [\n");
-        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
-            if (i2 == n && ne[2] > 2 * n) {
-                LOG_ERR("                                      ..., \n");
-                i2 = ne[2] - n;
-            }
-            LOG_ERR("                                      [\n");
-            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
-                if (i1 == n && ne[1] > 2 * n) {
-                    LOG_ERR("                                       ..., \n");
-                    i1 = ne[1] - n;
-                }
-                LOG_ERR("                                       [");
-                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
-                    if (i0 == n && ne[0] > 2 * n) {
-                        LOG_ERR("..., ");
-                        i0 = ne[0] - n;
-                    }
-                    const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
-                    LOG_ERR("%12.4f", v);
-                    if (i0 < ne[0] - 1) {
-                        LOG_ERR(", ");
-                    }
-                }
-                LOG_ERR("],\n");
-            }
-            LOG_ERR("                                      ],\n");
-        }
-        LOG_ERR("                                     ]\n");
-        LOG_ERR("                                     sum = %f\n", sum);
-    }
-
-    if constexpr (abort) {
-        if (std::isnan(sum)) {
-            LOG_ERR("encountered NaN - aborting\n");
-            exit(0);
-        }
-    }
-}
-
-/**
- * GGML operations callback during the graph execution.
- *
- * @param t current tensor
- * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
- *            if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
- *            see ggml_backend_sched_eval_callback
- * @param user_data user data to pass at each call back
- * @return true to receive data or continue the graph, false otherwise
- */
-template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
-    auto * cb_data = (base_callback_data *) user_data;
-
-    const struct ggml_tensor * src0 = t->src[0];
-    const struct ggml_tensor * src1 = t->src[1];
-
-    if (ask) {
-        return true;  // Always retrieve data
-    }
-
-    bool matches_filter = cb_data->tensor_filters.empty();
-
-    if (!matches_filter) {
-        for (const auto & filter : cb_data->tensor_filters) {
-            if (std::regex_search(t->name, filter)) {
-                matches_filter = true;
-                break;
-            }
-        }
-    }
-
-    char src1_str[128] = { 0 };
-    if (src1) {
-        snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, common_ggml_ne_string(src1).c_str());
-    }
-
-    if (matches_filter) {
-        LOG_ERR("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, t->name, ggml_type_name(t->type),
-                ggml_op_desc(t), src0->name, common_ggml_ne_string(src0).c_str(), src1 ? src1_str : "",
-                common_ggml_ne_string(t).c_str());
-    }
-
-    const bool is_host = ggml_backend_buffer_is_host(t->buffer);
-
-    if (!is_host) {
-        auto n_bytes = ggml_nbytes(t);
-        cb_data->data.resize(n_bytes);
-        ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
-    }
-
-    if (!ggml_is_quantized(t->type) && matches_filter) {
-        uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
-        common_debug_print_tensor<abort_on_nan>(data, t->type, t->ne, t->nb, 3);
-    }
-
-    return true;
-}
-
-// Explicit template instantiations
-template bool common_debug_cb_eval<false>(ggml_tensor *, bool, void *);
-template bool common_debug_cb_eval<true>(ggml_tensor *, bool, void *);
-template void common_debug_print_tensor<false>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);
-template void common_debug_print_tensor<true>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);

common/debug.h

@@ -1,43 +0,0 @@
-#pragma once
-#include "common.h"
-#include <string>
-#include <vector>
-#include <regex>
-
-// common debug functions and structs
-
-// Print a tensor's detailed data
-// data - the tensor's data in byte format
-// type - the tensor's quantization type
-// ne   - the tensor dimensions array
-// nb   - the tensor strides array
-// n    - the number of rows/columns to fully print
-template <bool abort_on_nan> void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n);
-
-// Intended to use as callback for ggml_backend_sched_eval_callback
-// prints tensors that are processed in the computation graph
-// by default prints all tensors, but can be configured by creating a `base_callback_data` instance with
-// non-empty filter_patterns. See examples/debug.ccp for possible usage patterns
-// The template parameter determins whether an error should be thrown whenever a NaN is encountered
-// in a tensor (useful for stopping debug sessions on first erroneous tensor)
-// The callback data will be passed as the third parameter (user_data)
-template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data);
-struct base_callback_data {
-    std::vector<uint8_t>    data;
-    std::vector<std::regex> tensor_filters;
-
-    base_callback_data() = default;
-
-    base_callback_data(common_params & params, const std::vector<std::string> & filter_patterns) {
-        for (const auto & pattern : filter_patterns) {
-            try {
-                std::string anchored_pattern = "^" + pattern;
-                tensor_filters.emplace_back(anchored_pattern, std::regex::optimize);
-            } catch (const std::regex_error & e) {
-                throw std::runtime_error("Invalid regex pattern '" + pattern + "': " + e.what());
-            }
-        }
-        params.cb_eval           = common_debug_cb_eval<false>;
-        params.cb_eval_user_data = this;
-    }
-};

common/download.cpp

@@ -19,7 +19,10 @@
 #include <thread>
 #include <vector>
 
-#if defined(LLAMA_USE_HTTPLIB)
+#if defined(LLAMA_USE_CURL)
+#include <curl/curl.h>
+#include <curl/easy.h>
+#elif defined(LLAMA_USE_HTTPLIB)
 #include "http.h"
 #endif
 
@@ -154,21 +157,322 @@ static std::string read_etag(const std::string & path) {
     return none;
 }
 
-static bool is_http_status_ok(int status) {
-    return status >= 200 && status < 400;
-}
+#ifdef LLAMA_USE_CURL
 
-std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag) {
-    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
-    std::string tag = parts.size() > 1 ? parts.back() : "latest";
-    std::string hf_repo = parts[0];
-    if (string_split<std::string>(hf_repo, '/').size() != 2) {
-        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
+//
+// CURL utils
+//
+
+using curl_ptr = std::unique_ptr<CURL, decltype(&curl_easy_cleanup)>;
+
+// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one
+struct curl_slist_ptr {
+    struct curl_slist * ptr = nullptr;
+    ~curl_slist_ptr() {
+        if (ptr) {
+            curl_slist_free_all(ptr);
+        }
     }
-    return {hf_repo, tag};
+};
+
+static CURLcode common_curl_perf(CURL * curl) {
+    CURLcode res = curl_easy_perform(curl);
+    if (res != CURLE_OK) {
+        LOG_ERR("%s: curl_easy_perform() failed\n", __func__);
+    }
+
+    return res;
 }
 
-#if defined(LLAMA_USE_HTTPLIB)
+// Send a HEAD request to retrieve the etag and last-modified headers
+struct common_load_model_from_url_headers {
+    std::string etag;
+    std::string last_modified;
+    std::string accept_ranges;
+};
+
+struct FILE_deleter {
+    void operator()(FILE * f) const { fclose(f); }
+};
+
+static size_t common_header_callback(char * buffer, size_t, size_t n_items, void * 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);
+    static std::regex                    last_modified_regex("Last-Modified", std::regex_constants::icase);
+    static std::regex                    accept_ranges_regex("Accept-Ranges", std::regex_constants::icase);
+    std::string                          header(buffer, n_items);
+    std::smatch                          match;
+    if (std::regex_match(header, match, header_regex)) {
+        const std::string & key   = match[1];
+        const std::string & value = match[2];
+        if (std::regex_match(key, match, etag_regex)) {
+            headers->etag = value;
+        } else if (std::regex_match(key, match, last_modified_regex)) {
+            headers->last_modified = value;
+        } else if (std::regex_match(key, match, accept_ranges_regex)) {
+            headers->accept_ranges = value;
+        }
+    }
+
+    return n_items;
+}
+
+static size_t common_write_callback(void * data, size_t size, size_t nmemb, void * fd) {
+    return std::fwrite(data, size, nmemb, static_cast<FILE *>(fd));
+}
+
+// helper function to hide password in URL
+static std::string llama_download_hide_password_in_url(const std::string & url) {
+    // Use regex to match and replace the user[:password]@ pattern in URLs
+    // Pattern: scheme://[user[:password]@]host[...]
+    static const std::regex url_regex(R"(^(?:[A-Za-z][A-Za-z0-9+.-]://)(?:[^/@]+@)?.$)");
+    std::smatch             match;
+
+    if (std::regex_match(url, match, url_regex)) {
+        // match[1] = scheme (e.g., "https://")
+        // match[2] = user[:password]@ part
+        // match[3] = rest of URL (host and path)
+        return match[1].str() + "********@" + match[3].str();
+    }
+
+    return url;  // No credentials found or malformed URL
+}
+
+static void common_curl_easy_setopt_head(CURL * curl, const std::string & url) {
+    // Set the URL, allow to follow http redirection
+    curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
+    curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
+
+#    if defined(_WIN32)
+    // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
+    //   operating system. Currently implemented under MS-Windows.
+    curl_easy_setopt(curl, CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+#    endif
+
+    curl_easy_setopt(curl, CURLOPT_NOBODY, 1L);      // will trigger the HEAD verb
+    curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 1L);  // hide head request progress
+    curl_easy_setopt(curl, CURLOPT_HEADERFUNCTION, common_header_callback);
+}
+
+static void common_curl_easy_setopt_get(CURL * curl) {
+    curl_easy_setopt(curl, CURLOPT_NOBODY, 0L);
+    curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, common_write_callback);
+
+    //  display download progress
+    curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
+}
+
+static bool common_pull_file(CURL * curl, const std::string & path_temporary) {
+    if (std::filesystem::exists(path_temporary)) {
+        const std::string partial_size = std::to_string(std::filesystem::file_size(path_temporary));
+        LOG_INF("%s: server supports range requests, resuming download from byte %s\n", __func__, partial_size.c_str());
+        const std::string range_str = partial_size + "-";
+        curl_easy_setopt(curl, CURLOPT_RANGE, range_str.c_str());
+    }
+
+    // Always open file in append mode could be resuming
+    std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "ab"));
+    if (!outfile) {
+        LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path_temporary.c_str());
+        return false;
+    }
+
+    common_curl_easy_setopt_get(curl);
+    curl_easy_setopt(curl, CURLOPT_WRITEDATA, outfile.get());
+
+    return common_curl_perf(curl) == CURLE_OK;
+}
+
+static bool common_download_head(CURL *              curl,
+                                 curl_slist_ptr &    http_headers,
+                                 const std::string & url,
+                                 const std::string & bearer_token) {
+    if (!curl) {
+        LOG_ERR("%s: error initializing libcurl\n", __func__);
+        return false;
+    }
+
+    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
+    // Check if hf-token or bearer-token was specified
+    if (!bearer_token.empty()) {
+        std::string auth_header = "Authorization: Bearer " + bearer_token;
+        http_headers.ptr        = curl_slist_append(http_headers.ptr, auth_header.c_str());
+    }
+
+    curl_easy_setopt(curl, CURLOPT_HTTPHEADER, http_headers.ptr);
+    common_curl_easy_setopt_head(curl, url);
+    return common_curl_perf(curl) == CURLE_OK;
+}
+
+// download one single file from remote URL to local path
+static bool common_download_file_single_online(const std::string & url,
+                                               const std::string & path,
+                                               const std::string & bearer_token) {
+    static const int max_attempts        = 3;
+    static const int retry_delay_seconds = 2;
+    for (int i = 0; i < max_attempts; ++i) {
+        std::string etag;
+
+        // Check if the file already exists locally
+        const auto file_exists = std::filesystem::exists(path);
+        if (file_exists) {
+            etag = read_etag(path);
+        } else {
+            LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
+        }
+
+        bool head_request_ok = false;
+        bool should_download = !file_exists;  // by default, we should download if the file does not exist
+
+        // Initialize libcurl
+        curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
+        common_load_model_from_url_headers headers;
+        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
+        curl_slist_ptr http_headers;
+        const bool     was_perform_successful = common_download_head(curl.get(), http_headers, url, bearer_token);
+        if (!was_perform_successful) {
+            head_request_ok = false;
+        }
+
+        long http_code = 0;
+        curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
+        if (http_code == 200) {
+            head_request_ok = true;
+        } else {
+            LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
+            head_request_ok = false;
+        }
+
+        // if head_request_ok is false, we don't have the etag or last-modified headers
+        // we leave should_download as-is, which is true if the file does not exist
+        bool should_download_from_scratch = false;
+        if (head_request_ok) {
+            // check if ETag or Last-Modified headers are different
+            // if it is, we need to download the file again
+            if (!etag.empty() && etag != headers.etag) {
+                LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(),
+                        headers.etag.c_str());
+                should_download              = true;
+                should_download_from_scratch = true;
+            }
+        }
+
+        const bool accept_ranges_supported = !headers.accept_ranges.empty() && headers.accept_ranges != "none";
+        if (should_download) {
+            if (file_exists &&
+                !accept_ranges_supported) {  // Resumable downloads not supported, delete and start again.
+                LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
+                if (remove(path.c_str()) != 0) {
+                    LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
+                    return false;
+                }
+            }
+
+            const std::string path_temporary = path + ".downloadInProgress";
+            if (should_download_from_scratch) {
+                if (std::filesystem::exists(path_temporary)) {
+                    if (remove(path_temporary.c_str()) != 0) {
+                        LOG_ERR("%s: unable to delete file: %s\n", __func__, path_temporary.c_str());
+                        return false;
+                    }
+                }
+
+                if (std::filesystem::exists(path)) {
+                    if (remove(path.c_str()) != 0) {
+                        LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
+                        return false;
+                    }
+                }
+            }
+            if (head_request_ok) {
+                write_etag(path, headers.etag);
+            }
+
+            // start the download
+            LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n",
+                    __func__, llama_download_hide_password_in_url(url).c_str(), path_temporary.c_str(),
+                    headers.etag.c_str(), headers.last_modified.c_str());
+            const bool was_pull_successful = common_pull_file(curl.get(), path_temporary);
+            if (!was_pull_successful) {
+                if (i + 1 < max_attempts) {
+                    const int exponential_backoff_delay = std::pow(retry_delay_seconds, i) * 1000;
+                    LOG_WRN("%s: retrying after %d milliseconds...\n", __func__, exponential_backoff_delay);
+                    std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
+                } else {
+                    LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
+                }
+
+                continue;
+            }
+
+            long http_code = 0;
+            curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
+            if (http_code < 200 || http_code >= 400) {
+                LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code);
+                return false;
+            }
+
+            if (rename(path_temporary.c_str(), path.c_str()) != 0) {
+                LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
+                return false;
+            }
+        } else {
+            LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
+        }
+
+        break;
+    }
+
+    return true;
+}
+
+std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params) {
+    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
+    curl_slist_ptr http_headers;
+    std::vector<char> res_buffer;
+
+    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
+    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
+    curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
+    curl_easy_setopt(curl.get(), CURLOPT_VERBOSE, 0L);
+    typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
+    auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
+        auto data_vec = static_cast<std::vector<char> *>(data);
+        data_vec->insert(data_vec->end(), (char *)ptr, (char *)ptr + size * nmemb);
+        return size * nmemb;
+    };
+    curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
+    curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_buffer);
+#if defined(_WIN32)
+    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+#endif
+    if (params.timeout > 0) {
+        curl_easy_setopt(curl.get(), CURLOPT_TIMEOUT, params.timeout);
+    }
+    if (params.max_size > 0) {
+        curl_easy_setopt(curl.get(), CURLOPT_MAXFILESIZE, params.max_size);
+    }
+    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
+    for (const auto & header : params.headers) {
+        http_headers.ptr = curl_slist_append(http_headers.ptr, header.c_str());
+    }
+    curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
+
+    CURLcode res = curl_easy_perform(curl.get());
+
+    if (res != CURLE_OK) {
+        std::string error_msg = curl_easy_strerror(res);
+        throw std::runtime_error("error: cannot make GET request: " + error_msg);
+    }
+
+    long res_code;
+    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
+
+    return { res_code, std::move(res_buffer) };
+}
+
+#elif defined(LLAMA_USE_HTTPLIB)
 
 class ProgressBar {
     static inline std::mutex mutex;
@@ -313,11 +617,9 @@ static bool common_pull_file(httplib::Client & cli,
 }
 
 // download one single file from remote URL to local path
-// returns status code or -1 on error
-static int common_download_file_single_online(const std::string & url,
+static bool common_download_file_single_online(const std::string & url,
                                                const std::string & path,
-                                               const std::string & bearer_token,
-                                               const common_header_list & custom_headers) {
+                                               const std::string & bearer_token) {
     static const int max_attempts        = 3;
     static const int retry_delay_seconds = 2;
 
@@ -327,9 +629,6 @@ static int common_download_file_single_online(const std::string & url,
     if (!bearer_token.empty()) {
         default_headers.insert({"Authorization", "Bearer " + bearer_token});
     }
-    for (const auto & h : custom_headers) {
-        default_headers.emplace(h.first, h.second);
-    }
     cli.set_default_headers(default_headers);
 
     const bool file_exists = std::filesystem::exists(path);
@@ -348,10 +647,8 @@ static int common_download_file_single_online(const std::string & url,
             LOG_WRN("%s: HEAD invalid http status code received: %d\n", __func__, head ? head->status : -1);
             if (file_exists) {
                 LOG_INF("%s: Using cached file (HEAD failed): %s\n", __func__, path.c_str());
-                return 304; // 304 Not Modified - fake cached response
+                return true;
             }
-            return head->status; // cannot use cached file, return raw status code
-            // TODO: maybe retry only on certain codes
         }
 
         std::string etag;
@@ -383,12 +680,12 @@ static int common_download_file_single_online(const std::string & url,
         if (file_exists) {
             if (!should_download_from_scratch) {
                 LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
-                return 304; // 304 Not Modified - fake cached response
+                return true;
             }
             LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
             if (remove(path.c_str()) != 0) {
                 LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
-                return -1;
+                return false;
             }
         }
 
@@ -400,7 +697,7 @@ static int common_download_file_single_online(const std::string & url,
                 existing_size = std::filesystem::file_size(path_temporary);
             } else if (remove(path_temporary.c_str()) != 0) {
                 LOG_ERR("%s: unable to delete file: %s\n", __func__, path_temporary.c_str());
-                return -1;
+                return false;
             }
         }
 
@@ -421,16 +718,15 @@ static int common_download_file_single_online(const std::string & url,
 
         if (std::rename(path_temporary.c_str(), path.c_str()) != 0) {
             LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
-            return -1;
+            return false;
         }
         if (!etag.empty()) {
             write_etag(path, etag);
         }
-
-        return head->status; // TODO: use actual GET status?
+        break;
     }
 
-    return -1; // max attempts reached
+    return true;
 }
 
 std::pair<long, std::vector<char>> common_remote_get_content(const std::string          & url,
@@ -438,9 +734,13 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string
     auto [cli, parts] = common_http_client(url);
 
     httplib::Headers headers = {{"User-Agent", "llama-cpp"}};
-
     for (const auto & header : params.headers) {
-        headers.emplace(header.first, header.second);
+        size_t pos = header.find(':');
+        if (pos != std::string::npos) {
+            headers.emplace(header.substr(0, pos), header.substr(pos + 1));
+        } else {
+            headers.emplace(header, "");
+        }
     }
 
     if (params.timeout > 0) {
@@ -465,45 +765,36 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string
     return { res->status, std::move(buf) };
 }
 
-int common_download_file_single(const std::string & url,
-                                const std::string & path,
-                                const std::string & bearer_token,
-                                bool offline,
-                                const common_header_list & headers) {
+#endif // LLAMA_USE_CURL
+
+#if defined(LLAMA_USE_CURL) || defined(LLAMA_USE_HTTPLIB)
+
+static bool common_download_file_single(const std::string & url,
+                                        const std::string & path,
+                                        const std::string & bearer_token,
+                                        bool                offline) {
     if (!offline) {
-        return common_download_file_single_online(url, path, bearer_token, headers);
+        return common_download_file_single_online(url, path, bearer_token);
     }
 
     if (!std::filesystem::exists(path)) {
         LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
-        return -1;
+        return false;
     }
 
     LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
-    return 304; // Not Modified - fake cached response
+    return true;
 }
 
 // download multiple files from remote URLs to local paths
 // the input is a vector of pairs <url, path>
-static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls,
-                                          const std::string & bearer_token,
-                                          bool offline,
-                                          const common_header_list & headers) {
+static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token, bool offline) {
     // Prepare download in parallel
     std::vector<std::future<bool>> futures_download;
-    futures_download.reserve(urls.size());
-
     for (auto const & item : urls) {
-        futures_download.push_back(
-            std::async(
-                std::launch::async,
-                [&bearer_token, offline, &headers](const std::pair<std::string, std::string> & it) -> bool {
-                    const int http_status = common_download_file_single(it.first, it.second, bearer_token, offline, headers);
-                    return is_http_status_ok(http_status);
-                },
-                item
-            )
-        );
+        futures_download.push_back(std::async(std::launch::async, [bearer_token, offline](const std::pair<std::string, std::string> & it) -> bool {
+            return common_download_file_single(it.first, it.second, bearer_token, offline);
+        }, item));
     }
 
     // Wait for all downloads to complete
@@ -516,18 +807,17 @@ static bool common_download_file_multiple(const std::vector<std::pair<std::strin
     return true;
 }
 
-bool common_download_model(const common_params_model & model,
-                           const std::string & bearer_token,
-                           bool offline,
-                           const common_header_list & headers) {
+bool common_download_model(
+        const common_params_model & model,
+        const std::string & bearer_token,
+        bool offline) {
     // Basic validation of the model.url
     if (model.url.empty()) {
         LOG_ERR("%s: invalid model url\n", __func__);
         return false;
     }
 
-    const int http_status = common_download_file_single(model.url, model.path, bearer_token, offline, headers);
-    if (!is_http_status_ok(http_status)) {
+    if (!common_download_file_single(model.url, model.path, bearer_token, offline)) {
         return false;
     }
 
@@ -586,26 +876,27 @@ bool common_download_model(const common_params_model & model,
         }
 
         // Download in parallel
-        common_download_file_multiple(urls, bearer_token, offline, headers);
+        common_download_file_multiple(urls, bearer_token, offline);
     }
 
     return true;
 }
 
-common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag,
-                                      const std::string & bearer_token,
-                                      bool offline,
-                                      const common_header_list & custom_headers) {
-    // the returned hf_repo is without tag
-    auto [hf_repo, tag] = common_download_split_repo_tag(hf_repo_with_tag);
+common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token, bool offline) {
+    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
+    std::string tag = parts.size() > 1 ? parts.back() : "latest";
+    std::string hf_repo = parts[0];
+    if (string_split<std::string>(hf_repo, '/').size() != 2) {
+        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
+    }
 
     std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag;
 
     // headers
-    common_header_list headers = custom_headers;
-    headers.push_back({"Accept", "application/json"});
+    std::vector<std::string> headers;
+    headers.push_back("Accept: application/json");
     if (!bearer_token.empty()) {
-        headers.push_back({"Authorization", "Bearer " + bearer_token});
+        headers.push_back("Authorization: Bearer " + bearer_token);
     }
     // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
     // User-Agent header is already set in common_remote_get_content, no need to set it here
@@ -661,7 +952,7 @@ common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag,
     } else if (res_code == 401) {
         throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
     } else {
-        throw std::runtime_error(string_format("error from HF API (%s), response code: %ld, data: %s", url.c_str(), res_code, res_str.c_str()));
+        throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str()));
     }
 
     // check response
@@ -740,10 +1031,9 @@ std::string common_docker_resolve_model(const std::string & docker) {
         const std::string    url_prefix = "https://registry-1.docker.io/v2/" + repo;
         std::string          manifest_url = url_prefix + "/manifests/" + tag;
         common_remote_params manifest_params;
-        manifest_params.headers.push_back({"Authorization", "Bearer " + token});
-        manifest_params.headers.push_back({"Accept",
-            "application/vnd.docker.distribution.manifest.v2+json,application/vnd.oci.image.manifest.v1+json"
-        });
+        manifest_params.headers.push_back("Authorization: Bearer " + token);
+        manifest_params.headers.push_back(
+            "Accept: application/vnd.docker.distribution.manifest.v2+json,application/vnd.oci.image.manifest.v1+json");
         auto manifest_res = common_remote_get_content(manifest_url, manifest_params);
         if (manifest_res.first != 200) {
             throw std::runtime_error("Failed to get Docker manifest, HTTP code: " + std::to_string(manifest_res.first));
@@ -780,8 +1070,7 @@ std::string common_docker_resolve_model(const std::string & docker) {
         std::string local_path = fs_get_cache_file(model_filename);
 
         const std::string blob_url = url_prefix + "/blobs/" + gguf_digest;
-        const int http_status = common_download_file_single(blob_url, local_path, token, false, {});
-        if (!is_http_status_ok(http_status)) {
+        if (!common_download_file_single(blob_url, local_path, token, false)) {
             throw std::runtime_error("Failed to download Docker Model");
         }
 
@@ -795,11 +1084,11 @@ std::string common_docker_resolve_model(const std::string & docker) {
 
 #else
 
-common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool, const common_header_list &) {
+common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) {
     throw std::runtime_error("download functionality is not enabled in this build");
 }
 
-bool common_download_model(const common_params_model &, const std::string &, bool, const common_header_list &) {
+bool common_download_model(const common_params_model &, const std::string &, bool) {
     throw std::runtime_error("download functionality is not enabled in this build");
 }
 
@@ -807,15 +1096,7 @@ std::string common_docker_resolve_model(const std::string &) {
     throw std::runtime_error("download functionality is not enabled in this build");
 }
 
-int common_download_file_single(const std::string &,
-                                const std::string &,
-                                const std::string &,
-                                bool,
-                                const common_header_list &) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
-
-#endif // defined(LLAMA_USE_HTTPLIB)
+#endif // LLAMA_USE_CURL || LLAMA_USE_HTTPLIB
 
 std::vector<common_cached_model_info> common_list_cached_models() {
     std::vector<common_cached_model_info> models;

common/download.h

@@ -1,27 +1,12 @@
 #pragma once
 
 #include <string>
-#include <vector>
 
 struct common_params_model;
 
-using common_header      = std::pair<std::string, std::string>;
-using common_header_list = std::vector<common_header>;
-
-struct common_remote_params {
-    common_header_list headers;
-    long timeout  = 0;           // in seconds, 0 means no timeout
-    long max_size = 0;           // unlimited if 0
-};
-
-// get remote file content, returns <http_code, raw_response_body>
-std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params);
-
-// split HF repo with tag into <repo, tag>
-// for example: "user/model:tag" -> <"user/model", "tag">
-// if tag is not present, default to "latest"
-// example: "user/model" -> <"user/model", "latest">
-std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag);
+//
+// download functionalities
+//
 
 struct common_cached_model_info {
     std::string manifest_path;
@@ -56,29 +41,17 @@ struct common_hf_file_res {
 common_hf_file_res common_get_hf_file(
     const std::string & hf_repo_with_tag,
     const std::string & bearer_token,
-    bool offline,
-    const common_header_list & headers = {}
-);
+    bool offline);
 
 // returns true if download succeeded
 bool common_download_model(
     const common_params_model & model,
     const std::string & bearer_token,
-    bool offline,
-    const common_header_list & headers = {}
-);
+    bool offline);
 
 // returns list of cached models
 std::vector<common_cached_model_info> common_list_cached_models();
 
-// download single file from url to local path
-// returns status code or -1 on error
-int common_download_file_single(const std::string & url,
-                                const std::string & path,
-                                const std::string & bearer_token,
-                                bool offline,
-                                const common_header_list & headers = {});
-
 // resolve and download model from Docker registry
 // return local path to downloaded model file
 std::string common_docker_resolve_model(const std::string & docker);

common/llguidance.cpp

@@ -106,16 +106,12 @@ static void llama_sampler_llg_free(llama_sampler * smpl) {
 }
 
 static llama_sampler_i llama_sampler_llg_i = {
-    /* .name              = */ llama_sampler_llg_name,
-    /* .accept            = */ llama_sampler_llg_accept_impl,
-    /* .apply             = */ llama_sampler_llg_apply,
-    /* .reset             = */ llama_sampler_llg_reset,
-    /* .clone             = */ llama_sampler_llg_clone,
-    /* .free              = */ llama_sampler_llg_free,
-    /* .backend_init      = */ NULL,
-    /* .backend_accept    = */ NULL,
-    /* .backend_apply     = */ NULL,
-    /* .backend_set_input = */ NULL,
+    /* .name   = */ llama_sampler_llg_name,
+    /* .accept = */ llama_sampler_llg_accept_impl,
+    /* .apply  = */ llama_sampler_llg_apply,
+    /* .reset  = */ llama_sampler_llg_reset,
+    /* .clone  = */ llama_sampler_llg_clone,
+    /* .free   = */ llama_sampler_llg_free,
 };
 
 static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,

common/preset.cpp

@@ -2,7 +2,6 @@
 #include "preset.h"
 #include "peg-parser.h"
 #include "log.h"
-#include "download.h"
 
 #include <fstream>
 #include <sstream>
@@ -16,64 +15,11 @@ static std::string rm_leading_dashes(const std::string & str) {
     return str.substr(pos);
 }
 
-// only allow a subset of args for remote presets for security reasons
-// do not add more args unless absolutely necessary
-// args that output to files are strictly prohibited
-static std::set<std::string> get_remote_preset_whitelist(const std::map<std::string, common_arg> & key_to_opt) {
-    static const std::set<std::string> allowed_options = {
-        "model-url",
-        "hf-repo",
-        "hf-repo-draft",
-        "hf-repo-v", // vocoder
-        "hf-file-v", // vocoder
-        "mmproj-url",
-        "pooling",
-        "jinja",
-        "batch-size",
-        "ubatch-size",
-        "cache-reuse",
-        "chat-template-kwargs",
-        "mmap",
-        // note: sampling params are automatically allowed by default
-        // negated args will be added automatically if the positive arg is specified above
-    };
-
-    std::set<std::string> allowed_keys;
-
-    for (const auto & it : key_to_opt) {
-        const std::string & key = it.first;
-        const common_arg & opt = it.second;
-        if (allowed_options.find(key) != allowed_options.end() || opt.is_sparam) {
-            allowed_keys.insert(key);
-            // also add variant keys (args without leading dashes and env vars)
-            for (const auto & arg : opt.get_args()) {
-                allowed_keys.insert(rm_leading_dashes(arg));
-            }
-            for (const auto & env : opt.get_env()) {
-                allowed_keys.insert(env);
-            }
-        }
-    }
-
-    return allowed_keys;
-}
-
-std::vector<std::string> common_preset::to_args(const std::string & bin_path) const {
+std::vector<std::string> common_preset::to_args() const {
     std::vector<std::string> args;
 
-    if (!bin_path.empty()) {
-        args.push_back(bin_path);
-    }
-
     for (const auto & [opt, value] : options) {
-        if (opt.is_preset_only) {
-            continue; // skip preset-only options (they are not CLI args)
-        }
-
-        // use the last arg as the main arg (i.e. --long-form)
-        args.push_back(opt.args.back());
-
-        // handle value(s)
+        args.push_back(opt.args.back()); // use the last arg as the main arg
         if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) {
             // flag option, no value
             if (common_arg_utils::is_falsey(value)) {
@@ -117,75 +63,6 @@ std::string common_preset::to_ini() const {
     return ss.str();
 }
 
-void common_preset::set_option(const common_preset_context & ctx, const std::string & env, const std::string & value) {
-    // try if option exists, update it
-    for (auto & [opt, val] : options) {
-        if (opt.env && env == opt.env) {
-            val = value;
-            return;
-        }
-    }
-    // if option does not exist, we need to add it
-    if (ctx.key_to_opt.find(env) == ctx.key_to_opt.end()) {
-        throw std::runtime_error(string_format(
-            "%s: option with env '%s' not found in ctx_params",
-            __func__, env.c_str()
-        ));
-    }
-    options[ctx.key_to_opt.at(env)] = value;
-}
-
-void common_preset::unset_option(const std::string & env) {
-    for (auto it = options.begin(); it != options.end(); ) {
-        const common_arg & opt = it->first;
-        if (opt.env && env == opt.env) {
-            it = options.erase(it);
-            return;
-        } else {
-            ++it;
-        }
-    }
-}
-
-bool common_preset::get_option(const std::string & env, std::string & value) const {
-    for (const auto & [opt, val] : options) {
-        if (opt.env && env == opt.env) {
-            value = val;
-            return true;
-        }
-    }
-    return false;
-}
-
-void common_preset::merge(const common_preset & other) {
-    for (const auto & [opt, val] : other.options) {
-        options[opt] = val; // overwrite existing options
-    }
-}
-
-void common_preset::apply_to_params(common_params & params) const {
-    for (const auto & [opt, val] : options) {
-        // apply each option to params
-        if (opt.handler_string) {
-            opt.handler_string(params, val);
-        } else if (opt.handler_int) {
-            opt.handler_int(params, std::stoi(val));
-        } else if (opt.handler_bool) {
-            opt.handler_bool(params, common_arg_utils::is_truthy(val));
-        } else if (opt.handler_str_str) {
-            // not supported yet
-            throw std::runtime_error(string_format(
-                "%s: option with two values is not supported yet",
-                __func__
-            ));
-        } else if (opt.handler_void) {
-            opt.handler_void(params);
-        } else {
-            GGML_ABORT("unknown handler type");
-        }
-    }
-}
-
 static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_file(const std::string & path) {
     std::map<std::string, std::map<std::string, std::string>> parsed;
 
@@ -295,20 +172,9 @@ static std::string parse_bool_arg(const common_arg & arg, const std::string & ke
     return value;
 }
 
-common_preset_context::common_preset_context(llama_example ex, bool only_remote_allowed)
-        : ctx_params(common_params_parser_init(default_params, ex)) {
-    common_params_add_preset_options(ctx_params.options);
-    key_to_opt = get_map_key_opt(ctx_params);
-
-    // setup allowed keys if only_remote_allowed is true
-    if (only_remote_allowed) {
-        filter_allowed_keys = true;
-        allowed_keys = get_remote_preset_whitelist(key_to_opt);
-    }
-}
-
-common_presets common_preset_context::load_from_ini(const std::string & path, common_preset & global) const {
+common_presets common_presets_load(const std::string & path, common_params_context & ctx_params) {
     common_presets out;
+    auto key_to_opt = get_map_key_opt(ctx_params);
     auto ini_data = parse_ini_from_file(path);
 
     for (auto section : ini_data) {
@@ -320,20 +186,9 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
         }
         LOG_DBG("loading preset: %s\n", preset.name.c_str());
         for (const auto & [key, value] : section.second) {
-            if (key == "version") {
-                // skip version key (reserved for future use)
-                continue;
-            }
-
             LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
-            if (filter_allowed_keys && allowed_keys.find(key) == allowed_keys.end()) {
-                throw std::runtime_error(string_format(
-                    "option '%s' is not allowed in remote presets",
-                    key.c_str()
-                ));
-            }
             if (key_to_opt.find(key) != key_to_opt.end()) {
-                const auto & opt = key_to_opt.at(key);
+                auto & opt = key_to_opt[key];
                 if (is_bool_arg(opt)) {
                     preset.options[opt] = parse_bool_arg(opt, key, value);
                 } else {
@@ -341,143 +196,11 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
                 }
                 LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
             } else {
-                throw std::runtime_error(string_format(
-                    "option '%s' not recognized in preset '%s'",
-                    key.c_str(), preset.name.c_str()
-                ));
+                // TODO: maybe warn about unknown key?
             }
         }
-
-        if (preset.name == "*") {
-            // handle global preset
-            global = preset;
-        } else {
-            out[preset.name] = preset;
-        }
-    }
-
-    return out;
-}
-
-common_presets common_preset_context::load_from_cache() const {
-    common_presets out;
-
-    auto cached_models = common_list_cached_models();
-    for (const auto & model : cached_models) {
-        common_preset preset;
-        preset.name = model.to_string();
-        preset.set_option(*this, "LLAMA_ARG_HF_REPO", model.to_string());
-        out[preset.name] = preset;
-    }
-
-    return out;
-}
-
-struct local_model {
-    std::string name;
-    std::string path;
-    std::string path_mmproj;
-};
-
-common_presets common_preset_context::load_from_models_dir(const std::string & models_dir) const {
-    if (!std::filesystem::exists(models_dir) || !std::filesystem::is_directory(models_dir)) {
-        throw std::runtime_error(string_format("error: '%s' does not exist or is not a directory\n", models_dir.c_str()));
-    }
-
-    std::vector<local_model> models;
-    auto scan_subdir = [&models](const std::string & subdir_path, const std::string & name) {
-        auto files = fs_list(subdir_path, false);
-        common_file_info model_file;
-        common_file_info first_shard_file;
-        common_file_info mmproj_file;
-        for (const auto & file : files) {
-            if (string_ends_with(file.name, ".gguf")) {
-                if (file.name.find("mmproj") != std::string::npos) {
-                    mmproj_file = file;
-                } else if (file.name.find("-00001-of-") != std::string::npos) {
-                    first_shard_file = file;
-                } else {
-                    model_file = file;
-                }
-            }
-        }
-        // single file model
-        local_model model{
-            /* name        */ name,
-            /* path        */ first_shard_file.path.empty() ? model_file.path : first_shard_file.path,
-            /* path_mmproj */ mmproj_file.path // can be empty
-        };
-        if (!model.path.empty()) {
-            models.push_back(model);
-        }
-    };
-
-    auto files = fs_list(models_dir, true);
-    for (const auto & file : files) {
-        if (file.is_dir) {
-            scan_subdir(file.path, file.name);
-        } else if (string_ends_with(file.name, ".gguf")) {
-            // single file model
-            std::string name = file.name;
-            string_replace_all(name, ".gguf", "");
-            local_model model{
-                /* name        */ name,
-                /* path        */ file.path,
-                /* path_mmproj */ ""
-            };
-            models.push_back(model);
-        }
-    }
-
-    // convert local models to presets
-    common_presets out;
-    for (const auto & model : models) {
-        common_preset preset;
-        preset.name = model.name;
-        preset.set_option(*this, "LLAMA_ARG_MODEL", model.path);
-        if (!model.path_mmproj.empty()) {
-            preset.set_option(*this, "LLAMA_ARG_MMPROJ", model.path_mmproj);
-        }
         out[preset.name] = preset;
     }
 
     return out;
 }
-
-common_preset common_preset_context::load_from_args(int argc, char ** argv) const {
-    common_preset preset;
-    preset.name = COMMON_PRESET_DEFAULT_NAME;
-
-    bool ok = common_params_to_map(argc, argv, ctx_params.ex, preset.options);
-    if (!ok) {
-        throw std::runtime_error("failed to parse CLI arguments into preset");
-    }
-
-    return preset;
-}
-
-common_presets common_preset_context::cascade(const common_presets & base, const common_presets & added) const {
-    common_presets out = base; // copy
-    for (const auto & [name, preset_added] : added) {
-        if (out.find(name) != out.end()) {
-            // if exists, merge
-            common_preset & target = out[name];
-            target.merge(preset_added);
-        } else {
-            // otherwise, add directly
-            out[name] = preset_added;
-        }
-    }
-    return out;
-}
-
-common_presets common_preset_context::cascade(const common_preset & base, const common_presets & presets) const {
-    common_presets out;
-    for (const auto & [name, preset] : presets) {
-        common_preset tmp = base; // copy
-        tmp.name = name;
-        tmp.merge(preset);
-        out[name] = std::move(tmp);
-    }
-    return out;
-}

common/preset.h

@@ -6,7 +6,6 @@
 #include <string>
 #include <vector>
 #include <map>
-#include <set>
 
 //
 // INI preset parser and writer
@@ -14,70 +13,20 @@
 
 constexpr const char * COMMON_PRESET_DEFAULT_NAME = "default";
 
-struct common_preset_context;
-
 struct common_preset {
     std::string name;
-
-    // options are stored as common_arg to string mapping, representing CLI arg and its value
+    // TODO: support repeated args in the future
     std::map<common_arg, std::string> options;
 
     // convert preset to CLI argument list
-    std::vector<std::string> to_args(const std::string & bin_path = "") const;
+    std::vector<std::string> to_args() const;
 
     // convert preset to INI format string
     std::string to_ini() const;
 
     // TODO: maybe implement to_env() if needed
-
-    // modify preset options where argument is identified by its env variable
-    void set_option(const common_preset_context & ctx, const std::string & env, const std::string & value);
-
-    // unset option by its env variable
-    void unset_option(const std::string & env);
-
-    // get option value by its env variable, return false if not found
-    bool get_option(const std::string & env, std::string & value) const;
-
-    // merge another preset into this one, overwriting existing options
-    void merge(const common_preset & other);
-
-    // apply preset options to common_params
-    void apply_to_params(common_params & params) const;
 };
 
 // interface for multiple presets in one file
 using common_presets = std::map<std::string, common_preset>;
-
-// context for loading and editing presets
-struct common_preset_context {
-    common_params default_params; // unused for now
-    common_params_context ctx_params;
-    std::map<std::string, common_arg> key_to_opt;
-
-    bool filter_allowed_keys = false;
-    std::set<std::string> allowed_keys;
-
-    // if only_remote_allowed is true, only accept whitelisted keys
-    common_preset_context(llama_example ex, bool only_remote_allowed = false);
-
-    // load presets from INI file
-    common_presets load_from_ini(const std::string & path, common_preset & global) const;
-
-    // generate presets from cached models
-    common_presets load_from_cache() const;
-
-    // generate presets from local models directory
-    // for the directory structure, see "Using multiple models" in server/README.md
-    common_presets load_from_models_dir(const std::string & models_dir) const;
-
-    // generate one preset from CLI arguments
-    common_preset load_from_args(int argc, char ** argv) const;
-
-    // cascade multiple presets if exist on both: base < added
-    // if preset does not exist in base, it will be added without modification
-    common_presets cascade(const common_presets & base, const common_presets & added) const;
-
-    // apply presets over a base preset (same idea as CSS cascading)
-    common_presets cascade(const common_preset & base, const common_presets & presets) const;
-};
+common_presets common_presets_load(const std::string & path, common_params_context & ctx_params);

common/regex-partial.cpp

@@ -27,7 +27,7 @@ common_regex_match common_regex::search(const std::string & input, size_t pos, b
         return res;
     }
     std::match_results<std::string::const_reverse_iterator> srmatch;
-    if (std::regex_search(input.rbegin(), input.rend() - pos, srmatch, rx_reversed_partial, std::regex_constants::match_continuous)) {
+    if (std::regex_match(input.rbegin(), input.rend() - pos, srmatch, rx_reversed_partial)) {
         auto group = srmatch[1].str();
         if (group.length() != 0) {
             auto it = srmatch[1].second.base();
@@ -55,18 +55,18 @@ common_regex_match common_regex::search(const std::string & input, size_t pos, b
   to see if a string ends with a partial regex match, but but it's not in std::regex yet.
   Instead, we'll the regex into a partial match regex operating as a full match on the reverse iterators of the input.
 
-  - /abcd/ -> ^(dcba|cba|ba|a) -> ^((?:(?:(?:(?:d)?c)?b)?a)
-  - /a|b/ -> ^(a|b)
+  - /abcd/ -> (dcba|cba|ba|a).* -> ((?:(?:(?:(?:d)?c)?b)?a).*
+  - /a|b/ -> (a|b).*
   - /a*?/ -> error, could match ""
-  - /a*b/ -> ^((?:b)?a*+) (final repetitions become eager)
-  - /.*?ab/ -> ^((?:b)?a) (omit .*)
-  - /a.*?b/ -> ^((?:b)?.*?a) (keep reluctant matches)
-  - /a(bc)d/ -> ^((?:(?:d)?(?:(?:c)?b))?a)
-  - /a(bc|de)/ -> ^((?:(?:(?:e)?d)?|(?:(?:c)?b)?)?a)
-  - /ab{2,4}c/ -> ^cbbb?b?a -> ^((?:(?:(?:(?:(?:c)?b)?b)?b?)?b?)?a)
+  - /a*b/ -> ((?:b)?a*+).* (final repetitions become eager)
+  - /.*?ab/ -> ((?:b)?a).* (merge .*)
+  - /a.*?b/ -> ((?:b)?.*?a).* (keep reluctant matches)
+  - /a(bc)d/ -> ((?:(?:d)?(?:(?:c)?b))?a).*
+  - /a(bc|de)/ -> ((?:(?:(?:e)?d)?|(?:(?:c)?b)?)?a).*
+  - /ab{2,4}c/ -> abbb?b?c -> ((?:(?:(?:(?:(?:c)?b)?b)?b?)?b?)?a).*
 
-  The regex will match a reversed string fully, and the end of the first (And only) capturing group will indicate the reversed start of the original partial pattern.
-  All other groups are turned into non-capturing groups, and reluctant quantifiers are ignored.
+  The regex will match a reversed string fully, and the end of the first (And only) capturing group will indicate the reversed start of the original partial pattern
+  (i.e. just where the final .* starts in the inverted pattern; all other groups are turned into non-capturing groups, and reluctant quantifiers are ignored)
 */
 std::string regex_to_reversed_partial_regex(const std::string & pattern) {
     auto it = pattern.begin();
@@ -177,7 +177,7 @@ std::string regex_to_reversed_partial_regex(const std::string & pattern) {
             }
         }
 
-        // /abcd/ -> ^(dcba|cba|ba|a) -> ^((?:(?:(?:d)?c)?b)?a)
+        // /abcd/ -> (dcba|cba|ba|a).* -> ((?:(?:(?:d)?c)?b)?a).*
         // if n(=4) parts, opening n-1(=3) non-capturing groups after the 1 capturing group
         // We'll do the outermost capturing group and final .* in the enclosing function.
         std::vector<std::string> res_alts;
@@ -200,5 +200,5 @@ std::string regex_to_reversed_partial_regex(const std::string & pattern) {
         throw std::runtime_error("Unmatched '(' in pattern");
     }
 
-    return "^(" + res + ")";
+    return "(" + res + ")[\\s\\S]*";
 }

common/sampling.cpp

@@ -120,34 +120,17 @@ struct common_sampler {
     }
 
     void set_logits(struct llama_context * ctx, int idx) {
-        const float *       sampled_probs  = llama_get_sampled_probs_ith     (ctx, idx);
-        const float *       sampled_logits = llama_get_sampled_logits_ith    (ctx, idx);
-        const llama_token * sampled_ids    = llama_get_sampled_candidates_ith(ctx, idx);
+        const auto * logits = llama_get_logits_ith(ctx, idx);
 
         const llama_model * model = llama_get_model(ctx);
         const llama_vocab * vocab = llama_model_get_vocab(model);
 
         const int n_vocab = llama_vocab_n_tokens(vocab);
 
-        if (sampled_probs) {
-            const uint32_t sampled_probs_count = llama_get_sampled_probs_count_ith(ctx, idx);
-            cur.resize(sampled_probs_count);
-            for (uint32_t i = 0; i < sampled_probs_count; ++i) {
-                cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]};
-            }
-        } else if (sampled_logits) {
-            const uint32_t sampled_logits_count = llama_get_sampled_logits_count_ith(ctx, idx);
-            cur.resize(sampled_logits_count);
-            for (uint32_t i = 0; i < sampled_logits_count; i++) {
-                cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f};
-            }
-        } else {
-            const auto * logits = llama_get_logits_ith(ctx, idx);
-            GGML_ASSERT(logits != nullptr);
-            cur.resize(n_vocab);
-            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-                cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
-            }
+        cur.resize(n_vocab);
+
+        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+            cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
         }
 
         cur_p = { cur.data(), cur.size(), -1, false };
@@ -176,7 +159,7 @@ std::string common_params_sampling::print() const {
     return std::string(result);
 }
 
-struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params) {
+struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
@@ -196,30 +179,24 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
 #endif // LLAMA_USE_LLGUIDANCE
     } else {
         std::vector<std::string> trigger_patterns;
+        std::vector<std::string> patterns_anywhere;
         std::vector<llama_token> trigger_tokens;
         for (const auto & trigger : params.grammar_triggers) {
             switch (trigger.type) {
                 case COMMON_GRAMMAR_TRIGGER_TYPE_WORD:
                 {
                     const auto & word = trigger.value;
-                    trigger_patterns.push_back(regex_escape(word));
+                    patterns_anywhere.push_back(regex_escape(word));
                     break;
                 }
                 case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN:
                 {
-                    trigger_patterns.push_back(trigger.value);
+                    patterns_anywhere.push_back(trigger.value);
                     break;
                 }
                 case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL:
                 {
-                    const auto & pattern = trigger.value;
-                    std::string anchored = "^$";
-                    if (!pattern.empty()) {
-                        anchored = (pattern.front() != '^' ? "^" : "")
-                            + pattern
-                            + (pattern.back() != '$' ? "$" : "");
-                    }
-                    trigger_patterns.push_back(anchored);
+                    trigger_patterns.push_back(trigger.value);
                     break;
                 }
                 case COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN:
@@ -233,6 +210,10 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
             }
         }
 
+        if (!patterns_anywhere.empty()) {
+            trigger_patterns.push_back("^[\\s\\S]*?(" + string_join(patterns_anywhere, "|") + ")[\\s\\S]*");
+        }
+
         std::vector<const char *> trigger_patterns_c;
         trigger_patterns_c.reserve(trigger_patterns.size());
         for (const auto & regex : trigger_patterns) {
@@ -315,12 +296,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
         llama_sampler_chain_add(chain, smpl);
     }
 
-    if (grmr && params.backend_sampling) {
-        LOG_WRN("%s: backend sampling is not compatible with grammar, disabling\n", __func__);
-
-        params.backend_sampling = false;
-    }
-
     auto * result = new common_sampler {
         /* .params  = */ params,
         /* .grmr    = */ grmr,
@@ -430,25 +405,6 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     auto & chain = gsmpl->chain;
     auto & cur_p = gsmpl->cur_p; // initialized by set_logits
 
-    // Check if a backend sampler has already sampled a token in which case we
-    // return that token id directly.
-    {
-        id = llama_get_sampled_token_ith(ctx, idx);
-
-        if (id != LLAMA_TOKEN_NULL) {
-            LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, id);
-
-            GGML_ASSERT(!gsmpl->grmr && "using grammar in combination with backend sampling is not supported");
-
-            // TODO: simplify
-            gsmpl->cur.resize(1);
-            gsmpl->cur[0] = { id, 0.0f, 1.0f };
-            cur_p = { gsmpl->cur.data(), gsmpl->cur.size(), 0, true };
-
-            return id;
-        }
-    }
-
     gsmpl->set_logits(ctx, idx);
 
     if (grammar_first) {

common/sampling.h

@@ -36,8 +36,7 @@ struct common_sampler;
 
 // llama_sampler API overloads
 
-// note: can mutate params in some cases
-struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params);
+struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
 
 void common_sampler_free(struct common_sampler * gsmpl);
 
@@ -49,7 +48,6 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
 // arguments can be nullptr to skip printing
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
 
-// get the underlying llama_sampler_chain
 struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
 
 // extended sampling implementation:

convert_hf_to_gguf_update.py

@@ -139,15 +139,11 @@ models = [
     {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
     {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
     {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
-    {"name": "modern-bert",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/answerdotai/ModernBERT-base", },
     {"name": "afmoe",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/arcee-ai/Trinity-Tokenizer", },
     {"name": "bailingmoe2",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", },
     {"name": "granite-docling",  "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
     {"name": "minimax-m2",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2", },
     {"name": "kormo",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/KORMo-Team/KORMo-tokenizer", },
-    {"name": "youtu",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Youtu-LLM-2B", },
-    {"name": "solar-open",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/upstage/Solar-Open-100B", },
-    {"name": "exaone-moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -168,8 +164,6 @@ pre_computed_hashes = [
     {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
     {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
     {"name": "grok-2",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
-    # jina-v2-de variants
-    {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
 ]
 
 

docs/android.md

@@ -1,27 +1,27 @@
 
 # Android
 
-## Build GUI binding using Android Studio
+## Build with Android Studio
 
 Import the `examples/llama.android` directory into Android Studio, then perform a Gradle sync and build the project.
-![Project imported into Android Studio](./android/imported-into-android-studio.jpg)
+![Project imported into Android Studio](./android/imported-into-android-studio.png)
 
 This Android binding supports hardware acceleration up to `SME2` for **Arm** and `AMX` for **x86-64** CPUs on Android and ChromeOS devices.
 It automatically detects the host's hardware to load compatible kernels. As a result, it runs seamlessly on both the latest premium devices and older devices that may lack modern CPU features or have limited RAM, without requiring any manual configuration.
 
 A minimal Android app frontend is included to showcase the binding’s core functionalities:
-1.	**Parse GGUF metadata** via `GgufMetadataReader` from either a `ContentResolver` provided `Uri` from shared storage, or a local `File` from your app's private storage.
-2.	**Obtain a `InferenceEngine`** instance through the `AiChat` facade and load your selected model via its app-private file path.
-3.	**Send a raw user prompt** for automatic template formatting, prefill, and batch decoding. Then collect the generated tokens in a Kotlin `Flow`.
+1.	**Parse GGUF metadata** via `GgufMetadataReader` from either a `ContentResolver` provided `Uri` or a local `File`.
+2.	**Obtain a `TierDetection` or `InferenceEngine`** instance through the high-level facade APIs.
+3.	**Send a raw user prompt** for automatic template formatting, prefill, and decoding. Then collect the generated tokens in a Kotlin `Flow`.
 
-For a production-ready experience that leverages advanced features such as system prompts and benchmarks, plus friendly UI features such as model management and Arm feature visualizer, check out [Arm AI Chat](https://play.google.com/store/apps/details?id=com.arm.aichat) on Google Play.
+For a production-ready experience that leverages advanced features such as system prompts and benchmarks, check out [Arm AI Chat](https://play.google.com/store/apps/details?id=com.arm.aichat) on Google Play.
 This project is made possible through a collaborative effort by Arm's **CT-ML**, **CE-ML** and **STE** groups:
 
-| ![Home screen](https://naco-siren.github.io/ai-chat/policy/index/1-llm-starter-pack.png)  | ![System prompt](https://naco-siren.github.io/ai-chat/policy/index/5-system-prompt.png)  | !["Haiku"](https://naco-siren.github.io/ai-chat/policy/index/4-metrics.png)  |
+| ![Home screen](./android/arm-ai-chat-home-screen.png)  | ![System prompt](./android/system-prompt-setup.png)  | !["Haiku"](./android/chat-with-system-prompt-haiku.png)  |
 |:------------------------------------------------------:|:----------------------------------------------------:|:--------------------------------------------------------:|
 |                      Home screen                       |                    System prompt                     |                         "Haiku"                          |
 
-## Build CLI on Android using Termux
+## Build on Android using Termux
 
 [Termux](https://termux.dev/en/) is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid.
 
@@ -52,7 +52,7 @@ To see what it might look like visually, here's an old demo of an interactive se
 
 https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
 
-## Cross-compile CLI using Android NDK
+## Cross-compile using Android NDK
 It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.)
 
 Once you're ready and have cloned `llama.cpp`, invoke the following in the project directory:

docs/backend/CANN.md

@@ -327,7 +327,3 @@ Maximum number of compiled CANN graphs kept in the LRU cache, default is 12. Whe
 ### GGML_CANN_PREFILL_USE_GRAPH
 
 Enable ACL graph execution during the prefill stage, default is false. This option is only effective when FA is enabled.
-
-### GGML_CANN_OPERATOR_FUSION
-
-Enable operator fusion during computation, default is false. This option fuses compatible operators (e.g., ADD + RMS_NORM) to reduce overhead and improve performance.

docs/backend/OPENCL.md

@@ -17,7 +17,7 @@ OpenCL (Open Computing Language) is an open, royalty-free standard for cross-pla
 
 ### Llama.cpp + OpenCL
 
-The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adreno GPU** firstly via OpenCL. Thanks to the portabilty of OpenCL, the OpenCL backend can also run on certain Intel GPUs such as those that do not have [SYCL](/docs/backend/SYCL.md) support although the performance is not optimal.
+The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adreno GPU** firstly via OpenCL. Thanks to the portabilty of OpenCL, the OpenCL backend can also run on certain Intel GPUs although the performance is not optimal.
 
 ## OS
 
@@ -218,56 +218,6 @@ cmake .. -G Ninja `
 ninja
 ```
 
-## Linux
-
-The two steps just above also apply to Linux. When building for linux, the commands are mostly the same as those for PowerShell on Windows, but in the second step they do not have the `-DCMAKE_TOOLCHAIN_FILE` parameter, and then in both steps the backticks are replaced with back slashes.
-
-If not installed already, install Git, CMake, Clang, Ninja and Python, then run in the terminal the following:
-
-### I. Setup Environment
-
-1. **Install OpenCL Headers and Library**
-
-```bash
-mkdir -p ~/dev/llm
-
-cd ~/dev/llm
-git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers
-mkdir build && cd build
-cmake .. -G Ninja \
-  -DBUILD_TESTING=OFF \
-  -DOPENCL_HEADERS_BUILD_TESTING=OFF \
-  -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF \
-  -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
-cmake --build . --target install
-
-cd ~/dev/llm
-git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader
-mkdir build && cd build
-cmake .. -G Ninja \
-  -DCMAKE_BUILD_TYPE=Release \
-  -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" \
-  -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
-cmake --build . --target install
-```
-
-### II. Build llama.cpp
-
-```bash
-mkdir -p ~/dev/llm
-cd ~/dev/llm
-
-git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
-mkdir build && cd build
-
-cmake .. -G Ninja \
-  -DCMAKE_BUILD_TYPE=Release \
-  -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" \
-  -DBUILD_SHARED_LIBS=OFF \
-  -DGGML_OPENCL=ON
-ninja
-```
-
 ## Known Issues
 
 - Flash attention does not always improve performance.

docs/backend/SYCL.md

@@ -829,7 +829,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
   No. We can't support Ollama issue directly, because we aren't familiar with Ollama.
 
-  Suggest reproducing on llama.cpp and report similar issue to llama.cpp. We will support it.
+  Sugguest reproducing on llama.cpp and report similar issue to llama.cpp. We will surpport it.
 
   It's same for other projects including llama.cpp SYCL backend.
 

docs/backend/hexagon/CMakeUserPresets.json

@@ -1,4 +1,4 @@
-{
+{
   "version": 4,
   "configurePresets": [
     {
@@ -22,8 +22,7 @@
             "GGML_LLAMAFILE":   "OFF",
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
-            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
-            "LLAMA_OPENSSL":    "OFF"
+            "LLAMA_CURL":       "OFF"
         }
     },
 
@@ -37,8 +36,7 @@
             "GGML_LLAMAFILE":   "OFF",
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
-            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
-            "LLAMA_OPENSSL":    "OFF"
+            "LLAMA_CURL":       "OFF"
         }
     },
 

docs/backend/hexagon/README.md

@@ -106,7 +106,7 @@ Here are some examples of running various llama.cpp tools via ADB.
 Simple question for Llama-3.2-1B
 
 ```
-~/src/llama.cpp$ M=Llama-3.2-1B-Instruct-Q4_0.gguf D=HTP0 ./scripts/snapdragon/adb/run-completion.sh -p "what is the most popular cookie in the world?"
+~/src/llama.cpp$ M=Llama-3.2-1B-Instruct-Q4_0.gguf D=HTP0 ./scripts/snapdragon/adb/run-cli.sh -no-cnv -p "what is the most popular cookie in the world?"
 ...
 ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
 ggml-hex: Hexagon Arch version v79
@@ -136,7 +136,7 @@ llama_memory_breakdown_print: |   - HTP0-REPACK        |                  504 =
 Summary request for OLMoE-1B-7B. This is a large model that requires two HTP sessions/devices
 
 ```
-~/src/llama.cpp$ M=OLMoE-1B-7B-0125-Instruct-Q4_0.gguf NDEV=2 D=HTP0,HTP1 ./scripts/snapdragon/adb/run-completion.sh -f surfing.txt
+~/src/llama.cpp$ M=OLMoE-1B-7B-0125-Instruct-Q4_0.gguf NDEV=2 D=HTP0,HTP1 ./scripts/snapdragon/adb/run-cli.sh -f surfing.txt -no-cnv
 ...
 ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
 ggml-hex: Hexagon Arch version v81
@@ -210,10 +210,6 @@ build: 6a8cf8914 (6733)
   Controls whether the Hexagon backend allocates host buffers. By default, all buffers except for REPACK are host buffers.
   This option is required for testing Ops that require REPACK buffers (MUL_MAT and MUL_MAT_ID).
 
-- `GGML_HEXAGON_EXPERIMENTAL=1`
-  Controls whether the Hexagon backend enables experimental features.
-  This option is required for enabling/testing experimental Ops (FLASH_ATTN_EXT).
-
 - `GGML_HEXAGON_VERBOSE=1`
   Enables verbose logging of Ops from the backend. Example output:
 
@@ -238,6 +234,6 @@ build: 6a8cf8914 (6733)
 
   Examples:
 
-      `GGML_HEXAGON_OPMASK=0x1 llama-completion ...` - Ops are enqueued but NPU-side processing is stubbed out
-      `GGML_HEXAGON_OPMASK=0x3 llama-completion ...` - NPU performs dynamic quantization and skips the rest
-      `GGML_HEXAGON_OPMASK=0x7 llama-completion ...` - Full queuing and processing of Ops (default)
+      `GGML_HEXAGON_OPMASK=0x1 llama-cli ...` - Ops are enqueued but NPU-side processing is stubbed out
+      `GGML_HEXAGON_OPMASK=0x3 llama-cli ...` - NPU performs dynamic quantization and skips the rest
+      `GGML_HEXAGON_OPMASK=0x7 llama-cli ...` - Full queuing and processing of Ops (default)

docs/backend/hexagon/developer.md

@@ -49,7 +49,7 @@ Each Hexagon device behaves like a GPU from the offload and model splitting pers
 Here is an example of running GPT-OSS-20B model on a newer Snapdragon device with 16GB of DDR.
 
 ```
-M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapdragon/adb/run-completion.sh -f surfing.txt -n 32
+M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapdragon/adb/run-cli.sh -no-cnv -f surfing.txt -n 32
 ...
 LD_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
 ADSP_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib

docs/build-riscv64-spacemit.md

@@ -15,7 +15,7 @@ Below is the build script: it requires utilizing RISC-V vector instructions for
 cmake -B build \
     -DCMAKE_BUILD_TYPE=Release \
     -DGGML_CPU_RISCV64_SPACEMIT=ON \
-    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_CURL=OFF \
     -DGGML_RVV=ON \
     -DGGML_RV_ZFH=ON \
     -DGGML_RV_ZICBOP=ON \

docs/build.md

@@ -65,10 +65,10 @@ cmake --build build --config Release
       cmake --preset x64-windows-llvm-release
       cmake --build build-x64-windows-llvm-release
       ```
-- If you want HTTPS/TLS features, you may install OpenSSL development libraries. If not installed, the project will build and run without SSL support.
-  - **Debian / Ubuntu:** `sudo apt-get install libssl-dev`
-  - **Fedora / RHEL / Rocky / Alma:** `sudo dnf install openssl-devel`
-  - **Arch / Manjaro:** `sudo pacman -S openssl`
+- Curl usage is enabled by default and can be turned off with `-DLLAMA_CURL=OFF`. Otherwise you need to install development libraries for libcurl.
+  - **Debian / Ubuntu:** `sudo apt-get install libcurl4-openssl-dev`  # (or `libcurl4-gnutls-dev` if you prefer GnuTLS)
+  - **Fedora / RHEL / Rocky / Alma:** `sudo dnf install libcurl-devel`
+  - **Arch / Manjaro:** `sudo pacman -S curl`  # includes libcurl headers
 
 ## BLAS Build
 
@@ -150,38 +150,19 @@ We also have a [guide](./backend/CUDA-FEDORA.md) for setting up CUDA toolkit in
 
 
 ### Compilation
-
-Make sure to read the notes about the CPU build for general instructions for e.g. speeding up the compilation.
-
 ```bash
 cmake -B build -DGGML_CUDA=ON
 cmake --build build --config Release
 ```
 
-### Non-Native Builds
-
-By default llama.cpp will be built for the hardware that is connected to the system at that time.
-For a build covering all CUDA GPUs, disable `GGML_NATIVE`:
-
-```bash
-cmake -B build -DGGML_CUDA=ON -DGGML_NATIVE=OFF
-```
-
-The resulting binary should run on all CUDA GPUs with optimal performance, though some just-in-time compilation may be required.
-
 ### Override Compute Capability Specifications
 
-If `nvcc` cannot detect your gpu, you may get compile warnings such as:
+If `nvcc` cannot detect your gpu, you may get compile-warnings such as:
  ```text
 nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used
 ```
 
-One option is to do a non-native build as described above.
-However, this will result in a large binary that takes a long time to compile.
-Alternatively it is also possible to explicitly specify CUDA architectures.
-This may also make sense for a non-native build, for that one should look at the logic in `ggml/src/ggml-cuda/CMakeLists.txt` as a starting point.
-
-To override the default CUDA architectures:
+To override the `native` GPU detection:
 
 #### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus).
 

docs/development/parsing.md

@@ -55,7 +55,7 @@ auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder &
 ```
 
 For a more complete example, see `test_example_native()` in
-[tests/test-chat-peg-parser.cpp](/tests/test-chat-peg-parser.cpp).
+[tests/test-chat-peg-parser.cpp](tests/test-chat-peg-parser.cpp).
 
 ## Parsers/Combinators
 
@@ -175,7 +175,7 @@ Most model output can be placed in one of the following categories:
   (Qwen3-Coder, MiniMax M2) or pseudo-function calls (LFM2)
 
 To provide broad coverage,
-[`common/chat-peg-parser.h`](/common/chat-peg-parser.h) contains builders and
+[`common/chat-peg-parser.h`](common/chat-peg-parser.h) contains builders and
 mappers that help create parsers and visitors/extractors for these types. They
 require parsers to tag nodes to conform to an AST "shape". This normalization
 makes it easy to extract information and generalize parsing.

docs/ops.md

@@ -22,7 +22,7 @@ Legend:
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
@@ -32,7 +32,7 @@ Legend:
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
@@ -57,6 +57,7 @@ Legend:
 |                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|               GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -70,9 +71,10 @@ Legend:
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 |
+|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
 |                              PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -97,6 +99,7 @@ Legend:
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |

docs/ops/BLAS.csv

@@ -965,7 +965,6 @@
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,1,2560],ne_kernel=[3,3,1,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
-"BLAS","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
@@ -4965,7 +4964,6 @@
 "BLAS","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","BLAS"
 "BLAS","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","BLAS"
 "BLAS","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","BLAS"
-"BLAS","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","BLAS"
 "BLAS","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","BLAS"
 "BLAS","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","BLAS"
 "BLAS","ARGMAX","type=f32,ne=[32,1,1,1]","support","0","no","BLAS"
@@ -5717,15 +5715,15 @@
 "BLAS","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","BLAS"
 "BLAS","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","0","no","BLAS"
 "BLAS","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[3,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[6,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[3,1536,4,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[3,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[6,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[3,2048,4,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
@@ -5735,15 +5733,6 @@
 "BLAS","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[9,1024,1,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[18,1024,1,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[9,1024,4,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[9,1536,1,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[18,1536,1,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[9,1536,4,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[9,2048,1,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[18,2048,1,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
@@ -6603,30 +6592,6 @@
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=67,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f32,type_b=f32,m=64,n=77,k=77,bs=[12,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","BLAS"
 "BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=576,n=512,k=576,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","BLAS"
-"BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=2048,k=8192,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=f32,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=bf16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q4_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q5_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q8_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q2_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q3_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q4_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q5_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=q6_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq2_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq2_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq1_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq1_m,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq4_nl,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq3_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
-"BLAS","MUL_MAT","type_a=iq4_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
@@ -8951,11 +8916,6 @@
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
-"BLAS","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
-"BLAS","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
-"BLAS","SOFT_MAX","type=f32,ne=[200000,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
-"BLAS","SOFT_MAX","type=f32,ne=[200000,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
-"BLAS","SOFT_MAX","type=f32,ne=[643251,3,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[16,16,1,1],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[15,15,1,1],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[16,16,2,3],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
@@ -9008,7 +8968,6 @@
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9018,7 +8977,6 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9029,13 +8987,11 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9045,7 +9001,6 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9056,13 +9011,11 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9072,7 +9025,6 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9083,13 +9035,11 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9099,7 +9049,6 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9110,7 +9059,6 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9236,7 +9184,6 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9246,7 +9193,6 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9257,13 +9203,11 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9273,7 +9217,6 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9284,13 +9227,11 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9300,7 +9241,6 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9311,13 +9251,11 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9327,7 +9265,6 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9338,7 +9275,6 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
-"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9606,333 +9542,333 @@
 "BLAS","ARGSORT","type=f32,ne=[2048,2,1,3],order=1","support","0","no","BLAS"
 "BLAS","ARGSORT","type=f32,ne=[2049,2,1,3],order=1","support","0","no","BLAS"
 "BLAS","ARGSORT","type=f32,ne=[2,8,8192,1],order=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[1,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[12,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[2,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[13,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[2,1,1,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[13,1,2,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[4,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[15,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[4,1,1,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[15,1,2,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[4,1,1,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[15,1,2,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[8,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[19,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[8,1,1,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[19,1,2,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[8,1,1,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[19,1,2,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[8,1,1,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[19,1,2,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[16,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[27,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[16,1,1,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[27,1,2,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[16,1,1,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[27,1,2,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[16,1,1,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[27,1,2,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[16,1,1,1],k=15,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[27,1,2,1],k=15,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[32,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[43,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[32,1,1,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[43,1,2,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[32,1,1,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[43,1,2,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[32,1,1,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[43,1,2,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[32,1,1,1],k=15,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[43,1,2,1],k=15,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[64,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[75,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[64,1,1,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[75,1,2,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[64,1,1,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[75,1,2,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[64,1,1,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[75,1,2,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[64,1,1,1],k=15,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[75,1,2,1],k=15,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[128,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[139,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[128,1,1,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[139,1,2,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[128,1,1,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[139,1,2,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[128,1,1,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[139,1,2,1],k=7,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[128,1,1,1],k=15,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[139,1,2,1],k=15,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[128,1,1,1],k=100,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[139,1,2,1],k=100,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[256,1,1,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[267,1,2,1],k=1,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[256,1,1,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[267,1,2,1],k=2,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[256,1,1,1],k=3,ties=0","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[267,1,2,1],k=3,ties=0","support","0","no","BLAS"
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 "BLAS","SUM","type=f32,ne=[11,5,6,3],permute=[0,2,1,3]","support","0","no","BLAS"
@@ -9955,9 +9891,8 @@
 "BLAS","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","0","no","BLAS"
 "BLAS","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","0","no","BLAS"
 "BLAS","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","0","no","BLAS"
 "BLAS","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","BLAS"
 "BLAS","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","BLAS"
 "BLAS","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","BLAS"
@@ -9979,7 +9914,6 @@
 "BLAS","CUMSUM","type=f32,ne=[2048,5,4,3]","support","0","no","BLAS"
 "BLAS","CUMSUM","type=f32,ne=[242004,1,1,1]","support","0","no","BLAS"
 "BLAS","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","BLAS"
-"BLAS","CUMSUM","type=f32,ne=[20481,4,1,1]","support","0","no","BLAS"
 "BLAS","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","BLAS"
 "BLAS","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","BLAS"
 "BLAS","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","BLAS"
@@ -9989,41 +9923,17 @@
 "BLAS","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","BLAS"
 "BLAS","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","BLAS"
 "BLAS","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","BLAS"
-"BLAS","DIAG","type=f32,ne=[10,1,4,3]","support","0","no","BLAS"
-"BLAS","DIAG","type=f32,ne=[79,1,19,13]","support","0","no","BLAS"
-"BLAS","DIAG","type=f32,ne=[256,1,8,16]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[79,79,5,3],ne_rhs=[417,79,5,3]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,2],ne_rhs=[32,128,4,2]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[80,80,2,8]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[79,80,2,8]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[81,80,2,8]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[80,80,8,8]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[79,80,8,8]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[81,80,8,8]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[84,84,4,4],ne_rhs=[32,84,4,4]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[95,95,8,8],ne_rhs=[40,95,8,8]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[32,128,4,4]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,3,4],ne_rhs=[32,128,3,4]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,1],ne_rhs=[32,128,4,1]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[200,64,4,4]","support","0","no","BLAS"
-"BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","BLAS"

docs/ops/Metal.csv

@@ -965,7 +965,6 @@
 "Metal","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,1,2560],ne_kernel=[3,3,1,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","Metal"
 "Metal","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","Metal"
 "Metal","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","Metal"
-"Metal","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","Metal"
 "Metal","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","Metal"
 "Metal","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","Metal"
 "Metal","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","Metal"
@@ -4965,9 +4964,8 @@
 "Metal","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","Metal"
 "Metal","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","Metal"
 "Metal","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","Metal"
-"Metal","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","Metal"
-"Metal","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","Metal"
-"Metal","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","Metal"
+"Metal","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","Metal"
+"Metal","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","Metal"
 "Metal","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","Metal"
 "Metal","ARGMAX","type=f32,ne=[32,513,1,1]","support","1","yes","Metal"
 "Metal","ARGMAX","type=f32,ne=[100,10,1,1]","support","1","yes","Metal"
@@ -5717,15 +5715,15 @@
 "Metal","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Metal"
 "Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","Metal"
 "Metal","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[3,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[6,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[3,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[3,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[6,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[3,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
+"Metal","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
 "Metal","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Metal"
 "Metal","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Metal"
 "Metal","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","Metal"
@@ -5735,15 +5733,6 @@
 "Metal","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Metal"
 "Metal","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Metal"
 "Metal","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[9,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[18,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[9,1024,4,1],ne_b=[9,1024,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[9,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[18,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[9,1536,4,1],ne_b=[9,1536,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[9,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[18,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","Metal"
-"Metal","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","Metal"
 "Metal","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","1","yes","Metal"
 "Metal","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","1","yes","Metal"
 "Metal","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","1","yes","Metal"
@@ -8927,8 +8916,6 @@
 "Metal","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","1","yes","Metal"
 "Metal","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Metal"
 "Metal","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Metal"
-"Metal","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Metal"
-"Metal","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Metal"
 "Metal","SOFT_MAX_BACK","type=f32,ne=[16,16,1,1],scale=1.000000,max_bias=0.000000","support","0","no","Metal"
 "Metal","SOFT_MAX_BACK","type=f32,ne=[15,15,1,1],scale=1.000000,max_bias=0.000000","support","0","no","Metal"
 "Metal","SOFT_MAX_BACK","type=f32,ne=[16,16,2,3],scale=1.000000,max_bias=0.000000","support","0","no","Metal"
@@ -9555,311 +9542,311 @@
 "Metal","ARGSORT","type=f32,ne=[2048,2,1,3],order=1","support","1","yes","Metal"
 "Metal","ARGSORT","type=f32,ne=[2049,2,1,3],order=1","support","1","yes","Metal"
 "Metal","ARGSORT","type=f32,ne=[2,8,8192,1],order=1","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[12,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[13,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[13,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=100,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=500,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=1023,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=9999,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=1,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=2,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=3,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=7,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","1","yes","Metal"
-"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[12,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[2,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[13,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[2,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[13,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=100","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=100","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=100","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=100","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=100","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=100","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=500","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=500","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=7","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=1023","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=500","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=1023","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=2","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=3","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=100","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=500","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=1023","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=1023","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=2","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=3","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=7","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=100","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=1023","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=2","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=1023","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=9999","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=9999","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=2","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=1023","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=9999","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=9999","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=1","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=2","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=2","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=15","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=100","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=500","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=9999","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=9999","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=1","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=1","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=2","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=1","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=3","support","1","yes","Metal"
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+"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=3","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=7","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=15","support","1","yes","Metal"
+"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=15","support","1","yes","Metal"
 "Metal","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","Metal"
 "Metal","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","Metal"
 "Metal","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","Metal"
@@ -9904,9 +9891,8 @@
 "Metal","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","Metal"
 "Metal","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","Metal"
 "Metal","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","Metal"
-"Metal","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","Metal"
-"Metal","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","0","no","Metal"
-"Metal","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","0","no","Metal"
+"Metal","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","Metal"
+"Metal","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","0","no","Metal"
 "Metal","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","Metal"
 "Metal","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","Metal"
 "Metal","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","Metal"
@@ -9937,41 +9923,17 @@
 "Metal","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","Metal"
 "Metal","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","Metal"
 "Metal","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","Metal"
-"Metal","DIAG","type=f32,ne=[10,1,4,3]","support","0","no","Metal"
-"Metal","DIAG","type=f32,ne=[79,1,19,13]","support","0","no","Metal"
-"Metal","DIAG","type=f32,ne=[256,1,8,16]","support","0","no","Metal"
 "Metal","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","Metal"
 "Metal","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","Metal"
 "Metal","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","Metal"
 "Metal","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","Metal"
 "Metal","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","Metal"
 "Metal","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[79,79,5,3],ne_rhs=[417,79,5,3]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,2],ne_rhs=[32,128,4,2]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[80,80,2,8]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[79,80,2,8]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[81,80,2,8]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[80,80,8,8]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[79,80,8,8]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[81,80,8,8]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[84,84,4,4],ne_rhs=[32,84,4,4]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[95,95,8,8],ne_rhs=[40,95,8,8]","support","0","no","Metal"
 "Metal","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[32,128,4,4]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,3,4],ne_rhs=[32,128,3,4]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,1],ne_rhs=[32,128,4,1]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[200,64,4,4]","support","0","no","Metal"
-"Metal","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","0","no","Metal"
-"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","1","yes","Metal"
-"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","0","no","Metal"
-"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","Metal"
-"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","0","no","Metal"
-"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","1","yes","Metal"
-"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","Metal"
-"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","Metal"
-"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","Metal"
+"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","Metal"
+"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","0","no","Metal"
+"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","1","yes","Metal"
+"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","Metal"
 "Metal","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Metal"
 "Metal","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Metal"
 "Metal","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","Metal"

docs/preset.md

@@ -1,97 +0,0 @@
-# llama.cpp INI Presets
-
-## Introduction
-
-The INI preset feature, introduced in [PR#17859](https://github.com/ggml-org/llama.cpp/pull/17859), allows users to create reusable and shareable parameter configurations for llama.cpp.
-
-### Using Presets with the Server
-
-When running multiple models on the server (router mode), INI preset files can be used to configure model-specific parameters. Please refer to the [server documentation](../tools/server/README.md) for more details.
-
-### Using a Remote Preset
-
-> [!NOTE]
->
-> This feature is currently only supported via the `-hf` option.
-
-For GGUF models hosted on Hugging Face, you can include a `preset.ini` file in the root directory of the repository to define specific configurations for that model.
-
-Example:
-
-```ini
-hf-repo-draft = username/my-draft-model-GGUF
-temp = 0.5
-top-k = 20
-top-p = 0.95
-```
-
-For security reasons, only certain options are allowed. Please refer to [preset.cpp](../common/preset.cpp) for the complete list of permitted options.
-
-Example usage:
-
-Assuming your repository `username/my-model-with-preset` contains a `preset.ini` with the configuration above:
-
-```sh
-llama-cli -hf username/my-model-with-preset
-
-# This is equivalent to:
-llama-cli -hf username/my-model-with-preset \
-  --hf-repo-draft username/my-draft-model-GGUF \
-  --temp 0.5 \
-  --top-k 20 \
-  --top-p 0.95
-```
-
-You can also override preset arguments by specifying them on the command line:
-
-```sh
-# Force temp = 0.1, overriding the preset value
-llama-cli -hf username/my-model-with-preset --temp 0.1
-```
-
-If you want to define multiple preset configurations for one or more GGUF models, you can create a blank HF repo for each preset. Each HF repo should contain a `preset.ini` file that references the actual model(s):
-
-```ini
-hf-repo = user/my-model-main
-hf-repo-draft = user/my-model-draft
-temp = 0.8
-ctx-size = 1024
-; (and other configurations)
-```
-
-### Named presets
-
-If you want to define multiple preset configurations for one or more GGUF models, you can create a blank HF repo containing a single `preset.ini` file that references the actual model(s):
-
-```ini
-[*]
-mmap = 1
-
-[gpt-oss-20b-hf]
-hf          = ggml-org/gpt-oss-20b-GGUF
-batch-size  = 2048
-ubatch-size = 2048
-top-p       = 1.0
-top-k       = 0
-min-p       = 0.01
-temp        = 1.0
-chat-template-kwargs = {"reasoning_effort": "high"}
-
-[gpt-oss-120b-hf]
-hf          = ggml-org/gpt-oss-120b-GGUF
-batch-size  = 2048
-ubatch-size = 2048
-top-p       = 1.0
-top-k       = 0
-min-p       = 0.01
-temp        = 1.0
-chat-template-kwargs = {"reasoning_effort": "high"}
-```
-
-You can then use it via `llama-cli` or `llama-server`, example:
-
-```sh
-llama-server -hf user/repo:gpt-oss-120b-hf
-```
-
-Please make sure to provide the correct `hf-repo` for each child preset. Otherwise, you may get error: `The specified tag is not a valid quantization scheme.`

examples/CMakeLists.txt

@@ -15,7 +15,6 @@ llama_add_compile_flags()
 if (EMSCRIPTEN)
 else()
     add_subdirectory(batched)
-    add_subdirectory(debug)
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
 
@@ -35,6 +34,7 @@ else()
     add_subdirectory(gen-docs)
     add_subdirectory(training)
     add_subdirectory(diffusion)
+    add_subdirectory(model-conversion)
     if (NOT GGML_BACKEND_DL)
         add_subdirectory(convert-llama2c-to-ggml)
         # these examples use the backends directly and cannot be built with dynamic loading

examples/batched/batched.cpp

@@ -21,7 +21,7 @@ int main(int argc, char ** argv) {
     params.prompt = "Hello my name is";
     params.n_predict = 32;
 
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BATCHED, print_usage)) {
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
         return 1;
     }
 
@@ -68,7 +68,7 @@ int main(int argc, char ** argv) {
     auto sparams = llama_sampler_chain_default_params();
     sparams.no_perf = false;
 
-    std::vector<llama_sampler_seq_config> sampler_configs;
+    std::vector<llama_sampler *> samplers;
 
     for (int32_t i = 0; i < n_parallel; ++i) {
         llama_sampler * smpl = llama_sampler_chain_init(sparams);
@@ -78,13 +78,7 @@ int main(int argc, char ** argv) {
         llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
         llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
 
-        sampler_configs.push_back({ i, smpl });
-    }
-
-    // TODO: temporarily gated behind a flag
-    if (params.sampling.backend_sampling) {
-        ctx_params.samplers   = sampler_configs.data();
-        ctx_params.n_samplers = sampler_configs.size();
+        samplers.push_back(smpl);
     }
 
     llama_context * ctx = llama_init_from_model(model, ctx_params);
@@ -186,7 +180,7 @@ int main(int argc, char ** argv) {
                 continue;
             }
 
-            const llama_token new_token_id = llama_sampler_sample(sampler_configs[i].sampler, ctx, i_batch[i]);
+            const llama_token new_token_id = llama_sampler_sample(samplers[i], ctx, i_batch[i]);
 
             // is it an end of generation? -> mark the stream as finished
             if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
@@ -242,15 +236,15 @@ int main(int argc, char ** argv) {
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
     LOG("\n");
-    llama_perf_sampler_print(sampler_configs[0].sampler);
+    llama_perf_sampler_print(samplers[0]);
     llama_perf_context_print(ctx);
 
     fprintf(stderr, "\n");
 
     llama_batch_free(batch);
 
-    for (auto & sampler_config : sampler_configs) {
-        llama_sampler_free(sampler_config.sampler);
+    for (auto & sampler_config : samplers) {
+        llama_sampler_free(sampler_config);
     }
 
     llama_free(ctx);

examples/debug/README.md

@@ -1,54 +0,0 @@
-# llama.cpp/examples/debug
-
-This is a utility intended to help debug a model by registering a callback that
-logs GGML operations and tensor data. It can also store the generated logits or
-embeddings as well as the prompt and token ids for comparision with the original
-model.
-
-### Usage
-
-```shell
-llama-debug \
-  --hf-repo ggml-org/models \
-  --hf-file phi-2/ggml-model-q4_0.gguf \
-  --model phi-2-q4_0.gguf \
-  --prompt hello \
-  --save-logits \
-  --verbose
-```
-The tensor data is logged as debug and required the --verbose flag. The reason
-for this is that while useful for a model with many layers there can be a lot of
-output. You can filter the tensor names using the `--tensor-filter` option.
-
-A recommended approach is to first run without `--verbose` and see if the
-generated logits/embeddings are close to the original model. If they are not,
-then it might be required to inspect tensor by tensor and in that case it is
-useful to enable the `--verbose` flag along with `--tensor-filter` to focus on
-specific tensors.
-
-### Options
-This example supports all standard `llama.cpp` options and also accepts the
-following options:
-```console
-$ llama-debug --help
-...
-
------ example-specific params -----
-
---save-logits                           save final logits to files for verification (default: false)
---logits-output-dir PATH                directory for saving logits output files (default: data)
---tensor-filter REGEX                   filter tensor names for debug output (regex pattern, can be specified multiple times)
-```
-
-### Output Files
-
-When `--save-logits` is enabled, the following files are created in the output
-directory:
-
-* `llamacpp-<model>[-embeddings].bin`        - Binary output (logits or embeddings)
-* `llamacpp-<model>[-embeddings].txt`        - Text output (logits or embeddings, one per line)
-* `llamacpp-<model>[-embeddings]-prompt.txt` - Prompt text and token IDs
-* `llamacpp-<model>[-embeddings]-tokens.bin` - Binary token IDs for programmatic comparison
-
-These files can be compared against the original model's output to verify the
-converted model.

examples/debug/debug.cpp

@@ -1,253 +0,0 @@
-#include "debug.h"
-#include "arg.h"
-#include "common.h"
-#include "log.h"
-#include "llama.h"
-
-#include <cstdlib>
-#include <string>
-#include <vector>
-#include <filesystem>
-#include <fstream>
-#include <regex>
-
-static void print_usage(int /*argc*/, char ** argv) {
-    const std::string usage_template = R"(
-        example usage:
-
-          Print tensors:
-
-          {prog} -m model.gguf -p "Hello my name is" --verbose
-
-          The tensors to be printed can be filtered with --tensor-filter option.
-
-          Save logits/embeddings:
-
-          {prog} -m model.gguf -p "Hello my name is" --save-logits
-
-          Add --embedding to save embeddings)" "\n";
-
-    // Fix the source code indentation above that is introduced by the raw string literal.
-    std::string usage = std::regex_replace(usage_template, std::regex("\\n {8}"), "\n");
-    usage = std::regex_replace(usage, std::regex("\\{prog\\}"), argv[0]);
-    LOG("%s\n", usage.c_str());
-}
-
-static bool has_pooling(llama_context * ctx) {
-    switch (llama_pooling_type(ctx)) {
-        case LLAMA_POOLING_TYPE_NONE:
-        case LLAMA_POOLING_TYPE_UNSPECIFIED:
-            return false;
-        default:
-            return true;
-    }
-}
-
-struct output_data {
-    float *                  data_ptr    = nullptr;
-    int                      data_size   = 0;
-    std::string              type_suffix;
-    std::vector<float>       embd_norm;
-    std::string              prompt;
-    std::vector<llama_token> tokens;
-
-    output_data(llama_context * ctx, const llama_model * model, const common_params & params) {
-        const llama_vocab * vocab = llama_model_get_vocab(model);
-        const bool add_bos = llama_vocab_get_add_bos(vocab);
-
-        tokens = common_tokenize(ctx, params.prompt, add_bos);
-        prompt = params.prompt;
-
-        if (params.embedding) {
-            const int n_embd       = llama_model_n_embd_out(model);
-            const bool pooling     = has_pooling(ctx);
-            const int n_embd_count = pooling ? 1 : tokens.size();
-            const int n_floats     = n_embd * n_embd_count;
-
-            float * embd_raw = pooling ? llama_get_embeddings_seq(ctx, 0) : llama_get_embeddings(ctx);
-            if (embd_raw == nullptr) {
-                throw std::runtime_error("failed to get embeddings from the model");
-            }
-
-            LOG_DBG("pooling_enabled: %s\n", pooling ? "true" : "false");
-            LOG_DBG("n_embd: %d\n", n_embd);
-            LOG_DBG("n_floats: %d\n", n_floats);
-            LOG_DBG("n_embd_count: %d\n", n_embd_count);
-
-            data_ptr    = embd_raw;
-            data_size   = n_floats;
-            type_suffix = "-embeddings";
-
-            if (params.embd_normalize >= 0) {
-                embd_norm.resize(n_floats);
-                for (int i = 0; i < n_embd_count; i++) {
-                    common_embd_normalize(embd_raw+i*n_embd, embd_norm.data()+i*n_embd, n_embd, params.embd_normalize);
-                }
-                data_ptr = embd_norm.data();
-            }
-        } else {
-            const float * logits = llama_get_logits_ith(ctx, tokens.size() - 1);
-            const int n_logits = llama_vocab_n_tokens(vocab);
-
-            data_ptr = const_cast<float*>(logits);
-            data_size = n_logits;
-            type_suffix = "";
-        }
-    }
-};
-
-static void save_output_data(const output_data & output, const std::string & model_name, const std::string & output_dir) {
-    std::filesystem::create_directory(output_dir);
-    auto base_path = std::filesystem::path{output_dir} / ("llamacpp-" + model_name + output.type_suffix);
-
-    // Save logits/embeddings to binary file.
-    {
-        std::filesystem::path filepath{base_path.string() + ".bin"};
-        std::ofstream file{filepath, std::ios::binary};
-        if (!file) {
-            throw std::runtime_error("failed to open binary output file: " + filepath.string());
-        }
-        file.write(reinterpret_cast<const char*>(output.data_ptr), output.data_size * sizeof(float));
-        LOG("Data saved to %s\n", filepath.c_str());
-    }
-
-    // Save logits/embeddings to text file.
-    {
-        std::filesystem::path filepath{base_path.string() + ".txt"};
-        std::ofstream file{filepath};
-        if (!file) {
-            throw std::runtime_error("failed to open text output file: " + filepath.string());
-        }
-        for (int i = 0; i < output.data_size; i++) {
-            file << i << ": " << output.data_ptr[i] << '\n';
-        }
-        LOG("Data saved to %s\n", filepath.c_str());
-    }
-
-    // Save prompt and tokens to text file.
-    {
-        std::filesystem::path filepath{base_path.string() + "-prompt.txt"};
-        std::ofstream file{filepath};
-        if (!file) {
-            throw std::runtime_error("failed to open prompt output file: " + filepath.string());
-        }
-
-        file << "prompt: " << output.prompt << '\n';
-        file << "n_tokens: " << output.tokens.size() << '\n';
-
-        file << "token ids: ";
-        for (size_t i = 0; i < output.tokens.size(); i++) {
-            file << output.tokens[i];
-            if (i + 1 < output.tokens.size()) {
-                file << ", ";
-            }
-        }
-        file << '\n';
-        LOG("Prompt saved to %s\n", filepath.c_str());
-    }
-
-    // Save token ids to binary file.
-    {
-        std::filesystem::path filepath{base_path.string() + "-tokens.bin"};
-        std::ofstream file{filepath, std::ios::binary};
-        if (!file) {
-            throw std::runtime_error("failed to open tokens binary file: " + filepath.string());
-        }
-        file.write(reinterpret_cast<const char*>(output.tokens.data()), output.tokens.size() * sizeof(llama_token));
-        LOG("Tokens saved to %s\n", filepath.c_str());
-    }
-
-}
-
-static void print_tokenized_prompt(llama_context * ctx, const std::vector<llama_token> & tokens, const std::string & prompt) {
-    const llama_model * model = llama_get_model(ctx);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    LOG("Model add_bos: %s\n", llama_vocab_get_add_bos(vocab) ? "true" : "false");
-    LOG("Input prompt: \"%s\"\n", prompt.c_str());
-    LOG("Token ids (%zu):\n", tokens.size());
-
-    for (auto id : tokens) {
-        std::string piece(128, '\0');
-        int n = llama_token_to_piece(vocab, id, piece.data(), piece.size(), 0, true);
-        if (n < 0) {
-            LOG_ERR("failed to convert token %d to piece\n", id);
-            continue;
-        }
-        piece.resize(n);
-        LOG("%s(%d) ", piece.c_str(), id);
-    }
-    LOG("\n");
-}
-
-static bool run(llama_context * ctx, const common_params & params) {
-    const llama_model * model = llama_get_model(ctx);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    const bool add_bos = llama_vocab_get_add_bos(vocab);
-
-    std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
-
-    if (tokens.empty()) {
-        LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__);
-        return false;
-    }
-
-    if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
-        LOG_ERR("%s : failed to eval\n", __func__);
-        return false;
-    }
-
-    print_tokenized_prompt(ctx, tokens, params.prompt);
-
-    if (params.save_logits) {
-        output_data output {ctx, model, params};
-        std::filesystem::path model_path{params.model.path};
-        std::string model_name{model_path.stem().string()};
-        save_output_data(output, model_name, params.logits_output_dir);
-    }
-
-    return true;
-}
-
-int main(int argc, char ** argv) {
-    common_params params;
-
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DEBUG, print_usage)) {
-        return 1;
-    }
-
-    common_init();
-
-    llama_backend_init();
-    llama_numa_init(params.numa);
-
-    base_callback_data cb_data(params, params.tensor_filter);
-
-    auto llama_init = common_init_from_params(params);
-
-    auto * model = llama_init->model();
-    auto * ctx   = llama_init->context();
-
-    if (model == nullptr || ctx == nullptr) {
-        LOG_ERR("%s : failed to init\n", __func__);
-        return 1;
-    }
-
-    {
-        LOG_INF("\n");
-        LOG_INF("%s\n", common_params_get_system_info(params).c_str());
-        LOG_INF("\n");
-    }
-
-    if (!run(ctx, params)) {
-        return 1;
-    }
-
-    LOG("\n");
-    llama_perf_context_print(ctx);
-
-    llama_backend_free();
-
-    return 0;
-}

examples/diffusion/diffusion-cli.cpp

@@ -553,7 +553,6 @@ int main(int argc, char ** argv) {
     model_params.n_gpu_layers       = params.n_gpu_layers;
     model_params.devices            = params.devices.data();
     model_params.use_mmap           = params.use_mmap;
-    model_params.use_direct_io      = params.use_direct_io;
     model_params.use_mlock          = params.use_mlock;
     model_params.check_tensors      = params.check_tensors;
 

examples/embedding/embedding.cpp

@@ -33,7 +33,7 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke
     }
 }
 
-static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd_out, int embd_norm) {
+static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
     const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
 
     // clear previous kv_cache values (irrelevant for embeddings)
@@ -65,8 +65,8 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
             GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
         }
 
-        float * out = output + embd_pos * n_embd_out;
-        common_embd_normalize(embd, out, n_embd_out, embd_norm);
+        float * out = output + embd_pos * n_embd;
+        common_embd_normalize(embd, out, n_embd, embd_norm);
     }
 }
 
@@ -252,8 +252,8 @@ int main(int argc, char ** argv) {
     }
 
     // allocate output
-    const int n_embd_out = llama_model_n_embd_out(model);
-    std::vector<float> embeddings(n_embd_count * n_embd_out, 0);
+    const int n_embd = llama_model_n_embd(model);
+    std::vector<float> embeddings(n_embd_count * n_embd, 0);
     float * emb = embeddings.data();
 
     // break into batches
@@ -267,8 +267,8 @@ int main(int argc, char ** argv) {
 
         // encode if at capacity
         if (batch.n_tokens + n_toks > n_batch || s >= n_seq_max) {
-            float * out = emb + e * n_embd_out;
-            batch_decode(ctx, batch, out, s, n_embd_out, params.embd_normalize);
+            float * out = emb + e * n_embd;
+            batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
             e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
             s = 0;
             common_batch_clear(batch);
@@ -280,8 +280,8 @@ int main(int argc, char ** argv) {
     }
 
     // final batch
-    float * out = emb + e * n_embd_out;
-    batch_decode(ctx, batch, out, s, n_embd_out, params.embd_normalize);
+    float * out = emb + e * n_embd;
+    batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
 
     if (params.embd_out.empty()) {
         LOG("\n");
@@ -289,19 +289,19 @@ int main(int argc, char ** argv) {
         if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
             for (int j = 0; j < n_embd_count; j++) {
                 LOG("embedding %d: ", j);
-                for (int i = 0; i < std::min(3, n_embd_out); i++) {
+                for (int i = 0; i < std::min(3, n_embd); i++) {
                     if (params.embd_normalize == 0) {
-                        LOG("%6.0f ", emb[j * n_embd_out + i]);
+                        LOG("%6.0f ", emb[j * n_embd + i]);
                     } else {
-                        LOG("%9.6f ", emb[j * n_embd_out + i]);
+                        LOG("%9.6f ", emb[j * n_embd + i]);
                     }
                 }
                 LOG(" ... ");
-                for (int i = n_embd_out - 3; i < n_embd_out; i++) {
+                for (int i = n_embd - 3; i < n_embd; i++) {
                     if (params.embd_normalize == 0) {
-                        LOG("%6.0f ", emb[j * n_embd_out + i]);
+                        LOG("%6.0f ", emb[j * n_embd + i]);
                     } else {
-                        LOG("%9.6f ", emb[j * n_embd_out + i]);
+                        LOG("%9.6f ", emb[j * n_embd + i]);
                     }
                 }
                 LOG("\n");
@@ -320,9 +320,9 @@ int main(int argc, char ** argv) {
                 for (uint32_t i = 0; i < n_cls_out; i++) {
                     // NOTE: if you change this log - update the tests in ci/run.sh
                     if (n_cls_out == 1) {
-                        LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd_out]);
+                        LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd]);
                     } else {
-                        LOG("rerank score %d: %8.3f [%s]\n", j, emb[j * n_embd_out + i], cls_out_labels[i].c_str());
+                        LOG("rerank score %d: %8.3f [%s]\n", j, emb[j * n_embd + i], cls_out_labels[i].c_str());
                     }
                 }
             }
@@ -330,11 +330,11 @@ int main(int argc, char ** argv) {
             // print the first part of the embeddings or for a single prompt, the full embedding
             for (int j = 0; j < n_prompts; j++) {
                 LOG("embedding %d: ", j);
-                for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd_out) : n_embd_out); i++) {
+                for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
                     if (params.embd_normalize == 0) {
-                        LOG("%6.0f ", emb[j * n_embd_out + i]);
+                        LOG("%6.0f ", emb[j * n_embd + i]);
                     } else {
-                        LOG("%9.6f ", emb[j * n_embd_out + i]);
+                        LOG("%9.6f ", emb[j * n_embd + i]);
                     }
                 }
                 LOG("\n");
@@ -350,7 +350,7 @@ int main(int argc, char ** argv) {
                 LOG("\n");
                 for (int i = 0; i < n_prompts; i++) {
                     for (int j = 0; j < n_prompts; j++) {
-                        float sim = common_embd_similarity_cos(emb + i * n_embd_out, emb + j * n_embd_out, n_embd_out);
+                        float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
                         LOG("%6.2f ", sim);
                     }
                     LOG("%1.10s", prompts[i].c_str());
@@ -368,9 +368,9 @@ int main(int argc, char ** argv) {
             if (notArray) LOG("    {\n      \"object\": \"embedding\",\n      \"index\": %d,\n      \"embedding\": ",j);
             LOG("[");
             for (int i = 0;;) { // at least one iteration (n_embd > 0)
-                LOG(params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd_out + i]);
+                LOG(params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]);
                 i++;
-                if (i < n_embd_out) LOG(","); else break;
+                if (i < n_embd) LOG(","); else break;
             }
             LOG(notArray ? "]\n    }" : "]");
             j++;
@@ -383,7 +383,7 @@ int main(int argc, char ** argv) {
             for (int i = 0;;) { // at least two iteration (n_embd_count > 1)
                 LOG("    [");
                 for (int j = 0;;) { // at least two iteration (n_embd_count > 1)
-                    float sim = common_embd_similarity_cos(emb + i * n_embd_out, emb + j * n_embd_out, n_embd_out);
+                    float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
                     LOG("%6.2f", sim);
                     j++;
                     if (j < n_embd_count) LOG(", "); else break;
@@ -397,7 +397,7 @@ int main(int argc, char ** argv) {
 
         if (notArray) LOG("\n}\n");
     } else if (params.embd_out == "raw") {
-        print_raw_embeddings(emb, n_embd_count, n_embd_out, model, pooling_type, params.embd_normalize);
+        print_raw_embeddings(emb, n_embd_count, n_embd, model, pooling_type, params.embd_normalize);
     }
 
     LOG("\n");

examples/eval-callback/CMakeLists.txt

@@ -4,23 +4,12 @@ install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 
-if(LLAMA_BUILD_TESTS)
-    if(NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
-        set(MODEL_NAME "tinyllamas/stories15M-q4_0.gguf")
-        set(MODEL_HASH "SHA256=66967fbece6dbe97886593fdbb73589584927e29119ec31f08090732d1861739")
-    else()
-        set(MODEL_NAME "tinyllamas/stories15M-be.Q4_0.gguf")
-        set(MODEL_HASH "SHA256=9aec857937849d976f30397e97eb1cabb53eb9dcb1ce4611ba8247fb5f44c65d")
-    endif()
-    set(MODEL_DEST "${CMAKE_BINARY_DIR}/${MODEL_NAME}")
-    set(TEST_TARGET test-eval-callback)
-    add_test(NAME ${TEST_TARGET}-download-model COMMAND ${CMAKE_COMMAND}
-        -DDEST=${MODEL_DEST}
-        -DNAME=${MODEL_NAME}
-        -DHASH=${MODEL_HASH}
-        -P ${CMAKE_SOURCE_DIR}/cmake/download-models.cmake
-    )
-    set_tests_properties(${TEST_TARGET}-download-model PROPERTIES FIXTURES_SETUP ${TEST_TARGET}-download-model)
-    add_test(NAME ${TEST_TARGET} COMMAND llama-eval-callback -m "${MODEL_DEST}" --prompt hello --seed 42 -ngl 0)
-    set_tests_properties(${TEST_TARGET} PROPERTIES FIXTURES_REQUIRED ${TEST_TARGET}-download-model)
+set(TEST_TARGET test-eval-callback)
+if(NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
+        add_test(NAME ${TEST_TARGET}
+                        COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
+else()
+        add_test(NAME ${TEST_TARGET}
+                        COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K-be.gguf --model stories260K-be.gguf --prompt hello --seed 42 -ngl 0)
 endif()
+set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl)

examples/eval-callback/eval-callback.cpp

@@ -1,12 +1,165 @@
 #include "arg.h"
 #include "common.h"
-#include "debug.h"
 #include "log.h"
 #include "llama.h"
-#include "llama-cpp.h"
+#include "ggml.h"
+
+#include <cmath>
+#include <cstdio>
 #include <string>
 #include <vector>
 
+/**
+ * This the arbitrary data which will be passed to each callback.
+ * Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor.
+ */
+struct callback_data {
+    std::vector<uint8_t> data;
+};
+
+static std::string ggml_ne_string(const ggml_tensor * t) {
+    std::string str;
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        str += std::to_string(t->ne[i]);
+        if (i + 1 < GGML_MAX_DIMS) {
+            str += ", ";
+        }
+    }
+    return str;
+}
+
+static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
+    union {
+        float f;
+        uint32_t i;
+    } u;
+    u.i = (uint32_t)h.bits << 16;
+    return u.f;
+}
+
+static float ggml_get_float_value(const uint8_t * data, ggml_type type, const size_t * nb, size_t i0, size_t i1, size_t i2, size_t i3) {
+    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
+    float v;
+    if (type == GGML_TYPE_F16) {
+        v = ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
+    } else if (type == GGML_TYPE_F32) {
+        v = *(const float *) &data[i];
+    } else if (type == GGML_TYPE_I64) {
+        v = (float) *(const int64_t *) &data[i];
+    } else if (type == GGML_TYPE_I32) {
+        v = (float) *(const int32_t *) &data[i];
+    } else if (type == GGML_TYPE_I16) {
+        v = (float) *(const int16_t *) &data[i];
+    } else if (type == GGML_TYPE_I8) {
+        v = (float) *(const int8_t *) &data[i];
+    } else if (type == GGML_TYPE_BF16) {
+        v = ggml_compute_bf16_to_fp32(*(const ggml_bf16_t *) &data[i]);
+    } else {
+        GGML_ABORT("fatal error");
+    }
+    return v;
+}
+
+static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
+    GGML_ASSERT(n > 0);
+    float sum = 0;
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
+                    sum += v;
+                }
+            }
+        }
+    }
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        LOG("                                     [\n");
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            if (i2 == n && ne[2] > 2*n) {
+                LOG("                                      ..., \n");
+                i2 = ne[2] - n;
+            }
+            LOG("                                      [\n");
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                if (i1 == n && ne[1] > 2*n) {
+                    LOG("                                       ..., \n");
+                    i1 = ne[1] - n;
+                }
+                LOG("                                       [");
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    if (i0 == n && ne[0] > 2*n) {
+                        LOG("..., ");
+                        i0 = ne[0] - n;
+                    }
+                    const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
+                    LOG("%12.4f", v);
+                    if (i0 < ne[0] - 1) LOG(", ");
+                }
+                LOG("],\n");
+            }
+            LOG("                                      ],\n");
+        }
+        LOG("                                     ]\n");
+        LOG("                                     sum = %f\n", sum);
+    }
+
+    // TODO: make this abort configurable/optional?
+    if (std::isnan(sum)) {
+        LOG_ERR("encountered NaN - aborting\n");
+        exit(0);
+    }
+}
+
+/**
+ * GGML operations callback during the graph execution.
+ *
+ * @param t current tensor
+ * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
+ *            if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
+ *            see ggml_backend_sched_eval_callback
+ * @param user_data user data to pass at each call back
+ * @return true to receive data or continue the graph, false otherwise
+ */
+static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
+    auto * cb_data = (callback_data *) user_data;
+
+    const struct ggml_tensor * src0 = t->src[0];
+    const struct ggml_tensor * src1 = t->src[1];
+
+    if (ask) {
+        return true; // Always retrieve data
+    }
+
+    char src1_str[128] = {0};
+    if (src1) {
+        snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
+    }
+
+    LOG("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
+         t->name, ggml_type_name(t->type), ggml_op_desc(t),
+         src0->name, ggml_ne_string(src0).c_str(),
+         src1 ? src1_str : "",
+         ggml_ne_string(t).c_str());
+
+
+    // copy the data from the GPU memory if needed
+    const bool is_host = ggml_backend_buffer_is_host(t->buffer);
+
+    if (!is_host) {
+        auto n_bytes = ggml_nbytes(t);
+        cb_data->data.resize(n_bytes);
+        ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
+    }
+
+    if (!ggml_is_quantized(t->type)) {
+        uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
+        ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
+    }
+
+    return true;
+}
+
 static bool run(llama_context * ctx, const common_params & params) {
     const llama_model * model = llama_get_model(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
@@ -29,7 +182,7 @@ static bool run(llama_context * ctx, const common_params & params) {
 }
 
 int main(int argc, char ** argv) {
-    base_callback_data cb_data;
+    callback_data cb_data;
 
     common_params params;
 
@@ -44,7 +197,7 @@ int main(int argc, char ** argv) {
 
     // pass the callback to the backend scheduler
     // it will be executed for each node during the graph computation
-    params.cb_eval = common_debug_cb_eval<false>;
+    params.cb_eval = ggml_debug;
     params.cb_eval_user_data = &cb_data;
     params.warmup = false;
 

examples/gen-docs/gen-docs.cpp

@@ -2,74 +2,57 @@
 #include "common.h"
 
 #include <fstream>
-#include <sstream>
 #include <string>
 
 // Export usage message (-h) to markdown format
-// Automatically update the markdown docs
 
-#define HELP_START_MARKER "<!-- HELP_START -->"
-#define HELP_END_MARKER   "<!-- HELP_END -->"
-#define NOTE_MESSAGE      "<!-- IMPORTANT: The list below is auto-generated by llama-gen-docs; do NOT modify it manually -->"
-
-struct md_file {
-    llama_example ex;
-    std::string fname;
-    std::string specific_section_header;
-};
-
-std::vector<md_file> md_files = {
-    {LLAMA_EXAMPLE_CLI,        "tools/cli/README.md",        "CLI-specific params"},
-    {LLAMA_EXAMPLE_COMPLETION, "tools/completion/README.md", "Completion-specific params"},
-    {LLAMA_EXAMPLE_SERVER,     "tools/server/README.md",     "Server-specific params"},
-};
-
-static void write_table_header(std::ostringstream & ss) {
-    ss << "| Argument | Explanation |\n";
-    ss << "| -------- | ----------- |\n";
+static void write_table_header(std::ofstream & file) {
+    file << "| Argument | Explanation |\n";
+    file << "| -------- | ----------- |\n";
 }
 
-static void write_table_entry(std::ostringstream & ss, const common_arg & opt) {
-    ss << "| `";
+static void write_table_entry(std::ofstream & file, const common_arg & opt) {
+    file << "| `";
     // args
     auto all_args = opt.get_args();
     for (const auto & arg : all_args) {
     if (arg == all_args.front()) {
-            ss << arg;
-            if (all_args.size() > 1) ss << ", ";
+            file << arg;
+            if (all_args.size() > 1) file << ", ";
         } else {
-            ss << arg << (arg != all_args.back() ? ", " : "");
+            file << arg << (arg != all_args.back() ? ", " : "");
         }
     }
     // value hint
     if (opt.value_hint) {
         std::string md_value_hint(opt.value_hint);
         string_replace_all(md_value_hint, "|", "\\|");
-        ss << " " << md_value_hint;
+        file << " " << md_value_hint;
     }
     if (opt.value_hint_2) {
         std::string md_value_hint_2(opt.value_hint_2);
         string_replace_all(md_value_hint_2, "|", "\\|");
-        ss << " " << md_value_hint_2;
+        file << " " << md_value_hint_2;
     }
     // help text
     std::string md_help(opt.help);
-    md_help = string_strip(md_help);
     string_replace_all(md_help, "\n", "<br/>");
     string_replace_all(md_help, "|", "\\|");
-    ss << "` | " << md_help << " |\n";
+    file << "` | " << md_help << " |\n";
 }
 
-static void write_table(std::ostringstream & ss, std::vector<common_arg *> & opts) {
-    write_table_header(ss);
+static void write_table(std::ofstream & file, std::vector<common_arg *> & opts) {
+    write_table_header(file);
     for (const auto & opt : opts) {
-        write_table_entry(ss, *opt);
+        write_table_entry(file, *opt);
     }
 }
 
-static void write_help(std::ostringstream & ss, const md_file & md) {
+static void export_md(std::string fname, llama_example ex, std::string name) {
+    std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
+
     common_params params;
-    auto ctx_arg = common_params_parser_init(params, md.ex);
+    auto ctx_arg = common_params_parser_init(params, ex);
 
     std::vector<common_arg *> common_options;
     std::vector<common_arg *> sparam_options;
@@ -85,58 +68,18 @@ static void write_help(std::ostringstream & ss, const md_file & md) {
         }
     }
 
-    ss << HELP_START_MARKER << "\n\n";
-
-    ss << NOTE_MESSAGE << "\n\n";
-
-    ss << "### Common params\n\n";
-    write_table(ss, common_options);
-    ss << "\n\n### Sampling params\n\n";
-    write_table(ss, sparam_options);
-    ss << "\n\n### " << md.specific_section_header << "\n\n";
-    write_table(ss, specific_options);
-
-    ss << "\n" << HELP_END_MARKER;
+    file << "**Common params**\n\n";
+    write_table(file, common_options);
+    file << "\n\n**Sampling params**\n\n";
+    write_table(file, sparam_options);
+    file << "\n\n**" << name << "-specific params**\n\n";
+    write_table(file, specific_options);
 }
 
 int main(int, char **) {
-    for (const auto & md : md_files) {
-        std::ifstream infile(md.fname);
-        if (!infile.is_open()) {
-            fprintf(stderr, "failed to open file '%s' for reading\n", md.fname.c_str());
-            return 1;
-        }
-
-        std::ostringstream ss;
-        ss << infile.rdbuf();
-        infile.close();
-
-        std::string content = ss.str();
-
-        size_t help_start = content.find(HELP_START_MARKER);
-        size_t help_end   = content.find(HELP_END_MARKER);
-
-        if (help_start == std::string::npos || help_end == std::string::npos || help_end <= help_start) {
-            fprintf(stderr, "failed to find help markers in file '%s'\n", md.fname.c_str());
-            return 1;
-        }
-
-        std::ostringstream new_help_ss;
-        write_help(new_help_ss, md);
-        std::string new_help = new_help_ss.str();
-
-        content = content.substr(0, help_start) + new_help + content.substr(help_end + strlen(HELP_END_MARKER));
-
-        std::ofstream outfile(md.fname);
-        if (!outfile.is_open()) {
-            fprintf(stderr, "failed to open file '%s' for writing\n", md.fname.c_str());
-            return 1;
-        }
-        outfile << content;
-        outfile.close();
-
-        printf("Updated help in '%s'\n", md.fname.c_str());
-    }
+    // TODO: add CLI
+    export_md("autogen-completion.md", LLAMA_EXAMPLE_COMPLETION, "Tool");
+    export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER, "Server");
 
     return 0;
 }

examples/llama.android/app/build.gradle.kts

@@ -41,8 +41,11 @@ android {
         }
     }
     compileOptions {
-        sourceCompatibility = JavaVersion.VERSION_17
-        targetCompatibility = JavaVersion.VERSION_17
+        sourceCompatibility = JavaVersion.VERSION_1_8
+        targetCompatibility = JavaVersion.VERSION_1_8
+    }
+    kotlinOptions {
+        jvmTarget = "1.8"
     }
 }
 

examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt

@@ -6,7 +6,6 @@ import android.util.Log
 import android.widget.EditText
 import android.widget.TextView
 import android.widget.Toast
-import androidx.activity.addCallback
 import androidx.activity.enableEdgeToEdge
 import androidx.activity.result.contract.ActivityResultContracts
 import androidx.appcompat.app.AppCompatActivity
@@ -19,7 +18,6 @@ import com.arm.aichat.gguf.GgufMetadata
 import com.arm.aichat.gguf.GgufMetadataReader
 import com.google.android.material.floatingactionbutton.FloatingActionButton
 import kotlinx.coroutines.Dispatchers
-import kotlinx.coroutines.Job
 import kotlinx.coroutines.flow.onCompletion
 import kotlinx.coroutines.launch
 import kotlinx.coroutines.withContext
@@ -38,7 +36,6 @@ class MainActivity : AppCompatActivity() {
 
     // Arm AI Chat inference engine
     private lateinit var engine: InferenceEngine
-    private var generationJob: Job? = null
 
     // Conversation states
     private var isModelReady = false
@@ -50,13 +47,11 @@ class MainActivity : AppCompatActivity() {
         super.onCreate(savedInstanceState)
         enableEdgeToEdge()
         setContentView(R.layout.activity_main)
-        // View model boilerplate and state management is out of this basic sample's scope
-        onBackPressedDispatcher.addCallback { Log.w(TAG, "Ignore back press for simplicity") }
 
         // Find views
         ggufTv = findViewById(R.id.gguf)
         messagesRv = findViewById(R.id.messages)
-        messagesRv.layoutManager = LinearLayoutManager(this).apply { stackFromEnd = true }
+        messagesRv.layoutManager = LinearLayoutManager(this)
         messagesRv.adapter = messageAdapter
         userInputEt = findViewById(R.id.user_input)
         userActionFab = findViewById(R.id.fab)
@@ -162,35 +157,33 @@ class MainActivity : AppCompatActivity() {
      * Validate and send the user message into [InferenceEngine]
      */
     private fun handleUserInput() {
-        userInputEt.text.toString().also { userMsg ->
-            if (userMsg.isEmpty()) {
+        userInputEt.text.toString().also { userSsg ->
+            if (userSsg.isEmpty()) {
                 Toast.makeText(this, "Input message is empty!", Toast.LENGTH_SHORT).show()
             } else {
                 userInputEt.text = null
-                userInputEt.isEnabled = false
                 userActionFab.isEnabled = false
 
                 // Update message states
-                messages.add(Message(UUID.randomUUID().toString(), userMsg, true))
+                messages.add(Message(UUID.randomUUID().toString(), userSsg, true))
                 lastAssistantMsg.clear()
                 messages.add(Message(UUID.randomUUID().toString(), lastAssistantMsg.toString(), false))
 
-                generationJob = lifecycleScope.launch(Dispatchers.Default) {
-                    engine.sendUserPrompt(userMsg)
+                lifecycleScope.launch(Dispatchers.Default) {
+                    engine.sendUserPrompt(userSsg)
                         .onCompletion {
                             withContext(Dispatchers.Main) {
-                                userInputEt.isEnabled = true
                                 userActionFab.isEnabled = true
                             }
                         }.collect { token ->
+                            val messageCount = messages.size
+                            check(messageCount > 0 && !messages[messageCount - 1].isUser)
+
+                            messages.removeAt(messageCount - 1).copy(
+                                content = lastAssistantMsg.append(token).toString()
+                            ).let { messages.add(it) }
+
                             withContext(Dispatchers.Main) {
-                                val messageCount = messages.size
-                                check(messageCount > 0 && !messages[messageCount - 1].isUser)
-
-                                messages.removeAt(messageCount - 1).copy(
-                                    content = lastAssistantMsg.append(token).toString()
-                                ).let { messages.add(it) }
-
                                 messageAdapter.notifyItemChanged(messages.size - 1)
                             }
                         }
@@ -202,7 +195,6 @@ class MainActivity : AppCompatActivity() {
     /**
      * Run a benchmark with the model file
      */
-    @Deprecated("This benchmark doesn't accurately indicate GUI performance expected by app developers")
     private suspend fun runBenchmark(modelName: String, modelFile: File) =
         withContext(Dispatchers.Default) {
             Log.i(TAG, "Starts benchmarking $modelName")
@@ -231,16 +223,6 @@ class MainActivity : AppCompatActivity() {
             if (!it.exists()) { it.mkdir() }
         }
 
-    override fun onStop() {
-        generationJob?.cancel()
-        super.onStop()
-    }
-
-    override fun onDestroy() {
-        engine.destroy()
-        super.onDestroy()
-    }
-
     companion object {
         private val TAG = MainActivity::class.java.simpleName
 

examples/llama.android/app/src/main/res/layout/activity_main.xml

@@ -1,56 +1,55 @@
 <?xml version="1.0" encoding="utf-8"?>
 <androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
     xmlns:app="http://schemas.android.com/apk/res-auto"
-    xmlns:tools="http://schemas.android.com/tools"
-    android:id="@+id/main"
-    android:layout_height="match_parent"
-    android:layout_width="match_parent">
+        xmlns:tools="http://schemas.android.com/tools"
+        android:id="@+id/main"
+        android:layout_height="match_parent"
+        android:layout_width="match_parent">
 
     <LinearLayout
         android:fitsSystemWindows="true"
         android:layout_width="match_parent"
         android:layout_height="match_parent"
         android:orientation="vertical"
-        android:layout_marginEnd="4dp"
         tools:context=".MainActivity">
 
-        <ScrollView
+        <FrameLayout
             android:layout_width="match_parent"
             android:layout_height="0dp"
-            android:layout_weight="1"
-            android:fadeScrollbars="false">
+            android:layout_weight="1">
 
-            <TextView
-                android:id="@+id/gguf"
+            <ScrollView
                 android:layout_width="match_parent"
                 android:layout_height="wrap_content"
-                android:padding="16dp"
-                android:text="Selected GGUF model's metadata will show here."
-                style="@style/TextAppearance.MaterialComponents.Body2" />
+                android:fadeScrollbars="false">
 
-        </ScrollView>
+                <TextView
+                    android:id="@+id/gguf"
+                    android:layout_width="match_parent"
+                    android:layout_height="wrap_content"
+                    android:layout_margin="16dp"
+                    android:text="Selected GGUF model's metadata will show here."
+                    style="@style/TextAppearance.MaterialComponents.Body2"
+                    android:maxLines="100" />
 
-        <com.google.android.material.divider.MaterialDivider
-            android:layout_width="match_parent"
-            android:layout_height="2dp"
-            android:layout_marginHorizontal="16dp" />
+            </ScrollView>
+
+        </FrameLayout>
 
         <androidx.recyclerview.widget.RecyclerView
             android:id="@+id/messages"
             android:layout_width="match_parent"
             android:layout_height="0dp"
             android:layout_weight="4"
+            android:padding="16dp"
             android:fadeScrollbars="false"
-            android:scrollbars="vertical"
             app:reverseLayout="true"
             tools:listitem="@layout/item_message_assistant"/>
 
         <LinearLayout
             android:layout_width="match_parent"
             android:layout_height="wrap_content"
-            android:orientation="horizontal"
-            android:paddingStart="16dp"
-            android:paddingEnd="4dp">
+            android:orientation="horizontal">
 
             <EditText
                 android:id="@+id/user_input"
@@ -68,7 +67,7 @@
                 style="@style/Widget.Material3.FloatingActionButton.Primary"
                 android:layout_width="wrap_content"
                 android:layout_height="wrap_content"
-                android:layout_margin="12dp"
+                android:layout_margin="8dp"
                 android:src="@drawable/outline_folder_open_24" />
 
         </LinearLayout>

examples/llama.android/app/src/main/res/layout/item_message_assistant.xml

@@ -2,8 +2,7 @@
 <LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
     android:layout_width="match_parent"
     android:layout_height="wrap_content"
-    android:layout_marginHorizontal="16dp"
-    android:layout_marginVertical="8dp"
+    android:padding="8dp"
     android:gravity="start">
 
     <TextView

examples/llama.android/app/src/main/res/layout/item_message_user.xml

@@ -2,8 +2,7 @@
 <LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
     android:layout_width="match_parent"
     android:layout_height="wrap_content"
-    android:layout_marginHorizontal="16dp"
-    android:layout_marginVertical="8dp"
+    android:padding="8dp"
     android:gravity="end">
 
     <TextView

examples/llama.android/gradle/libs.versions.toml

@@ -1,15 +1,15 @@
 [versions]
 
 # Plugins
-agp = "8.13.2"
-kotlin = "2.3.0"
+agp = "8.13.0"
+kotlin = "2.2.20"
 
 # AndroidX
-activity = "1.12.2"
+activity = "1.11.0"
 appcompat = "1.7.1"
 core-ktx = "1.17.0"
 constraint-layout = "2.2.1"
-datastore-preferences = "1.2.0"
+datastore-preferences = "1.1.7"
 
 # Material
 material = "1.13.0"

examples/llama.android/lib/build.gradle.kts

@@ -26,7 +26,7 @@ android {
 
                 arguments += "-DBUILD_SHARED_LIBS=ON"
                 arguments += "-DLLAMA_BUILD_COMMON=ON"
-                arguments += "-DLLAMA_OPENSSL=OFF"
+                arguments += "-DLLAMA_CURL=OFF"
 
                 arguments += "-DGGML_NATIVE=OFF"
                 arguments += "-DGGML_BACKEND_DL=ON"

examples/llama.android/lib/src/main/cpp/ai_chat.cpp

@@ -560,6 +560,6 @@ Java_com_arm_aichat_internal_InferenceEngineImpl_unload(JNIEnv * /*unused*/, job
 
 extern "C"
 JNIEXPORT void JNICALL
-Java_com_arm_aichat_internal_InferenceEngineImpl_shutdown(JNIEnv *, jobject /*unused*/) {
+Java_com_arm_aichat_internal_InferenceEngineImpl_shutdown(JNIEnv *env, jobject /*unused*/) {
     llama_backend_free();
 }

examples/llama.android/lib/src/main/java/com/arm/aichat/InferenceEngine.kt

@@ -38,7 +38,7 @@ interface InferenceEngine {
     /**
      * Unloads the currently loaded model.
      */
-    fun cleanUp()
+    suspend fun cleanUp()
 
     /**
      * Cleans up resources when the engine is no longer needed.

examples/llama.android/lib/src/main/java/com/arm/aichat/internal/InferenceEngineImpl.kt

@@ -15,11 +15,9 @@ import kotlinx.coroutines.cancel
 import kotlinx.coroutines.flow.Flow
 import kotlinx.coroutines.flow.MutableStateFlow
 import kotlinx.coroutines.flow.StateFlow
-import kotlinx.coroutines.flow.asStateFlow
 import kotlinx.coroutines.flow.flow
 import kotlinx.coroutines.flow.flowOn
 import kotlinx.coroutines.launch
-import kotlinx.coroutines.runBlocking
 import kotlinx.coroutines.withContext
 import java.io.File
 import java.io.IOException
@@ -111,11 +109,9 @@ internal class InferenceEngineImpl private constructor(
 
     private val _state =
         MutableStateFlow<InferenceEngine.State>(InferenceEngine.State.Uninitialized)
-    override val state: StateFlow<InferenceEngine.State> = _state.asStateFlow()
+    override val state: StateFlow<InferenceEngine.State> = _state
 
     private var _readyForSystemPrompt = false
-    @Volatile
-    private var _cancelGeneration = false
 
     /**
      * Single-threaded coroutine dispatcher & scope for LLama asynchronous operations
@@ -173,8 +169,6 @@ internal class InferenceEngineImpl private constructor(
                 }
                 Log.i(TAG, "Model loaded!")
                 _readyForSystemPrompt = true
-
-                _cancelGeneration = false
                 _state.value = InferenceEngine.State.ModelReady
             } catch (e: Exception) {
                 Log.e(TAG, (e.message ?: "Error loading model") + "\n" + pathToModel, e)
@@ -237,19 +231,15 @@ internal class InferenceEngineImpl private constructor(
 
             Log.i(TAG, "User prompt processed. Generating assistant prompt...")
             _state.value = InferenceEngine.State.Generating
-            while (!_cancelGeneration) {
+            while (true) {
                 generateNextToken()?.let { utf8token ->
                     if (utf8token.isNotEmpty()) emit(utf8token)
                 } ?: break
             }
-            if (_cancelGeneration) {
-                Log.i(TAG, "Assistant generation aborted per requested.")
-            } else {
-                Log.i(TAG, "Assistant generation complete. Awaiting user prompt...")
-            }
+            Log.i(TAG, "Assistant generation complete. Awaiting user prompt...")
             _state.value = InferenceEngine.State.ModelReady
         } catch (e: CancellationException) {
-            Log.i(TAG, "Assistant generation's flow collection cancelled.")
+            Log.i(TAG, "Generation cancelled by user.")
             _state.value = InferenceEngine.State.ModelReady
             throw e
         } catch (e: Exception) {
@@ -278,9 +268,8 @@ internal class InferenceEngineImpl private constructor(
     /**
      * Unloads the model and frees resources, or reset error states
      */
-    override fun cleanUp() {
-        _cancelGeneration = true
-        runBlocking(llamaDispatcher) {
+    override suspend fun cleanUp() =
+        withContext(llamaDispatcher) {
             when (val state = _state.value) {
                 is InferenceEngine.State.ModelReady -> {
                     Log.i(TAG, "Unloading model and free resources...")
@@ -304,21 +293,17 @@ internal class InferenceEngineImpl private constructor(
                 else -> throw IllegalStateException("Cannot unload model in ${state.javaClass.simpleName}")
             }
         }
-    }
 
     /**
      * Cancel all ongoing coroutines and free GGML backends
      */
     override fun destroy() {
-        _cancelGeneration = true
-        runBlocking(llamaDispatcher) {
-            _readyForSystemPrompt = false
-            when(_state.value) {
-                is InferenceEngine.State.Uninitialized -> {}
-                is InferenceEngine.State.Initialized -> shutdown()
-                else -> { unload(); shutdown() }
-            }
-        }
+        _readyForSystemPrompt = false
         llamaScope.cancel()
+        when(_state.value) {
+            is InferenceEngine.State.Uninitialized -> {}
+            is InferenceEngine.State.Initialized -> shutdown()
+            else -> { unload(); shutdown() }
+        }
     }
 }

examples/model-conversion/CMakeLists.txt

@@ -1,5 +1,5 @@
-set(TARGET llama-debug)
-add_executable(${TARGET} debug.cpp)
+set(TARGET llama-logits)
+add_executable(${TARGET} logits.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)

examples/model-conversion/Makefile

@@ -25,8 +25,6 @@ define quantize_model
 	@echo "Export the quantized model path to $(2) variable in your environment"
 endef
 
-DEVICE ?= auto
-
 ###
 ### Casual Model targets/recipes
 ###
@@ -55,13 +53,13 @@ causal-convert-mm-model:
 
 causal-run-original-model:
 	$(call validate_model_path,causal-run-original-model)
-	@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/run-org-model.py --device "$(DEVICE)"
+	@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/run-org-model.py
 
 causal-run-converted-model:
 	@CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/causal/run-converted-model.sh
 
 causal-verify-logits: causal-run-original-model causal-run-converted-model
-	@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/compare-logits.py
+	@./scripts/causal/compare-logits.py
 	@MODEL_PATH="$(MODEL_PATH)" ./scripts/utils/check-nmse.py -m ${MODEL_PATH}
 
 causal-run-original-embeddings:
@@ -138,13 +136,16 @@ embedding-run-original-model-st: embedding-run-original-model
 embedding-run-converted-model:
 	@./scripts/embedding/run-converted-model.sh $(CONVERTED_EMBEDDING_MODEL) \
 	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") \
-	$(if $(EMBD_NORMALIZE),--embd-normalize "$(EMBD_NORMALIZE)")
+	$(if $(USE_POOLING),--pooling)
+
+embedding-run-converted-model-st: USE_POOLING=1
+embedding-run-converted-model-st: embedding-run-converted-model
 
 embedding-verify-logits: embedding-run-original-model embedding-run-converted-model
 	@./scripts/embedding/compare-embeddings-logits.sh \
 	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
 
-embedding-verify-logits-st: embedding-run-original-model-st embedding-run-converted-model
+embedding-verify-logits-st: embedding-run-original-model-st embedding-run-converted-model-st
 	@./scripts/embedding/compare-embeddings-logits.sh \
 	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
 

examples/model-conversion/README.md

@@ -198,13 +198,14 @@ model, and the other is a text file which allows for manual visual inspection.
 
 #### Using SentenceTransformer with numbered layers
 For models that have numbered SentenceTransformer layers (01_Pooling, 02_Dense,
-03_Dense, 04_Normalize), these will be applied automatically when running the
-converted model but currently there is a separate target to run the original
-version:
+03_Dense, 04_Normalize), use the `-st` targets to apply all these layers:
 
 ```console
 # Run original model with SentenceTransformer (applies all numbered layers)
 (venv) $ make embedding-run-original-model-st
+
+# Run converted model with pooling enabled
+(venv) $ make embedding-run-converted-model-st
 ```
 
 This will use the SentenceTransformer library to load and run the model, which
@@ -212,17 +213,6 @@ automatically applies all the numbered layers in the correct order. This is
 particularly useful when comparing with models that should include these
 additional transformation layers beyond just the base model output.
 
-The type of normalization can be specified for the converted model but is not
-strictly necessary as the verification uses cosine similarity and the magnitude
-of the output vectors does not affect this. But the normalization type can be
-specified as an argument to the target which might be useful for manual
-inspection:
-```console
-(venv) $ make embedding-verify-logits-st EMBD_NORMALIZE=1
-```
-The original model will apply the normalization according to the normalization
-layer specified in the modules.json configuration file.
-
 ### Model conversion
 After updates have been made to [gguf-py](../../gguf-py) to add support for the
 new model the model can be converted to GGUF format using the following command:

examples/model-conversion/logits.cpp

@@ -0,0 +1,268 @@
+#include "llama.h"
+#include "common.h"
+
+
+#include <cstdio>
+#include <cstring>
+#include <string>
+#include <vector>
+#include <ctype.h>
+#include <filesystem>
+
+static void print_usage(int, char ** argv) {
+    printf("\nexample usage:\n");
+    printf("\n    %s -m model.gguf [-ngl n_gpu_layers] -embd-mode [-pooling] [-embd-norm <norm>] [prompt]\n", argv[0]);
+    printf("\n");
+    printf("  -embd-norm: normalization type for pooled embeddings (default: 2)\n");
+    printf("              -1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm\n");
+    printf("\n");
+}
+
+int main(int argc, char ** argv) {
+    std::string model_path;
+    std::string prompt = "Hello, my name is";
+    int ngl = 0;
+    bool embedding_mode = false;
+    bool pooling_enabled = false;
+    int32_t embd_norm = 2;  // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm)
+
+    {
+        int i = 1;
+        for (; i < argc; i++) {
+            if (strcmp(argv[i], "-m") == 0) {
+                if (i + 1 < argc) {
+                    model_path = argv[++i];
+                } else {
+                    print_usage(argc, argv);
+                    return 1;
+                }
+            } else if (strcmp(argv[i], "-ngl") == 0) {
+                if (i + 1 < argc) {
+                    try {
+                        ngl = std::stoi(argv[++i]);
+                    } catch (...) {
+                        print_usage(argc, argv);
+                        return 1;
+                    }
+                } else {
+                    print_usage(argc, argv);
+                    return 1;
+                }
+            } else if (strcmp(argv[i], "-embd-mode") == 0) {
+                embedding_mode = true;
+            } else if (strcmp(argv[i], "-pooling") == 0) {
+                pooling_enabled = true;
+            } else if (strcmp(argv[i], "-embd-norm") == 0) {
+                if (i + 1 < argc) {
+                    try {
+                        embd_norm = std::stoi(argv[++i]);
+                    } catch (...) {
+                        print_usage(argc, argv);
+                        return 1;
+                    }
+                } else {
+                    print_usage(argc, argv);
+                    return 1;
+                }
+            } else {
+                // prompt starts here
+                break;
+            }
+        }
+
+        if (model_path.empty()) {
+            print_usage(argc, argv);
+            return 1;
+        }
+
+        if (i < argc) {
+            prompt = argv[i++];
+            for (; i < argc; i++) {
+                prompt += " ";
+                prompt += argv[i];
+            }
+        }
+    }
+
+    ggml_backend_load_all();
+    llama_model_params model_params = llama_model_default_params();
+    model_params.n_gpu_layers = ngl;
+
+    llama_model * model = llama_model_load_from_file(model_path.c_str(), model_params);
+
+    if (model == NULL) {
+        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    // Extract basename from model_path
+    const char * basename = strrchr(model_path.c_str(), '/');
+    basename = (basename == NULL) ? model_path.c_str() : basename + 1;
+
+    char model_name[256];
+    strncpy(model_name, basename, 255);
+    model_name[255] = '\0';
+
+    char * dot = strrchr(model_name, '.');
+    if (dot != NULL && strcmp(dot, ".gguf") == 0) {
+        *dot = '\0';
+    }
+    printf("Model name: %s\n", model_name);
+
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+    const int n_prompt = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, true, true);
+
+    std::vector<llama_token> prompt_tokens(n_prompt);
+    if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
+        fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
+        return 1;
+    }
+
+    llama_context_params ctx_params = llama_context_default_params();
+    ctx_params.n_ctx = n_prompt;
+    ctx_params.n_batch = n_prompt;
+    ctx_params.no_perf = false;
+    if (embedding_mode) {
+        ctx_params.embeddings = true;
+        ctx_params.pooling_type = pooling_enabled ? LLAMA_POOLING_TYPE_MEAN : LLAMA_POOLING_TYPE_NONE;
+        ctx_params.n_ubatch = ctx_params.n_batch;
+    }
+
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
+    if (ctx == NULL) {
+        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    printf("Input prompt: \"%s\"\n", prompt.c_str());
+    printf("Tokenized prompt (%d tokens): ", n_prompt);
+    for (auto id : prompt_tokens) {
+        char buf[128];
+        int n = llama_token_to_piece(vocab, id, buf, sizeof(buf), 0, true);
+        if (n < 0) {
+            fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
+            return 1;
+        }
+        std::string s(buf, n);
+        printf("%s (%d)", s.c_str(), id);
+    }
+    printf("\n");
+
+    llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
+
+    if (llama_decode(ctx, batch)) {
+        fprintf(stderr, "%s : failed to eval\n", __func__);
+        return 1;
+    }
+
+    float * data_ptr;
+    int data_size;
+    const char * type;
+    std::vector<float> embd_out;
+
+    if (embedding_mode) {
+        const int n_embd = llama_model_n_embd(model);
+        const int n_embd_count = pooling_enabled ? 1 : batch.n_tokens;
+        const int n_embeddings = n_embd * n_embd_count;
+        float * embeddings;
+        type = "-embeddings";
+
+        if (llama_pooling_type(ctx) != LLAMA_POOLING_TYPE_NONE) {
+            embeddings = llama_get_embeddings_seq(ctx, 0);
+            embd_out.resize(n_embeddings);
+            printf("Normalizing embeddings using norm: %d\n", embd_norm);
+            common_embd_normalize(embeddings, embd_out.data(), n_embeddings, embd_norm);
+            embeddings = embd_out.data();
+        } else {
+            embeddings = llama_get_embeddings(ctx);
+        }
+
+        printf("Embedding dimension: %d\n", n_embd);
+        printf("\n");
+
+        // Print embeddings in the specified format
+        for (int j = 0; j < n_embd_count; j++) {
+            printf("embedding %d: ", j);
+
+            // Print first 3 values
+            for (int i = 0; i < 3 && i < n_embd; i++) {
+                printf("%9.6f ", embeddings[j * n_embd + i]);
+            }
+
+            printf(" ... ");
+
+            // Print last 3 values
+            for (int i = n_embd - 3; i < n_embd; i++) {
+                if (i >= 0) {
+                    printf("%9.6f ", embeddings[j * n_embd + i]);
+                }
+            }
+
+            printf("\n");
+        }
+        printf("\n");
+
+        printf("Embeddings size: %d\n", n_embeddings);
+
+        data_ptr = embeddings;
+        data_size = n_embeddings;
+    } else {
+        float * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
+        const int n_logits = llama_vocab_n_tokens(vocab);
+        type = "";
+        printf("Vocab size: %d\n", n_logits);
+
+        data_ptr = logits;
+        data_size = n_logits;
+    }
+
+    std::filesystem::create_directory("data");
+
+    // Save data to binary file
+    char bin_filename[512];
+    snprintf(bin_filename, sizeof(bin_filename), "data/llamacpp-%s%s.bin", model_name, type);
+    printf("Saving data to %s\n", bin_filename);
+
+    FILE * f = fopen(bin_filename, "wb");
+    if (f == NULL) {
+        fprintf(stderr, "%s: error: failed to open binary output file\n", __func__);
+        return 1;
+    }
+    fwrite(data_ptr, sizeof(float), data_size, f);
+    fclose(f);
+
+    // Also save as text for debugging
+    char txt_filename[512];
+    snprintf(txt_filename, sizeof(txt_filename), "data/llamacpp-%s%s.txt", model_name, type);
+    f = fopen(txt_filename, "w");
+    if (f == NULL) {
+        fprintf(stderr, "%s: error: failed to open text output file\n", __func__);
+        return 1;
+    }
+    for (int i = 0; i < data_size; i++) {
+        fprintf(f, "%d: %.6f\n", i, data_ptr[i]);
+    }
+    fclose(f);
+
+    if (!embedding_mode) {
+        printf("First 10 logits: ");
+        for (int i = 0; i < 10 && i < data_size; i++) {
+            printf("%.6f ", data_ptr[i]);
+        }
+        printf("\n");
+
+        printf("Last 10 logits: ");
+        for (int i = data_size - 10; i < data_size; i++) {
+            if (i >= 0) printf("%.6f ", data_ptr[i]);
+        }
+        printf("\n\n");
+    }
+
+    printf("Data saved to %s\n", bin_filename);
+    printf("Data saved to %s\n", txt_filename);
+
+    llama_free(ctx);
+    llama_model_free(model);
+
+    return 0;
+}

examples/model-conversion/scripts/causal/compare-embeddings-logits.sh

@@ -5,11 +5,8 @@ set -e
 MODEL_PATH="${1:-"$MODEL_PATH"}"
 MODEL_NAME="${2:-$(basename "$MODEL_PATH")}"
 
-CONVERTED_MODEL_PATH="${1:-"$CONVERTED_MODEL"}"
-CONVERTED_MODEL_NAME="${2:-$(basename "$CONVERTED_MODEL_PATH" ".gguf")}"
-
 if [ -t 0 ]; then
-    CPP_EMBEDDINGS="data/llamacpp-${CONVERTED_MODEL_NAME}-embeddings.bin"
+    CPP_EMBEDDINGS="data/llamacpp-${MODEL_NAME}-embeddings.bin"
 else
     # Process piped JSON data and convert to binary (matching logits.cpp format)
     TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn)

examples/model-conversion/scripts/causal/compare-logits.py

@@ -3,11 +3,10 @@
 import sys
 import numpy as np
 from pathlib import Path
-import os
 
 # Add utils directory to path for direct script execution
 sys.path.insert(0, str(Path(__file__).parent.parent / "utils"))
-from common import get_model_name_from_env_path, compare_tokens, exit_with_warning  # type: ignore[import-not-found]
+from common import get_model_name_from_env_path  # type: ignore[import-not-found]
 
 def quick_logits_check(pytorch_file, llamacpp_file):
     """Lightweight sanity check before NMSE"""
@@ -39,7 +38,6 @@ def quick_logits_check(pytorch_file, llamacpp_file):
     return True
 
 def main():
-    model_path = os.environ.get('MODEL_PATH')
     model_name = get_model_name_from_env_path('MODEL_PATH')
     data_dir = Path("data")
     pytorch_file = data_dir / f"pytorch-{model_name}.bin"
@@ -60,12 +58,6 @@ def main():
 
     print("Checked all required files were found. Proceeding...\n")
 
-    # Verify tokens as they are a prerequisite for logits comparison.
-    print("🔍 Token Comparison Check")
-    print("=" * 40)
-    if not compare_tokens(f"pytorch-{model_name}", f"llamacpp-{llamacpp_model_name}"):
-        exit_with_warning("\n❌ Token mismatch detected", model_path)
-    print()
 
     print("🔍 GGML Model Validation for model ", model_name)
     print("=" * 40)
@@ -81,7 +73,8 @@ def main():
         print("       Ok to proceed with NMSE check...")
         sys.exit(0)
     else:
-        exit_with_warning(f"❌ NOK: Top 10 predictions don't match - generation will differ", model_path)
+        print(f"❌ NOK: Top 10 predictions don't match - generation will differ")
+        sys.exit(1)
 
 if __name__ == "__main__":
     main()

examples/model-conversion/scripts/causal/modelcard.template

@@ -7,7 +7,7 @@ base_model:
 Recommended way to run this model:
 
 ```sh
-llama-server -hf {namespace}/{model_name}-GGUF
+llama-server -hf {namespace}/{model_name}-GGUF -c 0
 ```
 
 Then, access http://localhost:8080