arg: allow -kvu flag for llama-perplexity (#18117 )

The -kvu (--kv-unified) flag is required for hellaswag and winogrande benchmarks which use coupled sequences. Without unified KV cache, these benchmarks fail with: split_equal: sequential split is not supported when there are coupled sequences in the input batch (you may need to use the -kvu flag) This change adds LLAMA_EXAMPLE_PERPLEXITY to the allowed examples for the -kvu argument, enabling its use with llama-perplexity.
ggml : use WARP_SIZE/2 for argmax reduction offset (#18092 )
2026-06-30 09:37:42 +02:00 · 2025-12-17 08:33:02 +02:00 · 2025-12-17 11:47:01 +08:00 · 2025-12-17 02:27:03 +01:00 · 2025-12-17 00:50:12 +01:00 · 2025-12-17 00:49:34 +01:00
142 changed files with 8327 additions and 4709 deletions
@@ -107,7 +107,7 @@ ENTRYPOINT ["/app/tools.sh"]
 # ENTRYPOINT ["/app/llama-server"]

 ### Target: light
-# Lightweight image containing only llama-cli
+# Lightweight image containing only llama-cli and llama-completion
 # ==============================================================================
 FROM base AS light

@@ -23,11 +23,12 @@ ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
 RUN echo "Building with static libs" && \
    source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \
    cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF -DLLAMA_BUILD_TESTS=OFF  && \
-    cmake --build build --config Release --target llama-cli
+    cmake --build build --config Release --target llama-cli && \
+    cmake --build build --config Release --target llama-completion

 # TODO: use image with NNRT
 FROM ascendai/cann:$ASCEND_VERSION AS runtime
-COPY --from=build /app/build/bin/llama-cli /llama-cli
+COPY --from=build /app/build/bin/llama-cli /app/build/bin/llama-completion /

 ENV LC_ALL=C.utf8

@@ -37,6 +37,7 @@ make -j GGML_CUDA=1
 %install
 mkdir -p %{buildroot}%{_bindir}/
 cp -p llama-cli %{buildroot}%{_bindir}/llama-cuda-cli
+cp -p llama-completion %{buildroot}%{_bindir}/llama-cuda-completion
 cp -p llama-server %{buildroot}%{_bindir}/llama-cuda-server
 cp -p llama-simple %{buildroot}%{_bindir}/llama-cuda-simple

@@ -68,6 +69,7 @@ rm -rf %{_builddir}/*

 %files
 %{_bindir}/llama-cuda-cli
+%{_bindir}/llama-cuda-completion
 %{_bindir}/llama-cuda-server
 %{_bindir}/llama-cuda-simple
 /usr/lib/systemd/system/llamacuda.service
@@ -39,6 +39,7 @@ make -j
 %install
 mkdir -p %{buildroot}%{_bindir}/
 cp -p llama-cli %{buildroot}%{_bindir}/llama-cli
+cp -p llama-completion %{buildroot}%{_bindir}/llama-completion
 cp -p llama-server %{buildroot}%{_bindir}/llama-server
 cp -p llama-simple %{buildroot}%{_bindir}/llama-simple

@@ -70,6 +71,7 @@ rm -rf %{_builddir}/*

 %files
 %{_bindir}/llama-cli
+%{_bindir}/llama-completion
 %{_bindir}/llama-server
 %{_bindir}/llama-simple
 /usr/lib/systemd/system/llama.service
@@ -11,7 +11,7 @@ body:
        (i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation.
        If you encountered the issue while using an external UI (e.g. ollama),
        please reproduce your issue using one of the examples/binaries in this repository.
-        The `llama-cli` binary can be used for simple and reproducible model inference.
+        The `llama-completion` binary can be used for simple and reproducible model inference.
  - type: textarea
    id: version
    attributes:
@@ -74,9 +74,12 @@ body:
        Please give us a summary of the problem and tell us how to reproduce it.
        If you can narrow down the bug to specific hardware, compile flags, or command line arguments,
        that information would be very much appreciated by us.
+
+        If possible, please try to reproduce the issue using `llama-completion` with `-fit off`.
+        If you can only reproduce the issue with `-fit on`, please provide logs both with and without `--verbose`.
      placeholder: >
-        e.g. when I run llama-cli with -ngl 99 I get garbled outputs.
-        When I use -ngl 0 it works correctly.
+        e.g. when I run llama-completion with `-fa on` I get garbled outputs for very long prompts.
+        With short prompts or `-fa off` it works correctly.
        Here are the exact commands that I used: ...
    validations:
      required: true
@@ -0,0 +1,295 @@
+# Server WebUI build and tests
+name: Server WebUI
+
+on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      slow_tests:
+        description: 'Run slow tests'
+        required: true
+        type: boolean
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/server-webui.yml', 'tools/server/webui/**.*', 'tools/server/tests/**.*', 'tools/server/public/**']
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: ['.github/workflows/server-webui.yml', 'tools/server/webui/**.*', 'tools/server/tests/**.*', 'tools/server/public/**']
+
+env:
+  LLAMA_LOG_COLORS: 1
+  LLAMA_LOG_PREFIX: 1
+  LLAMA_LOG_TIMESTAMPS: 1
+  LLAMA_LOG_VERBOSITY: 10
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  webui-setup:
+    name: WebUI Setup
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+          cache: "npm"
+          cache-dependency-path: "tools/server/webui/package-lock.json"
+
+      - name: Cache node_modules
+        uses: actions/cache@v4
+        id: cache-node-modules
+        with:
+          path: tools/server/webui/node_modules
+          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
+          restore-keys: |
+            ${{ runner.os }}-node-modules-
+
+      - name: Install dependencies
+        if: steps.cache-node-modules.outputs.cache-hit != 'true'
+        run: npm ci
+        working-directory: tools/server/webui
+
+  webui-check:
+    needs: webui-setup
+    name: WebUI Check
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+
+      - name: Restore node_modules cache
+        uses: actions/cache@v4
+        with:
+          path: tools/server/webui/node_modules
+          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
+          restore-keys: |
+            ${{ runner.os }}-node-modules-
+
+      - name: Run type checking
+        run: npm run check
+        working-directory: tools/server/webui
+
+      - name: Run linting
+        run: npm run lint
+        working-directory: tools/server/webui
+
+  webui-build:
+    needs: webui-check
+    name: WebUI Build
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+
+      - name: Restore node_modules cache
+        uses: actions/cache@v4
+        with:
+          path: tools/server/webui/node_modules
+          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
+          restore-keys: |
+            ${{ runner.os }}-node-modules-
+
+      - name: Build application
+        run: npm run build
+        working-directory: tools/server/webui
+
+  webui-tests:
+    needs: webui-build
+    name: Run WebUI tests
+    permissions:
+      contents: read
+
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+
+      - name: Restore node_modules cache
+        uses: actions/cache@v4
+        with:
+          path: tools/server/webui/node_modules
+          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
+          restore-keys: |
+            ${{ runner.os }}-node-modules-
+
+      - name: Install Playwright browsers
+        run: npx playwright install --with-deps
+        working-directory: tools/server/webui
+
+      - name: Build Storybook
+        run: npm run build-storybook
+        working-directory: tools/server/webui
+
+      - name: Run Client tests
+        run: npm run test:client
+        working-directory: tools/server/webui
+
+      - name: Run Server tests
+        run: npm run test:server
+        working-directory: tools/server/webui
+
+      - name: Run UI tests
+        run: npm run test:ui -- --testTimeout=60000
+        working-directory: tools/server/webui
+
+      - name: Run E2E tests
+        run: npm run test:e2e
+        working-directory: tools/server/webui
+
+  server-build:
+    needs: [webui-tests]
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
+        build_type: [RelWithDebInfo]
+        include:
+          - build_type: Release
+            sanitizer: ""
+      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
+
+    steps:
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get -y install \
+            build-essential \
+            xxd \
+            git \
+            cmake \
+            curl \
+            wget \
+            language-pack-en \
+            libssl-dev
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r tools/server/tests/requirements.txt
+
+      - name: Setup Node.js for WebUI
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+          cache: "npm"
+          cache-dependency-path: "tools/server/webui/package-lock.json"
+
+      - name: Install WebUI dependencies
+        run: npm ci
+        working-directory: tools/server/webui
+
+      - name: Build WebUI
+        run: npm run build
+        working-directory: tools/server/webui
+
+      - name: Build (no OpenMP)
+        id: cmake_build_no_openmp
+        if: ${{ matrix.sanitizer == 'THREAD' }}
+        run: |
+          cmake -B build \
+              -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+              -DGGML_OPENMP=OFF ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
+      - name: Build (sanitizers)
+        id: cmake_build_sanitizers
+        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
+        run: |
+          cmake -B build \
+              -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
+      - name: Build (sanitizers)
+        id: cmake_build
+        if: ${{ matrix.sanitizer == '' }}
+        run: |
+          cmake -B build \
+              -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ matrix.sanitizer == '' }}
+        env:
+          GITHUB_ACTIONS: "true"
+        run: |
+          cd tools/server/tests
+          ./tests.sh
+
+      - name: Tests (sanitizers)
+        id: server_integration_tests_sanitizers
+        if: ${{ matrix.sanitizer != '' }}
+        run: |
+          cd tools/server/tests
+          LLAMA_SANITIZE=1 ./tests.sh
+
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd tools/server/tests
+          SLOW_TESTS=1 ./tests.sh
@@ -76,270 +76,6 @@ jobs:
        run: |
          pip install -r tools/server/tests/requirements.txt

-  webui-setup:
-    name: WebUI Setup
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
-
-      - name: Cache node_modules
-        uses: actions/cache@v4
-        id: cache-node-modules
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Install dependencies
-        if: steps.cache-node-modules.outputs.cache-hit != 'true'
-        run: npm ci
-        working-directory: tools/server/webui
-
-  webui-check:
-    needs: webui-setup
-    name: WebUI Check
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Run type checking
-        run: npm run check
-        working-directory: tools/server/webui
-
-      - name: Run linting
-        run: npm run lint
-        working-directory: tools/server/webui
-
-  webui-build:
-    needs: webui-check
-    name: WebUI Build
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Build application
-        run: npm run build
-        working-directory: tools/server/webui
-
-  webui-tests:
-    needs: webui-build
-    name: Run WebUI tests
-    permissions:
-      contents: read
-
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Install Playwright browsers
-        run: npx playwright install --with-deps
-        working-directory: tools/server/webui
-
-      - name: Build Storybook
-        run: npm run build-storybook
-        working-directory: tools/server/webui
-
-      - name: Run Client tests
-        run: npm run test:client
-        working-directory: tools/server/webui
-
-      - name: Run Server tests
-        run: npm run test:server
-        working-directory: tools/server/webui
-
-      - name: Run UI tests
-        run: npm run test:ui -- --testTimeout=60000
-        working-directory: tools/server/webui
-
-      - name: Run E2E tests
-        run: npm run test:e2e
-        working-directory: tools/server/webui
-
-  server-build:
-    needs: [webui-tests]
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
-        build_type: [RelWithDebInfo]
-        include:
-          - build_type: Release
-            sanitizer: ""
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
-
-    steps:
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get -y install \
-            build-essential \
-            xxd \
-            git \
-            cmake \
-            curl \
-            wget \
-            language-pack-en \
-            libssl-dev
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
-
-      - name: Setup Node.js for WebUI
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
-
-      - name: Install WebUI dependencies
-        run: npm ci
-        working-directory: tools/server/webui
-
-      - name: Build WebUI
-        run: npm run build
-        working-directory: tools/server/webui
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-              -DGGML_OPENMP=OFF ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build_sanitizers
-        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build
-        if: ${{ matrix.sanitizer == '' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ matrix.sanitizer == '' }}
-        env:
-          GITHUB_ACTIONS: "true"
-        run: |
-          cd tools/server/tests
-          ./tests.sh
-
-      - name: Tests (sanitizers)
-        id: server_integration_tests_sanitizers
-        if: ${{ matrix.sanitizer != '' }}
-        run: |
-          cd tools/server/tests
-          LLAMA_SANITIZE=1 ./tests.sh
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd tools/server/tests
-          SLOW_TESTS=1 ./tests.sh
-
-
  server-windows:
    runs-on: windows-2022

@@ -87,7 +87,8 @@
 /tests/                                 @ggerganov
 /tests/test-chat-.*                     @pwilkin
 /tools/batched-bench/                   @ggerganov
-/tools/main/                            @ggerganov
+/tools/cli/                             @ngxson
+/tools/completion/                      @ggerganov
 /tools/mtmd/                            @ngxson
 /tools/perplexity/                      @ggerganov
 /tools/quantize/                        @ggerganov
@@ -313,7 +313,7 @@ The Hugging Face platform provides a variety of online tools for converting, qua

 To learn more about model quantization, [read this documentation](tools/quantize/README.md)

-## [`llama-cli`](tools/main)
+## [`llama-cli`](tools/cli)

 #### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.

@@ -525,7 +525,8 @@ To learn more about model quantization, [read this documentation](tools/quantize

 ## Other documentation

- [main (cli)](tools/main/README.md)
+- [cli](tools/cli/README.md)
+- [completion](tools/completion/README.md)
 - [server](tools/server/README.md)
 - [GBNF grammars](grammars/README.md)

@@ -68,3 +68,6 @@ Please disclose it as a private [security advisory](https://github.com/ggml-org/
 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
@@ -398,6 +398,8 @@ function gg_run_qwen3_0_6b {
    ./bin/llama-quantize ${model_bf16} ${model_q5_k} q5_k $(nproc)
    ./bin/llama-quantize ${model_bf16} ${model_q6_k} q6_k $(nproc)

+    (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
+
    (time ./bin/llama-completion -no-cnv --model ${model_f16}  -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
    (time ./bin/llama-completion -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
    (time ./bin/llama-completion -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -523,6 +525,8 @@ function gg_run_embd_bge_small {

    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0

+    (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
+
    (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
    (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log

@@ -563,6 +567,8 @@ function gg_run_rerank_tiny {

    model_f16="${path_models}/ggml-model-f16.gguf"

+    (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
+
    # for this model, the SEP token is "</s>"
    (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --no-op-offload --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log

@@ -20,6 +20,7 @@
 #include <nlohmann/json.hpp>

 #include <algorithm>
+#include <cinttypes>
 #include <climits>
 #include <cstdarg>
 #include <fstream>
@@ -529,7 +530,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        params.kv_overrides.back().key[0] = 0;
    }

-    if (!params.tensor_buft_overrides.empty()) {
+    // pad tensor_buft_overrides for llama_params_fit:
+    const size_t ntbo = llama_max_tensor_buft_overrides();
+    while (params.tensor_buft_overrides.size() < ntbo) {
        params.tensor_buft_overrides.push_back({nullptr, nullptr});
    }

@@ -832,6 +835,19 @@ bool common_arg_utils::is_autoy(const std::string & value) {
 }

 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
+    if (ex == LLAMA_EXAMPLE_COMPLETION) {
+        params.use_jinja = false;   // disable jinja by default
+
+    } else if (ex == LLAMA_EXAMPLE_MTMD) {
+        params.use_jinja = false;   // disable jinja by default
+        params.sampling.temp = 0.2; // lower temp by default for better quality
+
+    } else if (ex == LLAMA_EXAMPLE_SERVER) {
+        params.n_parallel = -1;     // auto by default
+    }
+
    params.use_color = tty_can_use_colors();

    // load dynamic backends
@@ -1104,7 +1120,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_env("LLAMA_ARG_SWA_FULL"));
    add_opt(common_arg(
        {"--ctx-checkpoints", "--swa-checkpoints"}, "N",
-        string_format("max number of context checkpoints to create per slot (default: %d)\n"
+        string_format("max number of context checkpoints to create per slot (default: %d)"
            "[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)", params.n_ctx_checkpoints),
        [](common_params & params, int value) {
            params.n_ctx_checkpoints = value;
@@ -1112,7 +1128,7 @@ 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(
        {"--cache-ram", "-cram"}, "N",
-        string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)\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) {
            params.cache_ram_mib = value;
@@ -1120,12 +1136,11 @@ 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(
        {"--kv-unified", "-kvu"},
-        string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n"
-            "[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)", params.kv_unified ? "true" : "false"),
+        "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_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY}));
    add_opt(common_arg(
        {"--context-shift"},
        {"--no-context-shift"},
@@ -1885,13 +1900,27 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            LOG_WRN("DEPRECATED: --defrag-thold is deprecated and no longer necessary to specify\n");
        }
    ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
-    add_opt(common_arg(
-        {"-np", "--parallel"}, "N",
-        string_format("number of parallel sequences to decode (default: %d)", params.n_parallel),
-        [](common_params & params, int value) {
-            params.n_parallel = value;
-        }
-    ).set_env("LLAMA_ARG_N_PARALLEL"));
+    if (ex == LLAMA_EXAMPLE_SERVER) {
+        // this is to make sure this option appears in the server-specific section of the help message
+        add_opt(common_arg(
+            {"-np", "--parallel"}, "N",
+            string_format("number of server slots (default: %d, -1 = auto)", params.n_parallel),
+            [](common_params & params, int value) {
+                if (value == 0) {
+                    throw std::invalid_argument("error: invalid value for n_parallel\n");
+                }
+                params.n_parallel = value;
+            }
+        ).set_env("LLAMA_ARG_N_PARALLEL").set_examples({LLAMA_EXAMPLE_SERVER}));
+    } else {
+        add_opt(common_arg(
+            {"-np", "--parallel"}, "N",
+            string_format("number of parallel sequences to decode (default: %d)", params.n_parallel),
+            [](common_params & params, int value) {
+                params.n_parallel = value;
+            }
+        ).set_env("LLAMA_ARG_N_PARALLEL"));
+    }
    add_opt(common_arg(
        {"-ns", "--sequences"}, "N",
        string_format("number of sequences to decode (default: %d)", params.n_sequences),
@@ -2153,6 +2182,34 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
        }
    ).set_env("LLAMA_ARG_MAIN_GPU"));
+    add_opt(common_arg(
+        { "-fit", "--fit" }, "[on|off]",
+        string_format("whether to adjust unset arguments to fit in device memory ('on' or 'off', default: '%s')", params.fit_params ? "on" : "off"),
+        [](common_params & params, const std::string & value) {
+            if (is_truthy(value)) {
+                params.fit_params = true;
+            } else if (is_falsey(value)) {
+                params.fit_params = false;
+            } else {
+                throw std::runtime_error(
+                    string_format("error: unkown value for --fit: '%s'\n", value.c_str()));
+            }
+        }
+    ).set_env("LLAMA_ARG_FIT"));
+    add_opt(common_arg(
+        { "-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(
+        { "-fitc", "--fit-ctx" }, "N",
+        string_format("minimum ctx size that can be set by --fit option, default: %" PRIu32, params.fit_params_min_ctx),
+        [](common_params & params, int value) {
+            params.fit_params_min_ctx = value;
+        }
+    ).set_env("LLAMA_ARG_FIT_CTX"));
    add_opt(common_arg(
        {"--check-tensors"},
        string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
@@ -4,9 +4,14 @@

 using json = nlohmann::json;

-static std::string_view trim_trailing_space(std::string_view sv) {
+static std::string_view trim_trailing_space(std::string_view sv, int max = -1) {
+    int count = 0;
    while (!sv.empty() && std::isspace(static_cast<unsigned char>(sv.back()))) {
+        if (max != -1 && count <= max) {
+            break;
+        }
        sv.remove_suffix(1);
+        count++;
    }
    return sv;
 }
@@ -93,7 +98,7 @@ void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) {

    if (is_arg_string && current_tool) {
        // Serialize to JSON, but exclude the end quote
-        std::string dumped = json(node.text).dump();
+        std::string dumped = json(trim_trailing_space(node.text)).dump();
        current_tool->arguments += dumped.substr(0, dumped.size() - 1);
        needs_closing_quote = true;
    }
@@ -101,6 +106,7 @@ void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) {
    if (is_arg_close && current_tool) {
        if (needs_closing_quote) {
            current_tool->arguments += "\"";
+            needs_closing_quote = false;
        }
    }

@@ -109,6 +115,10 @@ void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) {
    }

    if (is_tool_close && current_tool) {
+        if (needs_closing_quote) {
+            current_tool->arguments += "\"";
+            needs_closing_quote = false;
+        }
        current_tool->arguments += "}";
    }
 }
@@ -711,6 +711,25 @@ static void foreach_function(const json & tools, const std::function<void(const
    }
 }

+static void foreach_parameter(const json & function, const std::function<void(const std::string &, const json &, bool)> & fn) {
+    if (!function.contains("parameters") || !function.at("parameters").is_object()) {
+        return;
+    }
+    const auto & params = function.at("parameters");
+    if (!params.contains("properties") || !params.at("properties").is_object()) {
+        return;
+    }
+    const auto & props = params.at("properties");
+    std::set<std::string> required;
+    if (params.contains("required") && params.at("required").is_array()) {
+        params.at("required").get_to(required);
+    }
+    for (const auto & [name, prop] : props.items()) {
+        bool is_required = (required.find(name) != required.end());
+        fn(name, prop, is_required);
+    }
+}
+
 static std::string apply(
    const common_chat_template & tmpl,
    const struct templates_params & inputs,
@@ -1409,6 +1428,123 @@ static common_chat_params common_chat_params_init_nemotron_v2(const common_chat_
    return data;
 }

+static common_chat_params common_chat_params_init_nemotron_v3(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+
+    data.prompt = apply(tmpl, inputs);
+    data.format = COMMON_CHAT_FORMAT_PEG_CONSTRUCTED;
+
+    // Handle thinking tags appropriately based on inputs.enable_thinking
+    if (string_ends_with(data.prompt, "<think>\n")) {
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    }
+
+    data.preserved_tokens = {
+        "<think>",
+        "</think>",
+        "<tool_call>",
+        "</tool_call>",
+    };
+
+    auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar = true;
+
+    auto parser = build_chat_peg_constructed_parser([&](auto & p) {
+        auto reasoning = p.eps();
+        if (inputs.enable_thinking && extract_reasoning) {
+            auto reasoning_content = p.reasoning(p.until("</think>")) + ("</think>" | p.end());
+            if (data.thinking_forced_open) {
+                reasoning = reasoning_content;
+            }
+        }
+
+        // Response format parser
+        if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) {
+            return reasoning << p.content(p.schema(p.json(), "response-format", inputs.json_schema));
+        }
+
+        // Tool call parser
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+
+                auto schema_info = common_schema_info();
+                schema_info.resolve_refs(parameters);
+
+                auto tool_open = "<function=" + p.tool_name(p.literal(name)) + ">\n";
+                auto tool_close = p.literal("</function>\n");
+                auto args = p.sequence();
+                auto arg_string = p.rule("xml-arg-string", p.until_one_of({
+                    "\n</parameter>",
+                    "\n<parameter=",
+                    "\n</function>"
+                }));
+
+                foreach_parameter(function, [&](const auto & param_name, const json & param_schema, bool is_required) {
+                    auto rule_name = "tool-" + name + "-arg-" + param_name;
+
+                    auto arg_open = "<parameter=" + p.tool_arg_name(p.literal(param_name)) + ">\n";
+                    auto arg_close = p.literal("</parameter>\n");
+                    auto arg_value = p.eps();
+
+                    if (schema_info.resolves_to_string(param_schema)) {
+                        arg_value = p.tool_arg_string_value(arg_string) + "\n";
+                    } else {
+                        arg_value = p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", param_schema));
+                    }
+
+                    // Model may or my not close with </parameter>
+                    auto arg_rule = p.rule(rule_name, p.tool_arg_open(arg_open) + arg_value + p.optional(p.tool_arg_close(arg_close)));
+                    args += p.repeat(arg_rule, /* min = */ is_required ? 1 : 0, /* max = */ 1);
+                });
+
+                tool_choice |= p.rule("tool-" + name, p.tool_open(tool_open) + args + p.tool_close(tool_close));
+            });
+
+            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+            auto max_calls = inputs.parallel_tool_calls ? -1 : 1;
+            auto tool_call = p.rule("tool-call", "<tool_call>\n" + tool_choice + "</tool_call>" + p.space());
+            auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(tool_call, /* min = */ min_calls, /* max = */ max_calls));
+
+            return reasoning << p.content(p.until("<tool_call>")) << tool_calls;
+        }
+
+        // Content only parser
+        include_grammar = false;
+        return reasoning << p.content(p.rest());
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto schema = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<tool_call>"}
+        };
+    }
+
+    return data;
+}
+
+
 static common_chat_params common_chat_params_init_apertus(const common_chat_template & tmpl, const struct templates_params & inputs) {
    common_chat_params data;

@@ -2534,6 +2670,10 @@ static common_chat_params common_chat_templates_apply_jinja(
        src.find("<function=") != std::string::npos &&
        src.find("<parameters>") != std::string::npos &&
        src.find("<parameter=") != std::string::npos) {
+        // Nemotron 3 Nano 30B A3B
+        if (src.find("<think>") != std::string::npos) {
+            return common_chat_params_init_nemotron_v3(tmpl, params);
+        }
        return common_chat_params_init_qwen3_coder_xml(tmpl, params);
    }

@@ -1088,7 +1088,15 @@ struct common_init_result::impl {

 common_init_result::common_init_result(common_params & params) :
    pimpl(new impl{}) {
-    const auto mparams = common_model_params_to_llama(params);
+    auto mparams = common_model_params_to_llama(params);
+    auto cparams = common_context_params_to_llama(params);
+
+    if (params.fit_params) {
+        LOG_INF("%s: fitting params to device memory, to report bugs during this step use -fit off (or --verbose if you can't)\n", __func__);
+        llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
+            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);
+    }

    llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
    if (model == NULL) {
@@ -1103,8 +1111,6 @@ common_init_result::common_init_result(common_params & params) :
    // TODO: fix naming
    common_init_sampler_from_model(model, params.sampling);

-    auto cparams = common_context_params_to_llama(params);
-
    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
        params.sampling.ignore_eos = false;
@@ -1143,8 +1149,7 @@ common_init_result::common_init_result(common_params & params) :

    llama_context * lctx = llama_init_from_model(model, cparams);
    if (lctx == NULL) {
-        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-                __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
        return;
    }

@@ -1176,15 +1181,13 @@ common_init_result_ptr common_init_from_params(common_params & params) {

    llama_model * model = res->model();
    if (model == NULL) {
-        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
        return res;
    }

    llama_context * lctx = res->context();
    if (lctx == NULL) {
-        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
        return res;
    }

@@ -99,6 +99,7 @@ enum llama_example {
    LLAMA_EXAMPLE_TTS,
    LLAMA_EXAMPLE_DIFFUSION,
    LLAMA_EXAMPLE_FINETUNE,
+    LLAMA_EXAMPLE_FIT_PARAMS,

    LLAMA_EXAMPLE_COUNT,
 };
@@ -306,8 +307,8 @@ struct lr_opt {
 struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata);

 struct common_params {
-    int32_t n_predict             =    -1; // new tokens to predict
-    int32_t n_ctx                 =  4096; // context size
+    int32_t n_predict             =    -1; // max. number of new tokens to predict, -1 == no limit
+    int32_t n_ctx                 =     0; // context size, 0 == context the model was trained with
    int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
    int32_t n_ubatch              =   512; // physical batch size for prompt processing (must be >=32 to use BLAS)
    int32_t n_keep                =     0; // number of tokens to keep from initial prompt
@@ -328,9 +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 - 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
+    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

@@ -305,8 +305,9 @@ static std::string format_literal(const std::string & literal) {

 std::string gbnf_format_literal(const std::string & literal) { return format_literal(literal); }

-class SchemaConverter {
+class common_schema_converter {
 private:
+    friend class common_schema_info;
    friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
    std::function<json(const std::string &)> _fetch_json;
    bool _dotall;
@@ -729,7 +730,7 @@ private:
    }

 public:
-    SchemaConverter(
+    common_schema_converter(
        const std::function<json(const std::string &)> & fetch_json,
        bool dotall)
          : _fetch_json(fetch_json), _dotall(dotall)
@@ -990,6 +991,134 @@ public:
    }
 };

+// common_schema_info implementation (pimpl)
+
+common_schema_info::common_schema_info()
+    : impl_(std::make_unique<common_schema_converter>(
+        [](const std::string &) { return json(); },
+        false)) {}
+
+common_schema_info::~common_schema_info() = default;
+
+common_schema_info::common_schema_info(common_schema_info &&) noexcept = default;
+common_schema_info & common_schema_info::operator=(common_schema_info &&) noexcept = default;
+
+void common_schema_info::resolve_refs(nlohmann::ordered_json & schema) {
+    impl_->resolve_refs(schema, "");
+}
+
+// Determines if a JSON schema can resolve to a string type through any path.
+// Some models emit raw string values rather than JSON-encoded strings for string parameters.
+// If any branch of the schema (via oneOf, anyOf, $ref, etc.) permits a string, this returns
+// true, allowing callers to handle the value as a raw string for simplicity.
+bool common_schema_info::resolves_to_string(const nlohmann::ordered_json & schema) {
+    std::unordered_set<std::string> visited_refs;
+
+    std::function<bool(const json &)> check = [&](const json & s) -> bool {
+        if (!s.is_object()) {
+            return false;
+        }
+
+        // Handle $ref
+        if (s.contains("$ref")) {
+            const std::string & ref = s["$ref"];
+            if (visited_refs.find(ref) != visited_refs.end()) {
+                // Circular reference, assume not a string to be safe
+                return false;
+            }
+            visited_refs.insert(ref);
+            auto it = impl_->_refs.find(ref);
+            if (it != impl_->_refs.end()) {
+                return check(it->second);
+            }
+            return false;
+        }
+
+        // Check type field
+        if (s.contains("type")) {
+            const json & schema_type = s["type"];
+            if (schema_type.is_string()) {
+                if (schema_type == "string") {
+                    return true;
+                }
+            } else if (schema_type.is_array()) {
+                // Type can be an array like ["string", "null"]
+                for (const auto & t : schema_type) {
+                    if (t == "string") {
+                        return true;
+                    }
+                }
+            }
+        }
+
+        // Check oneOf/anyOf - if any alternative can be a string
+        if (s.contains("oneOf")) {
+            for (const auto & alt : s["oneOf"]) {
+                if (check(alt)) {
+                    return true;
+                }
+            }
+        }
+        if (s.contains("anyOf")) {
+            for (const auto & alt : s["anyOf"]) {
+                if (check(alt)) {
+                    return true;
+                }
+            }
+        }
+
+        // Check allOf - all components must be compatible with string type
+        if (s.contains("allOf")) {
+            bool all_string = true;
+            for (const auto & component : s["allOf"]) {
+                if (!check(component)) {
+                    all_string = false;
+                    break;
+                }
+            }
+            if (all_string) {
+                return true;
+            }
+        }
+
+        // Check const - if the constant value is a string
+        if (s.contains("const")) {
+            if (s["const"].is_string()) {
+                return true;
+            }
+        }
+
+        // Check enum - if any enum value is a string
+        if (s.contains("enum")) {
+            for (const auto & val : s["enum"]) {
+                if (val.is_string()) {
+                    return true;
+                }
+            }
+        }
+
+        // String-specific keywords imply string type
+        if (s.contains("pattern") || s.contains("minLength") || s.contains("maxLength")) {
+            return true;
+        }
+
+        // Check format - many formats imply string
+        if (s.contains("format")) {
+            const std::string & fmt = s["format"];
+            if (fmt == "date" || fmt == "time" || fmt == "date-time" ||
+                fmt == "uri" || fmt == "email" || fmt == "hostname" ||
+                fmt == "ipv4" || fmt == "ipv6" || fmt == "uuid" ||
+                fmt.find("uuid") == 0) {
+                return true;
+            }
+        }
+
+        return false;
+    };
+
+    return check(schema);
+}
+
 std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
 #ifdef LLAMA_USE_LLGUIDANCE
    if (!force_gbnf) {
@@ -1006,7 +1135,7 @@ std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
 }

 std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options) {
-    SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall);
+    common_schema_converter converter([&](const std::string &) { return json(); }, options.dotall);
    common_grammar_builder builder {
        /* .add_rule = */ [&](const std::string & name, const std::string & rule) {
            return converter._add_rule(name, rule);
@@ -3,11 +3,31 @@
 #include <nlohmann/json_fwd.hpp>

 #include <functional>
+#include <memory>
 #include <string>

 std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
                                   bool force_gbnf = false);

+class common_schema_converter;
+
+// Probes a JSON schema to extract information about its structure and type constraints.
+class common_schema_info {
+    std::unique_ptr<common_schema_converter> impl_;
+
+  public:
+    common_schema_info();
+    ~common_schema_info();
+
+    common_schema_info(const common_schema_info &) = delete;
+    common_schema_info & operator=(const common_schema_info &) = delete;
+    common_schema_info(common_schema_info &&) noexcept;
+    common_schema_info & operator=(common_schema_info &&) noexcept;
+
+    void resolve_refs(nlohmann::ordered_json & schema);
+    bool resolves_to_string(const nlohmann::ordered_json & schema);
+};
+
 struct common_grammar_builder {
    std::function<std::string(const std::string &, const std::string &)> add_rule;
    std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
@@ -425,7 +425,7 @@ struct parser_executor {

        if (result.need_more_input()) {
            // Propagate - need to know what child would match before negating
-            return result;
+            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos);
        }

        // Child failed, so negation succeeds
@@ -157,6 +157,21 @@ static std::map<std::string, common_arg> get_map_key_opt(common_params_context &
    return mapping;
 }

+static bool is_bool_arg(const common_arg & arg) {
+    return !arg.args_neg.empty();
+}
+
+static std::string parse_bool_arg(const common_arg & arg, const std::string & key, const std::string & value) {
+    // if this is a negated arg, we need to reverse the value
+    for (const auto & neg_arg : arg.args_neg) {
+        if (rm_leading_dashes(neg_arg) == key) {
+            return common_arg_utils::is_truthy(value) ? "false" : "true";
+        }
+    }
+    // otherwise, not negated
+    return value;
+}
+
 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);
@@ -173,8 +188,13 @@ common_presets common_presets_load(const std::string & path, common_params_conte
        for (const auto & [key, value] : section.second) {
            LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
            if (key_to_opt.find(key) != key_to_opt.end()) {
-                preset.options[key_to_opt[key]] = value;
-                LOG_DBG("accepted option: %s = %s\n", key.c_str(), value.c_str());
+                auto & opt = key_to_opt[key];
+                if (is_bool_arg(opt)) {
+                    preset.options[opt] = parse_bool_arg(opt, key, value);
+                } else {
+                    preset.options[opt] = value;
+                }
+                LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
            } else {
                // TODO: maybe warn about unknown key?
            }
@@ -143,6 +143,7 @@ models = [
    {"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", },
 ]

 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -103,6 +103,8 @@ SYCL backend supports Intel GPU Family:
 - Intel Built-in Arc GPU
 - Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to [oneAPI supported GPU](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html#inpage-nav-1-1)).

+On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the performance is not optimal, and some GPUs may not support OpenCL nor have any GPGPU capabilities.
+
 #### Verified devices

 | Intel GPU                     | Status  | Verified Model                        |
@@ -9,7 +9,8 @@ Adding a model requires few steps:
 After following these steps, you can open PR.

 Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
- [main](/tools/main/)
+- [cli](/tools/cli/)
+- [completion](/tools/completion/)
 - [imatrix](/tools/imatrix/)
 - [quantize](/tools/quantize/)
 - [server](/tools/server/)
@@ -96,7 +97,7 @@ The model params and tensors layout must be defined in `llama.cpp` source files:
 1. Define a new `llm_arch` enum value in `src/llama-arch.h`.
 2. In `src/llama-arch.cpp`:
    - Add the architecture name to the `LLM_ARCH_NAMES` map.
-    - Add the tensor mappings to the `LLM_TENSOR_NAMES` map.
+    - Add the list of model tensors to `llm_get_tensor_names` (you may also need to update `LLM_TENSOR_NAMES`)
 3. Add any non-standard metadata loading in the `llama_model_loader` constructor in `src/llama-model-loader.cpp`.
 4. If the model has a RoPE operation, add a case for the architecture in `llama_model_rope_type` function in `src/llama-model.cpp`.

@@ -7,9 +7,9 @@
 ## Images
 We have three Docker images available for this project:

-1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
-2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
-3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
+1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
+2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the `llama-cli` and `llama-completion` executables. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
+3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the `llama-server` executable. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)

 Additionally, there the following images, similar to the above:

@@ -44,13 +44,15 @@ docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --all-in-o
 On completion, you are ready to play!

 ```bash
-docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf
+docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run-legacy -m /models/32B/ggml-model-q8_0.gguf -no-cnv -p "Building a mobile app can be done in 15 steps:" -n 512
 ```

 or with a light image:

 ```bash
-docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+docker run -v /path/to/models:/models --entrypoint /app/llama-cli ghcr.io/ggml-org/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf
+docker run -v /path/to/models:/models --entrypoint /app/llama-completion ghcr.io/ggml-org/llama.cpp:light -m /models/32B/ggml-model-q8_0.gguf -no-cnv -p "Building a mobile app can be done in 15 steps:" -n 512
 ```

 or with a server image:
@@ -59,6 +61,8 @@ or with a server image:
 docker run -v /path/to/models:/models -p 8080:8080 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512
 ```

+In the above examples, `--entrypoint /app/llama-cli` is specified for clarity, but you can safely omit it since it's the default entrypoint in the container.
+
 ## Docker With CUDA

 Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
@@ -80,9 +84,9 @@ The defaults are:

 The resulting images, are essentially the same as the non-CUDA images:

-1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
-2. `local/llama.cpp:light-cuda`: This image only includes the main executable file.
-3. `local/llama.cpp:server-cuda`: This image only includes the server executable file.
+1. `local/llama.cpp:full-cuda`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
+2. `local/llama.cpp:light-cuda`: This image only includes the `llama-cli` and `llama-completion` executables.
+3. `local/llama.cpp:server-cuda`: This image only includes the `llama-server` executable.

 ## Usage

@@ -114,9 +118,9 @@ The defaults are:

 The resulting images, are essentially the same as the non-MUSA images:

-1. `local/llama.cpp:full-musa`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
-2. `local/llama.cpp:light-musa`: This image only includes the main executable file.
-3. `local/llama.cpp:server-musa`: This image only includes the server executable file.
+1. `local/llama.cpp:full-musa`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
+2. `local/llama.cpp:light-musa`: This image only includes the `llama-cli` and `llama-completion` executables.
+3. `local/llama.cpp:server-musa`: This image only includes the `llama-server` executable.

 ## Usage

@@ -18,12 +18,12 @@ Legend:
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
@@ -31,7 +31,7 @@ Legend:
 |                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
@@ -64,7 +64,7 @@ Legend:
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
@@ -98,14 +98,14 @@ Legend:
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -113,7 +113,7 @@ Legend:
 |                              SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
@@ -48,7 +48,7 @@ static void write_table(std::ofstream & file, std::vector<common_arg *> & opts)
    }
 }

-static void export_md(std::string fname, llama_example ex) {
+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;
@@ -72,13 +72,14 @@ static void export_md(std::string fname, llama_example ex) {
    write_table(file, common_options);
    file << "\n\n**Sampling params**\n\n";
    write_table(file, sparam_options);
-    file << "\n\n**Example-specific params**\n\n";
+    file << "\n\n**" << name << "-specific params**\n\n";
    write_table(file, specific_options);
 }

 int main(int, char **) {
-    export_md("autogen-main.md", LLAMA_EXAMPLE_COMPLETION);
-    export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER);
+    // TODO: add CLI
+    export_md("autogen-completion.md", LLAMA_EXAMPLE_COMPLETION, "Tool");
+    export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER, "Server");

    return 0;
 }
@@ -10,6 +10,13 @@ and in some cases perplexity checked of the quantized model. And finally the
 model/models need to the ggml-org on Hugging Face. This tool/example tries to
 help with this process.

+> 📝 **Note:** When adding a new model from an existing family, verify the
+> previous version passes logits verification first. Existing models can have
+> subtle numerical differences that don't affect generation quality but cause
+> logits mismatches. Identifying these upfront whether they exist in llama.cpp,
+> the conversion script, or in an upstream implementation, can save significant
+> debugging time.
+
 ### Overview
 The idea is that the makefile targets and scripts here can be used in the
 development/conversion process assisting with things like:
@@ -7,7 +7,7 @@ base_model:
 Recommended way to run this model:

 ```sh
-llama-server -hf {namespace}/{model_name}-GGUF -c 0 -fa
+llama-server -hf {namespace}/{model_name}-GGUF -c 0
 ```

 Then, access http://localhost:8080
@@ -34,8 +34,11 @@ done
 MODEL_PATH="${MODEL_PATH:-"$EMBEDDING_MODEL_PATH"}"
 MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"

+CONVERTED_MODEL_PATH="${CONVERTED_EMBEDDING_PATH:-"$CONVERTED_EMBEDDING_MODEL"}"
+CONVERTED_MODEL_NAME="${CONVERTED_MODEL_NAME:-$(basename "$CONVERTED_MODEL_PATH" .gguf)}"
+
 if [ -t 0 ]; then
-    CPP_EMBEDDINGS="data/llamacpp-${MODEL_NAME}-embeddings.bin"
+    CPP_EMBEDDINGS="data/llamacpp-${CONVERTED_MODEL_NAME}-embeddings.bin"
 else
    # Process piped JSON data and convert to binary (matching logits.cpp format)
    TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn)
@@ -53,7 +53,14 @@ GGML_API void           ggml_gallocr_free(ggml_gallocr_t galloc);
 // call with a worst-case graph to avoid buffer reallocations
 // not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
 // returns false if the buffer allocation failed
+// ggml_gallocr_resrve_n_size writes the buffer sizes per galloc buffer that would be allocated by ggml_gallocr_reserve_n to sizes
 GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
+GGML_API void ggml_gallocr_reserve_n_size(
+    ggml_gallocr_t galloc,
+    struct ggml_cgraph * graph,
+    const int * node_buffer_ids,
+    const int * leaf_buffer_ids,
+    size_t * sizes);
 GGML_API bool ggml_gallocr_reserve_n(
    ggml_gallocr_t galloc,
    struct ggml_cgraph * graph,
@@ -68,6 +75,8 @@ GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_i

 // Utils
 // Create a buffer and allocate all the tensors in a ggml_context
+// ggml_backend_alloc_ctx_tensors_from_buft_size returns the size of the buffer that would be allocated by ggml_backend_alloc_ctx_tensors_from_buft
+GGML_API size_t                       ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
 GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
 GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend);

@@ -307,6 +307,7 @@ extern "C" {
    GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);

    // Initialize backend buffers from a measure graph
+    GGML_API void                 ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes);
    GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success

    GGML_API int                  ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
@@ -2615,7 +2615,8 @@ extern "C" {

    // Set callback for all future logging events.
    // If this is not called, or NULL is supplied, everything is output on stderr.
-    GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data);
+    GGML_API void ggml_log_get(ggml_log_callback * log_callback, void ** user_data);
+    GGML_API void ggml_log_set(ggml_log_callback   log_callback, void *  user_data);

    GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);

@@ -594,7 +594,9 @@ static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) {
 }

 static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) {
-    return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated;
+    return t->data != NULL // tensor data already set externally
+        || t->buffer // tensor on external buffer (but not yet allocated)
+        || ggml_gallocr_is_own(galloc, t); // tensor will be allocated by galloc
 }

 // free the extra space at the end if the new tensor is smaller
@@ -823,7 +825,8 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
    }
 }

-bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
+static bool ggml_gallocr_reserve_n_impl(
+        ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, bool no_alloc) {
    size_t min_hash_size = graph->n_nodes + graph->n_leafs;
    // add 25% margin to avoid hash collisions
    min_hash_size += min_hash_size / 4;
@@ -928,16 +931,19 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
                size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0;
                if (cur_size > 0) {
                    GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n",
-                        __func__, ggml_backend_buft_name(galloc->bufts[i]),
-                        cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+                        __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
                }
            }
 #endif
            ggml_vbuffer_free(galloc->buffers[i]);
-            galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
-            if (galloc->buffers[i] == NULL) {
-                GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
-                return false;
+            if (no_alloc) {
+                galloc->buffers[i] = NULL;
+            } else {
+                galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
+                if (galloc->buffers[i] == NULL) {
+                    GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
+                    return false;
+                }
            }
        }
    }
@@ -945,6 +951,21 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
    return true;
 }

+void ggml_gallocr_reserve_n_size(
+        ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, size_t * sizes) {
+    GGML_ASSERT(ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ true));
+    for (int i = 0; i < galloc->n_buffers; i++) {
+        sizes[i] = 0;
+        for (int c = 0; c < galloc->buf_tallocs[i]->n_chunks; c++) {
+            sizes[i] += galloc->buf_tallocs[i]->chunks[c]->max_size;
+        }
+    }
+}
+
+bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
+    return ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ false);
+}
+
 bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
    return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
 }
@@ -1147,7 +1168,8 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
    return true;
 }

-ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
+static ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft_impl(
+        struct ggml_context * ctx, ggml_backend_buffer_type_t buft, size_t * nbytes_total, bool no_alloc) {
    GGML_ASSERT(ggml_get_no_alloc(ctx) == true);

    size_t alignment = ggml_backend_buft_get_alignment(buft);
@@ -1155,6 +1177,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte

    ggml_backend_buffer_t * buffers = NULL;
    size_t n_buffers = 0;
+    *nbytes_total = 0;

    size_t cur_buf_size = 0;
    struct ggml_tensor * first = ggml_get_first_tensor(ctx);
@@ -1166,10 +1189,11 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte

        if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) {
            // allocate tensors in the current buffer
-            if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
+            if (!no_alloc && !alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
                return NULL;
            }
            first = t;
+            *nbytes_total += cur_buf_size;
            cur_buf_size = this_size;
        } else {
            cur_buf_size += this_size;
@@ -1178,15 +1202,21 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte

    // allocate remaining tensors
    if (cur_buf_size > 0) {
-        if (!alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) {
+        *nbytes_total += cur_buf_size;
+        if (!no_alloc && !alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) {
            return NULL;
        }
    }

+    if (no_alloc) {
+        return NULL;
+    }
+
    if (n_buffers == 0) {
 #ifndef NDEBUG
        GGML_LOG_DEBUG("%s: all tensors in the context are already allocated\n", __func__);
 #endif
+        GGML_ASSERT(!buffers);
        return NULL;
    }

@@ -1196,10 +1226,24 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
    } else {
        buffer = ggml_backend_multi_buffer_alloc_buffer(buffers, n_buffers);
    }
-    free(buffers);
+    if (buffers) {
+        free(buffers); // can be NULL if context is empty or no_alloc
+    }
    return buffer;
 }

+size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
+    size_t nbytes_total = 0;
+    ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc=*/ true);
+    GGML_ASSERT(!buf);
+    return nbytes_total;
+}
+
+ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
+    size_t nbytes_total = 0;
+    return ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc =*/ false);
+}
+
 ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) {
    return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend));
 }
@@ -36,12 +36,11 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
 }

 ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    GGML_ASSERT(buft);
    if (size == 0) {
        // return a dummy buffer for zero-sized allocations
        return ggml_backend_buffer_init(buft, {}, NULL, 0);
    }
-
-    GGML_ASSERT(buft);
    return buft->iface.alloc_buffer(buft, size);
 }

@@ -128,6 +127,12 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
        return NULL;
    }

+    // FIXME JG: a multi_buffer has a non-zero size, according to the above comment get_base is not optional,
+    //     I don't know whether the above comment is correct
+    if (!buffer->iface.get_base) {
+        return NULL;
+    }
+
    void * base = buffer->iface.get_base(buffer);

    GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL");
@@ -1727,6 +1732,20 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
    sched->is_alloc = false;
 }

+void ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes) {
+    GGML_ASSERT(sched);
+    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
+    GGML_ASSERT(sizes);
+
+    ggml_backend_sched_reset(sched);
+
+    ggml_backend_sched_synchronize(sched);
+
+    ggml_backend_sched_split_graph(sched, measure_graph);
+
+    ggml_gallocr_reserve_n_size(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids, sizes);
+}
+
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
    GGML_ASSERT(sched);
    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
@@ -21,7 +21,7 @@ static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __rest
    }

 #pragma unroll
-    for (int offset = 16; offset > 0; offset >>= 1) {
+    for (int offset = WARP_SIZE/2; offset > 0; offset >>= 1) {
        const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
        const int   col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
        if (val > maxval) {
@@ -50,7 +50,7 @@ static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __rest
                argmax = shared_argmax[lane_id];
            }
 #pragma unroll
-            for (int offset = 16; offset > 0; offset >>= 1) {
+            for (int offset = WARP_SIZE/2; offset > 0; offset >>= 1) {
                const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
                const int   col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
                if (val > maxval) {
@@ -1976,9 +1976,6 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s
            break;

        case GGML_TYPE_F16:
-            if (!opt_experimental) {
-                return false;
-            }
            break;

        default:
@@ -903,7 +903,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri
        const float * restrict vy  = (const float * restrict) y;

        for (uint32_t i = 0; i < n; i++) {
-            rsum += vx[i] * (__fp16) vy[i];
+            rsum += (float)vx[i] * vy[i];
        }
        *s = rsum;
        return;
@@ -917,7 +917,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri

    // for some reason we need volatile here so that the compiler doesn't try anything funky
    volatile HVX_Vector rsum = Q6_V_vsplat_R(0);
-
+    float r_sum_scalar = 0.0f;
    uint32_t i = 0;

    for (i = 0; i < nv0; i++) {
@@ -926,31 +926,42 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri
        HVX_Vector     x  = vx[i];
        HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00));  // mul by 1.0

-        HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
-        HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
+        //NOTE: need volatile here to prevent compiler optimization
+        // Seem compiler cannot guarantee read-after-write??
+        volatile HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
+        volatile HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));

        HVX_Vector sum = Q6_Vqf32_vadd_Vqf32Vqf32(hi, lo);
        rsum           = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum);
    }

    if (nv1) {
-        HVX_VectorPair yp = vy[i];
+        // HVX_VectorPair yp = vy[i];

-        HVX_Vector     x  = vx[i];
-        HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00));  // mul by 1.0
+        // HVX_Vector     x  = vx[i];
+        // HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00));  // mul by 1.0

-        if (nv1 >= 32) {
-            HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
-            rsum          = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, hi);
-            nv1 -= 32;
-        }
+        // if (nv1 >= 32) {
+        //     volatile HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
+        //     rsum          = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, hi);
+        //     nv1 -= 32;
+        // }

+        // rsum = hvx_vec_qf32_reduce_sum(rsum);
+
+        // if (nv1) {
+        //     volatile HVX_Vector lo  = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
+        //     HVX_Vector sum = hvx_vec_qf32_reduce_sum_n(lo, nv1);
+        //     rsum           = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum);
+        // }
+
+        //process the remainder using scalar loop
        rsum = hvx_vec_qf32_reduce_sum(rsum);
+        const __fp16 * restrict sx = (const __fp16 * restrict) x;
+        const float * restrict sy  = (const float * restrict) y;

-        if (nv1) {
-            HVX_Vector lo  = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
-            HVX_Vector sum = hvx_vec_qf32_reduce_sum_n(lo, nv1);
-            rsum           = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum);
+        for (uint32_t i = nv0 * 64; i < n; i++) {
+            r_sum_scalar += (float) sx[i] * sy[i];
        }

        // hvx_vec_dump_fp16("X", x);
@@ -961,7 +972,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri
        rsum = hvx_vec_qf32_reduce_sum(rsum);
    }

-    *s = hvx_vec_get_fp32(Q6_Vsf_equals_Vqf32(rsum));
+    *s = hvx_vec_get_fp32(Q6_Vsf_equals_Vqf32(rsum)) + r_sum_scalar;

 #    ifdef HTP_DEBUG
    {
@@ -1498,9 +1509,6 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
    uint64_t t1, t2;
    t1 = HAP_perf_get_qtimer_count();

-    const size_t src0_row_size = sizeof(__fp16) * ne00;
-    const size_t src1_row_size = sizeof(float) * ne10;
-
    assert(ne12 % ne02 == 0);
    assert(ne13 % ne03 == 0);

@@ -1510,8 +1518,6 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
    // This is the size of the rest of the dimensions of the result
    const uint32_t nr1 = ne1 * ne2 * ne3;

-    uint32_t chunk_size = 64;
-
    // distribute the thread work across the inner or outer loop based on which one is larger
    uint32_t nchunk0 = nr0 > nr1 ? nth : 1;  // parallelize by src0 rows
    uint32_t nchunk1 = nr0 > nr1 ? 1 : nth;  // parallelize by src1 rows
@@ -1544,11 +1550,11 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
    const uint32_t blck_0 = 64;
    const uint32_t blck_1 = 64;

-    float tmp[32];
+    __attribute__((aligned(128))) float tmp[64];

    for (uint32_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
        for (uint32_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
-            for (uint32_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1++) {
+            for (uint32_t ir1 = iir1; ir1 < MIN(iir1 + blck_1, ir1_end); ir1++) {
                const uint32_t i13 = (ir1 / (ne12 * ne1));
                const uint32_t i12 = (ir1 - i13 * ne12 * ne1) / ne1;
                const uint32_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1);
@@ -1561,13 +1567,16 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
                const uint32_t i2 = i12;
                const uint32_t i3 = i13;

-                const uint8_t * restrict src0_row = (const uint8_t *) src0->data + (0 + i02 * nb02 + i03 * nb03);
+                const uint8_t * restrict src0_base = (const uint8_t *) src0->data + (0 + i02 * nb02 + i03 * nb03);
                const uint8_t * restrict src1_col =
-                    (const uint8_t *) src1->data + (i11 + i12 * ne11 + i13 * ne12 * ne11) * src1_row_size;
+                    (const uint8_t *) src1->data + (i11 * nb11 + i12 * nb12 + i13 * nb13);
                float * dst_col = (float *) ((uint8_t * restrict) dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));

-                for (uint32_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0++) {
-                    vec_dot_f16_f32(ne00, &tmp[ir0 - iir0], src0_row + ir0 * src0_row_size, src1_col);
+                const uint32_t ir0_block_end = MIN(iir0 + blck_0, ir0_end);
+                for (uint32_t ir0 = iir0; ir0 < ir0_block_end; ir0++) {
+                    // Use nb01 stride for non-contiguous src0 support
+                    const uint8_t * restrict src0_row = src0_base + ir0 * nb01;
+                    vec_dot_f16_f32(ne00, &tmp[ir0 - iir0], src0_row, src1_col);
                }

                hvx_copy_fp32_ua((uint8_t *) &dst_col[iir0], (uint8_t *) tmp, MIN(iir0 + blck_0, ir0_end) - iir0);
@@ -769,9 +769,16 @@ ggml_metal_device_t ggml_metal_device_init(void) {
 #endif

            dev->props.use_shared_buffers = dev->props.has_unified_memory;
+#if TARGET_OS_OSX
+            // In case of eGPU, shared memory may be preferable.
+            dev->props.use_shared_buffers |= [dev->mtl_device location] == MTLDeviceLocationExternal;
+#endif
            if (getenv("GGML_METAL_SHARED_BUFFERS_DISABLE") != NULL) {
                dev->props.use_shared_buffers = false;
            }
+            if (getenv("GGML_METAL_SHARED_BUFFERS_ENABLE") != NULL) {
+                dev->props.use_shared_buffers = true;
+            }

            dev->props.supports_gpu_family_apple7 = [dev->mtl_device supportsFamily:MTLGPUFamilyApple7];

@@ -0,0 +1,77 @@
+#include <sycl/sycl.hpp>
+#include "common.hpp"
+#include "add-id.hpp"
+
+static void add_id_kernel(
+    const float* src0,
+    const float* src1,
+    const int32_t* src2,
+    float* dst,
+    int64_t ne0,
+    int64_t ne1,
+    size_t nb01,
+    size_t nb02,
+    size_t nb11,
+    size_t nb21,
+    sycl::nd_item<3> item_ct1) {
+  const int64_t i1 = item_ct1.get_group(2);
+  const int64_t i2 = item_ct1.get_group(1);
+
+  const int i11 =
+      *(const int32_t*)((const char*)src2 + i1 * sizeof(int32_t) + i2 * nb21);
+
+  const size_t nb1 = ne0 * sizeof(float);
+  const size_t nb2 = ne1 * nb1;
+
+  float* dst_row = (float*)((char*)dst + i1 * nb1 + i2 * nb2);
+  const float* src0_row =
+      (const float*)((const char*)src0 + i1 * nb01 + i2 * nb02);
+  const float* src1_row = (const float*)((const char*)src1 + i11 * nb11);
+
+  for (int64_t i0 = item_ct1.get_local_id(2); i0 < ne0;
+       i0 += item_ct1.get_local_range(2)) {
+    dst_row[i0] = src0_row[i0] + src1_row[i0];
+  }
+}
+
+void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
+  const ggml_tensor* src0 = dst->src[0];
+  const ggml_tensor* src1 = dst->src[1];
+  const ggml_tensor* src2 = dst->src[2];
+
+  GGML_TENSOR_TERNARY_OP_LOCALS
+
+  GGML_ASSERT(dst->type == GGML_TYPE_F32);
+  GGML_ASSERT(src0->type == GGML_TYPE_F32);
+  GGML_ASSERT(src1->type == GGML_TYPE_F32);
+  GGML_ASSERT(src2->type == GGML_TYPE_I32);
+
+  GGML_ASSERT(nb00 == sizeof(float));
+  GGML_ASSERT(nb10 == sizeof(float));
+  GGML_ASSERT(nb20 == sizeof(int32_t));
+
+  const float* src0_d = (const float*)src0->data;
+  const float* src1_d = (const float*)src1->data;
+  const int32_t* src2_d = (const int32_t*)src2->data;
+  float* dst_d = (float*)dst->data;
+
+  int threads = std::min((int)ne00, 768);  // cols
+  ctx.stream()->parallel_for(
+      sycl::nd_range<3>(
+          sycl::range<3>(1, ne02, ne01) * sycl::range<3>(1, 1, threads),
+          sycl::range<3>(1, 1, threads)),
+      [=](sycl::nd_item<3> item_ct1) {
+        add_id_kernel(
+            src0_d,
+            src1_d,
+            src2_d,
+            dst_d,
+            ne0,
+            ne1,
+            nb01,
+            nb02,
+            nb11,
+            nb21,
+            item_ct1);
+      });
+}
@@ -0,0 +1,8 @@
+#ifndef GGML_SYCL_ADD_ID_HPP
+#define GGML_SYCL_ADD_ID_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_add_id(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_ADD_ID_HPP
@@ -642,5 +642,22 @@ static __dpct_inline__ sycl::uint2 fast_div_modulo(uint32_t n, const sycl::uint3
    return sycl::uint2(div_val, mod_val);
 }

+static __dpct_inline__ int ggml_sycl_dp4a(const int a, const int b, int c) {
+    return dpct::dp4a(a, b, c);
+}
+
+static __dpct_inline__ float ggml_sycl_e8m0_to_fp32(uint8_t x) {
+    uint32_t bits;
+    if (x == 0) {
+        bits = 0x00400000;
+    } else {
+        bits = (uint32_t) x << 23;
+    }
+
+    float result;
+    memcpy(&result, &bits, sizeof(float));
+    return result;
+}
+

 #endif // GGML_SYCL_COMMON_HPP
@@ -472,6 +472,16 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k
      }
 }

+template <typename dst_t>
+static void dequantize_row_mxfp4_sycl(const void * vx, dst_t * y, const int64_t k, dpct::queue_ptr stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
+        [=](sycl::nd_item<3> item_ct1) {
+            dequantize_block_mxfp4(vx, y, item_ct1);
+        });
+}
+
 template <typename src_t, typename dst_t>
 static void convert_unary_nc(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01,
                          const int64_t ne02, const int64_t s01, const int64_t s02, const int64_t s03,
@@ -518,6 +528,7 @@ static void convert_unary_sycl(const void * vx, dst_t * y, const int64_t k, dpct
    convert_unary_nc_sycl<src_t>(vx, y, k, 1, 1, 1, k, k, k, queue);
 }

+
 to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
    switch (type) {
        case GGML_TYPE_Q4_0:
@@ -571,6 +582,8 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
            return dequantize_row_iq4_xs_sycl;
        case GGML_TYPE_IQ4_NL:
            return dequantize_row_iq4_nl_sycl;
+        case GGML_TYPE_MXFP4:
+            return dequantize_row_mxfp4_sycl;
        case GGML_TYPE_F32:
            return convert_unary_sycl<float>;
 #ifdef GGML_SYCL_HAS_BF16
@@ -636,6 +649,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
            return dequantize_row_iq4_xs_sycl;
        case GGML_TYPE_IQ4_NL:
            return dequantize_row_iq4_nl_sycl;
+        case GGML_TYPE_MXFP4:
+            return dequantize_row_mxfp4_sycl;
        case GGML_TYPE_F16:
            return convert_unary_sycl<sycl::half>;
 #ifdef GGML_SYCL_HAS_BF16
@@ -819,5 +819,23 @@ dequantize_block_iq4_xs(const void *__restrict__ vx, dst_t *__restrict__ yy,
    }
 }

+template<typename dst_t>
+static void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                   const sycl::nd_item<3> &item_ct1) {
+    // auto                item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    const int64_t       i        = item_ct1.get_group(2);
+    const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4);
+
+    const int64_t    tid = item_ct1.get_local_id(2);
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = ggml_sycl_e8m0_to_fp32(x[ib].e);
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f;
+        y[j+16] = d * kvalues_mxfp4[q4[j] >>  4]*0.5f;
+    }
+}

 #endif // GGML_SYCL_DEQUANTIZE_HPP
@@ -1860,10 +1860,31 @@ namespace dpct
                                           : id);
    }

+    template <typename T1, typename T2>
+    using dot_product_acc_t = std::conditional_t<
+        std::is_unsigned_v<T1> && std::is_unsigned_v<T2>,
+        uint32_t,
+        int32_t>;
+
+    template <typename T>
+    sycl::vec<T, 4> extract_and_sign_or_zero_extend4(T val) {
+      return sycl::vec<T, 1>(val)
+          .template as<sycl::vec<
+              std::conditional_t<std::is_signed_v<T>, int8_t, uint8_t>,
+              4>>()
+          .template convert<T>();
+    }
+
    template <typename T1, typename T2, typename T3>
-    inline auto dp4a(T1 a, T2 b, T3 c)
-    {
-        return syclcompat::dp4a(a, b, c);
+    inline auto dp4a(T1 a, T2 b, T3 c) {
+      dot_product_acc_t<T1, T2> res = c;
+      auto va = extract_and_sign_or_zero_extend4(a);
+      auto vb = extract_and_sign_or_zero_extend4(b);
+      res += va[0] * vb[0];
+      res += va[1] * vb[1];
+      res += va[2] * vb[2];
+      res += va[3] * vb[3];
+      return res;
    }

    struct sub_sat
@@ -2972,6 +2993,38 @@ namespace dpct
    atomic_fetch_add<T1, addressSpace>(addr, operand, memoryOrder);
    }

+    inline unsigned int byte_level_permute(
+        unsigned int a, unsigned int b, unsigned int s) {
+      unsigned int ret;
+      ret = ((((std::uint64_t)b << 32 | a) >> (s & 0x7) * 8) & 0xff) |
+            (((((std::uint64_t)b << 32 | a) >> ((s >> 4) & 0x7) * 8) & 0xff)
+             << 8) |
+            (((((std::uint64_t)b << 32 | a) >> ((s >> 8) & 0x7) * 8) & 0xff)
+             << 16) |
+            (((((std::uint64_t)b << 32 | a) >> ((s >> 12) & 0x7) * 8) & 0xff)
+             << 24);
+      return ret;
+    }
+
+    inline uint32_t byte_level_permute_custom(
+        uint32_t low32, uint32_t high32, uint32_t sel, int mode = 0) {
+      constexpr uint16_t lookup[6][4] = {
+          {0x3210, 0x4321, 0x5432, 0x6543},  // Forward 4-byte extract
+          {0x5670, 0x6701, 0x7012, 0x0123},  // Backward 4-byte extract
+          {0x0000, 0x1111, 0x2222, 0x3333},  // Replicate 8-bit values
+          {0x3210, 0x3211, 0x3222, 0x3333},  // Edge clamp left
+          {0x0000, 0x1110, 0x2210, 0x3210},  // Edge clamp right
+          {0x1010, 0x3232, 0x1010, 0x3232}   // Replicate 16-bit values
+      };
+
+      if (mode >= 1 && mode <= 6) {
+        return byte_level_permute(low32, high32, lookup[mode - 1][sel & 0x3]);
+      } else if (!mode) {
+        return byte_level_permute(low32, high32, sel);
+      }
+      return 0;
+    }
+
 } // COPY from DPCT head files

 #endif // GGML_SYCL_DPCT_HELPER_HPP
@@ -911,6 +911,98 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten
        });
 }

+__dpct_inline__ float ggml_sycl_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) {
+    x = sycl::fmin(x, limit);
+    g = sycl::fmax(sycl::fmin(g, limit), -limit);
+
+    float out_glu = x / (1.0f + sycl::native::exp(-x * alpha));
+    out_glu = out_glu * (1.0f + g);
+    return out_glu;
+}
+
+
+template <typename T>
+static void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k,
+                              const int64_t n, const int64_t o0, const int64_t o1,
+                              float alpha, float limit, sycl::nd_item<3> item_ct1) {
+    const int64_t i = int64_t(item_ct1.get_local_range(2)) * item_ct1.get_group(2) + item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    const int64_t j0 = (i / n) * o0 + (i % n);
+    const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
+
+    float xi = x[j0];
+    float gi = g[j1];
+
+    dst[i] = ggml_sycl_op_swiglu_oai_single(xi, gi, alpha, limit);
+}
+
+template <typename T>
+static void swiglu_oai_sycl(const T *       x,
+                            const T *       g,
+                            T *             dst,
+                            const int64_t   k,
+                            const int64_t   n,
+                            const int64_t   o0,
+                            const int64_t   o1,
+                            const float     alpha,
+                            const float     limit,
+                            dpct::queue_ptr stream) {
+    const int64_t num_blocks = (k + SYCL_GLU_BLOCK_SIZE - 1) / SYCL_GLU_BLOCK_SIZE;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE),
+                                           sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             swiglu_oai_kernel(x, g, dst, k, n, o0, o1, alpha, limit, item_ct1);
+                         });
+}
+
+void ggml_sycl_op_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    void * src0_d = src0->data;
+    void * src1_d = src1 ? src1->data : src0->data;
+    const int64_t src0_o = src0->nb[1];
+    const int64_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
+    void * dst_d = dst->data;
+    const int64_t nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
+    dpct::queue_ptr     stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+    GGML_ASSERT(src0->nb[0] == ggml_element_size(src0));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(dst->ne[0] == nc);
+    GGML_ASSERT(ggml_nrows(dst) == ggml_nrows(src0));
+
+    if (src1) {
+        GGML_ASSERT(ggml_is_contiguous_1(src1));
+        GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
+        GGML_ASSERT(src1->ne[0] == nc);
+        GGML_ASSERT(src0->type == src1->type);
+    }
+
+    //const int32_t swapped = ((const int32_t *) dst->op_params)[1];
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    const float alpha = ggml_get_op_params_f32(dst, 2);
+    const float limit = ggml_get_op_params_f32(dst, 3);
+
+    float * src0_p = (float *) src0_d;
+    float * src1_p = (float *) src1_d;
+
+    if (!src1) {
+        src0_p += swapped ? nc : 0;
+        src1_p += swapped ? 0 : nc;
+    }
+
+    swiglu_oai_sycl(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream);
+}
+
 static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
        [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
@@ -1070,6 +1162,11 @@ void ggml_sycl_swiglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    ggml_sycl_op_swiglu(ctx, dst);
 }

+void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
+    ggml_sycl_op_swiglu_oai(ctx, dst);
+}
+
 void ggml_sycl_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_geglu_erf(ctx, dst);
@@ -5,6 +5,8 @@
 #include "ggml.h"
 #include <limits> // For std::numeric_limits

+#define SYCL_GLU_BLOCK_SIZE 256
+
 template <typename T>
 T neg_infinity() {
    return -std::numeric_limits<T>::infinity();
@@ -41,6 +43,8 @@ void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

 void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

+void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
 void ggml_sycl_gelu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

 void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
@@ -39,6 +39,7 @@
 #include "ggml-impl.h"
 #include "ggml-backend-impl.h"

+#include "ggml-sycl/add-id.hpp"
 #include "ggml-sycl/backend.hpp"
 #include "ggml-sycl/common.hpp"
 #include "ggml-sycl/element_wise.hpp"
@@ -3313,6 +3314,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
    bool use_mul_mat_q =  ggml_sycl_supports_mmq(src0->type)
        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;

+
    // mmvq and mmq need the __dp4a instruction which is available for gen12+
    // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
    use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
@@ -3320,7 +3322,6 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
    use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
 #endif // SYCL_USE_XMX

-
    // mmvq path is faster in the CUDA backend.
    if (!g_ggml_sycl_prioritize_dmmv && (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda
        // Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization
@@ -3711,6 +3712,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
        case GGML_OP_ADD1: // TODO: more efficient implementation
            ggml_sycl_add(ctx, dst);
            break;
+        case GGML_OP_ADD_ID:
+            ggml_sycl_add_id(ctx, dst);
+            break;
        case GGML_OP_SUB:
            ggml_sycl_sub(ctx, dst);
            break;
@@ -3803,6 +3807,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
                case GGML_GLU_OP_SWIGLU:
                    ggml_sycl_swiglu(ctx, dst);
                    break;
+                case GGML_GLU_OP_SWIGLU_OAI:
+                    ggml_sycl_swiglu_oai(ctx, dst);
+                    break;
                case GGML_GLU_OP_GEGLU_ERF:
                    ggml_sycl_geglu_erf(ctx, dst);
                    break;
@@ -4397,6 +4404,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                case GGML_GLU_OP_REGLU:
                case GGML_GLU_OP_GEGLU:
                case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
                    return ggml_is_contiguous_1(op->src[0]);
@@ -4424,15 +4432,18 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                    }
                }
                ggml_type src0_type = op->src[0]->type;
-                if (src0_type == GGML_TYPE_BF16 || src0_type == GGML_TYPE_MXFP4) {
-                    // TODO: support MXFP4
+                if (src0_type == GGML_TYPE_BF16 ) {
+                    // TODO: support GGML_TYPE_BF16
                    // FIXME: keep a list of supported types to avoid breaking the backend when a new type is added
                    return false;
                }
+
                // TODO: The configuration below needs more work to be supported with oneDNN
-                if (ggml_is_permuted(a) && !ggml_is_contiguous(a) && a->ne[2] > 1 && a->ne[3] > 1) {
-                    return false;
+                if (ggml_is_permuted(a) && !ggml_is_contiguous(a) &&
+                    a->ne[2] > 1 && a->ne[3] > 1 && src0_type == GGML_TYPE_F16) {
+                  return false;
                }
+
                // TODO: This specific configuration can fail with oneDNN and needs more debugging
                if (!ggml_is_permuted(a) && ggml_is_permuted(b) && b->ne[2] > 1 && b->ne[3] > 1 &&
                    a->ne[0] > 128 && a->ne[2] == 1 && src0_type == GGML_TYPE_F16) {
@@ -4553,9 +4564,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
        case GGML_OP_VIEW:
        case GGML_OP_PERMUTE:
        case GGML_OP_TRANSPOSE:
-            return true;
        case GGML_OP_ADD:
        case GGML_OP_ADD1:
+        case GGML_OP_ADD_ID:
        case GGML_OP_SUB:
        case GGML_OP_COUNT_EQUAL:
        case GGML_OP_MUL:
@@ -595,6 +595,25 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
    }
 }

+static void mul_mat_vec_mxfp4_q8_1_sycl(const void * vx, const void * vy, float * dst, const int ncols, const int nrows,
+                                        dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_MXFP4 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+
+    {
+        stream->submit([&](sycl::handler & cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                             [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                                 mul_mat_vec_q<QK_MXFP4, QI_MXFP4, block_mxfp4, VDR_MXFP4_Q8_1_MMVQ, vec_dot_mxfp4_q8_1>(
+                                     vx, vy, dst, ncols, nrows, item_ct1);
+                             });
+        });
+    }
+}
+
+
 static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
@@ -1123,6 +1142,9 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
            case GGML_TYPE_IQ4_XS:
                mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
                break;
+            case GGML_TYPE_MXFP4:
+                mul_mat_vec_mxfp4_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+                break;
            default:
                GGML_ABORT("fatal error");
        }
@@ -14,10 +14,10 @@
 #include "pad.hpp"

 static void pad_f32(const float * src, float * dst,
-                               const int lp0, const int rp0, const int lp1, const int rp1,
-                               const int lp2, const int rp2, const int lp3, const int rp3,
-                               const int ne0, const int ne1, const int ne2, const int ne3) {
-    auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+                    const int lp0, const int rp0, const int lp1, const int rp1,
+                    const int lp2, const int rp2, const int lp3, const int rp3,
+                    const int ne0, const int ne1, const int ne2, const int ne3,
+                    sycl::nd_item<3> item_ct1) {
    int i0 = item_ct1.get_local_id(2) +
             item_ct1.get_group(2) * item_ct1.get_local_range(2);
    int i1 = item_ct1.get_group(1);
@@ -63,7 +63,7 @@ static void pad_f32_sycl(const float *src, float *dst, const int lp0,
                          sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
        [=](sycl::nd_item<3> item_ct1) {
            pad_f32(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1,
-                    ne2, ne3);
+                    ne2, ne3, item_ct1);
        });
 }

@@ -88,7 +88,7 @@ void ggml_sycl_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    GGML_ASSERT(src0->nb[0] == sizeof(float));
    GGML_ASSERT(src1->nb[0] == sizeof(float));

-    GGML_ASSERT(src0->nb[1] == src0->ne[0] * static_cast<int>(sizeof(float)));
+    GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float));

    const int src_stride_inner = ncs;
    const int src_stride_seq   = ncs * d_inner;
@@ -20,6 +20,18 @@
 typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1,
                                  const int & iqs);

+static __dpct_inline__ int get_int_b1(const void * x, const int & i32) {
+    const uint8_t * x8 = (const uint8_t *) x;
+
+    int x32  = x8[4*i32 + 0] <<  0;
+    x32     |= x8[4*i32 + 1] <<  8;
+    x32     |= x8[4*i32 + 2] << 16;
+    x32     |= x8[4*i32 + 3] << 24;
+
+    return x32;
+}
+
+
 static __dpct_inline__ int get_int_from_int8(const int8_t* x8, const int& i32) {
  const uint16_t* x16 =
      (const uint16_t*)(x8 + sizeof(int) * i32); // assume at least 2 byte
@@ -75,6 +87,28 @@ static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4,
    val2 = v1 | (v2 << 16);
 }

+static __dpct_inline__ sycl::int2 get_int_from_table_16(
+    const int& q4, const int8_t* table) {
+  const uint32_t* table32 = (const uint32_t*)table;
+  uint32_t tmp[2];
+  const uint32_t low_high_selection_indices =
+      (0x32103210 | ((q4 & 0x88888888) >> 1));
+#pragma unroll
+  for (uint32_t i = 0; i < 2; ++i) {
+    const uint32_t shift = 16 * i;
+
+    const uint32_t low =
+        dpct::byte_level_permute(table32[0], table32[1], q4 >> shift);
+    const uint32_t high =
+        dpct::byte_level_permute(table32[2], table32[3], q4 >> shift);
+    tmp[i] = dpct::byte_level_permute(
+        low, high, low_high_selection_indices >> shift);
+  }
+  return sycl::int2(
+      dpct::byte_level_permute(tmp[0], tmp[1], 0x6420),
+      dpct::byte_level_permute(tmp[0], tmp[1], 0x7531));
+}
+
 #define VDR_Q2_K_Q8_1_MMVQ 1

 // contiguous v/x values
@@ -685,6 +719,30 @@ vec_dot_q4_1_q8_1(const void *__restrict__ vbq,
    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
 }

+#define VDR_MXFP4_Q8_1_MMVQ 2
+#define VDR_MXFP4_Q8_1_MMQ  4
+
+static __dpct_inline__ float vec_dot_mxfp4_q8_1(const void * __restrict__ vbq,
+                                                const block_q8_1 * __restrict__ bq8_1,
+                                                const int & iqs) {
+    const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq;
+
+    const int * q8 = (const int *) bq8_1->qs + iqs;
+
+    int sumi = 0;
+#pragma unroll
+    for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) {
+        const int aux_q4 = get_int_b1(bq4->qs, iqs + l);
+        const sycl::int2 v      = get_int_from_table_16(aux_q4, kvalues_mxfp4);
+        sumi = ggml_sycl_dp4a(v.x(), q8[l + 0], sumi);
+        sumi = ggml_sycl_dp4a(v.y(), q8[l + 4], sumi);
+    }
+
+    const float d = ggml_sycl_e8m0_to_fp32(bq4->e) * 0.5f * (bq8_1->ds)[0];
+    return d * sumi;
+}
+
+
 static __dpct_inline__ float
 vec_dot_q5_0_q8_1(const void *__restrict__ vbq,
                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
@@ -10,44 +10,44 @@ FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i,
                     const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
    const uint y_idx_base = i * QUANT_K + 32 * ib32;
-    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {    
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
        const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx_base) / 4;
-        [[unroll]] for (uint l = 0; l < 4; ++l) {            
+        [[unroll]] for (uint l = 0; l < 4; ++l) {
            const vec4 b_val_0 = vec4(data_b_v4[base_b_idx + 2 * l]);
            const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]);

            // index for data_a
            uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;

-            [[unroll]] for (uint n = 0; n < num_rows; ++n) {                
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
                const float d = float(data_a[ibi].d);
                const uint qh = data_a[ibi].qh[ib32];

                const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1);
                const uint qs = data_a[ibi].qs[4 * ib32 + l];
-                const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3);              
+                const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3);
                const uint16_t grid = uint16_t(iq1s_grid[qs | (idxhi << 8)]);

                const float delta_val = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
-                const vec4 delta_v = vec4(delta_val);				
+                const vec4 delta_v = vec4(delta_val);
                const vec4 fbits0 = vec4(
                    float(bitfieldExtract(grid, 0, 2)),
                    float(bitfieldExtract(grid, 2, 2)),
                    float(bitfieldExtract(grid, 4, 2)),
                    float(bitfieldExtract(grid, 6, 2))
-                );				
+                );
                const vec4 fbits1 = vec4(
                    float(bitfieldExtract(grid, 8, 2)),
                    float(bitfieldExtract(grid, 10, 2)),
                    float(bitfieldExtract(grid, 12, 2)),
                    float(bitfieldExtract(grid, 14, 2))
                );
-				
+
                vec4 sum_v = fma(b_val_0, fbits0 + delta_v, vec4(0.0));
                sum_v      = fma(b_val_1, fbits1 + delta_v, sum_v);
-				FLOAT_TYPE sum = dot(sum_v, vec4(1.0));
-				
-                temp[j][n] = fma(dl, sum, temp[j][n]);				
+                FLOAT_TYPE sum = dot(sum_v, vec4(1.0));
+
+                temp[j][n] = fma(dl, sum, temp[j][n]);
                ibi += num_blocks_per_row;
            }
        }
@@ -7566,6 +7566,11 @@ size_t ggml_quantize_chunk(

 ////////////////////////////////////////////////////////////////////////////////

+void ggml_log_get(ggml_log_callback * log_callback, void ** user_data) {
+    *log_callback = g_logger_state.log_callback;
+    *user_data    = g_logger_state.log_callback_user_data;
+}
+
 void ggml_log_set(ggml_log_callback log_callback, void * user_data) {
    g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default;
    g_logger_state.log_callback_user_data = user_data;
@@ -413,6 +413,7 @@ class MODEL_ARCH(IntEnum):
    JAIS             = auto()
    NEMOTRON         = auto()
    NEMOTRON_H       = auto()
+    NEMOTRON_H_MOE   = auto()
    EXAONE           = auto()
    EXAONE4          = auto()
    GRANITE          = auto()
@@ -642,6 +643,7 @@ class MODEL_TENSOR(IntEnum):
    V_MMPROJ_PEG         = auto()
    V_ENC_EMBD_CLS       = auto()
    V_ENC_EMBD_PATCH     = auto()
+    V_ENC_EMBD_NORM      = auto()
    V_ENC_EMBD_POS       = auto()
    V_ENC_INPUT_NORM     = auto()
    V_ENC_ATTN_QKV       = auto()
@@ -660,6 +662,7 @@ class MODEL_TENSOR(IntEnum):
    V_LAYER_SCALE_2      = auto()
    V_PRE_NORM           = auto()
    V_POST_NORM          = auto()
+    V_MM_POST_NORM       = auto()
    V_MM_INP_NORM        = auto()
    V_MM_INP_PROJ        = auto() # gemma3
    V_MM_SOFT_EMB_NORM   = auto() # gemma3
@@ -786,6 +789,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.JAIS:             "jais",
    MODEL_ARCH.NEMOTRON:         "nemotron",
    MODEL_ARCH.NEMOTRON_H:       "nemotron_h",
+    MODEL_ARCH.NEMOTRON_H_MOE:   "nemotron_h_moe",
    MODEL_ARCH.EXAONE:           "exaone",
    MODEL_ARCH.EXAONE4:          "exaone4",
    MODEL_ARCH.GRANITE:          "granite",
@@ -1014,6 +1018,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.V_MMPROJ_PEG:              "mm.model.peg.{bid}",
    MODEL_TENSOR.V_ENC_EMBD_CLS:            "v.class_embd",
    MODEL_TENSOR.V_ENC_EMBD_PATCH:          "v.patch_embd",
+    MODEL_TENSOR.V_ENC_EMBD_NORM:           "v.norm_embd",
    MODEL_TENSOR.V_ENC_EMBD_POS:            "v.position_embd",
    MODEL_TENSOR.V_ENC_ATTN_QKV:            "v.blk.{bid}.attn_qkv",
    MODEL_TENSOR.V_ENC_ATTN_Q:              "v.blk.{bid}.attn_q",
@@ -1032,6 +1037,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.V_LAYER_SCALE_2:           "v.blk.{bid}.ls2",
    MODEL_TENSOR.V_PRE_NORM:                "v.pre_ln",
    MODEL_TENSOR.V_POST_NORM:               "v.post_ln",
+    MODEL_TENSOR.V_MM_POST_NORM:            "mm.post_norm",
    MODEL_TENSOR.V_MM_INP_PROJ:             "mm.input_projection",
    MODEL_TENSOR.V_MM_INP_NORM:             "mm.input_norm",
    MODEL_TENSOR.V_MM_SOFT_EMB_NORM:        "mm.soft_emb_norm",
@@ -1092,6 +1098,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.V_MMPROJ_PEG,
        MODEL_TENSOR.V_ENC_EMBD_CLS,
        MODEL_TENSOR.V_ENC_EMBD_PATCH,
+        MODEL_TENSOR.V_ENC_EMBD_NORM,
        MODEL_TENSOR.V_ENC_EMBD_POS,
        MODEL_TENSOR.V_ENC_INPUT_NORM,
        MODEL_TENSOR.V_ENC_ATTN_QKV,
@@ -1110,6 +1117,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.V_LAYER_SCALE_2,
        MODEL_TENSOR.V_PRE_NORM,
        MODEL_TENSOR.V_POST_NORM,
+        MODEL_TENSOR.V_MM_POST_NORM,
        MODEL_TENSOR.V_MM_INP_PROJ,
        MODEL_TENSOR.V_MM_INP_NORM,
        MODEL_TENSOR.V_MM_SOFT_EMB_NORM,
@@ -2529,6 +2537,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
+    MODEL_ARCH.NEMOTRON_H_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_D,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_OUT,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        # experts
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        # shared expert
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+    ],
    MODEL_ARCH.EXAONE: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
@@ -3320,6 +3355,7 @@ class VisionProjectorType:
    ULTRAVOX = "ultravox"
    INTERNVL = "internvl"
    QWEN2A = "qwen2a" # audio
+    GLMA = "glma" # audio
    QWEN25O = "qwen2.5o" # omni
    VOXTRAL = "voxtral"
    LFM2 = "lfm2"
@@ -3327,6 +3363,7 @@ class VisionProjectorType:
    LIGHTONOCR = "lightonocr"
    COGVLM = "cogvlm"
    JANUS_PRO = "janus_pro"
+    GLM4V = "glm4v"


 # Items here are (block size, type size)
@@ -154,7 +154,8 @@ class TensorNameMap:
            "model.layers.{bid}.operator_norm",                     # lfm2
            "model.transformer.blocks.{bid}.attn_norm",             # llada
            "layers.{bid}.input_layernorm",                         # qwen3-embedding
-            "model.layers.{bid}.attention_layernorm"                # apertus
+            "model.layers.{bid}.attention_layernorm",               # apertus
+            "model.layers.{bid}.pre_attention_layernorm",           # kormo
        ),

        # Attention norm 2
@@ -342,6 +343,7 @@ class TensorNameMap:
            "model.transformer.blocks.{bid}.ff_norm",                        # llada
            "layers.{bid}.post_attention_layernorm",                         # qwen3-embedding
            "model.layers.{bid}.feedforward_layernorm",                      # apertus
+            "model.layers.{bid}.pre_mlp_layernorm",                          # kormo
        ),

        # Pre feed-forward norm
@@ -377,6 +379,7 @@ class TensorNameMap:
            "model.layers.{bid}.feed_forward.gate",               # lfm2moe
            "model.layers.{bid}.mlp.router.gate",               # afmoe
            "layers.{bid}.gate",                                # mistral-large
+            "backbone.layers.{bid}.mixer.gate",                 # nemotron-h-moe
        ),

        MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -390,6 +393,7 @@ class TensorNameMap:
            "model.layers.{bid}.mlp.expert_bias",                           # afmoe
            "model.layers.{bid}.feed_forward.expert_bias",                  # lfm2moe
            "model.layers.{bid}.block_sparse_moe.e_score_correction",       # minimax-m2
+            "backbone.layers.{bid}.mixer.gate.e_score_correction"           # nemotron-h-moe
        ),

        # Feed-forward up
@@ -438,7 +442,7 @@ class TensorNameMap:
            "layers.{bid}.feed_forward.experts.w3",                 # mixtral (merged)
            "transformer.decoder_layer.{bid}.moe.linear_v",         # Grok (merged)
            "transformer.blocks.{bid}.ffn.experts.mlp.v1",          # dbrx
-            "model.layers.{bid}.mlp.experts.up_proj",               # qwen2moe olmoe (merged) ernie4.5-moe
+            "model.layers.{bid}.mlp.experts.up_proj",               # qwen2moe olmoe (merged) ernie4.5-moe, nemotron-h-moe (merged)
            "model.layers.{bid}.block_sparse_moe.experts.w3",       # phimoe (merged)
            "model.layers.{bid}.feed_forward.experts.up_proj",      # llama4
            "encoder.layers.{bid}.mlp.experts.mlp.w1",              # nomic-bert-moe
@@ -452,6 +456,7 @@ class TensorNameMap:
            "model.layers.{bid}.feed_forward.down_proj",
            "model.layers.{bid}.mlp.shared_mlp.up_proj",             # hunyuan
            "layers.{bid}.shared_experts.w3",                        # mistral-large
+            "backbone.layers.{bid}.mixer.shared_experts.up_proj",    # nemotron-h-moe
        ),

        MODEL_TENSOR.FFN_UP_CHEXP: (
@@ -546,7 +551,7 @@ class TensorNameMap:
            "layers.{bid}.feed_forward.experts.w2",                 # mixtral (merged)
            "transformer.decoder_layer.{bid}.moe.linear_1",         # Grok (merged)
            "transformer.blocks.{bid}.ffn.experts.mlp.w2",          # dbrx
-            "model.layers.{bid}.mlp.experts.down_proj",             # qwen2moe olmoe (merged) ernie4.5-moe
+            "model.layers.{bid}.mlp.experts.down_proj",             # qwen2moe olmoe (merged) ernie4.5-moe nemotron-h-moe (merged)
            "model.layers.{bid}.block_sparse_moe.output_linear",    # granitemoe
            "model.layers.{bid}.block_sparse_moe.experts.w2",       # phimoe (merged)
            "model.layers.{bid}.feed_forward.experts.down_proj",    # llama4
@@ -561,6 +566,7 @@ class TensorNameMap:
            "model.layers.{bid}.shared_mlp.output_linear",             # granitemoe
            "model.layers.{bid}.mlp.shared_mlp.down_proj",             # hunyuan
            "layers.{bid}.shared_experts.w2",                          # mistral-large
+            "backbone.layers.{bid}.mixer.shared_experts.down_proj",    # nemotron-h-moe
        ),

        MODEL_TENSOR.FFN_DOWN_CHEXP: (
@@ -704,6 +710,7 @@ class TensorNameMap:
            "model.layers.{bid}.mamba.dt_proj",         # jamba falcon-h1 granite-hybrid
            "model.layers.layers.{bid}.mixer.dt_proj",  # plamo2
            "model.layers.{bid}.linear_attn.dt_proj",   # qwen3next
+            "backbone.layers.{bid}.mixer.dt",           # nemotron-h-moe
        ),

        MODEL_TENSOR.SSM_DT_NORM: (
@@ -1205,6 +1212,7 @@ class TensorNameMap:
        MODEL_TENSOR.V_MMPROJ_FC: (
            "model.connector.modality_projection.proj", # SmolVLM
            "model.vision.linear_proj.linear_proj", # cogvlm
+            "visual.merger.proj", # glm4v
        ),

        MODEL_TENSOR.V_MMPROJ_MLP: (
@@ -1238,6 +1246,10 @@ class TensorNameMap:
            "model.vision.patch_embedding.proj", # cogvlm
        ),

+        MODEL_TENSOR.V_ENC_EMBD_NORM: (
+            "visual.post_conv_layernorm", # glm4v
+        ),
+
        MODEL_TENSOR.V_ENC_EMBD_POS: (
            "vision_tower.vision_model.embeddings.position_embedding",
            "model.vision_tower.embeddings.position_embeddings", # Intern-S1
@@ -1247,6 +1259,7 @@ class TensorNameMap:
            "vision_tower.patch_embed.pos_emb", # kimi-vl
            "visual.pos_embed", # qwen3vl
            "model.vision.patch_embedding.position_embedding", # cogvlm
+            "visual.embeddings.position_embedding", # glm4v
        ),

        MODEL_TENSOR.V_ENC_ATTN_QKV: (
@@ -1402,6 +1415,11 @@ class TensorNameMap:
            "vision_model.layernorm_post", # llama4
            "visual.merger.ln_q", # qwen2vl
            "vision_tower.encoder.final_layernorm", # kimi-vl
+            "visual.post_layernorm", # glm4v
+        ),
+
+        MODEL_TENSOR.V_MM_POST_NORM: (
+            "visual.merger.post_projection_norm", # glm4v
        ),

        MODEL_TENSOR.V_MM_INP_PROJ: (
@@ -1471,6 +1489,7 @@ class TensorNameMap:
        MODEL_TENSOR.V_MM_PATCH_MERGER: (
            "multi_modal_projector.patch_merger.merging_layer", # mistral small 3.1 - hf
            "patch_merger.merging_layer", # mistral
+            "visual.downsample", # glm4v
        ),

        MODEL_TENSOR.V_DS_NORM: (
@@ -1491,14 +1510,17 @@ class TensorNameMap:

        MODEL_TENSOR.V_MM_UP: (
            "model.vision.linear_proj.dense_h_to_4h", # cogvlm
+            "visual.merger.up_proj", # glm4v
        ),

        MODEL_TENSOR.V_MM_DOWN: (
            "model.vision.linear_proj.dense_4h_to_h", # cogvlm
+            "visual.merger.down_proj", # glm4v
        ),

        MODEL_TENSOR.V_MM_GATE: (
            "model.vision.linear_proj.gate_proj", # cogvlm
+            "visual.merger.gate_proj", # glm4v
        ),

        MODEL_TENSOR.V_TOK_BOI: (
@@ -288,7 +288,7 @@ class LocalTensor:
    data_range: LocalTensorRange

    def mmap_bytes(self) -> np.ndarray:
-        return np.memmap(self.data_range.filename, offset=self.data_range.offset, shape=self.data_range.size)
+        return np.memmap(self.data_range.filename, mode='r', offset=self.data_range.offset, shape=self.data_range.size)


 class SafetensorsLocal:
@@ -1,6 +1,6 @@
 # GBNF Guide

-GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/main` and `tools/server`.
+GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/cli`, `tools/completion` and `tools/server`.

 ## Background

@@ -135,7 +135,7 @@ While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) ma
 You can use GBNF grammars:

 - In [llama-server](../tools/server)'s completion endpoints, passed as the `grammar` body field
- In [llama-cli](../tools/main), passed as the `--grammar` & `--grammar-file` flags
+- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--grammar` & `--grammar-file` flags
 - With [test-gbnf-validator](../tests/test-gbnf-validator.cpp), to test them against strings.

 ## JSON Schemas → GBNF
@@ -145,7 +145,7 @@ You can use GBNF grammars:
 - In [llama-server](../tools/server):
    - For any completion endpoints, passed as the `json_schema` body field
    - For the `/chat/completions` endpoint, passed inside the `response_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}` or `{ type: "json_schema", json_schema: {"schema": ...} }`)
- In [llama-cli](../tools/main), passed as the `--json` / `-j` flag
+- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--json` / `-j` flag
 - To convert to a grammar ahead of time:
    - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py)
    - in JavaScript with [json-schema-to-grammar.mjs](../tools/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../tools/server)'s Web UI)
@@ -313,6 +313,7 @@ extern "C" {
        bool check_tensors;   // validate model tensor data
        bool use_extra_bufts; // use extra buffer types (used for weight repacking)
        bool no_host;         // bypass host buffer allowing extra buffers to be used
+        bool no_alloc;        // only load metadata and simulate memory allocations
    };

    // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
@@ -466,10 +467,24 @@ extern "C" {
    // Frees all allocated memory
    LLAMA_API void llama_free(struct llama_context * ctx);

+    // fits mparams and cparams to free device memory (assumes system memory is unlimited)
+    // returns true if the parameters could be successfully modified to fit device memory
+    // this function is NOT thread safe because it modifies the global llama logger state
+    LLAMA_API bool llama_params_fit(
+                                   const char   * path_model,
+                    struct llama_model_params   * mparams,
+                    struct llama_context_params * cparams,
+                                          float * tensor_split,          // writable buffer for tensor split, needs at least llama_max_devices elements
+        struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
+                                         size_t   margin,                // margin of memory to leave per device in bytes
+                                       uint32_t   n_ctx_min,             // minimum context size to set when trying to reduce memory use
+                            enum ggml_log_level   log_level);            // minimum log level to print during fitting, lower levels go to debug log
+
    LLAMA_API int64_t llama_time_us(void);

    LLAMA_API size_t llama_max_devices(void);
    LLAMA_API size_t llama_max_parallel_sequences(void);
+    LLAMA_API size_t llama_max_tensor_buft_overrides(void);

    LLAMA_API bool llama_supports_mmap       (void);
    LLAMA_API bool llama_supports_mlock      (void);
@@ -1354,7 +1369,9 @@ extern "C" {

    // Set callback for all future logging events.
    // If this is not called, or NULL is supplied, everything is output on stderr.
-    LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
+    // The logger state is global so these functions are NOT thread safe.
+    LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data);
+    LLAMA_API void llama_log_set(ggml_log_callback   log_callback, void *  user_data);

    //
    // Performance utils
@@ -0,0 +1,204 @@
+{% macro render_extra_keys(json_dict, handled_keys) %}
+    {%- if json_dict is mapping %}
+        {%- for json_key in json_dict if json_key not in handled_keys %}
+            {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %}
+                {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}
+            {%- else %}
+                {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}
+            {%- endif %}
+        {%- endfor %}
+    {%- endif %}
+{% endmacro %}
+{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %}
+{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %}
+
+{%- set ns = namespace(last_user_idx = -1) %}
+{%- set loop_messages = messages %}
+{%- for m in loop_messages %}
+  {%- if m["role"] == "user" %}
+    {%- set ns.last_user_idx = loop.index0 %}
+  {%- endif %}
+{%- endfor %}
+
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = [] %}
+{%- endif %}
+{# Recompute last_user_idx relative to loop_messages after handling system #}
+{%- set ns = namespace(last_user_idx = -1) %}
+{%- for m in loop_messages %}
+  {%- if m["role"] == "user" %}
+    {%- set ns.last_user_idx = loop.index0 %}
+  {%- endif %}
+{%- endfor %}
+{%- if system_message is defined %}
+    {{- "<|im_start|>system\n" + system_message }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- "<|im_start|>system\n" }}
+    {%- endif %}
+{%- endif %}
+{%- if tools is iterable and tools | length > 0 %}
+    {%- if system_message is defined and system_message | length > 0 %}
+        {{- "\n\n" }}
+    {%- endif %}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n" }}
+    {{- "<tools>" }}
+    {%- for tool in tools %}
+        {%- if tool.function is defined %}
+            {%- set tool = tool.function %}
+        {%- endif %}
+        {{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }}
+        {%- if tool.description is defined %}
+            {{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
+        {%- endif %}
+        {{- '\n<parameters>' }}
+        {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
+            {%- for param_name, param_fields in tool.parameters.properties|items %}
+                {{- '\n<parameter>' }}
+                {{- '\n<name>' ~ param_name ~ '</name>' }}
+                {%- if param_fields.type is defined %}
+                    {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
+                {%- endif %}
+                {%- if param_fields.description is defined %}
+                    {{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}
+                {%- endif %}
+                {%- if param_fields.enum is defined %}
+                    {{- '\n<enum>' ~ (param_fields.enum | tojson | safe) ~ '</enum>' }}
+                {%- endif %}
+                {%- set handled_keys = ['name', 'type', 'description', 'enum'] %}
+                {{- render_extra_keys(param_fields, handled_keys) }}
+                {{- '\n</parameter>' }}
+            {%- endfor %}
+        {%- endif %}
+        {% set handled_keys = ['type', 'properties', 'required'] %}
+        {{- render_extra_keys(tool.parameters, handled_keys) }}
+        {%- if tool.parameters is defined and tool.parameters.required is defined %}
+            {{- '\n<required>' ~ (tool.parameters.required | tojson | safe) ~ '</required>' }}
+        {%- endif %}
+        {{- '\n</parameters>' }}
+        {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
+        {{- render_extra_keys(tool, handled_keys) }}
+        {{- '\n</function>' }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+{%- endif %}
+
+
+{%- if system_message is defined %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+
+{%- for message in loop_messages %}
+    {%- if message.role == "assistant" %}
+        {# Add reasoning content in to content field for unified processing below. #}
+        {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}
+            {%- set content = "<think>\n" ~ message.reasoning_content ~ "\n</think>\n" ~ (message.content | default('', true)) %}
+        {%- else %}
+            {%- set content = message.content | default('', true) %}
+            {%- if content is string -%}
+                {# Allow downstream logic to to take care of broken thought, only handle coherent reasoning here. #}
+                {%- if '<think>' not in content and '</think>' not in content -%}
+                    {%- set content = "<think></think>" ~ content -%}
+                {%- endif -%}
+            {%- else -%}
+                {%- set content = content -%}
+            {%- endif -%}
+        {%- endif %}
+        {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}
+            {# Assistant message has tool calls. #}
+            {{- '<|im_start|>assistant\n' }}
+                {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}
+                {%- if content is string and content | trim | length > 0 %}
+                    {%- if include_content %}
+                        {{- (content | trim) ~ '\n' -}}
+                    {%- else %}
+                        {%- set c = (content | string) %}
+                        {%- if '</think>' in c %}
+                            {# Keep only content after the last closing think. Also generation prompt causes this. #}
+                            {%- set c = c.split('</think>')[-1] %}
+                        {%- elif '<think>' in c %}
+                            {# If <think> was opened but never closed, drop the trailing think segment #}
+                            {%- set c = c.split('<think>')[0] %}
+                        {%- endif %}
+                        {%- set c = "<think></think>" ~ c | trim %}
+                        {%- if c | length > 0 %}
+                            {{- c ~ '\n' -}}
+                        {%- endif %}
+                    {%- endif %}
+                {%- else %}
+                    {{- "<think></think>" -}}
+                {%- endif %}
+                {%- for tool_call in message.tool_calls %}
+                    {%- if tool_call.function is defined %}
+                        {%- set tool_call = tool_call.function %}
+                    {%- endif %}
+                    {{- '<tool_call>\n<function=' ~ tool_call.name ~ '>\n' -}}
+                        {%- if tool_call.arguments is defined %}
+                            {%- for args_name, args_value in tool_call.arguments|items %}
+                                {{- '<parameter=' ~ args_name ~ '>\n' -}}
+                                    {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                                {{- args_value ~ '\n</parameter>\n' -}}
+                            {%- endfor %}
+                        {%- endif %}
+                    {{- '</function>\n</tool_call>\n' -}}
+                {%- endfor %}
+                {{- '<|im_end|>\n' }}
+        {%- else %}
+            {# Assistant message doesn't have tool calls. #}
+            {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}
+                {{- '<|im_start|>assistant\n' ~ (content | default('', true) | string | trim) ~ '<|im_end|>\n' }}
+            {%- else %}
+                {%- set c = (content | default('', true) | string) %}
+                {%- if '<think>' in c and '</think>' in c %}
+                    {%- set c = "<think></think>" ~ c.split('</think>')[-1] %}
+                {%- endif %}
+                {%- set c = c | trim %}
+                {%- if c | length > 0 %}
+                    {{- '<|im_start|>assistant\n' ~ c ~ '<|im_end|>\n' }}
+                {%- else %}
+                    {{- '<|im_start|>assistant\n<|im_end|>\n' }}
+                {%- endif %}
+            {%- endif %}
+        {%- endif %}
+    {%- elif message.role == "user" or message.role == "system" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- set content = message.content | string %}
+        {{- content }}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user\n' }}
+        {%- endif %}
+        {{- '<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>\n' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+    {%- if enable_thinking %}
+        {{- '<|im_start|>assistant\n<think>\n' }}
+    {%- else %}
+        {{- '<|im_start|>assistant\n<think></think>' }}
+    {%- endif %}
+{%- endif %}
@@ -0,0 +1,65 @@
+#!/bin/sh
+#
+
+# Basedir on device
+basedir=/data/local/tmp/llama.cpp
+
+cli_opts=
+
+branch=.
+[ "$B" != "" ] && branch=$B
+
+adbserial=
+[ "$S" != "" ] && adbserial="-s $S"
+
+model="gemma-3-4b-it-Q4_0.gguf"
+[ "$M" != "" ] && model="$M"
+
+mmproj="mmproj-F16.gguf"
+[ "$MMPROJ" != "" ] && mmproj="$MMPROJ"
+
+image=
+[ "$IMG" != "" ] && image="$IMG"
+
+device="HTP0"
+[ "$D" != "" ] && device="$D"
+
+verbose=
+[ "$V" != "" ] && verbose="GGML_HEXAGON_VERBOSE=$V"
+
+experimental="GGML_HEXAGON_EXPERIMENTAL=1"
+[ "$E" != "" ] && experimental="GGML_HEXAGON_EXPERIMENTAL=$E"
+
+sched=
+[ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v"
+
+profile=
+[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1"
+
+opmask=
+[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK"
+
+nhvx=
+[ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX"
+
+ndev=
+[ "$NDEV" != "" ] && ndev="GGML_HEXAGON_NDEV=$NDEV"
+
+# MTMD backend device for vision model (defaults to CPU if not set)
+mtmd_backend=
+[ "$MTMD_DEVICE" != "" ] && mtmd_backend="MTMD_BACKEND_DEVICE=$MTMD_DEVICE"
+
+set -x
+
+adb $adbserial shell " \
+  cd $basedir; ulimit -c unlimited;        \
+    LD_LIBRARY_PATH=$basedir/$branch/lib   \
+    ADSP_LIBRARY_PATH=$basedir/$branch/lib \
+    $verbose $experimental $sched $opmask $profile $nhvx $ndev $mtmd_backend       \
+      ./$branch/bin/llama-mtmd-cli --no-mmap -m $basedir/../gguf/$model   \
+         --mmproj $basedir/../gguf/$mmproj \
+         --image $basedir/../gguf/$image \
+         --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1             \
+         --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on \
+         -ngl 99 --device $device -v $cli_opts $@ \
+"
@@ -3,6 +3,7 @@
 #include "ggml.h" // ggml_op

 #include <string>
+#include <set>

 //
 // gguf constants (sync with gguf.py)
@@ -79,6 +80,7 @@ enum llm_arch {
    LLM_ARCH_JAIS,
    LLM_ARCH_NEMOTRON,
    LLM_ARCH_NEMOTRON_H,
+    LLM_ARCH_NEMOTRON_H_MOE,
    LLM_ARCH_EXAONE,
    LLM_ARCH_EXAONE4,
    LLM_ARCH_RWKV6,
@@ -315,6 +317,7 @@ enum llm_tensor {
    LLM_TENSOR_DENSE_3_OUT,
    LLM_TENSOR_OUTPUT,
    LLM_TENSOR_OUTPUT_NORM,
+    LLM_TENSOR_OUTPUT_NORM_LFM2, // fix for wrong tensor name
    LLM_TENSOR_ROPE_FREQS,
    LLM_TENSOR_ROPE_FACTORS_LONG,
    LLM_TENSOR_ROPE_FACTORS_SHORT,
@@ -525,6 +528,10 @@ struct LLM_TN_IMPL {
    const int bid;
    const int xid;

+    const std::set<llm_tensor> model_tensors;
+
+    LLM_TN_IMPL(llm_arch arch, llm_tensor tensor, const char * suffix, int bid, int xid);
+
    std::string str() const;

    operator std::string() const {
@@ -546,11 +553,11 @@ struct LLM_TN {
    llm_arch arch;

    LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const {
-        return { arch, tensor, suffix, bid, xid };
+        return LLM_TN_IMPL(arch, tensor, suffix, bid, xid);
    }

    LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const {
-        return { arch, tensor, nullptr, bid, xid };
+        return LLM_TN_IMPL(arch, tensor, nullptr, bid, xid);
    }
 };

@@ -258,6 +258,7 @@ llama_context::llama_context(

        backend_buft.clear();
        backend_ptrs.clear();
+        backend_buf_exp_size.clear();

        for (auto & backend : backends) {
            auto * buft = ggml_backend_get_default_buffer_type(backend.get());
@@ -274,6 +275,7 @@ llama_context::llama_context(

            backend_buft.push_back(buft);
            backend_ptrs.push_back(backend.get());
+            backend_buf_exp_size.push_back(0);
        }

        LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size());
@@ -389,7 +391,8 @@ llama_context::llama_context(

        // reserve pp (prompt processing) graph first so that buffers are only allocated once
        {
-            auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
+            auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(),
+                model.hparams.no_alloc, model.hparams.no_alloc ? backend_buf_exp_size.data() : nullptr);
            if (!gf) {
                if (pipeline_parallel) {
                    LLAMA_LOG_WARN("%s: compute buffer allocation failed, retrying without pipeline parallelism\n", __func__);
@@ -407,7 +410,7 @@ llama_context::llama_context(

        // reserve with tg (token generation) graph to get the number of splits and nodes
        {
-            auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get());
+            auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get(), model.hparams.no_alloc);
            if (!gf) {
                throw std::runtime_error("failed to allocate compute tg buffers");
            }
@@ -422,7 +425,7 @@ llama_context::llama_context(
            //
            // auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get());
            //
-            auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
+            auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), model.hparams.no_alloc);
            if (!gf) {
                throw std::runtime_error("failed to allocate compute pp buffers");
            }
@@ -431,11 +434,13 @@ llama_context::llama_context(
        for (size_t i = 0; i < backend_ptrs.size(); ++i) {
            ggml_backend_t             backend = backend_ptrs[i];
            ggml_backend_buffer_type_t buft    = backend_buft[i];
-            size_t size = ggml_backend_sched_get_buffer_size(sched.get(), backend);
-            if (size > 1) {
+            if (!model.hparams.no_alloc) {
+                backend_buf_exp_size[i] = ggml_backend_sched_get_buffer_size(sched.get(), backend);
+            }
+            if (backend_buf_exp_size[i] > 1) {
                LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
                        ggml_backend_buft_name(buft),
-                        size / 1024.0 / 1024.0);
+                        backend_buf_exp_size[i] / 1024.0 / 1024.0);
            }
        }

@@ -454,6 +459,23 @@ llama_context::llama_context(
 }

 llama_context::~llama_context() {
+    // FIXME this currently results in a use-after-free bug if the model is freed before the context
+    // if (!model.hparams.no_alloc) {
+    //     for (size_t i = 0; i < backend_ptrs.size(); ++i) {
+    //         ggml_backend_t             backend = backend_ptrs[i];
+    //         ggml_backend_buffer_type_t buft    = backend_buft[i];
+
+    //         const size_t size_exp = backend_buf_exp_size[i];
+    //         const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend);
+    //         if (size_exp == size_act) {
+    //             LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n",
+    //                 __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
+    //         } else {
+    //             LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n",
+    //                 __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
+    //         }
+    //     }
+    // }
    ggml_opt_free(opt_ctx);
 }

@@ -1428,7 +1450,8 @@ llm_graph_result * llama_context::get_gf_res_reserve() const {
    return static_cast<llm_graph_result *>(gf_res_reserve.get());
 }

-ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only) {
+ggml_cgraph * llama_context::graph_reserve(
+        uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only, size_t * sizes) {
    LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
    GGML_ASSERT(n_outputs >= 1);

@@ -1465,8 +1488,13 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u

    // initialize scheduler with the specified graph
    if (split_only) {
-        ggml_backend_sched_split_graph(sched.get(), gf);
+        if (sizes) {
+            ggml_backend_sched_reserve_size(sched.get(), gf, sizes);
+        } else {
+            ggml_backend_sched_split_graph(sched.get(), gf);
+        }
    } else if (!ggml_backend_sched_reserve(sched.get(), gf)) {
+        GGML_ASSERT(!sizes);
        LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
        return nullptr;
    }
@@ -2088,15 +2116,26 @@ void llama_context::perf_reset() {

 std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> llama_context::memory_breakdown() const {
    std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> ret;
-    for (const auto & buft_size : model.memory_breakdown()) {
-        ret[buft_size.first].model += buft_size.second;
+    for (const auto & [buft, size] : model.memory_breakdown()) {
+        ret[buft].model += size;
    }
-    for (const auto & buft_size : memory->memory_breakdown()) {
-        ret[buft_size.first].context += buft_size.second;
+    if (memory) {
+        for (const auto & [buft, size] : memory->memory_breakdown()) {
+            ret[buft].context += size;
+        }
    }
-    for (const auto & backend_ptr : backends) {
-        ggml_backend_t backend = backend_ptr.get();
-        ret[ggml_backend_sched_get_buffer_type(sched.get(), backend)].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend);
+    if (model.hparams.no_alloc) {
+        for (size_t i = 0; i < backends.size(); ++i) {
+            ggml_backend_t             backend = backends[i].get();
+            ggml_backend_buffer_type_t buft    = ggml_backend_sched_get_buffer_type(sched.get(), backend);
+            ret[buft].compute += backend_buf_exp_size[i];
+        }
+    } else {
+        for (const auto & backend_ptr : backends) {
+            ggml_backend_t             backend = backend_ptr.get();
+            ggml_backend_buffer_type_t buft    = ggml_backend_sched_get_buffer_type(sched.get(), backend);
+            ret[buft].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend);
+        }
    }
    return ret;
 }
@@ -26,6 +26,10 @@ struct llama_memory_breakdown_data {
    size_t model   = 0; // memory allocated for the model
    size_t context = 0; // memory allocated for the context
    size_t compute = 0; // memory allocated for temporary compute buffers
+
+    size_t total() const {
+        return model + context + compute;
+    }
 };

 struct llama_context {
@@ -206,7 +210,8 @@ public:
    ggml_status graph_compute(ggml_cgraph * gf, bool batched);

    // reserve a graph with a dummy ubatch of the specified size
-    ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false);
+    ggml_cgraph * graph_reserve(
+        uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false, size_t * sizes = nullptr);

 private:
    llm_graph_params graph_params(
@@ -281,9 +286,10 @@ private:

    std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;

-    // buffer types used for the compute buffer of each backend
+    // pointers and buffer types used for the compute buffer of each backend
    std::vector<ggml_backend_t>             backend_ptrs;
    std::vector<ggml_backend_buffer_type_t> backend_buft;
+    std::vector<size_t>                     backend_buf_exp_size; // expected buffer sizes

    llm_graph_result_ptr gf_res_prev;
    llm_graph_result_ptr gf_res_reserve;
@@ -254,6 +254,24 @@ void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
    }
 }

+bool llm_graph_input_rs::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_memory_recurrent_context *>(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    res &= s_copy->ne[0] == mctx->get_n_rs();
+
+    res &= s_copy_main->ne[0]  == params.ubatch.n_seqs;
+    res &= s_copy_extra->ne[0] == mctx->get_n_rs() - params.ubatch.n_seqs;
+
+    res &= head == mctx->get_head();
+    res &= rs_z == mctx->get_rs_z();
+
+    return res;
+}
+
 void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
    GGML_UNUSED(ubatch);

@@ -461,8 +479,46 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
 }

 void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
-    inp_attn->set_input(ubatch);
-    inp_rs->set_input(ubatch);
+    mctx->get_attn()->set_input_k_idxs(inp_attn->self_k_idxs, ubatch);
+    mctx->get_attn()->set_input_v_idxs(inp_attn->self_v_idxs, ubatch);
+
+    mctx->get_attn()->set_input_kq_mask(inp_attn->self_kq_mask, ubatch, cparams.causal_attn);
+
+    const int64_t n_rs = mctx->get_recr()->get_n_rs();
+
+    if (inp_rs->s_copy) {
+        GGML_ASSERT(ggml_backend_buffer_is_host(inp_rs->s_copy->buffer));
+        int32_t * data = (int32_t *) inp_rs->s_copy->data;
+
+        // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
+        for (uint32_t i = 0; i < n_rs; ++i) {
+            data[i] = mctx->get_recr()->s_copy(i);
+        }
+    }
+}
+
+bool llm_graph_input_mem_hybrid::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_memory_hybrid_context *>(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    res &= inp_attn->self_k_idxs->ne[0] == params.ubatch.n_tokens;
+  //res &= inp_attn->self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
+
+    res &= inp_attn->self_kq_mask->ne[0] == mctx->get_attn()->get_n_kv();
+    res &= inp_attn->self_kq_mask->ne[1] == params.ubatch.n_tokens;
+
+    res &= inp_rs->s_copy->ne[0] == mctx->get_recr()->get_n_rs();
+
+    res &= inp_rs->s_copy_main->ne[0]  == params.ubatch.n_seqs;
+    res &= inp_rs->s_copy_extra->ne[0] == mctx->get_recr()->get_n_rs() - params.ubatch.n_seqs;
+
+    res &= inp_rs->head == mctx->get_recr()->get_head();
+    res &= inp_rs->rs_z == mctx->get_recr()->get_rs_z();
+
+    return res;
 }

 //
@@ -1089,6 +1145,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
                cur = ggml_relu(ctx0, cur);
                cb(cur, "ffn_moe_relu", il);
            } break;
+        case LLM_FFN_RELU_SQR:
+            if (gate_exps) {
+                // TODO: add support for gated squared relu
+                GGML_ABORT("fatal error: gated squared relu not implemented");
+            } else {
+                cur = ggml_relu(ctx0, cur);
+                cur = ggml_sqr(ctx0, cur);
+                cb(cur, "ffn_moe_relu_sqr", il);
+            } break;
        default:
            GGML_ABORT("fatal error");
    }
@@ -1841,6 +1906,9 @@ static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
    inp->s_copy_main  = ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0);
    inp->s_copy_extra = ggml_view_1d(ctx0, inp->s_copy, n_rs - n_seqs, n_seqs * inp->s_copy->nb[0]);

+    inp->head = mctx_cur->get_head();
+    inp->rs_z = mctx_cur->get_rs_z();
+
    return inp;
 }

@@ -1909,10 +1977,10 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
 llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
    const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);

-    auto inp_rs   = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
+    auto inp_rs   = build_rs_inp_impl     (ctx0, ubatch, mctx_cur->get_recr());
    auto inp_attn = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());

-    auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);
+    auto inp = std::make_unique<llm_graph_input_mem_hybrid>(cparams, std::move(inp_attn), std::move(inp_rs), mctx_cur);

    return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
 }
@@ -225,6 +225,8 @@ public:

    void set_input(const llama_ubatch * ubatch) override;

+    bool can_reuse(const llm_graph_params & params) override;
+
    ggml_tensor * s_copy;  // I32 [n_rs]

    // views of s_copy, computed once per graph
@@ -233,6 +235,10 @@ public:
    ggml_tensor * s_copy_extra;  // I32 [n_rs - n_seqs]

    const llama_memory_recurrent_context * mctx;
+
+    // used in view offsets, need to match for valid graph reuse
+    uint32_t head;
+    int32_t rs_z;
 };

 class llm_graph_input_cross_embd : public llm_graph_input_i {
@@ -365,22 +371,28 @@ public:
 class llm_graph_input_mem_hybrid : public llm_graph_input_i {
 public:
    llm_graph_input_mem_hybrid(
+            const llama_cparams & cparams,
            std::unique_ptr<llm_graph_input_attn_kv> inp_attn,
-            std::unique_ptr<llm_graph_input_rs>              inp_rs,
-            const llama_memory_hybrid_context *              mctx) :
+            std::unique_ptr<llm_graph_input_rs>      inp_rs,
+            const llama_memory_hybrid_context *      mctx) :
        inp_attn(std::move(inp_attn)),
        inp_rs(std::move(inp_rs)),
+        cparams(cparams),
        mctx(mctx) { }
    virtual ~llm_graph_input_mem_hybrid() = default;

    void set_input(const llama_ubatch * ubatch) override;

+    bool can_reuse(const llm_graph_params & params) override;
+
    std::unique_ptr<llm_graph_input_attn_kv> inp_attn;
    std::unique_ptr<llm_graph_input_rs>      inp_rs;

    llm_graph_input_attn_kv * get_attn() const { return inp_attn.get(); }
    llm_graph_input_rs      * get_recr() const { return inp_rs.get(); }

+    const llama_cparams cparams;
+
    const llama_memory_hybrid_context * mctx;
 };

@@ -2,6 +2,7 @@

 #include "ggml.h"

+#include <algorithm>
 #include <cassert>

 void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
@@ -230,3 +231,7 @@ bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama

    return false;
 }
+
+bool llama_hparams::use_mrope() const {
+    return rope_sections[0] > 0 && rope_sections[1] > 0;
+}
@@ -34,6 +34,7 @@ struct llama_hparams_convnext {

 struct llama_hparams {
    bool vocab_only;
+    bool no_alloc;
    bool rope_finetuned;
    bool use_par_res;
    bool swin_norm;
@@ -269,6 +270,8 @@ struct llama_hparams {
    // TODO: think of a better place for this function
    // TODO: pack the SWA params in a struct?
    static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1);
+
+    bool use_mrope() const;
 };

 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
@@ -25,6 +25,10 @@ time_meas::~time_meas() {
    }
 }

+void llama_log_get(ggml_log_callback * log_callback, void ** user_data) {
+    ggml_log_get(log_callback, user_data);
+}
+
 void llama_log_set(ggml_log_callback log_callback, void * user_data) {
    ggml_log_set(log_callback, user_data);
    g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
@@ -175,7 +175,15 @@ llama_kv_cache::llama_kv_cache(

    // allocate tensors and initialize the buffers to avoid NaNs in the padding
    for (auto & [buft, ctx] : ctx_map) {
-        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft);
+        ggml_backend_buffer_t buf;
+        if (model.hparams.no_alloc) {
+            buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
+            for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != nullptr; t = ggml_get_next_tensor(ctx.get(), t)) {
+                t->buffer = buf; // set dummy buffer for KV cache so that the backend scheduler won't try to allocate it
+            }
+        } else {
+            buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); // real buffer
+        }
        if (!buf) {
            throw std::runtime_error("failed to allocate buffer for kv cache");
        }
@@ -482,9 +490,18 @@ llama_pos llama_kv_cache::seq_pos_max(llama_seq_id seq_id) const {

 std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache::memory_breakdown() const {
    std::map<ggml_backend_buffer_type_t, size_t> ret;
-    for (const auto & [_, buf] : ctxs_bufs) {
-        ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
+    for (const auto & [ctx, buf] : ctxs_bufs) {
+        ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf.get());
+
+        if (hparams.no_alloc) {
+            GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) == nullptr);
+            ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
+        } else {
+            // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
+            ret[buft] += ggml_backend_buffer_get_size(buf.get());
+        }
    }
+
    return ret;
 }

@@ -1544,9 +1561,11 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama

        const uint32_t strm = seq_id == -1 ? s : seq_to_stream[seq_id];

+        slot_info sinfo;
+
        bool res = true;
-        res = res && state_read_meta(io, strm, cell_count, seq_id);
-        res = res && state_read_data(io, strm, cell_count);
+        res = res && state_read_meta(io, strm, cell_count, sinfo, seq_id);
+        res = res && state_read_data(io, strm, cell_count, sinfo);

        if (!res) {
            if (seq_id == -1) {
@@ -1685,7 +1704,7 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
    }
 }

-bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id) {
+bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, slot_info & sinfo, llama_seq_id dest_seq_id) {
    auto & cells = v_cells[strm];
    auto & head  = v_heads[strm];

@@ -1722,7 +1741,7 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
            ubatch.seq_id[i]   = &dest_seq_id;
        }

-        const auto sinfo = find_slot(ubatch, true);
+        sinfo = find_slot(ubatch, false);
        if (sinfo.empty()) {
            LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
            return false;
@@ -1732,20 +1751,16 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
        //       see: https://github.com/ggml-org/llama.cpp/pull/16825#issuecomment-3460868350
        apply_ubatch(sinfo, ubatch);

-        const auto head_cur = sinfo.head();
+        LLAMA_LOG_DEBUG("%s: cell_count = %d, dest_seq_id = %d\n", __func__, cell_count, dest_seq_id);

-        // keep the head at the old position because we will read the KV data into it in state_read_data()
-        head = head_cur;
-
-        LLAMA_LOG_DEBUG("%s: head_cur = %d, head = %d, cell_count = %d, dest_seq_id = %d\n", __func__, head_cur, head, cell_count, dest_seq_id);
-
-        // DEBUG CHECK: head_cur should be our first cell, head_cur + cell_count - 1 should be our last cell (verify seq_id and pos values)
-        // Assume that this is one contiguous block of cells
-        GGML_ASSERT(head_cur + cell_count <= cells.size());
-        GGML_ASSERT(cells.pos_get(head_cur)                  == ubatch.pos[0]);
-        GGML_ASSERT(cells.pos_get(head_cur + cell_count - 1) == ubatch.pos[cell_count - 1]);
-        GGML_ASSERT(cells.seq_has(head_cur,                  dest_seq_id));
-        GGML_ASSERT(cells.seq_has(head_cur + cell_count - 1, dest_seq_id));
+        // DEBUG CHECK: verify that all cells were allocated and have correct seq_id and pos values
+        GGML_ASSERT(sinfo.n_stream() == 1);
+        GGML_ASSERT(sinfo.idxs[0].size() == cell_count);
+        for (uint32_t i = 0; i < cell_count; ++i) {
+            const uint32_t idx = sinfo.idxs[0][i];
+            GGML_ASSERT(cells.pos_get(idx) == ubatch.pos[i]);
+            GGML_ASSERT(cells.seq_has(idx, dest_seq_id));
+        }
    } else {
        // whole KV cache restore

@@ -1778,15 +1793,24 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
            }
        }

+        // Create contiguous slot_info for whole cache restore
+        sinfo.s0 = strm;
+        sinfo.s1 = strm;
+        sinfo.resize(1);
+        sinfo.strm[0] = strm;
+        sinfo.idxs[0].resize(cell_count);
+        for (uint32_t i = 0; i < cell_count; ++i) {
+            sinfo.idxs[0][i] = i;
+        }
+
        head = 0;
    }

    return true;
 }

-bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count) {
+bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo) {
    auto & cells = v_cells[strm];
-    auto & head  = v_heads[strm];

    uint32_t v_trans;
    uint32_t n_layer;
@@ -1836,8 +1860,17 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
        }

        if (cell_count) {
-            // Read and set the keys for the whole cell range
-            ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row);
+            if (sinfo.is_contiguous()) {
+                // Fast path: contiguous cells, single memcpy
+                ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), sinfo.head() * k_size_row, cell_count * k_size_row);
+            } else {
+                // Slow path: scatter to non-contiguous positions
+                const void * src = io.read(cell_count * k_size_row);
+                for (uint32_t i = 0; i < cell_count; ++i) {
+                    const size_t dst_offset = sinfo.idxs[0][i] * k_size_row;
+                    ggml_backend_tensor_set(k, (const char*)src + i * k_size_row, dst_offset, k_size_row);
+                }
+            }
        }
    }

@@ -1868,8 +1901,17 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
            }

            if (cell_count) {
-                // Read and set the values for the whole cell range
-                ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), head * v_size_row, cell_count * v_size_row);
+                if (sinfo.is_contiguous()) {
+                    // Fast path: contiguous cells, single memcpy
+                    ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), sinfo.head() * v_size_row, cell_count * v_size_row);
+                } else {
+                    // Slow path: scatter to non-contiguous positions
+                    const void * src = io.read(cell_count * v_size_row);
+                    for (uint32_t i = 0; i < cell_count; ++i) {
+                        const size_t dst_offset = sinfo.idxs[0][i] * v_size_row;
+                        ggml_backend_tensor_set(v, (const char*)src + i * v_size_row, dst_offset, v_size_row);
+                    }
+                }
            }
        }
    } else {
@@ -1908,10 +1950,22 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
            }

            if (cell_count) {
-                // For each row in the transposed matrix, read the values for the whole cell range
-                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                    const size_t dst_offset = (head + j * cells.size()) * v_size_el;
-                    ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+                if (sinfo.is_contiguous()) {
+                    // Fast path: contiguous cells
+                    const uint32_t h = sinfo.head();
+                    for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                        const size_t dst_offset = (h + j * cells.size()) * v_size_el;
+                        ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+                    }
+                } else {
+                    // Slow path: scatter to non-contiguous positions
+                    for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                        const void * src = io.read(cell_count * v_size_el);
+                        for (uint32_t i = 0; i < cell_count; ++i) {
+                            const size_t dst_offset = (sinfo.idxs[0][i] + j * cells.size()) * v_size_el;
+                            ggml_backend_tensor_set(v, (const char*)src + i * v_size_el, dst_offset, v_size_el);
+                        }
+                    }
                }
            }
        }
@@ -72,6 +72,23 @@ public:
        void clear() {
            idxs.clear();
        }
+
+        // check if indices are contiguous starting from head()
+        bool is_contiguous() const {
+            if (idxs.empty() || idxs[0].empty()) {
+                return true;
+            }
+            if (idxs.size() > 1) {
+                return false;
+            }
+            const uint32_t h = idxs[0][0];
+            for (size_t i = 0; i < idxs[0].size(); ++i) {
+                if (idxs[0][i] != h + i) {
+                    return false;
+                }
+            }
+            return true;
+        }
    };

    using slot_info_vec_t = std::vector<slot_info>;
@@ -264,8 +281,8 @@ private:
    void state_write_meta(llama_io_write_i & io, const cell_ranges_t & cr, llama_seq_id seq_id = -1) const;
    void state_write_data(llama_io_write_i & io, const cell_ranges_t & cr) const;

-    bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
-    bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count);
+    bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count,       slot_info & sinfo, llama_seq_id dest_seq_id = -1);
+    bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo);
 };

 class llama_kv_cache_context : public llama_memory_context_i {
@@ -222,7 +222,7 @@ llama_memory_hybrid_context::llama_memory_hybrid_context(
    ubatches(std::move(ubatches)),
    // note: here we copy the ubatches. not sure if this is ideal
    ctx_attn(new llama_kv_cache_context(mem->get_mem_attn(), std::move(sinfos_attn), this->ubatches)),
-    ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(),                        this->ubatches)),
+    ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
    status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
 }

@@ -473,6 +473,7 @@ llama_model_loader::llama_model_loader(
        std::vector<std::string> & splits,
        bool use_mmap,
        bool check_tensors,
+        bool no_alloc,
        const llama_model_kv_override * param_overrides_p,
        const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) {
    int trace = 0;
@@ -716,6 +717,7 @@ llama_model_loader::llama_model_loader(

    this->use_mmap = use_mmap;
    this->check_tensors = check_tensors;
+    this->no_alloc = no_alloc;
 }

 std::string llama_model_loader::get_arch_name() const {
@@ -71,6 +71,7 @@ struct llama_model_loader {

    bool use_mmap = false;
    bool check_tensors;
+    bool no_alloc;

    llama_files files;
    llama_ftype ftype;
@@ -97,6 +98,7 @@ struct llama_model_loader {
        std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
        bool use_mmap,
        bool check_tensors,
+        bool no_alloc,
        const llama_model_kv_override * param_overrides_p,
        const llama_model_tensor_buft_override * param_tensor_buft_overrides_p);

@@ -120,6 +120,7 @@ const char * llm_type_name(llm_type type) {
        case LLM_TYPE_16B_A1B:       return "16B.A1B";
        case LLM_TYPE_21B_A3B:       return "21B.A3B";
        case LLM_TYPE_30B_A3B:       return "30B.A3B";
+        case LLM_TYPE_31B_A3_5B:     return "31B.A3.5B";
        case LLM_TYPE_80B_A3B:       return "80B.A3B";
        case LLM_TYPE_100B_A6B:      return "100B.A6B";
        case LLM_TYPE_106B_A12B:     return "106B.A12B";
@@ -1688,7 +1689,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
            } break;
        case LLM_ARCH_GLM4:
            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
                switch (hparams.n_layer) {
                    case 40: type = LLM_TYPE_9B; break;
                    case 61: type = LLM_TYPE_32B; break;
@@ -1697,8 +1699,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
            } break;
        case LLM_ARCH_GLM4_MOE:
            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,     hparams.n_ff_exp);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);

                // MoE parameters
                ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
@@ -1797,6 +1800,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                }
            } break;
        case LLM_ARCH_NEMOTRON_H:
+        case LLM_ARCH_NEMOTRON_H_MOE:
            {
                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
@@ -1812,7 +1816,14 @@ void llama_model::load_hparams(llama_model_loader & ml) {

                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp,        false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp,      false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+
                switch (hparams.n_layer) {
+                    case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B
                    case 56: type = LLM_TYPE_9B; break;
                    default: type = LLM_TYPE_UNKNOWN;
                }
@@ -3388,9 +3399,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        // optional bias tensors
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

@@ -5159,6 +5170,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                    }
                } break;
            case LLM_ARCH_NEMOTRON_H:
+            case LLM_ARCH_NEMOTRON_H_MOE:
                {
                    // mamba2 Mixer SSM params
                    // NOTE: int64_t for tensor dimensions
@@ -5169,6 +5181,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                    const int64_t n_group    = hparams.ssm_n_group;
                    const int64_t d_in_proj  = 2*d_inner + 2*n_group*d_state + n_ssm_head;

+                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+                    const int64_t n_ff_shexp = hparams.n_ff_shexp;
+
                    // embeddings
                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

@@ -5218,12 +5233,26 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias",   i), {n_embd_k_gqa_i}, TENSOR_NOT_REQUIRED);
                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias",   i), {n_embd_v_gqa_i}, TENSOR_NOT_REQUIRED);
                            layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd},         TENSOR_NOT_REQUIRED);
-                        } else {
-                            // mlp layers
-                            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, 0);
-                            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, 0);
-                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias",   i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
+                        }  else {
+                            if (n_expert != 0) {
+                                layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert}, 0);
+                                layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert         }, 0);
+
+                                // MoE branch
+                                layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                                layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+                                // Shared expert branch
+                                layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
+                                layer.ffn_up_shexp    = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
+
+                            } else {
+                                // mlp layers
+                                layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, 0);
+                                layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, 0);
+                                layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+                                layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias",   i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
+                            }
                        }
                    }
                } break;
@@ -6207,8 +6236,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                {
                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM_LFM2, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,           "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);

                    if (output == NULL) {
                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
@@ -6606,9 +6635,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {

        std::vector<ggml_backend_buffer_ptr> bufs;
        if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
+            GGML_ASSERT(!ml.no_alloc);
            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
                // only the mmap region containing the tensors in the model is mapped to the backend buffer
-                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
+                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer,
+                //     then we could just use metal for all layers
                // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
                void * addr = nullptr;
                size_t first, last; // NOLINT
@@ -6624,9 +6655,16 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                bufs.emplace_back(buf);
                buf_map.emplace(idx, buf);
            }
-        }
-        else {
-            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+        } else {
+            ggml_backend_buffer_t buf;
+            if (ml.no_alloc) {
+                buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
+                for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+                    t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them
+                }
+            } else {
+                buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
+            }
            if (buf == nullptr) {
                throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
            }
@@ -6681,6 +6719,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
        }
    }

+    if (ml.no_alloc) {
+        return true;
+    }
+
    // load tensor data
    for (auto & [ctx, buf_map] : ctx_buf_maps) {
        if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) {
@@ -6723,9 +6765,18 @@ size_t llama_model::n_devices() const {

 std::map<ggml_backend_buffer_type_t, size_t> llama_model::memory_breakdown() const {
    std::map<ggml_backend_buffer_type_t, size_t> ret;
-    for (const auto & [_, bufs] : pimpl->ctxs_bufs) {
-        for (const auto & buf : bufs) {
-            ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
+    for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) {
+        if (hparams.no_alloc) {
+            GGML_ASSERT(bufs.size() == 1);
+            ggml_backend_buffer_t buf = bufs[0].get();
+            GGML_ASSERT(ggml_backend_buffer_get_base(buf) == nullptr);
+            ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf);
+            ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
+        } else {
+            for (const auto & buf : bufs) {
+                // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
+                ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
+            }
        }
    }
    return ret;
@@ -6770,6 +6821,7 @@ void llama_model::print_info() const {
    // hparams
    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, arch_name().c_str());
    LLAMA_LOG_INFO("%s: vocab_only       = %d\n",     __func__, hparams.vocab_only);
+    LLAMA_LOG_INFO("%s: no_alloc         = %d\n",     __func__, hparams.no_alloc);

    if (!hparams.vocab_only) {
        LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
@@ -6827,7 +6879,8 @@ void llama_model::print_info() const {
        arch == LLM_ARCH_PLAMO2 ||
        arch == LLM_ARCH_GRANITE_HYBRID ||
        arch == LLM_ARCH_QWEN3NEXT ||
-        arch == LLM_ARCH_NEMOTRON_H) {
+        arch == LLM_ARCH_NEMOTRON_H ||
+        arch == LLM_ARCH_NEMOTRON_H_MOE) {
        LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
        LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
        LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
@@ -6882,7 +6935,8 @@ void llama_model::print_info() const {
    if (arch == LLM_ARCH_MINICPM ||
        arch == LLM_ARCH_GRANITE ||
        arch == LLM_ARCH_GRANITE_MOE ||
-        arch == LLM_ARCH_GRANITE_HYBRID) {
+        arch == LLM_ARCH_GRANITE_HYBRID ||
+        arch == LLM_ARCH_NEMOTRON_H_MOE) {
        LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
        LLAMA_LOG_INFO("%s: f_residual_scale  = %f\n", __func__, hparams.f_residual_scale);
        LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
@@ -7063,7 +7117,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                    if (arch == LLM_ARCH_FALCON_H1) {
                        filter_attn = [&](int32_t) { return true; };
                        filter_recr = [&](int32_t) { return true; };
-                    } else if (arch == LLM_ARCH_NEMOTRON_H) {
+                    } else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) {
                        filter_attn = [&](int32_t il) {
                            return !hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
                        };
@@ -7434,6 +7488,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
                llm = std::make_unique<llm_build_nemotron>(*this, params);
            } break;
        case LLM_ARCH_NEMOTRON_H:
+        case LLM_ARCH_NEMOTRON_H_MOE:
            {
                llm = std::make_unique<llm_build_nemotron_h>(*this, params);
            } break;
@@ -7618,6 +7673,7 @@ llama_model_params llama_model_default_params() {
        /*.check_tensors               =*/ false,
        /*.use_extra_bufts             =*/ true,
        /*.no_host                     =*/ false,
+        /*.no_alloc                    =*/ false,
    };

    return result;
@@ -7717,6 +7773,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_ARWKV7:
        case LLM_ARCH_WAVTOKENIZER_DEC:
        case LLM_ARCH_NEMOTRON_H:
+        case LLM_ARCH_NEMOTRON_H_MOE:
            return LLAMA_ROPE_TYPE_NONE;

        // use what we call a normal RoPE, operating on pairs of consecutive head values
@@ -7737,7 +7794,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_DEEPSEEK2:
        case LLM_ARCH_PLM:
        case LLM_ARCH_CHATGLM:
-        case LLM_ARCH_GLM4:
        case LLM_ARCH_GRANITE:
        case LLM_ARCH_GRANITE_MOE:
        case LLM_ARCH_GRANITE_HYBRID:
@@ -7799,7 +7855,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_LFM2:
        case LLM_ARCH_LFM2MOE:
        case LLM_ARCH_SMALLTHINKER:
-        case LLM_ARCH_GLM4_MOE:
        case LLM_ARCH_SEED_OSS:
        case LLM_ARCH_GROVEMOE:
        case LLM_ARCH_APERTUS:
@@ -7816,6 +7871,11 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_QWEN3VLMOE:
            return LLAMA_ROPE_TYPE_IMROPE;

+        case LLM_ARCH_GLM4:
+            return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NORM;
+        case LLM_ARCH_GLM4_MOE:
+            return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NEOX;
+
        // all model arches should be listed explicitly here
        case LLM_ARCH_UNKNOWN:
            GGML_ABORT("unknown architecture");
@@ -113,6 +113,7 @@ enum llm_type {
    LLM_TYPE_16B_A1B,
    LLM_TYPE_21B_A3B, // Ernie MoE small
    LLM_TYPE_30B_A3B,
+    LLM_TYPE_31B_A3_5B,
    LLM_TYPE_80B_A3B, // Qwen3 Next
    LLM_TYPE_100B_A6B,
    LLM_TYPE_106B_A12B, // GLM-4.5-Air
@@ -596,7 +596,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
    }

    std::vector<std::string> splits = {};
-    llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, kv_overrides, nullptr);
+    llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
    ml.init_mappings(false); // no prefetching

    llama_model model(llama_model_default_params());
@@ -1895,7 +1895,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                clean_spaces = false;
            } else if (
                    tokenizer_pre == "qwen2" ||
-                    tokenizer_pre == "deepseek-r1-qwen") {
+                    tokenizer_pre == "deepseek-r1-qwen" ||
+                    tokenizer_pre == "kormo") {
                pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
                clean_spaces = false;
            } else if (
@@ -1,6 +1,9 @@
+#include "llama.h"
+
 #include "llama-impl.h"

 #include "llama-chat.h"
+#include "llama-context.h"
 #include "llama-mmap.h"
 #include "llama-vocab.h"
 #include "llama-model-loader.h"
@@ -11,11 +14,14 @@
 #include "ggml-backend.h"

 #include <algorithm>
+#include <cassert>
+#include <cinttypes>
 #include <cstddef>
 #include <cstdint>
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <stdexcept>

 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -37,6 +43,669 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty
    GGML_ABORT("fatal error");
 }

+struct llama_device_memory_data {
+    int64_t total;
+    int64_t free;
+    llama_memory_breakdown_data mb;
+};
+
+static std::vector<llama_device_memory_data> llama_get_device_memory_data(
+        const char * path_model, const llama_model_params * mparams, const llama_context_params * cparams,
+        std::vector<ggml_backend_dev_t> & devs, uint32_t & hp_ngl, uint32_t & hp_n_ctx_train, uint32_t & hp_n_expert,
+        const ggml_log_level log_level) {
+    struct user_data_t {
+        struct {
+            ggml_log_callback callback;
+            void * user_data;
+        } original_logger;
+        ggml_log_level min_level; // prints below this log level go to debug log
+    };
+    user_data_t ud;
+    llama_log_get(&ud.original_logger.callback, &ud.original_logger.user_data);
+    ud.min_level = log_level;
+
+    llama_log_set([](ggml_log_level level, const char * text, void * user_data) {
+        const user_data_t * ud = (const user_data_t *) user_data;
+        const ggml_log_level level_eff = level >= ud->min_level ? level : GGML_LOG_LEVEL_DEBUG;
+        ud->original_logger.callback(level_eff, text, ud->original_logger.user_data);
+    }, &ud);
+
+    llama_model_params mparams_copy = *mparams;
+    mparams_copy.no_alloc  = true;
+    mparams_copy.use_mmap  = false;
+    mparams_copy.use_mlock = false;
+
+    llama_model * model = llama_model_load_from_file(path_model, mparams_copy);
+    if (model == nullptr) {
+        llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
+        throw std::runtime_error("failed to load model");
+    }
+
+    llama_context * ctx = llama_init_from_model(model, *cparams);
+    if (ctx == nullptr) {
+        llama_model_free(model);
+        llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
+        throw std::runtime_error("failed to create llama_context from model");
+    }
+
+    std::vector<llama_device_memory_data> ret(model->devices.size());
+
+    std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> memory_breakdown = ctx->memory_breakdown();
+
+    for (const auto & [buft, mb] : memory_breakdown) {
+        if (ggml_backend_buft_is_host(buft)) {
+            continue;
+        }
+
+        ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
+        if (!dev) {
+            continue;
+        }
+        for (size_t i = 0; i < ret.size(); i++) {
+            if (model->devices[i] == dev) {
+                ret[i].mb.model   += mb.model;
+                ret[i].mb.context += mb.context;
+                ret[i].mb.compute += mb.compute;
+                break;
+            }
+        }
+    }
+    for (size_t i = 0; i < ret.size(); i++) {
+        size_t free, total;
+        ggml_backend_dev_memory(model->devices[i], &free, &total);
+        ret[i].free  = free;
+        ret[i].total = total;
+    }
+
+    devs           = model->devices;
+    hp_ngl         = model->hparams.n_layer;
+    hp_n_ctx_train = model->hparams.n_ctx_train;
+    hp_n_expert    = model->hparams.n_expert;
+
+    llama_memory_breakdown_print(ctx); // goes to debug log
+
+    llama_free(ctx);
+    llama_model_free(model);
+    llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
+    return ret;
+}
+
+// enum to identify part of a layer for distributing its tensors:
+enum layer_fraction_t {
+    LAYER_FRACTION_NONE = 0, // nothing
+    LAYER_FRACTION_ATTN = 1, // attention
+    LAYER_FRACTION_UP   = 2, // attention + up
+    LAYER_FRACTION_GATE = 3, // attention + up + gate
+    LAYER_FRACTION_MOE  = 4, // everything but sparse MoE weights
+};
+// this enum is only used in llama_params_fit_impl but needs to be defined outside of it to fix a Windows compilation issue
+
+static void llama_params_fit_impl(
+        const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
+        float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
+        size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
+    constexpr int64_t MiB = 1024*1024;
+    const int64_t margin = margin_s; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
+    typedef std::vector<llama_device_memory_data> dmds_t;
+    const llama_model_params default_mparams = llama_model_default_params();
+
+    std::vector<ggml_backend_dev_t> devs;
+    uint32_t hp_ngl = 0; // hparams.n_gpu_layers
+    uint32_t hp_nct = 0; // hparams.n_ctx_train
+    uint32_t hp_nex = 0; // hparams.n_expert
+
+    // step 1: get data for default parameters and check whether any changes are necessary in the first place
+
+    LLAMA_LOG_DEBUG("%s: getting device memory data for initial parameters:\n", __func__);
+    const dmds_t dmds_full = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+    const size_t nd = devs.size(); // number of devices
+    if (nd == 0) {
+        LLAMA_LOG_INFO("%s: no devices with dedicated memory found\n", __func__);
+        return;
+    }
+
+    std::vector<std::string> dev_names;
+    {
+        dev_names.reserve(nd);
+        size_t max_length = 0;
+        for (ggml_backend_dev_t dev : devs) {
+            std::string name = ggml_backend_dev_name(dev);
+            name += " (";
+            name += ggml_backend_dev_description(dev);
+            name += ")";
+            dev_names.push_back(name);
+            max_length = std::max(max_length, name.length());
+        }
+        for (std::string & dn : dev_names) {
+            dn.insert(dn.end(), max_length - dn.length(), ' ');
+        }
+    }
+
+    int64_t sum_total           = 0;
+    int64_t sum_projected_free  = 0;
+    int64_t min_projected_free  = INT64_MAX;
+    int64_t sum_projected_used  = 0;
+    int64_t sum_projected_model = 0;
+    int64_t sum_projected_ctx   = 0;
+
+    if (nd > 1) {
+        LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__);
+    }
+    for (size_t id = 0; id < nd; id++) {
+        const llama_device_memory_data & dmd = dmds_full[id];
+
+        const int64_t projected_used = dmd.mb.total();
+        const int64_t projected_free = dmd.free - projected_used;
+
+        sum_total           += dmd.total;
+        sum_projected_used  += projected_used;
+        sum_projected_free  += projected_free;
+        min_projected_free   = std::min(min_projected_free, projected_free);
+        sum_projected_model += dmd.mb.model;
+        sum_projected_ctx   += dmd.mb.context;
+
+        if (nd > 1) {
+            LLAMA_LOG_INFO("%s:   - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " %s\n",
+                __func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, std::abs(projected_free)/MiB,
+                projected_free >= 0 ? "surplus" : "deficit");
+        }
+    }
+    assert(sum_total >= 0 && sum_projected_used >= 0 && sum_projected_ctx >= 0);
+    assert(sum_projected_used >= sum_projected_ctx);
+    LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n",
+        __func__, sum_projected_used/MiB, sum_total/MiB);
+    if (min_projected_free >= margin) {
+        if (nd == 1) {
+            LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n",
+                __func__, min_projected_free/MiB, margin/MiB);
+            return;
+        }
+        LLAMA_LOG_INFO("%s: will leave at least %" PRId64 " >= %" PRId64 " MiB of free memory on all devices, no changes needed\n",
+            __func__, min_projected_free/MiB, margin/MiB);
+        return;
+    }
+
+    // step 2: try reducing memory use by reducing the context size
+
+    {
+        int64_t global_surplus = sum_projected_free - int64_t(nd)*margin;
+        if (global_surplus < 0) {
+            LLAMA_LOG_INFO(nd == 1 ?
+                "%s: cannot fulfill margin of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n" :
+                "%s: cannot fulfill margin of %" PRId64 " MiB on all devices, need to use %" PRId64 " MiB less in total\n",
+                __func__, margin/MiB, -global_surplus/MiB);
+            if (cparams->n_ctx == 0) {
+                if (hp_nct > n_ctx_min) {
+                    const int64_t bytes_per_ctx = sum_projected_ctx / hp_nct;
+
+                    int64_t memory_reduction = -global_surplus;
+                    if (nd > 1) {
+                        // for multiple devices we need to be more conservative in terms of how much context we think can fit:
+                        //   - for dense models only whole layers can be assigned to devices
+                        //   - for MoE models only whole tensors can be assigned to devices, which we estimate to be <= 1/3 of a layer
+                        //   - on average we expect a waste of 0.5 layers/tensors per device
+                        //   - use slightly more than the expected average for nd devices to be safe
+                        const int64_t model_per_layer = sum_projected_model / std::min(uint32_t(mparams->n_gpu_layers), hp_ngl);
+                        memory_reduction += (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6);
+                    }
+
+                    uint32_t ctx_reduction = std::min(uint32_t((memory_reduction + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min);
+                    cparams->n_ctx = hp_nct - ctx_reduction;
+                    cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend
+
+                    ctx_reduction = hp_nct - cparams->n_ctx;
+                    memory_reduction = ctx_reduction * bytes_per_ctx;
+                    global_surplus += memory_reduction;
+                    LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
+                        __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
+                    if (global_surplus >= 0) {
+                        if (nd == 1) {
+                            LLAMA_LOG_INFO("%s: entire model can be fit by reducing context\n", __func__);
+                            return;
+                        }
+                        LLAMA_LOG_INFO("%s: entire model should be fit across devices by reducing context\n", __func__);
+                    }
+                } else {
+                    LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
+                        __func__, hp_nct, n_ctx_min);
+                }
+            } else {
+                LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
+            }
+        }
+    }
+
+    if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) {
+        throw std::runtime_error("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort");
+    }
+    if (nd > 1) {
+        if (!tensor_split) {
+            throw std::runtime_error("did not provide a buffer to write the tensor_split to, abort");
+        }
+        if (mparams->tensor_split) {
+            for (size_t id = 0; id < nd; id++) {
+                if (mparams->tensor_split[id] != 0.0f) {
+                    throw std::runtime_error("model_params::tensor_split already set by user, abort");
+                }
+            }
+        }
+        if (mparams->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            throw std::runtime_error("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort");
+        }
+        if (hp_ngl < 2*nd) {
+            throw std::runtime_error("model has only " + std::to_string(hp_ngl) + " layers but need at least "
+                + std::to_string(2*nd) + " to fit memory for " + std::to_string(nd) + " devices, abort");
+        }
+    }
+    if (!tensor_buft_overrides) {
+        throw std::runtime_error("did not provide buffer to set tensor_buft_overrides, abort");
+    }
+    if (mparams->tensor_buft_overrides && (mparams->tensor_buft_overrides->pattern || mparams->tensor_buft_overrides->buft)) {
+        throw std::runtime_error("model_params::tensor_buft_overrides already set by user, abort");
+    }
+
+    // step 3: iteratively fill the back to front with "dense" layers
+    //   - for a dense model simply fill full layers, giving each device a contiguous slice of the model
+    //   - for a MoE model, same as dense model but with all MoE tensors in system memory
+
+    // utility function that returns a static C string matching the tensors for a specific layer index and layer fraction:
+    auto get_overflow_pattern = [&](const size_t il, const layer_fraction_t lf) -> const char * {
+        constexpr size_t n_strings = 1000;
+        if (il >= n_strings) {
+            throw std::runtime_error("at most " + std::to_string(n_strings) + " model layers are supported");
+        }
+        switch (lf) {
+            case LAYER_FRACTION_ATTN: {
+                static std::array<std::string, n_strings> patterns;
+                if (patterns[il].empty()) {
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|gate|down).*";
+                }
+                return patterns[il].c_str();
+            }
+            case LAYER_FRACTION_UP: {
+                static std::array<std::string, n_strings> patterns;
+                if (patterns[il].empty()) {
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|down).*";
+                }
+                return patterns[il].c_str();
+            }
+            case LAYER_FRACTION_GATE: {
+                static std::array<std::string, n_strings> patterns;
+                if (patterns[il].empty()) {
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_down.*";
+                }
+                return patterns[il].c_str();
+            }
+            case LAYER_FRACTION_MOE: {
+                static std::array<std::string, n_strings> patterns;
+                if (patterns[il].empty()) {
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|down|gate)_(ch|)exps";
+                }
+                return patterns[il].c_str();
+            }
+            default:
+                GGML_ABORT("fatal error");
+        }
+    };
+
+    struct ngl_t {
+        uint32_t n_layer = 0; // number of total layers
+        uint32_t n_part  = 0; // number of partial layers, <= n_layer
+
+        // for the first partial layer varying parts can overflow, all further layers use LAYER_FRACTION_MOE:
+        layer_fraction_t overflow_type = LAYER_FRACTION_MOE;
+    };
+
+    const size_t ntbo = llama_max_tensor_buft_overrides();
+
+    // utility function to set n_gpu_layers and tensor_split
+    auto set_ngl_tensor_split_tbo = [&](
+            const std::vector<ngl_t> & ngl_per_device,
+            const std::vector<ggml_backend_buffer_type_t> & overflow_bufts,
+            llama_model_params & mparams,
+            const bool add_nonrepeating) {
+        mparams.n_gpu_layers = 0;
+        for (size_t id = 0; id < nd; id++) {
+            mparams.n_gpu_layers += ngl_per_device[id].n_layer;
+            if (nd > 1) {
+                tensor_split[id] = ngl_per_device[id].n_layer;
+            }
+        }
+        assert(uint32_t(mparams.n_gpu_layers) <= hp_ngl);
+        uint32_t il0 = hp_ngl - mparams.n_gpu_layers; // start index for tensor buft overrides
+
+        if (add_nonrepeating) {
+            mparams.n_gpu_layers += 1;
+            tensor_split[nd - 1] += 1;
+        }
+        mparams.tensor_split = tensor_split;
+
+        size_t itbo = 0;
+        for (size_t id = 0; id < nd; id++) {
+            il0 += ngl_per_device[id].n_layer - ngl_per_device[id].n_part;
+            for (uint32_t il = il0; il < il0 + ngl_per_device[id].n_part; il++) {
+                if (itbo + 1 >= ntbo) {
+                    tensor_buft_overrides[itbo].pattern = nullptr;
+                    tensor_buft_overrides[itbo].buft    = nullptr;
+                    itbo++;
+                    mparams.tensor_buft_overrides = tensor_buft_overrides;
+                    throw std::runtime_error("llama_params_fit_n_tensor_buft_overrides() == "
+                        + std::to_string(ntbo) + " is insufficient for model\n");
+                }
+                tensor_buft_overrides[itbo].pattern = get_overflow_pattern(il, il == il0 ? ngl_per_device[id].overflow_type : LAYER_FRACTION_MOE);
+                tensor_buft_overrides[itbo].buft = overflow_bufts[id];
+                itbo++;
+            }
+            il0 += ngl_per_device[id].n_part;
+        }
+        tensor_buft_overrides[itbo].pattern = nullptr;
+        tensor_buft_overrides[itbo].buft    = nullptr;
+        itbo++;
+        mparams.tensor_buft_overrides = tensor_buft_overrides;
+    };
+
+    // utility function that returns the memory use per device for given numbers of layers per device
+    auto get_memory_for_layers = [&](
+            const char * func_name,
+            const std::vector<ngl_t> & ngl_per_device,
+            const std::vector<ggml_backend_buffer_type_t> & overflow_bufts,
+            const bool add_nonrepeating) -> std::vector<int64_t> {
+        llama_model_params mparams_copy = *mparams;
+        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy, add_nonrepeating);
+
+        const dmds_t dmd_nl = llama_get_device_memory_data(
+            path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+
+        LLAMA_LOG_DEBUG("%s: memory for test allocation by device:\n", func_name);
+        for (size_t id = 0; id < nd; id++) {
+            const ngl_t & n = ngl_per_device[id];
+            LLAMA_LOG_DEBUG(
+                "%s: id=%zu, n_layer=%2" PRIu32 ", n_part=%2" PRIu32 ", overflow_type=%d, mem=%6" PRId64 " MiB\n",
+                func_name, id, n.n_layer, n.n_part, int(n.overflow_type), dmd_nl[id].mb.total()/MiB);
+        }
+
+        std::vector<int64_t> ret;
+        ret.reserve(nd);
+        for (const llama_device_memory_data & dmd : dmd_nl) {
+            ret.push_back(dmd.mb.total());
+        }
+        return ret;
+    };
+
+    int64_t global_surplus_cpu_moe = 0;
+    if (hp_nex > 0) {
+        const static std::string pattern_moe_all = "blk\\.\\d+\\.ffn_(up|down|gate)_(ch|)exps"; // matches all MoE tensors
+        ggml_backend_buffer_type_t cpu_buft = ggml_backend_cpu_buffer_type();
+        tensor_buft_overrides[0] = {pattern_moe_all.c_str(), cpu_buft};
+        tensor_buft_overrides[1] = {nullptr, nullptr};
+        mparams->tensor_buft_overrides = tensor_buft_overrides;
+
+        LLAMA_LOG_DEBUG("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
+        const dmds_t dmds_cpu_moe = llama_get_device_memory_data(
+            path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+
+        for (const llama_device_memory_data & dmd : dmds_cpu_moe) {
+            global_surplus_cpu_moe += dmd.free;
+            global_surplus_cpu_moe -= int64_t(dmd.mb.total()) + margin;
+        }
+
+        if (global_surplus_cpu_moe > 0) {
+            LLAMA_LOG_INFO("%s: with only dense weights in device memory there is a total surplus of %" PRId64 " MiB\n",
+                __func__, global_surplus_cpu_moe/MiB);
+        } else {
+            LLAMA_LOG_INFO("%s: with only dense weights in device memory there is still a total deficit of %" PRId64 " MiB\n",
+                __func__, -global_surplus_cpu_moe/MiB);
+        }
+
+        // reset
+        tensor_buft_overrides[0] = {nullptr, nullptr};
+        mparams->tensor_buft_overrides = tensor_buft_overrides;
+    }
+
+    std::vector<int64_t> targets; // maximum acceptable memory use per device
+    targets.reserve(nd);
+    for (size_t id = 0; id < nd; id++) {
+        targets.push_back(dmds_full[id].free - margin);
+        LLAMA_LOG_DEBUG("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB);
+    }
+
+    // whether for the optimal memory use we expect to load at least some MoE tensors:
+    const bool partial_moe = hp_nex > 0 && global_surplus_cpu_moe > 0;
+
+    std::vector<ggml_backend_buffer_type_t> overflow_bufts; // which bufts the partial layers of a device overflow to:
+    overflow_bufts.reserve(nd);
+    for (size_t id = 0; id < nd - 1; ++id) {
+        overflow_bufts.push_back(ggml_backend_dev_buffer_type(devs[id + 1]));
+    }
+    overflow_bufts.push_back(ggml_backend_cpu_buffer_type());
+
+    std::vector<ngl_t> ngl_per_device(nd);
+    std::vector<int64_t> mem = get_memory_for_layers(__func__, ngl_per_device, overflow_bufts, partial_moe);
+    if (hp_nex > 0) {
+        for (size_t id = 0; id < nd; id++) {
+            ngl_per_device[id].overflow_type = LAYER_FRACTION_MOE;
+        }
+    }
+
+    // optimize the number of layers per device using the method of false position:
+    //   - ngl_per_device has 0 layers for each device, lower bound
+    //   - try a "high" configuration where a device is given all unassigned layers
+    //   - interpolate the memory use / layer between low and high linearly to get a guess where it meets our target
+    //   - check memory use of our guess, replace either the low or high bound
+    //   - once we only have a difference of a single layer, stop and return the lower bound that just barely still fits
+    if (hp_nex == 0) {
+        LLAMA_LOG_INFO("%s: filling dense layers back-to-front:\n", __func__);
+    } else {
+        LLAMA_LOG_INFO("%s: filling dense-only layers back-to-front:\n", __func__);
+    }
+    for (int id = nd - 1; id >= 0; id--) {
+        uint32_t n_unassigned = hp_ngl;
+        for (size_t jd = id + 1; jd < nd; ++jd) {
+            assert(n_unassigned >= ngl_per_device[jd].n_layer);
+            n_unassigned -= ngl_per_device[jd].n_layer;
+        }
+
+        std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
+        ngl_per_device_high[id].n_layer = n_unassigned;
+        if (hp_nex > 0) {
+            ngl_per_device_high[id].n_part = ngl_per_device_high[id].n_layer;
+        }
+        if (ngl_per_device_high[id].n_layer > 0) {
+            std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe);
+            if (mem_high[id] > targets[id]) {
+                assert(ngl_per_device_high[id].n_layer > ngl_per_device[id].n_layer);
+                uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
+                LLAMA_LOG_DEBUG("%s: start filling device %" PRIu32 ", delta=%" PRIu32 "\n", __func__, id, delta);
+                while (delta > 1) {
+                    uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
+                    step_size = std::max(step_size, uint32_t(1));
+                    step_size = std::min(step_size, delta - 1);
+
+                    std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
+                    ngl_per_device_test[id].n_layer += step_size;
+                    if (hp_nex) {
+                        ngl_per_device_test[id].n_part += step_size;
+                    }
+                    const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+
+                    if (mem_test[id] <= targets[id]) {
+                        ngl_per_device = ngl_per_device_test;
+                        mem            = mem_test;
+                        LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
+                    } else {
+                        ngl_per_device_high = ngl_per_device_test;
+                        mem_high            = mem_test;
+                        LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device_high[id].n_layer);
+                    }
+                    delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
+                }
+            } else {
+                assert(ngl_per_device_high[id].n_layer == n_unassigned);
+                ngl_per_device = ngl_per_device_high;
+                LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
+            }
+        }
+
+        const int64_t projected_margin = dmds_full[id].free - mem[id];
+        LLAMA_LOG_INFO(
+            "%s:   - %s: %2" PRIu32 " layers, %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
+            __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, mem[id]/MiB, projected_margin/MiB);
+    }
+    if (hp_nex == 0 || global_surplus_cpu_moe <= 0) {
+        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe);
+        return;
+    }
+
+    // step 4: for a MoE model where all dense tensors fit,
+    //     convert the dense-only layers in the back to full layers in the front until all devices are full
+    // essentially the same procedure as for the dense-only layers except front-to-back
+    // also, try fitting at least part of one more layer to reduce waste for "small" GPUs with e.g. 24 GiB VRAM
+
+    size_t id_dense_start = nd;
+    for (int id = nd - 1; id >= 0; id--) {
+        if (ngl_per_device[id].n_layer > 0) {
+            id_dense_start = id;
+            continue;
+        }
+        break;
+    }
+    assert(id_dense_start < nd);
+
+    LLAMA_LOG_INFO("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__);
+    for (size_t id = 0; id <= id_dense_start; id++) {
+        std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
+        for (size_t jd = id_dense_start; jd < nd; jd++) {
+            const uint32_t n_layer_move = ngl_per_device_high[jd].n_layer;
+            ngl_per_device_high[id].n_layer += n_layer_move;
+            ngl_per_device_high[jd].n_layer -= n_layer_move;
+            ngl_per_device_high[jd].n_part = 0;
+        }
+        size_t id_dense_start_high = nd - 1;
+        std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe);
+
+        if (mem_high[id] > targets[id]) {
+            assert(ngl_per_device_high[id].n_layer >= ngl_per_device_high[id].n_part);
+            assert(ngl_per_device[id].n_layer >= ngl_per_device[id].n_part);
+            assert((ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
+                   >= ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
+            uint32_t delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
+                - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
+            while (delta > 1) {
+                uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
+                step_size = std::max(step_size, uint32_t(1));
+                step_size = std::min(step_size, delta - 1);
+
+                std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
+                size_t id_dense_start_test = id_dense_start;
+                uint32_t n_converted_test = 0;
+                for (;id_dense_start_test < nd; id_dense_start_test++) {
+                    const uint32_t n_convert_jd = std::min(step_size - n_converted_test, ngl_per_device_test[id_dense_start_test].n_part);
+                    ngl_per_device_test[id_dense_start_test].n_layer -= n_convert_jd;
+                    ngl_per_device_test[id_dense_start_test].n_part -= n_convert_jd;
+                    ngl_per_device_test[id].n_layer += n_convert_jd;
+                    n_converted_test += n_convert_jd;
+
+                    if (ngl_per_device_test[id_dense_start_test].n_layer > 0) {
+                        break;
+                    }
+                }
+                const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+
+                if (mem_test[id] <= targets[id]) {
+                    ngl_per_device = ngl_per_device_test;
+                    mem            = mem_test;
+                    id_dense_start = id_dense_start_test;
+                    LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
+                        __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+                } else {
+                    ngl_per_device_high = ngl_per_device_test;
+                    mem_high            = mem_test;
+                    id_dense_start_high = id_dense_start_test;
+                    LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n",
+                        __func__, id, ngl_per_device_high[id].n_layer, ngl_per_device_high[id].n_part, id_dense_start_high);
+                }
+                delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
+                    - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
+            }
+        } else {
+            ngl_per_device = ngl_per_device_high;
+            id_dense_start = id_dense_start_high;
+            LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
+                __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+        }
+
+        // try to fit at least part of one more layer
+        if (ngl_per_device[id_dense_start].n_layer > 0) {
+            std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
+            size_t id_dense_start_test = id_dense_start;
+            ngl_per_device_test[id_dense_start_test].n_layer--;
+            ngl_per_device_test[id_dense_start_test].n_part--;
+            ngl_per_device_test[id].n_layer++;
+            ngl_per_device_test[id].n_part++;
+            if (ngl_per_device_test[id_dense_start_test].n_layer == 0) {
+                id_dense_start_test++;
+            }
+            ngl_per_device_test[id].overflow_type = LAYER_FRACTION_UP;
+            LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__);
+            std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+            if (mem_test[id] < targets[id]) {
+                ngl_per_device = ngl_per_device_test;
+                mem            = mem_test;
+                id_dense_start = id_dense_start_test;
+                LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n",
+                    __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+
+                ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE;
+                LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__);
+                mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+                if (mem_test[id] < targets[id]) {
+                    ngl_per_device = ngl_per_device_test;
+                    mem            = mem_test;
+                    id_dense_start = id_dense_start_test;
+                    LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n",
+                        __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+                }
+            } else {
+                ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN;
+                LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__);
+                mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+                if (mem_test[id] < targets[id]) {
+                    ngl_per_device = ngl_per_device_test;
+                    mem            = mem_test;
+                    id_dense_start = id_dense_start_test;
+                    LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n",
+                        __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+                }
+            }
+        }
+
+        const int64_t projected_margin = dmds_full[id].free - mem[id];
+        LLAMA_LOG_INFO(
+            "%s:   - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
+            __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
+    }
+
+    set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe);
+}
+
+bool llama_params_fit(
+        const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
+        float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
+        size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
+    const int64_t t0_us = llama_time_us();
+    bool ok = true;
+    try {
+        llama_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margin_s, n_ctx_min, log_level);
+        LLAMA_LOG_INFO("%s: successfully fit params to free device memory\n", __func__);
+    } catch (const std::runtime_error & e) {
+        LLAMA_LOG_WARN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
+        ok = false;
+    }
+    const int64_t t1_us = llama_time_us();
+    LLAMA_LOG_INFO("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6);
+    return ok;
+}
+
 struct llama_sampler_chain_params llama_sampler_chain_default_params() {
    struct llama_sampler_chain_params result = {
        /*.no_perf                     =*/ true,
@@ -49,6 +718,10 @@ size_t llama_max_devices(void) {
    return 16;
 }

+size_t llama_max_tensor_buft_overrides() {
+    return 4096;
+}
+
 bool llama_supports_mmap(void) {
    return llama_mmap::SUPPORTED;
 }
@@ -108,11 +781,12 @@ static int llama_model_load(const std::string & fname, std::vector<std::string>
    model.t_start_us = tm.t_start_us;

    try {
-        llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.kv_overrides, params.tensor_buft_overrides);
+        llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);

        ml.print_info();

        model.hparams.vocab_only = params.vocab_only;
+        model.hparams.no_alloc   = params.no_alloc;

        try {
            model.load_arch(ml);
@@ -5,11 +5,20 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

+    bool use_mrope = hparams.use_mrope();
+    if (ubatch.embd && !use_mrope) {
+        // unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
+        GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
+    }
+
    // inp_pos - contains the positions
    ggml_tensor * inp_pos = build_inp_pos();

@@ -60,17 +69,25 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
                cb(Kcur, "Kcur_normed", il);
            }
-            Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                    );

-            Kcur = ggml_rope_ext(
-                    ctx0, Kcur, inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                    );
+            if (use_mrope) {
+                Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+            } else {
+                // Normal RoPE
+                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
+                                    rope_type, n_ctx_orig, freq_base, freq_scale,
+                                    ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
+                                    rope_type, n_ctx_orig, freq_base, freq_scale,
+                                    ext_factor, attn_factor, beta_fast, beta_slow);
+            }

            cb(Qcur, "Qcur", il);
            cb(Kcur, "Kcur", il);
@@ -8,11 +8,20 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

+    bool use_mrope = hparams.use_mrope();
+    if (ubatch.embd && !use_mrope) {
+        // unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
+        GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
+    }
+
    // inp_pos - contains the positions
    ggml_tensor * inp_pos = build_inp_pos();

@@ -63,11 +72,25 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params
                Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
                                    cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
            }
-            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                                 ext_factor, attn_factor, beta_fast, beta_slow);

-            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                                 ext_factor, attn_factor, beta_fast, beta_slow);
+            if (use_mrope) {
+                Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+            } else {
+                // Normal RoPE
+                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
+                                    rope_type, n_ctx_orig, freq_base, freq_scale,
+                                    ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
+                                    rope_type, n_ctx_orig, freq_base, freq_scale,
+                                    ext_factor, attn_factor, beta_fast, beta_slow);
+            }

            cb(Qcur, "Qcur", il);
            cb(Kcur, "Kcur", il);
@@ -441,23 +441,13 @@ private:
                ggml_tensor * cur,
                ggml_tensor * causal_mask,
                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
                        int   il);

    ggml_tensor * build_layer_ffn(
                ggml_tensor * cur,
                        int   il);

-    ggml_tensor * build_delta_net_recurrent(
-                ggml_tensor * q,
-                ggml_tensor * k,
-                ggml_tensor * v,
-                ggml_tensor * g,
-                ggml_tensor * beta,
-                ggml_tensor * state,
-                ggml_tensor * causal_mask,
-                ggml_tensor * identity,
-                        int   il);
-
    ggml_tensor * build_delta_net_chunking(
                ggml_tensor * q,
                ggml_tensor * k,
@@ -467,8 +457,18 @@ private:
                ggml_tensor * state,
                ggml_tensor * causal_mask,
                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
                        int   il);

+    ggml_tensor * build_delta_net_autoregressive(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                int           il);
+
    ggml_tensor * build_norm_gated(
                ggml_tensor * input,
                ggml_tensor * weights,
@@ -107,12 +107,41 @@ ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor *
 }

 ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const llama_model & model, const int il) {
-    cur = build_ffn(cur,
-            model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
-            NULL, NULL, NULL,
-            model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-            NULL, LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
-    cb(cur, "ffn_out", il);
+    if (model.layers[il].ffn_gate_inp == nullptr) {
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                NULL,                      NULL,                        NULL,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                NULL,
+                LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+    } else {
+        ggml_tensor * ffn_inp = cur;
+        ggml_tensor * moe_out =
+            build_moe_ffn(ffn_inp,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    nullptr, // no gate
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_RELU_SQR, hparams.expert_weights_norm,
+                    true, hparams.expert_weights_scale,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
+                    il);
+        cb(moe_out, "ffn_moe_out", il);
+
+        ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
+                    model.layers[il].ffn_up_shexp,  NULL, NULL,
+                    NULL /* no gate */           ,  NULL, NULL,
+                    model.layers[il].ffn_down_shexp, NULL, NULL,
+                    NULL,
+                    LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "ffn_shexp", il);
+
+        cur = ggml_add(ctx0, moe_out, ffn_shexp);
+        cb(cur, "ffn_out", il);
+    }

    cur = build_cvec(cur, il);
    cb(cur, "l_out", il);
@@ -31,16 +31,25 @@ llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_para
        {
            // compute Q and K and RoPE them
            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-            Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
            cb(Qcur, "Qcur", il);
+            if (model.layers[il].bq) {
+                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                cb(Qcur, "Qcur", il);
+            }

            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-            Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
            cb(Kcur, "Kcur", il);
+            if (model.layers[il].bk) {
+                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                cb(Kcur, "Kcur", il);
+            }

            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-            Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
            cb(Vcur, "Vcur", il);
+            if (model.layers[il].bv) {
+                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                cb(Vcur, "Vcur", il);
+            }

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
@@ -17,13 +17,15 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
    ggml_tensor * inp_out_ids = build_inp_out_ids();

    ggml_tensor * causal_mask =
-        ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens, ubatch.n_seq_tokens), 1.0f),
+        ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
                    GGML_TRI_TYPE_LOWER);

-    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens), 1.0f));
+    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);

    ggml_build_forward_expand(gf, causal_mask);
    ggml_build_forward_expand(gf, identity);
+    ggml_build_forward_expand(gf, diag_mask);

    for (int il = 0; il < n_layer; ++il) {
        ggml_tensor * inpSA = inpL;
@@ -34,7 +36,7 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
        // Determine layer type and build appropriate attention mechanism
        if (hparams.is_recurrent(il)) {
            // Linear attention layer (gated delta net)
-            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, il);
+            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
        } else {
            // Full attention layer
            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
@@ -93,14 +95,8 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
        ggml_tensor * state,
        ggml_tensor * causal_mask,
        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
        int           il) {
-    GGML_ASSERT(ggml_is_contiguous(q));
-    GGML_ASSERT(ggml_is_contiguous(k));
-    GGML_ASSERT(ggml_is_contiguous(v));
-    GGML_ASSERT(ggml_is_contiguous(g));
-    GGML_ASSERT(ggml_is_contiguous(beta));
-    GGML_ASSERT(ggml_is_contiguous(state));
-
    const int64_t S_k      = q->ne[0];
    const int64_t H_k      = q->ne[1];
    const int64_t n_tokens = q->ne[2];
@@ -120,15 +116,10 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(

    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case

-    // TODO: can this ever be false?
-    const bool use_qk_l2norm = true;
+    const float eps_norm = hparams.f_norm_rms_eps;

-    if (use_qk_l2norm) {
-        const float eps_norm = hparams.f_norm_rms_eps;
-
-        q = ggml_l2_norm(ctx0, q, eps_norm);
-        k = ggml_l2_norm(ctx0, k, eps_norm);
-    }
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);

    const float scale = 1.0f / sqrtf(S_v);

@@ -136,8 +127,6 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(

    beta = ggml_sigmoid(ctx0, beta);

-    ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
-
    cb(q, "q_in", il);
    cb(k, "k_in", il);
    cb(v, "v_in", il);
@@ -188,36 +177,21 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
    cb(v_beta, "v_beta", il);
    cb(k_beta, "k_beta", il);

-    ggml_tensor * chunked_mask =
-        ggml_view_4d(ctx0, causal_mask, chunk_size,
-                chunk_size,         causal_mask->ne[2], causal_mask->ne[3],
-                causal_mask->nb[1], causal_mask->nb[2], causal_mask->nb[3], 0);
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);

-    ggml_tensor * chunked_diag_mask =
-        ggml_view_4d(ctx0, causal_diag_mask, chunk_size,
-                chunk_size,              causal_diag_mask->ne[2], causal_diag_mask->ne[3],
-                causal_diag_mask->nb[1], causal_diag_mask->nb[2], causal_diag_mask->nb[3], 0);
-
-    ggml_tensor * chunked_identity =
-        ggml_view_4d(ctx0, identity, chunk_size,
-            chunk_size,      identity->ne[2], identity->ne[3],
-            identity->nb[1], identity->nb[2], identity->nb[3], 0);
-
-    q      = ggml_cont_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k      = ggml_cont_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k_beta = ggml_cont_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
-    v      = ggml_cont_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
-    v_beta = ggml_cont_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
-
-    g    = ggml_cont_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
-    beta = ggml_cont_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);

    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);

    cb(g_cumsum, "g_cumsum", il);

-    ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
-    ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);

    ggml_tensor * gcs_j_broadcast =
        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
@@ -226,23 +200,23 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(

    cb(decay_mask, "decay_mask", il);

-    decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
    decay_mask = ggml_exp(ctx0, decay_mask);
-    decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);

    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);

    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, chunked_mask));
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));

    cb(attn, "attn_pre_solve", il);

-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, chunked_mask);
-    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, chunked_identity, attn_lower), attn_lower);
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);

    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn                     = ggml_mul(ctx0, lin_solve, chunked_mask);
-    attn                     = ggml_add(ctx0, attn, chunked_identity);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);

    cb(attn, "attn_solved", il);

@@ -291,7 +265,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
        attn = ggml_mul_mat(ctx0, k_chunk, q_chunk);
        attn = ggml_mul(ctx0, attn, decay_mask_chunk);
-        attn = ggml_mul(ctx0, attn, ggml_add(ctx0, chunked_identity, chunked_mask));
+        attn = ggml_mul(ctx0, attn, diag_mask);

        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);

@@ -361,23 +335,14 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
    return ggml_concat(ctx0, flat_output, flat_state, 0);
 }

-ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
+ggml_tensor * llm_build_qwen3next::build_delta_net_autoregressive(
        ggml_tensor * q,
        ggml_tensor * k,
        ggml_tensor * v,
        ggml_tensor * g,
        ggml_tensor * beta,
        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
        int           il) {
-    GGML_ASSERT(ggml_is_contiguous(q));
-    GGML_ASSERT(ggml_is_contiguous(k));
-    GGML_ASSERT(ggml_is_contiguous(v));
-    GGML_ASSERT(ggml_is_contiguous(g));
-    GGML_ASSERT(ggml_is_contiguous(beta));
-    GGML_ASSERT(ggml_is_contiguous(state));
-
    const int64_t S_k      = q->ne[0];
    const int64_t H_k      = q->ne[1];
    const int64_t n_tokens = q->ne[2];
@@ -386,6 +351,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
    const int64_t S_v = v->ne[0];
    const int64_t H_v = v->ne[1];

+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
    GGML_ASSERT(v->ne[2] == n_tokens);
    GGML_ASSERT(k->ne[2] == n_tokens);
    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
@@ -397,215 +363,65 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(

    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case

-    // TODO: can this ever be false?
-    const bool use_qk_l2norm = true;
+    const float eps_norm = hparams.f_norm_rms_eps;

-    if (use_qk_l2norm) {
-        const float eps_norm = hparams.f_norm_rms_eps;
-
-        q = ggml_l2_norm(ctx0, q, eps_norm);
-        k = ggml_l2_norm(ctx0, k, eps_norm);
-    }
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);

    const float scale = 1.0f / sqrtf(S_v);

-    q = ggml_scale(ctx0, q, scale);
-
+    q    = ggml_scale(ctx0, q, scale);
    beta = ggml_sigmoid(ctx0, beta);

-    ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
-
    cb(q, "q_in", il);
    cb(k, "k_in", il);
    cb(v, "v_in", il);
    cb(beta, "beta_in", il);
    cb(g, "g_in", il);

-    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
-
-    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);

-    cb(q, "q_perm", il);
-    cb(k, "k_perm", il);
-    cb(v, "v_perm", il);
-    cb(beta, "beta_perm", il);
-    cb(g, "g_perm", il);
-    cb(state, "state_in", il);
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);

-    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
-    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);

-    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);

-    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));

-    cb(k_beta, "k_beta", il);
-    cb(v_beta, "v_beta", il);
-    cb(g_cumsum, "g_cumsum", il);
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);

-    ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, n_tokens, 1, H_v, n_seqs);  // [chunk_size, 1, n_tokens, n_seqs]
-    ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, n_tokens, H_v, n_seqs);  // [1, chunk_size, n_tokens, n_seqs]
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);

-    // Broadcast both tensors to [chunk_size, chunk_size, H_v, n_seqs]
-    // ggml_tensor * gcs_i_broadcast =
-    //     ggml_repeat_4d(ctx0, gcs_i, GGML_DELTA_NET_CHUNK, GGML_DELTA_NET_CHUNK, num_chunks * H_v,
-    //                     n_seqs);  // [chunk_size, 1, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
-    // Don't need this, this one will get auto-broadcast
-    ggml_tensor * gcs_j_broadcast =
-        ggml_repeat_4d(ctx0, gcs_j, n_tokens, n_tokens, H_v, n_seqs);  // [1, chunk_size, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
-
-    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-
-    // Apply lower triangular mask to ensure attention is causal (only past tokens influence current)
-    decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
-    // Apply exponential to get the decay mask values
-    decay_mask = ggml_exp(ctx0, decay_mask);
-    // Apply lower triangular mask again to ensure only lower triangular values remain
-    decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
-
-    cb(decay_mask, "decay_mask", il);
-
-    // attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
-    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
-
-    cb(kmulkbeta, "kmulkbeta", il);
-
-    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-
-    cb(attn, "attn_pre_rec", il);
-
-    // for i in range(1, chunk_size):
-    //          row = attn[..., i, :i].clone()
-    //          sub = attn[..., :i, :i].clone()
-    //          attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
-    // attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
-    //
-    // We reduce this to a linear triangular solve: AX = B, where B = attn, A = I - tril(A)
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
-    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
-
-    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
-    attn                     = ggml_add(ctx0, attn, identity);
-
-    // value = attn @ v_beta
-    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
-
-    cb(v, "value_beta", il);
-
-    // k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
-    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
-    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
-
-    cb(gexp, "g_cum_exp", il);
-
-    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
-
-    cb(kbeta_gexp, "kbeta_gexp", il);
-
-    ggml_tensor * k_cumdecay =
-        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
-
-    cb(k_cumdecay, "k_cumdecay", il);
-
-    // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
-    attn = ggml_mul_mat(ctx0, k, q);
-    attn = ggml_mul(ctx0, attn, decay_mask);
-    attn = ggml_mul(ctx0, attn, ggml_add(ctx0, identity, causal_mask));
-
-    cb(attn, "attn_decay_key", il);
-
-    ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
-
-    // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
-    ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay);
-
-    cb(v_prime, "v_prime", il);
-
-    // v_new = v_i - v_prime
-    ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v, v_prime), v_prime);
-
-    ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
-
-    cb(v_new, "v_new", il);
-
-    // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
-    ggml_tensor * q_g_exp    = ggml_mul(ctx0, q, gexp);
-    ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
-
-    cb(attn_inter, "attn_inter", il);
-
-    // core_attn_out[:, :, i] = attn_inter + attn @ v_new
-    ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn);
-
-    cb(v_attn, "v_attn", il);
-
-    ggml_tensor * core_attn_out = ggml_add(ctx0, attn_inter, v_attn);
-
-    cb(core_attn_out, "core_attn_out", il);
-
-    // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
-    // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
-    // key_gdiff = key * g_diff.unsqueeze(-1)
-    // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
-    // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
-
-    ggml_tensor * g_cum_last =
-        ggml_cont(ctx0, ggml_view_4d(ctx0, g_cumsum_t, g_cumsum_t->ne[0], 1, g_cumsum_t->ne[2], g_cumsum_t->ne[3],
-                                    g_cumsum_t->nb[1], g_cumsum_t->nb[2], g_cumsum_t->nb[3],
-                                    g_cumsum_t->nb[0] * (g_cumsum_t->ne[1] - 1)));
-
-    cb(g_cum_last, "g_cum_last", il);
-
-    ggml_tensor * gexp_last =
-        ggml_reshape_4d(ctx0, ggml_exp(ctx0, g_cum_last), 1, 1, g_cum_last->ne[0] * g_cum_last->ne[2], g_cum_last->ne[3]);
-
-    cb(gexp_last, "gexp_last", il);
-
-    ggml_tensor * g_cum_last_3d =
-        ggml_reshape_3d(ctx0, g_cum_last, g_cum_last->ne[0], g_cum_last->ne[2], g_cum_last->ne[3]);
-
-    cb(g_cum_last_3d, "g_cum_last_3d", il);
-
-    ggml_tensor * g_cumsum_3d = ggml_reshape_3d(ctx0, g_cumsum, g_cumsum->ne[0], g_cumsum->ne[2], g_cumsum->ne[3]);
-
-    cb(g_cumsum_3d, "g_cumsum_3d", il);
-
-    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum_3d, g_cum_last_3d));
-
-    cb(g_diff, "g_diff", il);
-
-    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
-
-    cb(g_diff_exp, "g_diff_exp", il);
-
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k,
-                                    ggml_reshape_4d(ctx0, g_diff_exp, 1, g_diff_exp->ne[0], g_diff_exp->ne[1],
-                                                    g_diff_exp->ne[2] * g_diff_exp->ne[3]));
-
-    cb(key_gdiff, "key_gdiff", il);
-
-    ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)));
-
-    cb(kgdmulvnew, "kgdmulvnew", il);
-
-    state = ggml_add(ctx0, ggml_mul(ctx0, state, gexp_last), kgdmulvnew);
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));

+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
    cb(state, "new_state", il);

-    // flatten output
-    ggml_tensor * flat_output =
-        ggml_cont_1d(ctx0, ggml_permute(ctx0, core_attn_out, 0, 2, 1, 3), S_v * H_v * n_tokens * n_seqs);
-
-    ggml_tensor * flat_state = ggml_cont_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
+    // flatten output, no need to permute since n_tokens is 1 so [S_v, 1, H_v, n_seqs] and [S_v, H_v, 1, n_seqs] are equivalent memory-layout wise
+    ggml_tensor * flat_output = ggml_reshape_1d(ctx0, core_attn_out, S_v * H_v * n_tokens * n_seqs);
+    ggml_tensor * flat_state  = ggml_reshape_1d(ctx0, state, S_v * S_v * H_v * n_seqs);

    return ggml_concat(ctx0, flat_output, flat_state, 0);
 }
@@ -712,6 +528,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
        ggml_tensor *        cur,
        ggml_tensor *        causal_mask,
        ggml_tensor *        identity,
+        ggml_tensor *        diag_mask,
        int                  il) {
    const auto * mctx_cur = inp->mctx;

@@ -737,11 +554,11 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
    cb(mixed_ba, "linear_attn_mixed_ba", il);

    int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
-    ggml_tensor * mixed_qkvz_reshaped = ggml_cont_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+    ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);

    // Reshape mixed_ba: [batch, seq_len, hidden_size] -> [batch, seq_len, num_k_heads, 2*num_v_heads/num_k_heads]
    int64_t       ba_new_dim        = 2 * num_v_heads / num_k_heads;
-    ggml_tensor * mixed_ba_reshaped = ggml_cont_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+    ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);

    // Split mixed_ba into b and a (beta and alpha parameters)
    int64_t split_sizes_ba[2] = {
@@ -762,8 +579,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
    ggml_tensor * beta  = ggml_cont_3d(ctx0, b, num_v_heads, n_seq_tokens, n_seqs);
    ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);

-    GGML_ASSERT(ggml_nelements(beta) + ggml_nelements(alpha) == ggml_nelements(mixed_ba));
-
    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
    cb(alpha_softplus, "a_softplus", il);
@@ -799,9 +614,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
                                   (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * sizeof(float));
    cb(z, "z", il);

-    GGML_ASSERT(ggml_nelements(query) + ggml_nelements(key) + ggml_nelements(value) + ggml_nelements(z) ==
-                ggml_nelements(mixed_qkvz));
-
    // After creating query, key, and value_reshaped, reshape each to flatten the head dimensions
    // query: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
    ggml_tensor * query_flat = ggml_cont_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
@@ -925,10 +737,13 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
    cb(k_conv, "k_conv_predelta", il);
    cb(v_conv, "v_conv_predelta", il);

-    // Choose between build_delta_net_chunking and build_delta_net_recurrent based on n_tokens
-    ggml_tensor * attn_out = n_seq_tokens > CHUNK_SIZE ?
-        build_delta_net_chunking (q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il) :
-        build_delta_net_recurrent(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il);
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    ggml_tensor * attn_out;
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
    cb(attn_out, "attn_out", il);

    // The tensors were concatenated 1d, so we need to extract them 1d as well
@@ -222,6 +222,14 @@ llama_build_and_test(test-backend-ops.cpp)
 llama_build_and_test(test-model-load-cancel.cpp  LABEL "model")
 llama_build_and_test(test-autorelease.cpp        LABEL "model")

+# Test for state restore with fragmented KV cache
+# Requires a model, uses same args pattern as test-thread-safety
+if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
+    llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -hf ggml-org/models -hff tinyllamas/stories15M-q4_0.gguf)
+else()
+    llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -hf ggml-org/models -hff tinyllamas/stories15M-be.Q4_0.gguf)
+endif()
+
 if (NOT GGML_BACKEND_DL)
    # these tests use the backends directly and cannot be built with dynamic loading
    llama_build_and_test(test-barrier.cpp)
@@ -3588,6 +3588,163 @@ static void test_template_output_peg_parsers() {
            t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})";
        });
    }
+
+    {
+        // NVIDIA Nemotron-3 Nano
+        auto tmpls = read_templates("models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja");
+
+        // Test basic message
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "Hello, world!\nWhat's up?";
+            t.expect = message_assist;
+        });
+
+        // Test basic message and reasoning with reasoning_format = none
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "I'm\nthinking\n</think>\nHello, world!\nWhat's up?";
+            t.expect.content = "I'm\nthinking\n</think>\nHello, world!\nWhat's up?";
+        });
+
+        // Test basic message and reasoning with reasoning_format = auto
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "I'm\nthinking\n</think>\nHello, world!\nWhat's up?";
+            t.params.enable_thinking = true;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+
+            t.expect = message_assist_thoughts;
+        });
+
+        // Test tool call
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "<tool_call>\n"
+                "<function=special_function>\n"
+                "<parameter=arg1>\n"
+                "1\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.enable_thinking = false;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {special_function_tool};
+
+            t.expect = message_assist_call;
+        });
+
+        // Test tool call with reasoning
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "I'm\nthinking\n</think>\n"
+                "<tool_call>\n"
+                "<function=special_function>\n"
+                "<parameter=arg1>\n"
+                "1\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {special_function_tool};
+
+            t.expect = message_assist_call_thoughts;
+        });
+
+        // Test parallel tool calls
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "<tool_call>\n"
+                "<function=special_function>\n"
+                "<parameter=arg1>\n"
+                "1\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>\n"
+                "<tool_call>\n"
+                "<function=special_function_with_opt>\n"
+                "<parameter=arg1>\n"
+                "1\n"
+                "</parameter>\n"
+                "<parameter=arg2>\n"
+                "2\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.enable_thinking = false;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.parallel_tool_calls = true;
+            t.params.tools = {special_function_tool, special_function_tool_with_optional_param};
+
+            t.expect.tool_calls = {{
+                /* .name = */      "special_function",
+                /* .arguments = */ R"({"arg1": 1})",
+                /* .id = */        {},
+            }, {
+                /* .name = */      "special_function_with_opt",
+                /* .arguments = */ R"({"arg1": 1, "arg2": 2})",
+                /* .id = */        {},
+            }};
+        });
+
+        // Test tool call with string parameter
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "<tool_call>\n"
+                "<function=python>\n"
+                "<parameter=code>\n"
+                "def hello():\n"
+                "    print(\"Hello, world!\")\n"
+                "\n"
+                "hello()\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.enable_thinking = false;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {python_tool};
+
+            t.expect.tool_calls = {{
+                /* .name = */      "python",
+                /* .arguments = */ "{\"code\": \"def hello():\\n    print(\\\"Hello, world!\\\")\\n\\nhello()\"}",
+                /* .id = */        {},
+            }};
+        });
+
+        // Test tool call with string parameter and no closing </parameter> tag
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "<tool_call>\n"
+                "<function=python>\n"
+                "<parameter=code>\n"
+                "def hello():\n"
+                "    print(\"Hello, world!\")\n"
+                "\n"
+                "hello()\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.enable_thinking = false;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {python_tool};
+
+            t.expect.tool_calls = {{
+                /* .name = */      "python",
+                /* .arguments = */ "{\"code\": \"def hello():\\n    print(\\\"Hello, world!\\\")\\n\\nhello()\"}",
+                /* .id = */        {},
+            }};
+        });
+
+        // Test response format
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+              "I need to output the invoice details in JSON\n"
+              "</think>\n"
+              R"({"amount": 123.45, "date": "2025-12-03"})";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.json_schema = invoice_schema;
+
+            t.expect.reasoning_content = "I need to output the invoice details in JSON";
+            t.expect.content = R"({"amount": 123.45, "date": "2025-12-03"})";
+        });
+    }
+
 }

 static void test_msg_diffs_compute() {
@@ -1367,10 +1367,85 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
    });
 }

+static void test_resolves_to_string() {
+    fprintf(stderr, "#\n# Testing resolves_to_string\n#\n");
+
+    auto test = [](const std::string & name, const std::string & schema_str, bool expected) {
+        fprintf(stderr, "- %s\n", name.c_str());
+        common_schema_info info;
+        auto schema = nlohmann::ordered_json::parse(schema_str);
+        info.resolve_refs(schema);
+        bool result = info.resolves_to_string(schema);
+        if (result != expected) {
+            fprintf(stderr, "#\n# Test '%s' failed.\n#\n", name.c_str());
+            fprintf(stderr, "Schema: %s\n", schema_str.c_str());
+            fprintf(stderr, "Expected: %s, Got: %s\n", expected ? "true" : "false", result ? "true" : "false");
+            assert(false);
+        }
+    };
+
+    // Basic type checks
+    test("type string", R"({"type": "string"})", true);
+    test("type integer", R"({"type": "integer"})", false);
+    test("type number", R"({"type": "number"})", false);
+    test("type boolean", R"({"type": "boolean"})", false);
+    test("type object", R"({"type": "object"})", false);
+    test("type array", R"({"type": "array"})", false);
+
+    // Type array (nullable string)
+    test("type array with string", R"({"type": ["string", "null"]})", true);
+    test("type array without string", R"({"type": ["integer", "null"]})", false);
+
+    // String-specific keywords
+    test("minLength implies string", R"({"minLength": 1})", true);
+    test("maxLength implies string", R"({"maxLength": 10})", true);
+    test("pattern implies string", R"({"pattern": "^[a-z]+$"})", true);
+
+    // Format
+    test("format date", R"({"format": "date"})", true);
+    test("format uuid", R"({"format": "uuid"})", true);
+    test("format email", R"({"format": "email"})", true);
+
+    // Const
+    test("const string", R"({"const": "hello"})", true);
+    test("const number", R"({"const": 123})", false);
+
+    // Enum
+    test("enum with strings", R"({"enum": ["a", "b", "c"]})", true);
+    test("enum with numbers", R"({"enum": [1, 2, 3]})", false);
+    test("enum mixed with string", R"({"enum": [1, "a", null]})", true);
+
+    // anyOf
+    test("anyOf with string", R"({"anyOf": [{"type": "string"}, {"type": "integer"}]})", true);
+    test("anyOf without string", R"({"anyOf": [{"type": "integer"}, {"type": "boolean"}]})", false);
+
+    // oneOf
+    test("oneOf with string", R"({"oneOf": [{"type": "string"}, {"type": "number"}]})", true);
+    test("oneOf without string", R"({"oneOf": [{"type": "object"}, {"type": "array"}]})", false);
+
+    // allOf - all must be strings
+    test("allOf all strings", R"({"allOf": [{"type": "string"}, {"minLength": 1}]})", true);
+    test("allOf mixed types", R"({"allOf": [{"type": "string"}, {"type": "integer"}]})", false);
+
+    // $ref
+    test("$ref to string",
+        R"({"$ref": "#/$defs/str", "$defs": {"str": {"type": "string"}}})", true);
+    test("$ref to integer",
+        R"({"$ref": "#/$defs/num", "$defs": {"num": {"type": "integer"}}})", false);
+
+    // Nested
+    test("nested anyOf with string",
+        R"({"anyOf": [{"anyOf": [{"type": "integer"}, {"type": "string"}]}, {"type": "boolean"}]})", true);
+
+    fprintf(stderr, "All resolves_to_string tests passed!\n");
+}
+
 int main() {
    fprintf(stderr, "LLAMA_NODE_AVAILABLE = %s\n", getenv("LLAMA_NODE_AVAILABLE") ? "true" : "false");
    fprintf(stderr, "LLAMA_PYTHON_AVAILABLE = %s\n", getenv("LLAMA_PYTHON_AVAILABLE") ? "true" : "false");

+    test_resolves_to_string();
+
    test_all("C++", [](const TestCase & tc) {
        try {
            tc.verify(json_schema_to_grammar(nlohmann::ordered_json::parse(tc.schema), true));
@@ -0,0 +1,122 @@
+// Test for state restore with fragmented KV cache
+// This tests the fix for: https://github.com/ggml-org/llama.cpp/issues/17527
+// The issue was that state restore required contiguous KV cache slots,
+// which fails when the cache is fragmented.
+//
+// The fix changes find_slot(ubatch, true) to find_slot(ubatch, false)
+// in state_read_meta(), allowing non-contiguous slot allocation.
+
+#include "arg.h"
+#include "common.h"
+#include "llama.h"
+
+#include <vector>
+#include <cstdio>
+#include <cstring>
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    params.sampling.seed = 1234;
+    params.kv_unified = true;
+    params.n_parallel = 3;
+    params.n_ctx = 256;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
+        return 1;
+    }
+
+    common_init();
+
+    // init
+    common_init_result_ptr llama_init = common_init_from_params(params);
+
+    llama_model * model = llama_init->model();
+    llama_context * ctx = llama_init->context();
+
+    if (model == nullptr || ctx == nullptr) {
+        fprintf(stderr, "%s : failed to init\n", __func__);
+        return 1;
+    }
+
+    GGML_UNUSED(model);
+
+    // tokenize prompt
+    std::vector<llama_token> tokens(70, 1);
+
+    // interleave the 3 sequences:
+    // 01201230123...
+    llama_batch batch = llama_batch_init(params.n_parallel*tokens.size(), 0, 1);
+    for (size_t i = 0; i < tokens.size(); i++) {
+        for (int s = 0; s < params.n_parallel; ++s) {
+            common_batch_add(batch, tokens[i], i, {s}, false);
+        }
+    }
+    batch.logits[batch.n_tokens - 1] = true;
+
+    if (llama_decode(ctx, batch)) {
+        fprintf(stderr, "%s : failed to decode seq 0\n", __func__);
+        return 1;
+    }
+
+    fprintf(stderr, "%s : processed prompt on seq 0, 1, 2 (%zu tokens each)\n", __func__, tokens.size());
+
+    // Save state of seq 1
+    std::vector<uint8_t> seq_state(llama_state_seq_get_size(ctx, 1));
+    const size_t ncopy = llama_state_seq_get_data(ctx, seq_state.data(), seq_state.size(), 1);
+    if (ncopy != seq_state.size()) {
+        fprintf(stderr, "%s : failed to save seq 1 state\n", __func__);
+        return 1;
+    }
+    fprintf(stderr, "%s : saved seq 1 state, %zu bytes\n", __func__, ncopy);
+
+    // clear seq 1 to create a "hole" in the KV cache (fragmentation)
+    // 0.20.20.20.2....
+    llama_memory_t mem = llama_get_memory(ctx);
+    llama_memory_seq_rm(mem, 1, -1, -1);
+    fprintf(stderr, "%s : cleared seq 1 to create fragmentation\n", __func__);
+
+    // Now the cache has holes where seq 1 was
+    // This creates fragmentation - there's no contiguous block large enough
+    // for the seq 1 state if we only look for contiguous slots
+
+    // Restore seq 1 state into seq 1 (should work with non-contiguous allocation)
+    // We use seq 1 since it's a valid sequence ID (0 to n_parallel-1)
+    // Before the fix, this would fail with "failed to find available cells in kv cache"
+    const size_t nset = llama_state_seq_set_data(ctx, seq_state.data(), seq_state.size(), 1);
+    if (nset != seq_state.size()) {
+        fprintf(stderr, "%s : FAILED to restore seq state into fragmented cache (got %zu, expected %zu)\n",
+                __func__, nset, seq_state.size());
+        fprintf(stderr, "%s : This is the bug - state restore fails with fragmented KV cache\n", __func__);
+        llama_batch_free(batch);
+        return 1;
+    }
+    fprintf(stderr, "%s : restored state into seq 1, %zu bytes\n", __func__, nset);
+
+    // Verify we can decode with the restored state
+    // Generate one token to verify the restored state is usable
+    auto sparams = llama_sampler_chain_default_params();
+    llama_sampler * smpl = llama_sampler_chain_init(sparams);
+    llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed));
+
+    auto next_token = llama_sampler_sample(smpl, ctx, -1);
+    auto next_token_str = common_token_to_piece(ctx, next_token);
+
+    common_batch_clear(batch);
+    common_batch_add(batch, next_token, (int)tokens.size(), {1}, true);
+
+    if (llama_decode(ctx, batch)) {
+        fprintf(stderr, "%s : failed to decode with restored state\n", __func__);
+        llama_sampler_free(smpl);
+        llama_batch_free(batch);
+        return 1;
+    }
+
+    fprintf(stderr, "%s : successfully decoded with restored state, generated: '%s'\n", __func__, next_token_str.c_str());
+    fprintf(stderr, "%s : SUCCESS - state restore works with fragmented KV cache\n", __func__);
+
+    llama_sampler_free(smpl);
+    llama_batch_free(batch);
+
+    return 0;
+}
@@ -37,4 +37,5 @@ else()
        add_subdirectory(cvector-generator)
        add_subdirectory(export-lora)
    endif()
+    add_subdirectory(fit-params)
 endif()
@@ -0,0 +1 @@
+TODO
@@ -1,4 +1,4 @@
-# llama.cpp/tools/main
+# llama.cpp/tools/completion

 This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts.

@@ -27,64 +27,64 @@ Once downloaded, place your model in the models folder in llama.cpp.
 ##### Input prompt (One-and-done)

 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
 ```
 ##### Conversation mode (Allow for continuous interaction with the model)

 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
 ```

 ##### Conversation mode using built-in jinja chat template

 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja
 ```

 ##### One-and-done query using jinja with custom system prompt and a starting prompt

 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
 ```

 ##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 ```

 ### Windows:

 ##### Input prompt (One-and-done)
 ```powershell
-./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
+./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
 ```
 ##### Conversation mode (Allow for continuous interaction with the model)

 ```powershell
-./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
+./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
 ```

 ##### Conversation mode using built-in jinja chat template

 ```powershell
-./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja
+./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja
 ```

 ##### One-and-done query using jinja with custom system prompt and a starting prompt

 ```powershell
-./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
+./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
 ```

 #### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):

 ```powershell
-llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
+llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 ```

 ## Common Options

-In this section, we cover the most commonly used options for running the `llama-cli` program with the LLaMA models:
+In this section, we cover the most commonly used options for running the `llama-completion` program with the LLaMA models:

 -   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/gemma-1.1-7b-it.Q4_K_M.gguf`; inferred from `--model-url` if set).
 -   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)).
@@ -97,7 +97,7 @@ In this section, we cover the most commonly used options for running the `llama-

 ## Input Prompts

-The `llama-cli` program provides several ways to interact with the LLaMA models using input prompts:
+The `llama-completion` program provides several ways to interact with the LLaMA models using input prompts:

 -   `--prompt PROMPT`: Provide a prompt directly as a command-line option.
 -   `--file FNAME`: Provide a file containing a prompt or multiple prompts.
@@ -107,7 +107,7 @@ The `llama-cli` program provides several ways to interact with the LLaMA models

 ## Interaction

-The `llama-cli` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`.
+The `llama-completion` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`.

 In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing.

@@ -136,7 +136,7 @@ To overcome this limitation, you can use the `--in-prefix` flag to add a space o
 The `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag:

 ```sh
-./llama-cli -r "User:" --in-prefix " "
+./llama-completion -r "User:" --in-prefix " "
 ```

 ### In-Suffix
@@ -144,7 +144,7 @@ The `--in-prefix` flag is used to add a prefix to your input, primarily, this is
 The `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (`\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag:

 ```sh
-./llama-cli -r "User:" --in-prefix " " --in-suffix "Assistant:"
+./llama-completion -r "User:" --in-prefix " " --in-suffix "Assistant:"
 ```
 When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled

@@ -87,9 +87,6 @@ int main(int argc, char ** argv) {
    common_params params;
    g_params = &params;

-    // disable jinja by default
-    params.use_jinja = false;
-
    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) {
        return 1;
    }
@@ -0,0 +1,8 @@
+set(TARGET llama-fit-params)
+add_executable(${TARGET} fit-params.cpp)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+if(LLAMA_TOOLS_INSTALL)
+    install(TARGETS ${TARGET} RUNTIME)
+endif()
@@ -0,0 +1,55 @@
+# fit-params
+
+llama.cpp binaries can automatically fit the projected memory use of a model to the free device memory available at runtime,
+this is controlled using the CLI arguments starting with `-fit`/`--fit`.
+Internally the code is calling `llama_params_fit` to adjust the `llama_model_params` and `llama_context_params` structs.
+`llama-fit-params` is a simple utility that prints the CLI arguments corresponding to these adjustments to stdout.
+Example usage:
+
+``` bash
+# First, run llama-fit-params and store the results in a file:
+> ./build/bin/llama-fit-params --model /opt/models/qwen_3-30b3a-f16.gguf | tee args.txt
+ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
+ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
+ggml_cuda_init: found 1 CUDA devices:
+  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
+build: 6895 (4341dc8bc) with cc (GCC) 15.2.1 20250813 for x86_64-pc-linux-gnu
+llama_params_fit_impl: projected to use 61807 MiB of device memory vs. 24077 MiB of free device memory
+llama_params_fit_impl: cannot fulfill margin of 1024 MiB, need to reduce device memory by 42444 MiB
+llama_params_fit_impl: context size reduced from 40960 to 4096 -> need 3456 MiB less memory in total
+llama_params_fit_impl: with only dense weights in device memory there is a total surplus of 16164 MiB
+llama_params_fit_impl: distributing layers across devices with overflow to next device/system memory:
+llama_params_fit_impl:   - CUDA0 (NVIDIA GeForce RTX 4090): 48 layers (34 overflowing),  19187 MiB used,   1199 MiB free
+llama_params_fit: successfully fit params to free device memory
+llama_params_fit: fitting params to free memory took 1.15 seconds
+Printing fitted CLI arguments to stdout...
+-c 4096 -ngl 48 -ot blk\.14\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.15\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.16\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.17\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.18\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.19\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.20\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.21\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.22\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.23\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.24\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.25\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.26\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.27\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.28\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.29\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.30\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.31\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.32\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.33\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.34\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.35\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.36\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.37\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.38\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.39\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.40\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.41\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.42\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.43\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.44\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.45\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.46\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.47\.ffn_(up|down|gate)_(ch|)exps=CPU
+
+# Next, use those results for a llama.cpp binary:
+> cat args.txt | xargs ./build/bin/llama-server --model /opt/models/qwen_3-30b3a-f16.gguf
+ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
+ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
+ggml_cuda_init: found 1 CUDA devices:
+  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
+build: 6895 (4341dc8bc) with cc (GCC) 15.2.1 20250813 for x86_64-pc-linux-gnu
+system info: n_threads = 16, n_threads_batch = 16, total_threads = 32
+
+system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
+
+main: binding port with default address family
+main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 31
+main: loading model
+srv    load_model: loading model '/opt/models/qwen_3-30b3a-f16.gguf'
+llama_params_fit_impl: projected to use 19187 MiB of device memory vs. 24077 MiB of free device memory
+llama_params_fit_impl: will leave 1199 >= 1024 MiB of free device memory, no changes needed
+llama_params_fit: successfully fit params to free device memory
+llama_params_fit: fitting params to free memory took 0.28 seconds
+[...]
+main: server is listening on http://127.0.0.1:8080 - starting the main loop
+srv  update_slots: all slots are idle
+^Csrv    operator(): operator(): cleaning up before exit...
+
+llama_memory_breakdown_print: | memory breakdown [MiB] | total   free     self   model   context   compute    unaccounted |
+llama_memory_breakdown_print: |   - CUDA0 (RTX 4090)   | 24077 =  945 + (19187 = 17904 +     384 +     898) +        3945 |
+llama_memory_breakdown_print: |   - Host               |                 58271 = 58259 +       0 +      12                |
+```
@@ -0,0 +1,66 @@
+#include "llama.h"
+
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+
+#include <chrono>
+#include <cinttypes>
+#include <thread>
+
+using namespace std::chrono_literals;
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
+        return 1;
+    }
+
+    common_init();
+    llama_backend_init();
+    llama_numa_init(params.numa);
+    auto mparams = common_model_params_to_llama(params);
+    auto cparams = common_context_params_to_llama(params);
+    const bool success = llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
+        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);
+    if (!success) {
+        LOG_ERR("%s: failed to fit CLI arguments to free memory, exiting...\n", __func__);
+        exit(1);
+    }
+
+    LOG_INF("%s: printing fitted CLI arguments to stdout...\n", __func__);
+    std::this_thread::sleep_for(10ms); // to avoid a race between stderr and stdout
+    printf("-c %" PRIu32 " -ngl %" PRIu32, cparams.n_ctx, mparams.n_gpu_layers);
+
+    size_t nd = llama_max_devices();
+    while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) {
+        nd--;
+    }
+    if (nd > 1) {
+        for (size_t id = 0; id < nd; id++) {
+            if (id == 0) {
+                printf(" -ts ");
+            }
+            printf("%s%" PRIu32, id > 0 ? "," : "", uint32_t(mparams.tensor_split[id]));
+        }
+    }
+
+    const size_t ntbo = llama_max_tensor_buft_overrides();
+    bool any_tbo = false;
+    for (size_t itbo = 0; itbo < ntbo && mparams.tensor_buft_overrides[itbo].pattern != nullptr; itbo++) {
+        if (itbo == 0) {
+            printf(" -ot \"");
+        }
+        printf("%s%s=%s", itbo > 0 ? "," : "", mparams.tensor_buft_overrides[itbo].pattern, ggml_backend_buft_name(mparams.tensor_buft_overrides[itbo].buft));
+        any_tbo = true;
+    }
+    printf("%s\n", any_tbo ? "\"" : "");
+
+    return 0;
+}
@@ -80,7 +80,7 @@ Each test is repeated the number of times given by `-r`, and the results are ave

 Using the `-d <n>` option, each test can be run at a specified context depth, prefilling the KV cache with `<n>` tokens.

-For a description of the other options, see the [main example](../main/README.md).
+For a description of the other options, see the [completion example](../completion/README.md).

 > [!NOTE]
 > The measurements with `llama-bench` do not include the times for tokenization and for sampling.
@@ -15,6 +15,7 @@ add_library(mtmd
            clip-graph.h
            models/models.h
            models/cogvlm.cpp
+            models/glm4v.cpp
            models/internvl.cpp
            models/kimivl.cpp
            models/llama4.cpp
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
TrevorS	4b2a4778f8	arg: allow -kvu flag for llama-perplexity (#18117 ) The -kvu (--kv-unified) flag is required for hellaswag and winogrande benchmarks which use coupled sequences. Without unified KV cache, these benchmarks fail with: split_equal: sequential split is not supported when there are coupled sequences in the input batch (you may need to use the -kvu flag) This change adds LLAMA_EXAMPLE_PERPLEXITY to the allowed examples for the -kvu argument, enabling its use with llama-perplexity.	2025-12-17 08:33:02 +02:00
Aadeshveer Singh	58062860af	ggml : use WARP_SIZE/2 for argmax reduction offset (#18092 )	2025-12-17 11:47:01 +08:00
Yuri Khrustalev	2973a65ecb	gguf-py : allow converting multi-tensor models from read-only locations (#18100 )	2025-12-17 02:27:03 +01:00
Johannes Gäßler	d0794e89d9	llama-fit-params: force disable mlock (#18103 )	2025-12-17 00:50:12 +01:00
Johannes Gäßler	9dcac6cf9f	llama-fit-params: lower ctx size for multi GPU (#18101 )	2025-12-17 00:49:34 +01:00
Johannes Gäßler	0e49a7b8b4	llama-fit-params: fix underflow for dense models (#18095 )	2025-12-17 00:47:37 +01:00
Johannes Gäßler	4164596c76	llama-fit-params: QoL impr. for prints/errors (#18089 )	2025-12-17 00:03:19 +01:00
Xuan-Son Nguyen	ef83fb8601	model: fix LFM2 missing tensors (#18105 )	2025-12-16 19:07:43 +01:00
Johannes Gäßler	ec98e20021	llama: fix early stop in params_fit if ctx is set (#18070 )	2025-12-16 14:24:00 +01:00
yifant-code	59977eba7b	server: fix crash when batch > ubatch with embeddings (#17912 ) * server: fix crash when batch > ubatch with embeddings (#12836) Fixes #12836 where the server crashes with GGML_ASSERT failure when running with embeddings enabled and n_batch > n_ubatch. Root cause: Embeddings use non-causal attention which requires all tokens to be processed within a single ubatch. When n_batch > n_ubatch, the server attempts to split processing, causing assertion failure. Solution: - Add parameter validation in main() after common_params_parse() - When embeddings enabled and n_batch > n_ubatch: * Log warnings explaining the issue * Automatically set n_batch = n_ubatch * Prevent server crash This follows the approach suggested by @ggerganov in issue #12836. Note: This supersedes stalled PR #12940 which attempted a runtime fix in the old examples/server/server.cpp location. This implementation validates at startup in tools/server/server.cpp (current location). Testing: - Build: Compiles successfully - Validation triggers: Warns when -b > -ub with --embedding - Auto-correction works: Adjusts n_batch = n_ubatch - No false positives: Valid params don't trigger warnings - Verified on macOS M3 Pro with embedding model * Update tools/server/server.cpp --------- Co-authored-by: ytian218 <ytian218@bloomberg.net> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2025-12-16 14:27:36 +02:00
Daniel Bevenius	79dbae034a	model-conversion : remove -fa option in model card template [no ci] (#18088 ) This commit updates the causal model card template and removes the -fa option as it is no longer required (fa is auto detected).	2025-12-16 13:25:09 +01:00
Xuan-Son Nguyen	7f2b2f3c77	arch: refactor LLM_TENSOR_NAMES (#18051 ) * arch: refactor LLM_TENSOR_NAMES * update docs * typo * fix LLM_ARCH_NEMOTRON_H_MOE * show more meaningful error message on missing tensor * fix and tested LLM_ARCH_NEMOTRON_H_MOE	2025-12-16 13:22:30 +01:00
Xuan-Son Nguyen	7b1db3d3b7	arg: clarify auto kvu/np being set on server (#17997 ) * arg: clarify auto kvu/np being set on server * improve docs * use invalid_argument	2025-12-16 12:01:27 +01:00
Piotr Wilkin (ilintar)	a5251ca11d	Optimization: Qwen3 next autoregressive pass (#17996 ) * It's Qwen3 Next, the lean mean token generation machine! * Apply patches from thread * Remove recurrent version, only keep chunked and autoregressive * Remove unnecessary conts and asserts * Remove more extra conts and asserts * Cleanup masking	2025-12-16 11:59:53 +01:00
Andrew Aladjev	fb644247de	CLI: fixed adding cli and completion into docker containers, improved docs (#18003 ) Co-authored-by: Andrew Aladjev <andrew.aladjev@gmail.com>	2025-12-16 11:52:23 +01:00
2114L3	5f5f9b4637	server: Update README.md incorrect argument (#18073 ) n-gpu-layer is incorrect argument is n-gpu-layers with the 's'	2025-12-16 11:50:43 +01:00
Xuan-Son Nguyen	3d86c6c2b5	model: support GLM4V vision encoder (#18042 ) * convert ok * no deepstack * less new tensors * cgraph ok * add mrope for text model * faster patch merger * add GGML_ROPE_TYPE_MRNORM * add support for metal * move glm4v do dedicated graph * convert: add norm_embd * clip: add debugging fn * working correctly * fix style * use bicubic * fix mrope metal * improve cpu * convert to neox ordering on conversion * revert backend changes * force stop if using old weight * support moe variant * fix conversion * fix convert (2) * Update tools/mtmd/clip-graph.h Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * process mrope_section on TextModel base class * resolve conflict merge --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2025-12-16 11:25:26 +01:00
Daniel Bevenius	9963b81f63	model-conversion : add note about verifying previous models (#18082 ) This commit adds a note to the README in the model-conversion examples, advising developers to verify that previous versions of models pass logits verification before adding new models from the same family.	2025-12-16 11:17:40 +01:00
Daniel Bevenius	db81d5ec4b	model-conversion : use CONVERTED_EMBEDDING_MODEL for embedding_verify_logits (#18079 ) This commit updates the embedding model verification script to use the CONVERTED_EMBEDDING_MODEL environment variable instead of using the EMBEDDING_MODEL_PATH (the original embedding model path) as the basis for the converted model file name. The motivation for this that currently if the converted embedding model file name differs from the original embedding model directory/name the verification script will look for the wrong .bin files that were generating when running the models.	2025-12-16 11:17:20 +01:00
Aldehir Rojas	c05aa69f32	common : add nemotron 3 parsing (#18077 ) * common : expose json-schema functionality to extract type info * common : fix peg parser negation during needs_more_input * common : add some defensive measures in constructed peg parser * common : add nemotron nano 3 support * common : add nemotron nano 3 tests * remove debug line	2025-12-16 04:05:23 -06:00
Francisco Herrera	279cef27c2	added note for old Intel hardware pre sycl (#18017 ) * added note for old Intel hardware pre sycl Older hardware used opencl * typo * use consistent terms	2025-12-16 17:45:09 +08:00
Georgi Gerganov	5ba95754ee	security : add collaborator guidance (#18081 )	2025-12-16 11:17:11 +02:00
Chris Peterson	2aa45ef9e3	llama: Include algorithm header needed for C++23 (#18078 )	2025-12-16 09:37:55 +02:00
Georgi Gerganov	c560316440	graph : reuse SSM graphs (#16490 ) * graph : reuse hybrid graphs * graph : reuse recurrent graphs * graph : fix reuse check for recurrent inputs * memory : move the recurrent state into the memory context * Revert "memory : move the recurrent state into the memory context" This reverts commit 00f115fe810815d4a22a6dee0acc346131e970e1. * cont : fix build	2025-12-16 09:36:21 +02:00
Sigbjørn Skjæret	d6742125c3	ci : separate webui from server (#18072 ) * separate webui from server * add public to path	2025-12-16 08:17:26 +01:00
Aleksander Grygier	3034836d36	webui: Improve copy to clipboard with text attachments (#17969 ) * feat: Create copy/paste user message including "pasted text" attachments * chore: update webui build output * chore: update webui static output * fix: UI issues * chore: update webui static output * fix: Decode HTML entities using `DOMParser` * chore: update webui build output * chore: update webui static output	2025-12-16 07:38:46 +01:00
Aleksander Grygier	a20979d433	webui: Add setting to always show sidebar on Desktop (#17809 ) * feat: Add setting to always show Sidebar on Desktop * chore: update webui build output * feat: Add auto-show sidebar setting * fix: Mobile settings dialog UI * chore: update webui build output * feat: UI label update * chore: update webui build output * chore: update webui build output * chore: update webui build output * refactor: Cleanup * chore: update webui build output	2025-12-16 07:31:37 +01:00
Daniel Bevenius	2995341730	llama : add support for NVIDIA Nemotron 3 Nano (#18058 ) * llama : add support for NVIDIA Nemotron Nano 3 This commit adds support for the NVIDIA Nemotron Nano 3 model, enabling the conversion and running of this model. Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2025-12-16 07:19:26 +01:00
Darius Lukas	40d9c394f4	Webui: Disable attachment button and model selector button when prompt textbox is disabled. (#17925 ) * Pass disabled state to the file attachments button and the model selector button. * Update index.html.gz * Fix model info card in non-router mode. * Update index.html.gz	2025-12-16 07:15:49 +01:00
Sigbjørn Skjæret	d6a1e18c65	convert : move rope_parameters to TextModel class (#18061 ) * make sure to search text_config for rope parameters * move rope_parameters to TextModel class	2025-12-15 22:03:16 +01:00
Shouyu	c45f89d551	ggml-hexagon: mm for mtmd (#17894 ) * feat: add run_mtmd script for hexagon * fix: fix issue in fp16xfp32 mm * fix: remove opt_experiment for fp16xfp32 mm * fix: ggml-hexagon: matmul fp16xfp32 support non-contigious src0 * fix: fix syntax check for run-mtmd.sh for cli	2025-12-15 10:53:56 -08:00
HelloKS	9d52f17ae3	model : add KORMo model (#18032 ) * vocab: add KORMo Tokenizer * model: add KORMoForCausalLM * vocab: change pretokenizer to qwen2 * lint: fix unintended line removal * model: make qwen2 bias tensor optional * model: use qwen2 architecture for KORMo	2025-12-15 18:51:43 +01:00
ssweens	4529c660c8	kv-cache: Fix state restore fragmented cache (#17982 ) * kv-cache : fix state restore with fragmented cache (#17527) Change find_slot to allow non-contiguous allocation during state restore. Fixes 'failed to find available cells in kv cache' error when restoring state to fragmented cache. * tests : update logic * cleanup: tightened state_read_meta sig, added is_contiguous case * fix: state_read_meta arg reorder loose ends --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2025-12-15 19:28:35 +02:00
Pascal	0f4f35e7be	Fix unreadable user markdown colors and truncate long texts in deletion dialogs (#17555 ) * webui: limit conversation name length in dialogs * webui: fix unreadable colors on links and table cell hover in user markdown * webui: keep table borders visible in user markdown * webui: updating unified exports * Update tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com> * chore: update webui build output * chore: update webui build output * chore: update webui build output --------- Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>	2025-12-15 16:34:53 +01:00
Jeremy Demeule	165caaf5fb	metal: use shared buffers on eGPU (#17866 ) * metal: use shared buffers on eGPU With #15906, I noticed on important regression when using metal backend on eGPU. This commit restore the previous behavior and add an option to force its activation. * metal: use shared buffers on eGPU * metal: use shared buffers on eGPU	2025-12-15 16:14:49 +02:00
Xuan-Son Nguyen	96a181a933	mtmd: refactor audio preprocessing (#17978 ) * mtmd: refactor audio preprocessing * refactor Co-authored-by: Tarek <tdakhran@users.noreply.github.com> * wip * wip (2) * improve constructor * fix use_natural_log * fix padding for short input * clean up * remove need_chunking --------- Co-authored-by: Tarek <tdakhran@users.noreply.github.com>	2025-12-15 14:16:52 +01:00
Andrew Aladjev	4a4f7e6550	cli: fixed dead links to tools/main for cli and completion, fixed code owners (#17993 ) Co-authored-by: Andrew Aladjev <andrew.aladjev@gmail.com>	2025-12-15 11:47:04 +01:00
Thomas Jarosch	e73d548659	webui: add "delete all conversations" button to import/export tab (#17444 ) * webui: add "delete all conversations" button to import/export tab - Add 'Delete all conversations' functionality with confirmation dialog - Add Trash icon and destructive styling for clear visual indication - Redirects to "?new_chat=true#/" by using conversationsStore.deleteAll() * chore: update webui build output	2025-12-15 11:29:29 +01:00
Johannes Gäßler	b1f3a6e5db	llama: automatically set parameters not set by the user in such a way that maximizes GPU utilization (#16653 ) * llama: automatically fit args to free memory llama-fit-params tool * fix CI * hints for bug reports, ensure no reallocation * fix segfault with Vulkan * add llama-fit-params to CI * fix CI * fix CI * fix CI * minor adjustments * fix assignment of 1 dense layer * fix logger not being reset on model load failure * remove --n-gpu-layer hint on model load failure * fix llama-fit-params verbosity * fix edge case * fix typo [no ci]	2025-12-15 09:24:59 +01:00
Neo Zhang Jianyu	4aced7a631	[SYCL] Support gpt-oss by OPs add-id, mul_mat for mxfp4, swiglu_oai (#17826 ) * support gpt-oss GPU by OP add-id, mul_mat for mxfp4, swiglu_oai, fix warning * fix fault ut case, update ops.md * rebase, fix format issue	2025-12-15 10:35:15 +08:00
piDack	745fa0e78b	model : add glm-asr support (#17901 ) * [model] add glm-asr support * fix format for ci * fix convert format for ci * update glm_asr convert script & use build_ffn for glm_asr clip & use build_stack for padding and review * check root architecture for convert hf script * fix conficlt with upstream * fix convert script for glm asr & format clip-impl * format * restore hparams text * improved conversion --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>	2025-12-15 03:18:46 +01:00
Xuan-Son Nguyen	52392291b2	preset: handle negated arg, reverse the meaning if needed (#18041 )	2025-12-14 22:08:10 +01:00
Sigbjørn Skjæret	5c8a717128	convert : refactor rope scaling handling (#18013 ) * refactor rope scaling handling * ws-- * missed a couple * use find_hparam	2025-12-14 16:04:37 +01:00
Haowei Wu	37f5a1093b	mtmd: enhance image resizing in llava_uhd (#18014 )	2025-12-14 15:57:52 +01:00
Ruben Ortlam	9e6649ecf2	vulkan: fix mul_mat_vec_iq1_s formatting (#18026 )	2025-12-14 14:52:46 +01:00