ggml-hexagon: swiglu_oai operation (#18114)

* snapshot: debug ggml-hexagon swiglu-oai

* fix: fix hvx_min_scalar_f32

* feat: working swiglu-oai

* chore: fix formating isue

.devops/cann.Dockerfile

@@ -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
 

.devops/llama-cli-cann.Dockerfile

@@ -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
 

.devops/llama-cpp-cuda.srpm.spec

@@ -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

.devops/llama-cpp.srpm.spec

@@ -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

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

@@ -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

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

@@ -86,6 +86,7 @@ body:
       description: >
           If applicable, please copy and paste any relevant log output, including any generated text.
           This will be automatically formatted into code, so no need for backticks.
+          If you are encountering problems specifically with the `llama_params_fit` module, always upload `--verbose` logs as well.
       render: shell
     validations:
       required: false

.github/workflows/build.yml

@@ -20,7 +20,8 @@ on:
       '**/*.swift',
       '**/*.m',
       '**/*.metal',
-      '**/*.comp'
+      '**/*.comp',
+      '**/*.glsl'
     ]
 
   pull_request:
@@ -40,7 +41,8 @@ on:
       '**/*.swift',
       '**/*.m',
       '**/*.metal',
-      '**/*.comp'
+      '**/*.comp',
+      '**/*.glsl'
     ]
 
 concurrency:

.github/workflows/server-webui.yml

@@ -0,0 +1,225 @@
+# 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-check:
+    name: WebUI Checks
+    runs-on: ubuntu-latest
+    continue-on-error: true
+    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
+        id: node
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+          cache: "npm"
+          cache-dependency-path: "tools/server/webui/package-lock.json"
+
+      - name: Install dependencies
+        id: setup
+        if: ${{ steps.node.conclusion == 'success' }}
+        run: npm ci
+        working-directory: tools/server/webui
+
+      - name: Run type checking
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
+        run: npm run check
+        working-directory: tools/server/webui
+
+      - name: Run linting
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
+        run: npm run lint
+        working-directory: tools/server/webui
+
+      - name: Build application
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
+        run: npm run build
+        working-directory: tools/server/webui
+
+      - name: Install Playwright browsers
+        id: playwright
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
+        run: npx playwright install --with-deps
+        working-directory: tools/server/webui
+
+      - name: Build Storybook
+        if: ${{ always() && steps.playwright.conclusion == 'success' }}
+        run: npm run build-storybook
+        working-directory: tools/server/webui
+
+      - name: Run Client tests
+        if: ${{ always() && steps.playwright.conclusion == 'success' }}
+        run: npm run test:client
+        working-directory: tools/server/webui
+
+      - name: Run Unit tests
+        if: ${{ always() && steps.playwright.conclusion == 'success' }}
+        run: npm run test:unit
+        working-directory: tools/server/webui
+
+      - name: Run UI tests
+        if: ${{ always() && steps.playwright.conclusion == 'success' }}
+        run: npm run test:ui -- --testTimeout=60000
+        working-directory: tools/server/webui
+
+      - name: Run E2E tests
+        if: ${{ always() && steps.playwright.conclusion == 'success' }}
+        run: npm run test:e2e
+        working-directory: tools/server/webui
+
+  server-build:
+    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

.github/workflows/server.yml

@@ -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
 

.gitignore

@@ -54,6 +54,7 @@
 /out/
 /tmp/
 /autogen-*.md
+/common/build-info.cpp
 
 # Deprecated
 

CODEOWNERS

@@ -32,7 +32,7 @@
 /examples/export-docs/                  @ggerganov
 /examples/gen-docs/                     @ggerganov
 /examples/gguf/                         @ggerganov
-/examples/llama.android/                @ggerganov
+/examples/llama.android/                @ggerganov @hanyin-arm @naco-siren
 /examples/llama.swiftui/                @ggerganov
 /examples/llama.vim                     @ggerganov
 /examples/lookahead/                    @ggerganov
@@ -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

README.md

@@ -190,6 +190,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
 - Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi)
 - Go (no CGo needed): [hybridgroup/yzma](https://github.com/hybridgroup/yzma)
+- Android: [llama.android](/examples/llama.android)
 
 </details>
 
@@ -313,7 +314,7 @@ 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 +526,8 @@ 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)
 

SECURITY.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

ci/run.sh

@@ -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
 

common/arg.cpp

@@ -20,6 +20,7 @@
 #include <nlohmann/json.hpp>
 
 #include <algorithm>
+#include <cinttypes>
 #include <climits>
 #include <cstdarg>
 #include <fstream>
@@ -419,6 +420,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         }
     };
 
+    std::set<std::string> seen_args;
+
     for (int i = 1; i < argc; i++) {
         const std::string arg_prefix = "--";
 
@@ -429,6 +432,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         if (arg_to_options.find(arg) == arg_to_options.end()) {
             throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
         }
+        if (!seen_args.insert(arg).second) {
+            LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+        }
         auto & tmp = arg_to_options[arg];
         auto opt = *tmp.first;
         bool is_positive = tmp.second;
@@ -504,7 +510,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
 
     // model is required (except for server)
     // TODO @ngxson : maybe show a list of available models in CLI in this case
-    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage) {
+    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage && !params.completion) {
         throw std::invalid_argument("error: --model is required\n");
     }
 
@@ -529,7 +535,9 @@ 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});
     }
 
@@ -724,7 +732,7 @@ static void add_rpc_devices(const std::string & servers) {
     }
 }
 
-bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map) {
+bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map) {
     common_params dummy_params;
     common_params_context ctx_arg = common_params_parser_init(dummy_params, ex, nullptr);
 
@@ -733,6 +741,9 @@ bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<comm
         for (const auto & arg : opt.args) {
             arg_to_options[arg] = &opt;
         }
+        for (const auto & arg : opt.args_neg) {
+            arg_to_options[arg] = &opt;
+        }
     }
 
     // TODO @ngxson : find a way to deduplicate this code
@@ -744,6 +755,8 @@ bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<comm
         }
     };
 
+    std::set<std::string> seen_args;
+
     for (int i = 1; i < argc; i++) {
         const std::string arg_prefix = "--";
 
@@ -754,6 +767,9 @@ bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<comm
         if (arg_to_options.find(arg) == arg_to_options.end()) {
             throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
         }
+        if (!seen_args.insert(arg).second) {
+            LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
+        }
         auto opt = *arg_to_options[arg];
         std::string val;
         if (opt.value_hint != nullptr) {
@@ -829,6 +845,19 @@ 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
@@ -1101,7 +1130,7 @@ 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;
@@ -1109,7 +1138,7 @@ 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;
@@ -1117,12 +1146,11 @@ 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"},
@@ -1208,13 +1236,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION}));
     add_opt(common_arg(
         {"--in-file"}, "FNAME",
-        "an input file (repeat to specify multiple files)",
+        "an input file (use comma-separated values to specify multiple files)",
         [](common_params & params, const std::string & value) {
-            std::ifstream file(value);
-            if (!file) {
-                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
+            for (const auto & item : string_split<std::string>(value, ',')) {
+                std::ifstream file(item);
+                if (!file) {
+                    throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
+                }
+                params.in_files.push_back(item);
             }
-            params.in_files.push_back(value);
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
@@ -1412,7 +1442,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.top_k = value;
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K;
         }
-    ).set_sparam());
+    ).set_sparam().set_env("LLAMA_ARG_TOP_K"));
     add_opt(common_arg(
         {"--top-p"}, "N",
         string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p),
@@ -1882,13 +1912,27 @@ 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),
@@ -1937,9 +1981,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_OFFLOAD"));
     add_opt(common_arg(
         {"--image", "--audio"}, "FILE",
-        "path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n",
+        "path to an image or audio file. use with multimodal models, use comma-separated values for multiple files\n",
         [](common_params & params, const std::string & value) {
-            params.image.emplace_back(value);
+            for (const auto & item : string_split<std::string>(value, ',')) {
+                params.image.emplace_back(item);
+            }
         }
     ).set_examples({LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
@@ -2150,6 +2196,34 @@ 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"),
@@ -2158,12 +2232,39 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ));
     add_opt(common_arg(
-        {"--override-kv"}, "KEY=TYPE:VALUE",
-        "advanced option to override model metadata by key. may be specified multiple times.\n"
-        "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
+        {"--override-kv"}, "KEY=TYPE:VALUE,...",
+        "advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.\n"
+        "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false",
         [](common_params & params, const std::string & value) {
-            if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) {
-                throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", value.c_str()));
+            std::vector<std::string> kv_overrides;
+
+            std::string current;
+            bool escaping = false;
+
+            for (const char c : value) {
+                if (escaping) {
+                    current.push_back(c);
+                    escaping = false;
+                } else if (c == '\\') {
+                    escaping = true;
+                } else if (c == ',') {
+                    kv_overrides.push_back(current);
+                    current.clear();
+                } else {
+                    current.push_back(c);
+                }
+            }
+
+            if (escaping) {
+                current.push_back('\\');
+            }
+
+            kv_overrides.push_back(current);
+
+            for (const auto & kv_override : kv_overrides) {
+                if (!string_parse_kv_override(kv_override.c_str(), params.kv_overrides)) {
+                    throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", kv_override.c_str()));
+                }
             }
         }
     ));
@@ -2177,33 +2278,50 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--lora"}, "FNAME",
-        "path to LoRA adapter (can be repeated to use multiple adapters)",
+        "path to LoRA adapter (use comma-separated values to load multiple adapters)",
         [](common_params & params, const std::string & value) {
-            params.lora_adapters.push_back({ std::string(value), 1.0, "", "", nullptr });
+            for (const auto & item : string_split<std::string>(value, ',')) {
+                params.lora_adapters.push_back({ item, 1.0, "", "", nullptr });
+            }
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
     add_opt(common_arg(
-        {"--lora-scaled"}, "FNAME", "SCALE",
-        "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
-        [](common_params & params, const std::string & fname, const std::string & scale) {
-            params.lora_adapters.push_back({ fname, std::stof(scale), "", "", nullptr });
+        {"--lora-scaled"}, "FNAME:SCALE,...",
+        "path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)\n"
+        "note: use comma-separated values",
+        [](common_params & params, const std::string & value) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
+                auto parts = string_split<std::string>(item, ':');
+                if (parts.size() != 2) {
+                    throw std::invalid_argument("lora-scaled format: FNAME:SCALE");
+                }
+                params.lora_adapters.push_back({ parts[0], std::stof(parts[1]), "", "", nullptr });
+            }
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
     add_opt(common_arg(
         {"--control-vector"}, "FNAME",
-        "add a control vector\nnote: this argument can be repeated to add multiple control vectors",
+        "add a control vector\nnote: use comma-separated values to add multiple control vectors",
         [](common_params & params, const std::string & value) {
-            params.control_vectors.push_back({ 1.0f, value, });
+            for (const auto & item : string_split<std::string>(value, ',')) {
+                params.control_vectors.push_back({ 1.0f, item, });
+            }
         }
     ));
     add_opt(common_arg(
-        {"--control-vector-scaled"}, "FNAME", "SCALE",
+        {"--control-vector-scaled"}, "FNAME:SCALE,...",
         "add a control vector with user defined scaling SCALE\n"
-        "note: this argument can be repeated to add multiple scaled control vectors",
-        [](common_params & params, const std::string & fname, const std::string & scale) {
-            params.control_vectors.push_back({ std::stof(scale), fname });
+        "note: use comma-separated values (format: FNAME:SCALE,...)",
+        [](common_params & params, const std::string & value) {
+            for (const auto & item : string_split<std::string>(value, ',')) {
+                auto parts = string_split<std::string>(item, ':');
+                if (parts.size() != 2) {
+                    throw std::invalid_argument("control-vector-scaled format: FNAME:SCALE");
+                }
+                params.control_vectors.push_back({ std::stof(parts[1]), parts[0] });
+            }
         }
     ));
     add_opt(common_arg(
@@ -2293,13 +2411,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("HF_TOKEN"));
     add_opt(common_arg(
         {"--context-file"}, "FNAME",
-        "file to load context from (repeat to specify multiple files)",
+        "file to load context from (use comma-separated values to specify multiple files)",
         [](common_params & params, const std::string & value) {
-            std::ifstream file(value, std::ios::binary);
-            if (!file) {
-                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
+            for (const auto & item : string_split<std::string>(value, ',')) {
+                std::ifstream file(item, std::ios::binary);
+                if (!file) {
+                    throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
+                }
+                params.context_files.push_back(item);
             }
-            params.context_files.push_back(value);
         }
     ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
     add_opt(common_arg(
@@ -2490,6 +2610,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.api_prefix = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX"));
+    add_opt(common_arg(
+        {"--webui-config"}, "JSON",
+        "JSON that provides default WebUI settings (overrides WebUI defaults)",
+        [](common_params & params, const std::string & value) {
+            params.webui_config_json = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG"));
+    add_opt(common_arg(
+        {"--webui-config-file"}, "PATH",
+        "JSON file that provides default WebUI settings (overrides WebUI defaults)",
+        [](common_params & params, const std::string & value) {
+            params.webui_config_json = read_file(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG_FILE"));
     add_opt(common_arg(
         {"--webui"},
         {"--no-webui"},

common/arg.h

@@ -115,7 +115,7 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
 
 // parse input arguments from CLI into a map
 // TODO: support repeated args in the future
-bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
+bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
 
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

common/chat-peg-parser.cpp

@@ -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 += "}";
     }
 }

common/chat.cpp

@@ -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);
     }
 

common/common.cpp

@@ -1013,31 +1013,40 @@ bool tty_can_use_colors() {
 // Model utils
 //
 
-static inline void common_init_sampler_from_model(
+// TODO: move to common/sampling
+static void common_init_sampler_from_model(
     const llama_model * model,
     common_params_sampling & sparams) {
 
     const uint64_t config = sparams.user_sampling_config;
 
     auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) {
-        if (config & user_config) return;
+        if (config & user_config) {
+            return;
+        }
 
         char buf[64] = {0};
         if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
             char * end = nullptr;
             int32_t v = strtol(buf, &end, 10);
-            if (end && end != buf) dst = v;
+            if (end && end != buf) {
+                dst = v;
+            }
         }
     };
 
     auto get_float = [&](const char * key, float & dst, uint64_t user_config) {
-        if (config & user_config) return;
+        if (config & user_config) {
+            return;
+        }
 
         char buf[128] = {0};
         if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
             char * end = nullptr;
             float v = strtof(buf, &end);
-            if (end && end != buf) dst = v;
+            if (end && end != buf) {
+                dst = v;
+            }
         }
     };
 
@@ -1065,31 +1074,125 @@ static inline void common_init_sampler_from_model(
     get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA),    sparams.mirostat_eta,    common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA);
 }
 
-struct common_init_result common_init_from_params(common_params & params) {
-    common_init_result iparams;
+struct common_init_result::impl {
+    impl() = default;
+    ~impl() = default;
+
+    llama_model_ptr   model;
+    llama_context_ptr context;
+
+    std::vector<llama_adapter_lora_ptr> lora;
+
+    std::vector<common_sampler_ptr> samplers;
+};
+
+common_init_result::common_init_result(common_params & params) :
+    pimpl(new impl{}) {
     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, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
+        llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
+            params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, 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) {
-        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());
-        return iparams;
+        return;
     }
 
-    common_init_sampler_from_model(model, params.sampling);
+    pimpl->model.reset(model);
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
-    auto cparams = common_context_params_to_llama(params);
+    // updates params.sampling
+    // TODO: fix naming
+    common_init_sampler_from_model(model, params.sampling);
+
+    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;
+    }
+
+    // initialize once
+    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
+        if (llama_vocab_is_eog(vocab, i)) {
+            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
+            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
+        }
+    }
+
+    if (params.sampling.ignore_eos) {
+        // add EOG biases to the active set of logit biases
+        params.sampling.logit_bias.insert(
+                params.sampling.logit_bias.end(),
+                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
+    }
+
+    //if (params.sampling.penalty_last_n == -1) {
+    //    LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    params.sampling.penalty_last_n = llama_n_ctx(lctx);
+    //}
+
+    //if (params.sampling.dry_penalty_last_n == -1) {
+    //    LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
+    //}
+
+    pimpl->samplers.resize(cparams.n_seq_max);
+
+    for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
+        pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
+    }
 
     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());
-        llama_model_free(model);
-        return iparams;
+        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
+        return;
     }
 
+    pimpl->context.reset(lctx);
+}
+
+llama_model * common_init_result::model() {
+    return pimpl->model.get();
+}
+
+llama_context * common_init_result::context() {
+    return pimpl->context.get();
+}
+
+common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
+    return pimpl->samplers[seq_id].get();
+}
+
+std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
+    return pimpl->lora;
+}
+
+void common_init_result::free_context() {
+    pimpl->context.reset();
+}
+
+common_init_result_ptr common_init_from_params(common_params & params) {
+    common_init_result_ptr res(new common_init_result(params));
+
+    llama_model * model = res->model();
+    if (model == NULL) {
+        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'\n", __func__, params.model.path.c_str());
+        return res;
+    }
+
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
         LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
         params.ctx_shift = false;
@@ -1101,10 +1204,7 @@ struct common_init_result common_init_from_params(common_params & params) {
 
         const auto cvec = common_control_vector_load(params.control_vectors);
         if (cvec.n_embd == -1) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
 
         int err = llama_apply_adapter_cvec(
@@ -1115,10 +1215,7 @@ struct common_init_result common_init_from_params(common_params & params) {
                 params.control_vector_layer_start,
                 params.control_vector_layer_end);
         if (err) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
     }
 
@@ -1142,10 +1239,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         }
 
         if (!ok) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
     }
 
@@ -1155,9 +1249,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
         if (lora == nullptr) {
             LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
-            llama_free(lctx);
-            llama_model_free(model);
-            return iparams;
+            return res;
         }
 
         char buf[1024];
@@ -1166,43 +1258,13 @@ struct common_init_result common_init_from_params(common_params & params) {
         la.task_name = buf;
         llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
         la.prompt_prefix = buf;
-        iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
+        res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters
     }
 
     if (!params.lora_init_without_apply) {
         common_set_adapter_lora(lctx, params.lora_adapters);
     }
 
-    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;
-    }
-
-    // initialize once
-    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
-        if (llama_vocab_is_eog(vocab, i)) {
-            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
-            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
-        }
-    }
-
-    if (params.sampling.ignore_eos) {
-        // add EOG biases to the active set of logit biases
-        params.sampling.logit_bias.insert(
-                params.sampling.logit_bias.end(),
-                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
-    }
-
-    if (params.sampling.penalty_last_n == -1) {
-        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.penalty_last_n = llama_n_ctx(lctx);
-    }
-
-    if (params.sampling.dry_penalty_last_n == -1) {
-        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
-    }
-
     if (params.warmup) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
@@ -1241,12 +1303,11 @@ struct common_init_result common_init_from_params(common_params & params) {
         llama_set_warmup(lctx, false);
     }
 
-    iparams.model.reset(model);
-    iparams.context.reset(lctx);
-
-    return iparams;
+    return res;
 }
 
+common_init_result::~common_init_result() = default;
+
 std::string get_model_endpoint() {
     const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
     // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
@@ -1255,7 +1316,9 @@ std::string get_model_endpoint() {
     std::string model_endpoint = "https://huggingface.co/";
     if (endpoint_env) {
         model_endpoint = endpoint_env;
-        if (model_endpoint.back() != '/') model_endpoint += '/';
+        if (model_endpoint.back() != '/') {
+            model_endpoint += '/';
+        }
     }
     return model_endpoint;
 }

common/common.h

@@ -99,6 +99,7 @@ enum llama_example {
     LLAMA_EXAMPLE_TTS,
     LLAMA_EXAMPLE_DIFFUSION,
     LLAMA_EXAMPLE_FINETUNE,
+    LLAMA_EXAMPLE_FIT_PARAMS,
 
     LLAMA_EXAMPLE_COUNT,
 };
@@ -195,7 +196,6 @@ struct common_params_sampling {
 
     std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"};     // default sequence breakers for DRY
 
-
     std::vector<enum common_sampler_type> samplers = {
         COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
@@ -216,6 +216,10 @@ struct common_params_sampling {
     std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
     std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
 
+    bool has_logit_bias() const {
+        return !logit_bias.empty();
+    }
+
     // print the parameters into a string
     std::string print() const;
 };
@@ -303,8 +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
@@ -325,9 +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
 
@@ -477,8 +484,11 @@ struct common_params {
 
     std::map<std::string, std::string> default_template_kwargs;
 
+    // webui configs
+    bool webui = true;
+    std::string webui_config_json;
+
     // "advanced" endpoints are disabled by default for better security
-    bool webui            = true;
     bool endpoint_slots   = true;
     bool endpoint_props   = false; // only control POST requests, not GET
     bool endpoint_metrics = false;
@@ -669,15 +679,29 @@ bool tty_can_use_colors();
 // Model utils
 //
 
-// note: defines object's lifetime
-struct common_init_result {
-    llama_model_ptr   model;
-    llama_context_ptr context;
+struct common_sampler;
 
-    std::vector<llama_adapter_lora_ptr> lora;
+// note: defines the model, context, samplers, ets. lifetimes
+struct common_init_result {
+    common_init_result(common_params & params);
+    ~common_init_result();
+
+    llama_model * model();
+    llama_context * context();
+    common_sampler * sampler(llama_seq_id seq_id);
+
+    std::vector<llama_adapter_lora_ptr> & lora();
+
+    void free_context();
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
 };
 
-struct common_init_result     common_init_from_params(common_params & params);
+using common_init_result_ptr = std::unique_ptr<common_init_result>;
+
+common_init_result_ptr common_init_from_params(common_params & params);
 
 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);

common/json-schema-to-grammar.cpp

@@ -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);

common/json-schema-to-grammar.h

@@ -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;

common/peg-parser.cpp

@@ -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

common/preset.cpp

@@ -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?
             }

common/sampling.cpp

@@ -116,7 +116,6 @@ struct common_sampler {
     void reset() {
         prev.clear();
 
-        llama_sampler_reset(grmr);
         llama_sampler_reset(chain);
     }
 
@@ -167,7 +166,11 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 
     lparams.no_perf = params.no_perf;
 
-    struct llama_sampler * grmr;
+    llama_sampler * grmr = nullptr;
+    llama_sampler * chain = llama_sampler_chain_init(lparams);
+
+    std::vector<llama_sampler *> samplers;
+
     if (params.grammar.compare(0, 11, "%llguidance") == 0) {
 #ifdef LLAMA_USE_LLGUIDANCE
         grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
@@ -217,30 +220,20 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
             trigger_patterns_c.push_back(regex.c_str());
         }
 
-        grmr = params.grammar_lazy
-             ? llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
-                                                        trigger_patterns_c.data(), trigger_patterns_c.size(),
-                                                        trigger_tokens.data(), trigger_tokens.size())
-             :      llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
-        if (!grmr) {
-            return nullptr;
+        if (!params.grammar.empty()) {
+             if (params.grammar_lazy) {
+                 grmr = llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
+                         trigger_patterns_c.data(), trigger_patterns_c.size(),
+                         trigger_tokens.data(), trigger_tokens.size());
+             } else {
+                 grmr = llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
+             }
         }
     }
 
-    auto * result = new common_sampler {
-        /* .params = */ params,
-        /* .grmr   = */ grmr,
-        /* .chain  = */ llama_sampler_chain_init(lparams),
-        /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
-        /* .cur    = */ {},
-        /* .cur_p  = */ {},
-    };
-
-    llama_sampler_chain_add(result->chain,
-            llama_sampler_init_logit_bias(
-                llama_vocab_n_tokens(vocab),
-                params.logit_bias.size(),
-                params.logit_bias.data()));
+    if (params.has_logit_bias()) {
+        samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data()));
+    }
 
     if (params.mirostat == 0) {
         for (const auto & cnstr : params.samplers) {
@@ -253,58 +246,71 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                             c_breakers.push_back(str.c_str());
                         }
 
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        samplers.push_back(llama_sampler_init_dry    (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_k       (params.top_k));
+                    samplers.push_back(llama_sampler_init_top_k      (params.top_k));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_p       (params.top_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_top_p      (params.top_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma));
+                    samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
                     break;
                 case COMMON_SAMPLER_TYPE_MIN_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_min_p       (params.min_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_min_p      (params.min_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_XTC:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_xtc         (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                    samplers.push_back(llama_sampler_init_xtc        (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
                     break;
                 case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_typical     (params.typ_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_typical    (params.typ_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext    (params.temp, params.dynatemp_range, params.dynatemp_exponent));
+                    samplers.push_back(llama_sampler_init_temp_ext   (params.temp, params.dynatemp_range, params.dynatemp_exponent));
                     break;
                 case COMMON_SAMPLER_TYPE_INFILL:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill      (vocab));
+                    samplers.push_back(llama_sampler_init_infill     (vocab));
                     break;
                 case COMMON_SAMPLER_TYPE_PENALTIES:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties   (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    samplers.push_back(llama_sampler_init_penalties  (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
                     break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
             }
         }
-        llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
+
+        samplers.push_back(llama_sampler_init_dist(params.seed));
     } else if (params.mirostat == 1) {
-        llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
+        samplers.push_back(llama_sampler_init_temp(params.temp));
+        samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
     } else if (params.mirostat == 2) {
-        llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
+        samplers.push_back(llama_sampler_init_temp(params.temp));
+        samplers.push_back(llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
     } else {
         GGML_ASSERT(false && "unknown mirostat version");
     }
 
+    for (auto * smpl : samplers) {
+        llama_sampler_chain_add(chain, smpl);
+    }
+
+    auto * result = new common_sampler {
+        /* .params  = */ params,
+        /* .grmr    = */ grmr,
+        /* .chain   = */ chain,
+        /* .prev    = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
+        /* .cur     = */ {},
+        /* .cur_p   = */ {},
+    };
+
     return result;
 }
 
 void common_sampler_free(struct common_sampler * gsmpl) {
     if (gsmpl) {
         llama_sampler_free(gsmpl->grmr);
-
         llama_sampler_free(gsmpl->chain);
 
         delete gsmpl;
@@ -314,7 +320,7 @@ void common_sampler_free(struct common_sampler * gsmpl) {
 void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
     const auto tm = gsmpl->tm();
 
-    if (accept_grammar) {
+    if (gsmpl->grmr && accept_grammar) {
         llama_sampler_accept(gsmpl->grmr, token);
     }
 
@@ -329,12 +335,12 @@ void common_sampler_reset(struct common_sampler * gsmpl) {
 
 struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
     return new common_sampler {
-        /* .params = */ gsmpl->params,
-        /* .grmr   = */ llama_sampler_clone(gsmpl->grmr),
-        /* .chain  = */ llama_sampler_clone(gsmpl->chain),
-        /* .prev   = */ gsmpl->prev,
-        /* .cur    = */ gsmpl->cur,
-        /* .cur_p  = */ gsmpl->cur_p,
+        /* .params  = */ gsmpl->params,
+        /* .grmr    = */ llama_sampler_clone(gsmpl->grmr),
+        /* .chain   = */ llama_sampler_clone(gsmpl->chain),
+        /* .prev    = */ gsmpl->prev,
+        /* .cur     = */ gsmpl->cur,
+        /* .cur_p   = */ gsmpl->cur_p,
     };
 }
 
@@ -383,33 +389,37 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
     }
 }
 
+struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
+    return gsmpl->chain;
+}
+
 llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
     llama_synchronize(ctx);
 
     // start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations
     const auto tm = gsmpl->tm();
 
-    gsmpl->set_logits(ctx, idx);
+    llama_token id = LLAMA_TOKEN_NULL;
 
     auto & grmr  = gsmpl->grmr;
     auto & chain = gsmpl->chain;
     auto & cur_p = gsmpl->cur_p; // initialized by set_logits
 
+    gsmpl->set_logits(ctx, idx);
+
     if (grammar_first) {
         llama_sampler_apply(grmr, &cur_p);
     }
 
     llama_sampler_apply(chain, &cur_p);
 
-    GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
-
-    const llama_token id = cur_p.data[cur_p.selected].id;
+    id = cur_p.data[cur_p.selected].id;
 
     if (grammar_first) {
         return id;
     }
 
-    // check if it the sampled token fits the grammar
+    // check if it the sampled token fits the grammar (grammar-based rejection sampling)
     {
         llama_token_data       single_token_data       = { id, 1.0f, 0.0f };
         llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
@@ -429,9 +439,11 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     llama_sampler_apply(grmr,  &cur_p);
     llama_sampler_apply(chain, &cur_p);
 
-    GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration");
+    GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
 
-    return cur_p.data[cur_p.selected].id;
+    id = cur_p.data[cur_p.selected].id;
+
+    return id;
 }
 
 std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
@@ -515,7 +527,8 @@ std::string common_sampler_print(const struct common_sampler * gsmpl) {
 
     for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
         const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
-        result += std::string("-> ") + llama_sampler_name(smpl) + " ";
+        result += std::string("-> ");
+        result += std::string(llama_sampler_name(smpl)) + " ";
     }
 
     return result;

common/sampling.h

@@ -48,6 +48,8 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
 // arguments can be nullptr to skip printing
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
 
+struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
+
 // extended sampling implementation:
 //
 // - set logits
@@ -107,3 +109,9 @@ std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std:
 
 llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
                 const char * grammar_kind, const char * grammar_data);
+
+struct common_sampler_deleter {
+    void operator()(common_sampler * s) { common_sampler_free(s); }
+};
+
+typedef std::unique_ptr<common_sampler, common_sampler_deleter> common_sampler_ptr;

convert_hf_to_gguf_update.py

@@ -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

docs/android.md

@@ -1,6 +1,26 @@
 
 # Android
 
+## Build with Android Studio
+
+Import the `examples/llama.android` directory into Android Studio, then perform a Gradle sync and build the project.
+![Project imported into Android Studio](./android/imported-into-android-studio.png)
+
+This Android binding supports hardware acceleration up to `SME2` for **Arm** and `AMX` for **x86-64** CPUs on Android and ChromeOS devices.
+It automatically detects the host's hardware to load compatible kernels. As a result, it runs seamlessly on both the latest premium devices and older devices that may lack modern CPU features or have limited RAM, without requiring any manual configuration.
+
+A minimal Android app frontend is included to showcase the binding’s core functionalities:
+1.	**Parse GGUF metadata** via `GgufMetadataReader` from either a `ContentResolver` provided `Uri` or a local `File`.
+2.	**Obtain a `TierDetection` or `InferenceEngine`** instance through the high-level facade APIs.
+3.	**Send a raw user prompt** for automatic template formatting, prefill, and decoding. Then collect the generated tokens in a Kotlin `Flow`.
+
+For a production-ready experience that leverages advanced features such as system prompts and benchmarks, check out [Arm AI Chat](https://play.google.com/store/apps/details?id=com.arm.aichat) on Google Play.
+This project is made possible through a collaborative effort by Arm's **CT-ML**, **CE-ML** and **STE** groups:
+
+| ![Home screen](./android/arm-ai-chat-home-screen.png)  | ![System prompt](./android/system-prompt-setup.png)  | !["Haiku"](./android/chat-with-system-prompt-haiku.png)  |
+|:------------------------------------------------------:|:----------------------------------------------------:|:--------------------------------------------------------:|
+|                      Home screen                       |                    System prompt                     |                         "Haiku"                          |
+
 ## Build on Android using Termux
 
 [Termux](https://termux.dev/en/) is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid.

docs/backend/SYCL.md

@@ -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                        |

docs/development/HOWTO-add-model.md

@@ -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`.
 

docs/docker.md

@@ -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
 

docs/ops.md

@@ -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 | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |

examples/batched/batched.cpp

@@ -2,6 +2,7 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "sampling.h"
 
 #include <algorithm>
 #include <cstdio>
@@ -64,17 +65,23 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_predict, n_parallel);
 
-    llama_context * ctx = llama_init_from_model(model, ctx_params);
-
     auto sparams = llama_sampler_chain_default_params();
     sparams.no_perf = false;
 
-    llama_sampler * smpl = llama_sampler_chain_init(sparams);
+    std::vector<llama_sampler *> samplers;
 
-    llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
-    llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
-    llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
-    llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
+    for (int32_t i = 0; i < n_parallel; ++i) {
+        llama_sampler * smpl = llama_sampler_chain_init(sparams);
+
+        llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
+        llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
+        llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
+        llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
+
+        samplers.push_back(smpl);
+    }
+
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
 
     if (ctx == NULL) {
         LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);
@@ -173,7 +180,7 @@ int main(int argc, char ** argv) {
                 continue;
             }
 
-            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
+            const llama_token new_token_id = llama_sampler_sample(samplers[i], ctx, i_batch[i]);
 
             // is it an end of generation? -> mark the stream as finished
             if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
@@ -229,14 +236,17 @@ int main(int argc, char ** argv) {
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
     LOG("\n");
-    llama_perf_sampler_print(smpl);
+    llama_perf_sampler_print(samplers[0]);
     llama_perf_context_print(ctx);
 
     fprintf(stderr, "\n");
 
     llama_batch_free(batch);
 
-    llama_sampler_free(smpl);
+    for (auto & sampler_config : samplers) {
+        llama_sampler_free(sampler_config);
+    }
+
     llama_free(ctx);
     llama_model_free(model);
 

examples/embedding/embedding.cpp

@@ -131,10 +131,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);

examples/eval-callback/eval-callback.cpp

@@ -202,10 +202,10 @@ int main(int argc, char ** argv) {
     params.warmup = false;
 
     // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == nullptr || ctx == nullptr) {
         LOG_ERR("%s : failed to init\n", __func__);

examples/gen-docs/gen-docs.cpp

@@ -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;
 }

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

@@ -1,16 +1,18 @@
 plugins {
-    id("com.android.application")
-    id("org.jetbrains.kotlin.android")
+    alias(libs.plugins.android.application)
+    alias(libs.plugins.jetbrains.kotlin.android)
 }
 
 android {
     namespace = "com.example.llama"
-    compileSdk = 34
+    compileSdk = 36
 
     defaultConfig {
-        applicationId = "com.example.llama"
+        applicationId = "com.example.llama.aichat"
+
         minSdk = 33
-        targetSdk = 34
+        targetSdk = 36
+
         versionCode = 1
         versionName = "1.0"
 
@@ -21,8 +23,17 @@ android {
     }
 
     buildTypes {
+        debug {
+            isMinifyEnabled = true
+            isShrinkResources = true
+            proguardFiles(
+                getDefaultProguardFile("proguard-android.txt"),
+                "proguard-rules.pro"
+            )
+        }
         release {
-            isMinifyEnabled = false
+            isMinifyEnabled = true
+            isShrinkResources = true
             proguardFiles(
                 getDefaultProguardFile("proguard-android-optimize.txt"),
                 "proguard-rules.pro"
@@ -36,30 +47,15 @@ android {
     kotlinOptions {
         jvmTarget = "1.8"
     }
-    buildFeatures {
-        compose = true
-    }
-    composeOptions {
-        kotlinCompilerExtensionVersion = "1.5.1"
-    }
 }
 
 dependencies {
+    implementation(libs.bundles.androidx)
+    implementation(libs.material)
 
-    implementation("androidx.core:core-ktx:1.12.0")
-    implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2")
-    implementation("androidx.activity:activity-compose:1.8.2")
-    implementation(platform("androidx.compose:compose-bom:2023.08.00"))
-    implementation("androidx.compose.ui:ui")
-    implementation("androidx.compose.ui:ui-graphics")
-    implementation("androidx.compose.ui:ui-tooling-preview")
-    implementation("androidx.compose.material3:material3")
-    implementation(project(":llama"))
-    testImplementation("junit:junit:4.13.2")
-    androidTestImplementation("androidx.test.ext:junit:1.1.5")
-    androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
-    androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00"))
-    androidTestImplementation("androidx.compose.ui:ui-test-junit4")
-    debugImplementation("androidx.compose.ui:ui-tooling")
-    debugImplementation("androidx.compose.ui:ui-test-manifest")
+    implementation(project(":lib"))
+
+    testImplementation(libs.junit)
+    androidTestImplementation(libs.androidx.junit)
+    androidTestImplementation(libs.androidx.espresso.core)
 }

examples/llama.android/app/proguard-rules.pro

@@ -19,3 +19,11 @@
 # If you keep the line number information, uncomment this to
 # hide the original source file name.
 #-renamesourcefileattribute SourceFile
+
+-keep class com.arm.aichat.* { *; }
+-keep class com.arm.aichat.gguf.* { *; }
+
+-assumenosideeffects class android.util.Log {
+    public static int v(...);
+    public static int d(...);
+}

examples/llama.android/app/src/main/AndroidManifest.xml

@@ -1,24 +1,21 @@
 <?xml version="1.0" encoding="utf-8"?>
-<manifest xmlns:android="http://schemas.android.com/apk/res/android"
-    xmlns:tools="http://schemas.android.com/tools">
-
-    <uses-permission android:name="android.permission.INTERNET" />
+<manifest xmlns:android="http://schemas.android.com/apk/res/android">
 
     <application
         android:allowBackup="true"
         android:dataExtractionRules="@xml/data_extraction_rules"
+        android:extractNativeLibs="true"
         android:fullBackupContent="@xml/backup_rules"
-        android:icon="@mipmap/ic_launcher"
+        android:icon="@mipmap/ic_launcher_round"
         android:label="@string/app_name"
         android:roundIcon="@mipmap/ic_launcher_round"
         android:supportsRtl="true"
-        android:theme="@style/Theme.LlamaAndroid"
+        android:theme="@style/Theme.AiChatSample"
         >
 
         <activity
             android:name=".MainActivity"
-            android:exported="true"
-            android:theme="@style/Theme.LlamaAndroid">
+            android:exported="true">
             <intent-filter>
                 <action android:name="android.intent.action.MAIN" />
 

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

@@ -1,119 +0,0 @@
-package com.example.llama
-
-import android.app.DownloadManager
-import android.net.Uri
-import android.util.Log
-import androidx.compose.material3.Button
-import androidx.compose.material3.Text
-import androidx.compose.runtime.Composable
-import androidx.compose.runtime.getValue
-import androidx.compose.runtime.mutableDoubleStateOf
-import androidx.compose.runtime.mutableStateOf
-import androidx.compose.runtime.remember
-import androidx.compose.runtime.rememberCoroutineScope
-import androidx.compose.runtime.setValue
-import androidx.core.database.getLongOrNull
-import androidx.core.net.toUri
-import kotlinx.coroutines.delay
-import kotlinx.coroutines.launch
-import java.io.File
-
-data class Downloadable(val name: String, val source: Uri, val destination: File) {
-    companion object {
-        @JvmStatic
-        private val tag: String? = this::class.qualifiedName
-
-        sealed interface State
-        data object Ready: State
-        data class Downloading(val id: Long): State
-        data class Downloaded(val downloadable: Downloadable): State
-        data class Error(val message: String): State
-
-        @JvmStatic
-        @Composable
-        fun Button(viewModel: MainViewModel, dm: DownloadManager, item: Downloadable) {
-            var status: State by remember {
-                mutableStateOf(
-                    if (item.destination.exists()) Downloaded(item)
-                    else Ready
-                )
-            }
-            var progress by remember { mutableDoubleStateOf(0.0) }
-
-            val coroutineScope = rememberCoroutineScope()
-
-            suspend fun waitForDownload(result: Downloading, item: Downloadable): State {
-                while (true) {
-                    val cursor = dm.query(DownloadManager.Query().setFilterById(result.id))
-
-                    if (cursor == null) {
-                        Log.e(tag, "dm.query() returned null")
-                        return Error("dm.query() returned null")
-                    }
-
-                    if (!cursor.moveToFirst() || cursor.count < 1) {
-                        cursor.close()
-                        Log.i(tag, "cursor.moveToFirst() returned false or cursor.count < 1, download canceled?")
-                        return Ready
-                    }
-
-                    val pix = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR)
-                    val tix = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES)
-                    val sofar = cursor.getLongOrNull(pix) ?: 0
-                    val total = cursor.getLongOrNull(tix) ?: 1
-                    cursor.close()
-
-                    if (sofar == total) {
-                        return Downloaded(item)
-                    }
-
-                    progress = (sofar * 1.0) / total
-
-                    delay(1000L)
-                }
-            }
-
-            fun onClick() {
-                when (val s = status) {
-                    is Downloaded -> {
-                        viewModel.load(item.destination.path)
-                    }
-
-                    is Downloading -> {
-                        coroutineScope.launch {
-                            status = waitForDownload(s, item)
-                        }
-                    }
-
-                    else -> {
-                        item.destination.delete()
-
-                        val request = DownloadManager.Request(item.source).apply {
-                            setTitle("Downloading model")
-                            setDescription("Downloading model: ${item.name}")
-                            setAllowedNetworkTypes(DownloadManager.Request.NETWORK_WIFI)
-                            setDestinationUri(item.destination.toUri())
-                        }
-
-                        viewModel.log("Saving ${item.name} to ${item.destination.path}")
-                        Log.i(tag, "Saving ${item.name} to ${item.destination.path}")
-
-                        val id = dm.enqueue(request)
-                        status = Downloading(id)
-                        onClick()
-                    }
-                }
-            }
-
-            Button(onClick = { onClick() }, enabled = status !is Downloading) {
-                when (status) {
-                    is Downloading -> Text(text = "Downloading ${(progress * 100).toInt()}%")
-                    is Downloaded -> Text("Load ${item.name}")
-                    is Ready -> Text("Download ${item.name}")
-                    is Error -> Text("Download ${item.name}")
-                }
-            }
-        }
-
-    }
-}

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

@@ -1,154 +1,257 @@
 package com.example.llama
 
-import android.app.ActivityManager
-import android.app.DownloadManager
-import android.content.ClipData
-import android.content.ClipboardManager
 import android.net.Uri
 import android.os.Bundle
-import android.os.StrictMode
-import android.os.StrictMode.VmPolicy
-import android.text.format.Formatter
-import androidx.activity.ComponentActivity
-import androidx.activity.compose.setContent
-import androidx.activity.viewModels
-import androidx.compose.foundation.layout.Box
-import androidx.compose.foundation.layout.Column
-import androidx.compose.foundation.layout.Row
-import androidx.compose.foundation.layout.fillMaxSize
-import androidx.compose.foundation.layout.padding
-import androidx.compose.foundation.lazy.LazyColumn
-import androidx.compose.foundation.lazy.items
-import androidx.compose.foundation.lazy.rememberLazyListState
-import androidx.compose.material3.Button
-import androidx.compose.material3.LocalContentColor
-import androidx.compose.material3.MaterialTheme
-import androidx.compose.material3.OutlinedTextField
-import androidx.compose.material3.Surface
-import androidx.compose.material3.Text
-import androidx.compose.runtime.Composable
-import androidx.compose.ui.Modifier
-import androidx.compose.ui.unit.dp
-import androidx.core.content.getSystemService
-import com.example.llama.ui.theme.LlamaAndroidTheme
+import android.util.Log
+import android.widget.EditText
+import android.widget.TextView
+import android.widget.Toast
+import androidx.activity.enableEdgeToEdge
+import androidx.activity.result.contract.ActivityResultContracts
+import androidx.appcompat.app.AppCompatActivity
+import androidx.lifecycle.lifecycleScope
+import androidx.recyclerview.widget.LinearLayoutManager
+import androidx.recyclerview.widget.RecyclerView
+import com.arm.aichat.AiChat
+import com.arm.aichat.InferenceEngine
+import com.arm.aichat.gguf.GgufMetadata
+import com.arm.aichat.gguf.GgufMetadataReader
+import com.google.android.material.floatingactionbutton.FloatingActionButton
+import kotlinx.coroutines.Dispatchers
+import kotlinx.coroutines.flow.onCompletion
+import kotlinx.coroutines.launch
+import kotlinx.coroutines.withContext
 import java.io.File
+import java.io.FileOutputStream
+import java.io.InputStream
+import java.util.UUID
 
-class MainActivity(
-    activityManager: ActivityManager? = null,
-    downloadManager: DownloadManager? = null,
-    clipboardManager: ClipboardManager? = null,
-): ComponentActivity() {
-    private val tag: String? = this::class.simpleName
+class MainActivity : AppCompatActivity() {
 
-    private val activityManager by lazy { activityManager ?: getSystemService<ActivityManager>()!! }
-    private val downloadManager by lazy { downloadManager ?: getSystemService<DownloadManager>()!! }
-    private val clipboardManager by lazy { clipboardManager ?: getSystemService<ClipboardManager>()!! }
+    // Android views
+    private lateinit var ggufTv: TextView
+    private lateinit var messagesRv: RecyclerView
+    private lateinit var userInputEt: EditText
+    private lateinit var userActionFab: FloatingActionButton
 
-    private val viewModel: MainViewModel by viewModels()
+    // Arm AI Chat inference engine
+    private lateinit var engine: InferenceEngine
 
-    // Get a MemoryInfo object for the device's current memory status.
-    private fun availableMemory(): ActivityManager.MemoryInfo {
-        return ActivityManager.MemoryInfo().also { memoryInfo ->
-            activityManager.getMemoryInfo(memoryInfo)
-        }
-    }
+    // Conversation states
+    private var isModelReady = false
+    private val messages = mutableListOf<Message>()
+    private val lastAssistantMsg = StringBuilder()
+    private val messageAdapter = MessageAdapter(messages)
 
     override fun onCreate(savedInstanceState: Bundle?) {
         super.onCreate(savedInstanceState)
+        enableEdgeToEdge()
+        setContentView(R.layout.activity_main)
 
-        StrictMode.setVmPolicy(
-            VmPolicy.Builder(StrictMode.getVmPolicy())
-                .detectLeakedClosableObjects()
-                .build()
-        )
+        // Find views
+        ggufTv = findViewById(R.id.gguf)
+        messagesRv = findViewById(R.id.messages)
+        messagesRv.layoutManager = LinearLayoutManager(this)
+        messagesRv.adapter = messageAdapter
+        userInputEt = findViewById(R.id.user_input)
+        userActionFab = findViewById(R.id.fab)
 
-        val free = Formatter.formatFileSize(this, availableMemory().availMem)
-        val total = Formatter.formatFileSize(this, availableMemory().totalMem)
-
-        viewModel.log("Current memory: $free / $total")
-        viewModel.log("Downloads directory: ${getExternalFilesDir(null)}")
-
-        val extFilesDir = getExternalFilesDir(null)
-
-        val models = listOf(
-            Downloadable(
-                "Phi-2 7B (Q4_0, 1.6 GiB)",
-                Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true"),
-                File(extFilesDir, "phi-2-q4_0.gguf"),
-            ),
-            Downloadable(
-                "TinyLlama 1.1B (f16, 2.2 GiB)",
-                Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true"),
-                File(extFilesDir, "tinyllama-1.1-f16.gguf"),
-            ),
-            Downloadable(
-                "Phi 2 DPO (Q3_K_M, 1.48 GiB)",
-                Uri.parse("https://huggingface.co/TheBloke/phi-2-dpo-GGUF/resolve/main/phi-2-dpo.Q3_K_M.gguf?download=true"),
-                File(extFilesDir, "phi-2-dpo.Q3_K_M.gguf")
-            ),
-        )
-
-        setContent {
-            LlamaAndroidTheme {
-                // A surface container using the 'background' color from the theme
-                Surface(
-                    modifier = Modifier.fillMaxSize(),
-                    color = MaterialTheme.colorScheme.background
-                ) {
-                    MainCompose(
-                        viewModel,
-                        clipboardManager,
-                        downloadManager,
-                        models,
-                    )
-                }
+        // Arm AI Chat initialization
+        lifecycleScope.launch(Dispatchers.Default) {
+            engine = AiChat.getInferenceEngine(applicationContext)
+        }
 
+        // Upon CTA button tapped
+        userActionFab.setOnClickListener {
+            if (isModelReady) {
+                // If model is ready, validate input and send to engine
+                handleUserInput()
+            } else {
+                // Otherwise, prompt user to select a GGUF metadata on the device
+                getContent.launch(arrayOf("*/*"))
             }
         }
     }
-}
 
-@Composable
-fun MainCompose(
-    viewModel: MainViewModel,
-    clipboard: ClipboardManager,
-    dm: DownloadManager,
-    models: List<Downloadable>
-) {
-    Column {
-        val scrollState = rememberLazyListState()
+    private val getContent = registerForActivityResult(
+        ActivityResultContracts.OpenDocument()
+    ) { uri ->
+        Log.i(TAG, "Selected file uri:\n $uri")
+        uri?.let { handleSelectedModel(it) }
+    }
 
-        Box(modifier = Modifier.weight(1f)) {
-            LazyColumn(state = scrollState) {
-                items(viewModel.messages) {
-                    Text(
-                        it,
-                        style = MaterialTheme.typography.bodyLarge.copy(color = LocalContentColor.current),
-                        modifier = Modifier.padding(16.dp)
-                    )
+    /**
+     * Handles the file Uri from [getContent] result
+     */
+    private fun handleSelectedModel(uri: Uri) {
+        // Update UI states
+        userActionFab.isEnabled = false
+        userInputEt.hint = "Parsing GGUF..."
+        ggufTv.text = "Parsing metadata from selected file \n$uri"
+
+        lifecycleScope.launch(Dispatchers.IO) {
+            // Parse GGUF metadata
+            Log.i(TAG, "Parsing GGUF metadata...")
+            contentResolver.openInputStream(uri)?.use {
+                GgufMetadataReader.create().readStructuredMetadata(it)
+            }?.let { metadata ->
+                // Update UI to show GGUF metadata to user
+                Log.i(TAG, "GGUF parsed: \n$metadata")
+                withContext(Dispatchers.Main) {
+                    ggufTv.text = metadata.toString()
                 }
-            }
-        }
-        OutlinedTextField(
-            value = viewModel.message,
-            onValueChange = { viewModel.updateMessage(it) },
-            label = { Text("Message") },
-        )
-        Row {
-            Button({ viewModel.send() }) { Text("Send") }
-            Button({ viewModel.bench(8, 4, 1) }) { Text("Bench") }
-            Button({ viewModel.clear() }) { Text("Clear") }
-            Button({
-                viewModel.messages.joinToString("\n").let {
-                    clipboard.setPrimaryClip(ClipData.newPlainText("", it))
-                }
-            }) { Text("Copy") }
-        }
 
-        Column {
-            for (model in models) {
-                Downloadable.Button(viewModel, dm, model)
+                // Ensure the model file is available
+                val modelName = metadata.filename() + FILE_EXTENSION_GGUF
+                contentResolver.openInputStream(uri)?.use { input ->
+                    ensureModelFile(modelName, input)
+                }?.let { modelFile ->
+                    loadModel(modelName, modelFile)
+
+                    withContext(Dispatchers.Main) {
+                        isModelReady = true
+                        userInputEt.hint = "Type and send a message!"
+                        userInputEt.isEnabled = true
+                        userActionFab.setImageResource(R.drawable.outline_send_24)
+                        userActionFab.isEnabled = true
+                    }
+                }
             }
         }
     }
+
+    /**
+     * Prepare the model file within app's private storage
+     */
+    private suspend fun ensureModelFile(modelName: String, input: InputStream) =
+        withContext(Dispatchers.IO) {
+            File(ensureModelsDirectory(), modelName).also { file ->
+                // Copy the file into local storage if not yet done
+                if (!file.exists()) {
+                    Log.i(TAG, "Start copying file to $modelName")
+                    withContext(Dispatchers.Main) {
+                        userInputEt.hint = "Copying file..."
+                    }
+
+                    FileOutputStream(file).use { input.copyTo(it) }
+                    Log.i(TAG, "Finished copying file to $modelName")
+                } else {
+                    Log.i(TAG, "File already exists $modelName")
+                }
+            }
+        }
+
+    /**
+     * Load the model file from the app private storage
+     */
+    private suspend fun loadModel(modelName: String, modelFile: File) =
+        withContext(Dispatchers.IO) {
+            Log.i(TAG, "Loading model $modelName")
+            withContext(Dispatchers.Main) {
+                userInputEt.hint = "Loading model..."
+            }
+            engine.loadModel(modelFile.path)
+        }
+
+    /**
+     * Validate and send the user message into [InferenceEngine]
+     */
+    private fun handleUserInput() {
+        userInputEt.text.toString().also { userSsg ->
+            if (userSsg.isEmpty()) {
+                Toast.makeText(this, "Input message is empty!", Toast.LENGTH_SHORT).show()
+            } else {
+                userInputEt.text = null
+                userActionFab.isEnabled = false
+
+                // Update message states
+                messages.add(Message(UUID.randomUUID().toString(), userSsg, true))
+                lastAssistantMsg.clear()
+                messages.add(Message(UUID.randomUUID().toString(), lastAssistantMsg.toString(), false))
+
+                lifecycleScope.launch(Dispatchers.Default) {
+                    engine.sendUserPrompt(userSsg)
+                        .onCompletion {
+                            withContext(Dispatchers.Main) {
+                                userActionFab.isEnabled = true
+                            }
+                        }.collect { token ->
+                            val messageCount = messages.size
+                            check(messageCount > 0 && !messages[messageCount - 1].isUser)
+
+                            messages.removeAt(messageCount - 1).copy(
+                                content = lastAssistantMsg.append(token).toString()
+                            ).let { messages.add(it) }
+
+                            withContext(Dispatchers.Main) {
+                                messageAdapter.notifyItemChanged(messages.size - 1)
+                            }
+                        }
+                }
+            }
+        }
+    }
+
+    /**
+     * Run a benchmark with the model file
+     */
+    private suspend fun runBenchmark(modelName: String, modelFile: File) =
+        withContext(Dispatchers.Default) {
+            Log.i(TAG, "Starts benchmarking $modelName")
+            withContext(Dispatchers.Main) {
+                userInputEt.hint = "Running benchmark..."
+            }
+            engine.bench(
+                pp=BENCH_PROMPT_PROCESSING_TOKENS,
+                tg=BENCH_TOKEN_GENERATION_TOKENS,
+                pl=BENCH_SEQUENCE,
+                nr=BENCH_REPETITION
+            ).let { result ->
+                messages.add(Message(UUID.randomUUID().toString(), result, false))
+                withContext(Dispatchers.Main) {
+                    messageAdapter.notifyItemChanged(messages.size - 1)
+                }
+            }
+        }
+
+    /**
+     * Create the `models` directory if not exist.
+     */
+    private fun ensureModelsDirectory() =
+        File(filesDir, DIRECTORY_MODELS).also {
+            if (it.exists() && !it.isDirectory) { it.delete() }
+            if (!it.exists()) { it.mkdir() }
+        }
+
+    companion object {
+        private val TAG = MainActivity::class.java.simpleName
+
+        private const val DIRECTORY_MODELS = "models"
+        private const val FILE_EXTENSION_GGUF = ".gguf"
+
+        private const val BENCH_PROMPT_PROCESSING_TOKENS = 512
+        private const val BENCH_TOKEN_GENERATION_TOKENS = 128
+        private const val BENCH_SEQUENCE = 1
+        private const val BENCH_REPETITION = 3
+    }
+}
+
+fun GgufMetadata.filename() = when {
+    basic.name != null -> {
+        basic.name?.let { name ->
+            basic.sizeLabel?.let { size ->
+                "$name-$size"
+            } ?: name
+        }
+    }
+    architecture?.architecture != null -> {
+        architecture?.architecture?.let { arch ->
+            basic.uuid?.let { uuid ->
+                "$arch-$uuid"
+            } ?: "$arch-${System.currentTimeMillis()}"
+        }
+    }
+    else -> {
+        "model-${System.currentTimeMillis().toHexString()}"
+    }
 }

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

@@ -1,105 +0,0 @@
-package com.example.llama
-
-import android.llama.cpp.LLamaAndroid
-import android.util.Log
-import androidx.compose.runtime.getValue
-import androidx.compose.runtime.mutableStateOf
-import androidx.compose.runtime.setValue
-import androidx.lifecycle.ViewModel
-import androidx.lifecycle.viewModelScope
-import kotlinx.coroutines.flow.catch
-import kotlinx.coroutines.launch
-
-class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() {
-    companion object {
-        @JvmStatic
-        private val NanosPerSecond = 1_000_000_000.0
-    }
-
-    private val tag: String? = this::class.simpleName
-
-    var messages by mutableStateOf(listOf("Initializing..."))
-        private set
-
-    var message by mutableStateOf("")
-        private set
-
-    override fun onCleared() {
-        super.onCleared()
-
-        viewModelScope.launch {
-            try {
-                llamaAndroid.unload()
-            } catch (exc: IllegalStateException) {
-                messages += exc.message!!
-            }
-        }
-    }
-
-    fun send() {
-        val text = message
-        message = ""
-
-        // Add to messages console.
-        messages += text
-        messages += ""
-
-        viewModelScope.launch {
-            llamaAndroid.send(text)
-                .catch {
-                    Log.e(tag, "send() failed", it)
-                    messages += it.message!!
-                }
-                .collect { messages = messages.dropLast(1) + (messages.last() + it) }
-        }
-    }
-
-    fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) {
-        viewModelScope.launch {
-            try {
-                val start = System.nanoTime()
-                val warmupResult = llamaAndroid.bench(pp, tg, pl, nr)
-                val end = System.nanoTime()
-
-                messages += warmupResult
-
-                val warmup = (end - start).toDouble() / NanosPerSecond
-                messages += "Warm up time: $warmup seconds, please wait..."
-
-                if (warmup > 5.0) {
-                    messages += "Warm up took too long, aborting benchmark"
-                    return@launch
-                }
-
-                messages += llamaAndroid.bench(512, 128, 1, 3)
-            } catch (exc: IllegalStateException) {
-                Log.e(tag, "bench() failed", exc)
-                messages += exc.message!!
-            }
-        }
-    }
-
-    fun load(pathToModel: String) {
-        viewModelScope.launch {
-            try {
-                llamaAndroid.load(pathToModel)
-                messages += "Loaded $pathToModel"
-            } catch (exc: IllegalStateException) {
-                Log.e(tag, "load() failed", exc)
-                messages += exc.message!!
-            }
-        }
-    }
-
-    fun updateMessage(newMessage: String) {
-        message = newMessage
-    }
-
-    fun clear() {
-        messages = listOf()
-    }
-
-    fun log(message: String) {
-        messages += message
-    }
-}

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

@@ -0,0 +1,51 @@
+package com.example.llama
+
+import android.view.LayoutInflater
+import android.view.View
+import android.view.ViewGroup
+import android.widget.TextView
+import androidx.recyclerview.widget.RecyclerView
+
+data class Message(
+    val id: String,
+    val content: String,
+    val isUser: Boolean
+)
+
+class MessageAdapter(
+    private val messages: List<Message>
+) : RecyclerView.Adapter<RecyclerView.ViewHolder>() {
+
+    companion object {
+        private const val VIEW_TYPE_USER = 1
+        private const val VIEW_TYPE_ASSISTANT = 2
+    }
+
+    override fun getItemViewType(position: Int): Int {
+        return if (messages[position].isUser) VIEW_TYPE_USER else VIEW_TYPE_ASSISTANT
+    }
+
+    override fun onCreateViewHolder(parent: ViewGroup, viewType: Int): RecyclerView.ViewHolder {
+        val layoutInflater = LayoutInflater.from(parent.context)
+        return if (viewType == VIEW_TYPE_USER) {
+            val view = layoutInflater.inflate(R.layout.item_message_user, parent, false)
+            UserMessageViewHolder(view)
+        } else {
+            val view = layoutInflater.inflate(R.layout.item_message_assistant, parent, false)
+            AssistantMessageViewHolder(view)
+        }
+    }
+
+    override fun onBindViewHolder(holder: RecyclerView.ViewHolder, position: Int) {
+        val message = messages[position]
+        if (holder is UserMessageViewHolder || holder is AssistantMessageViewHolder) {
+            val textView = holder.itemView.findViewById<TextView>(R.id.msg_content)
+            textView.text = message.content
+        }
+    }
+
+    override fun getItemCount(): Int = messages.size
+
+    class UserMessageViewHolder(view: View) : RecyclerView.ViewHolder(view)
+    class AssistantMessageViewHolder(view: View) : RecyclerView.ViewHolder(view)
+}

examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt

@@ -1,11 +0,0 @@
-package com.example.llama.ui.theme
-
-import androidx.compose.ui.graphics.Color
-
-val Purple80 = Color(0xFFD0BCFF)
-val PurpleGrey80 = Color(0xFFCCC2DC)
-val Pink80 = Color(0xFFEFB8C8)
-
-val Purple40 = Color(0xFF6650a4)
-val PurpleGrey40 = Color(0xFF625b71)
-val Pink40 = Color(0xFF7D5260)

examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt

@@ -1,70 +0,0 @@
-package com.example.llama.ui.theme
-
-import android.app.Activity
-import android.os.Build
-import androidx.compose.foundation.isSystemInDarkTheme
-import androidx.compose.material3.MaterialTheme
-import androidx.compose.material3.darkColorScheme
-import androidx.compose.material3.dynamicDarkColorScheme
-import androidx.compose.material3.dynamicLightColorScheme
-import androidx.compose.material3.lightColorScheme
-import androidx.compose.runtime.Composable
-import androidx.compose.runtime.SideEffect
-import androidx.compose.ui.graphics.toArgb
-import androidx.compose.ui.platform.LocalContext
-import androidx.compose.ui.platform.LocalView
-import androidx.core.view.WindowCompat
-
-private val DarkColorScheme = darkColorScheme(
-    primary = Purple80,
-    secondary = PurpleGrey80,
-    tertiary = Pink80
-)
-
-private val LightColorScheme = lightColorScheme(
-    primary = Purple40,
-    secondary = PurpleGrey40,
-    tertiary = Pink40
-
-    /* Other default colors to override
-    background = Color(0xFFFFFBFE),
-    surface = Color(0xFFFFFBFE),
-    onPrimary = Color.White,
-    onSecondary = Color.White,
-    onTertiary = Color.White,
-    onBackground = Color(0xFF1C1B1F),
-    onSurface = Color(0xFF1C1B1F),
-    */
-)
-
-@Composable
-fun LlamaAndroidTheme(
-    darkTheme: Boolean = isSystemInDarkTheme(),
-    // Dynamic color is available on Android 12+
-    dynamicColor: Boolean = true,
-    content: @Composable () -> Unit
-) {
-    val colorScheme = when {
-        dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> {
-            val context = LocalContext.current
-            if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context)
-        }
-
-        darkTheme -> DarkColorScheme
-        else -> LightColorScheme
-    }
-    val view = LocalView.current
-    if (!view.isInEditMode) {
-        SideEffect {
-            val window = (view.context as Activity).window
-            window.statusBarColor = colorScheme.primary.toArgb()
-            WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme
-        }
-    }
-
-    MaterialTheme(
-        colorScheme = colorScheme,
-        typography = Typography,
-        content = content
-    )
-}

examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt

@@ -1,34 +0,0 @@
-package com.example.llama.ui.theme
-
-import androidx.compose.material3.Typography
-import androidx.compose.ui.text.TextStyle
-import androidx.compose.ui.text.font.FontFamily
-import androidx.compose.ui.text.font.FontWeight
-import androidx.compose.ui.unit.sp
-
-// Set of Material typography styles to start with
-val Typography = Typography(
-    bodyLarge = TextStyle(
-        fontFamily = FontFamily.Default,
-        fontWeight = FontWeight.Normal,
-        fontSize = 16.sp,
-        lineHeight = 24.sp,
-        letterSpacing = 0.5.sp
-    )
-    /* Other default text styles to override
-    titleLarge = TextStyle(
-        fontFamily = FontFamily.Default,
-        fontWeight = FontWeight.Normal,
-        fontSize = 22.sp,
-        lineHeight = 28.sp,
-        letterSpacing = 0.sp
-    ),
-    labelSmall = TextStyle(
-        fontFamily = FontFamily.Default,
-        fontWeight = FontWeight.Medium,
-        fontSize = 11.sp,
-        lineHeight = 16.sp,
-        letterSpacing = 0.5.sp
-    )
-    */
-)

examples/llama.android/app/src/main/res/drawable/bg_assistant_message.xml

@@ -0,0 +1,4 @@
+<shape xmlns:android="http://schemas.android.com/apk/res/android" android:shape="rectangle">
+    <solid android:color="#E5E5EA" />
+    <corners android:radius="16dp" />
+</shape>

examples/llama.android/app/src/main/res/drawable/bg_user_message.xml

@@ -0,0 +1,4 @@
+<shape xmlns:android="http://schemas.android.com/apk/res/android" android:shape="rectangle">
+    <solid android:color="#4285F4" />
+    <corners android:radius="16dp" />
+</shape>

examples/llama.android/app/src/main/res/drawable/outline_folder_open_24.xml

@@ -0,0 +1,10 @@
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="24dp"
+    android:height="24dp"
+    android:viewportWidth="24"
+    android:viewportHeight="24"
+    android:tint="?attr/colorControlNormal">
+  <path
+      android:fillColor="@android:color/white"
+      android:pathData="M20,6h-8l-2,-2L4,4c-1.1,0 -1.99,0.9 -1.99,2L2,18c0,1.1 0.9,2 2,2h16c1.1,0 2,-0.9 2,-2L22,8c0,-1.1 -0.9,-2 -2,-2zM20,18L4,18L4,8h16v10z"/>
+</vector>

examples/llama.android/app/src/main/res/drawable/outline_send_24.xml

@@ -0,0 +1,11 @@
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="24dp"
+    android:height="24dp"
+    android:viewportWidth="24"
+    android:viewportHeight="24"
+    android:tint="?attr/colorControlNormal"
+    android:autoMirrored="true">
+  <path
+      android:fillColor="@android:color/white"
+      android:pathData="M4.01,6.03l7.51,3.22 -7.52,-1 0.01,-2.22m7.5,8.72L4,17.97v-2.22l7.51,-1M2.01,3L2,10l15,2 -15,2 0.01,7L23,12 2.01,3z"/>
+</vector>

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

@@ -0,0 +1,76 @@
+<?xml version="1.0" encoding="utf-8"?>
+<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:app="http://schemas.android.com/apk/res-auto"
+        xmlns:tools="http://schemas.android.com/tools"
+        android:id="@+id/main"
+        android:layout_height="match_parent"
+        android:layout_width="match_parent">
+
+    <LinearLayout
+        android:fitsSystemWindows="true"
+        android:layout_width="match_parent"
+        android:layout_height="match_parent"
+        android:orientation="vertical"
+        tools:context=".MainActivity">
+
+        <FrameLayout
+            android:layout_width="match_parent"
+            android:layout_height="0dp"
+            android:layout_weight="1">
+
+            <ScrollView
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content"
+                android:fadeScrollbars="false">
+
+                <TextView
+                    android:id="@+id/gguf"
+                    android:layout_width="match_parent"
+                    android:layout_height="wrap_content"
+                    android:layout_margin="16dp"
+                    android:text="Selected GGUF model's metadata will show here."
+                    style="@style/TextAppearance.MaterialComponents.Body2"
+                    android:maxLines="100" />
+
+            </ScrollView>
+
+        </FrameLayout>
+
+        <androidx.recyclerview.widget.RecyclerView
+            android:id="@+id/messages"
+            android:layout_width="match_parent"
+            android:layout_height="0dp"
+            android:layout_weight="4"
+            android:padding="16dp"
+            android:fadeScrollbars="false"
+            app:reverseLayout="true"
+            tools:listitem="@layout/item_message_assistant"/>
+
+        <LinearLayout
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+            android:orientation="horizontal">
+
+            <EditText
+                android:id="@+id/user_input"
+                android:enabled="false"
+                android:layout_width="0dp"
+                android:layout_weight="1"
+                android:layout_height="match_parent"
+                android:padding="8dp"
+                style="@style/TextAppearance.MaterialComponents.Body2"
+                android:hint="Please first pick a GGUF model file to import." />
+
+            <com.google.android.material.floatingactionbutton.FloatingActionButton
+                android:id="@+id/fab"
+                android:enabled="true"
+                style="@style/Widget.Material3.FloatingActionButton.Primary"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_margin="8dp"
+                android:src="@drawable/outline_folder_open_24" />
+
+        </LinearLayout>
+
+    </LinearLayout>
+</androidx.constraintlayout.widget.ConstraintLayout>

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

@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="utf-8"?>
+<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    android:layout_width="match_parent"
+    android:layout_height="wrap_content"
+    android:padding="8dp"
+    android:gravity="start">
+
+    <TextView
+        android:id="@+id/msg_content"
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:background="@drawable/bg_assistant_message"
+        android:padding="12dp"
+        android:textColor="@android:color/black" />
+</LinearLayout>

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

@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="utf-8"?>
+<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    android:layout_width="match_parent"
+    android:layout_height="wrap_content"
+    android:padding="8dp"
+    android:gravity="end">
+
+    <TextView
+        android:id="@+id/msg_content"
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:background="@drawable/bg_user_message"
+        android:padding="12dp"
+        android:textColor="@android:color/white" />
+</LinearLayout>

examples/llama.android/app/src/main/res/values/strings.xml

@@ -1,3 +1,3 @@
 <resources>
-    <string name="app_name">LlamaAndroid</string>
+    <string name="app_name">AI Chat basic sample</string>
 </resources>

examples/llama.android/app/src/main/res/values/themes.xml

@@ -1,5 +1,10 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
 
-    <style name="Theme.LlamaAndroid" parent="android:Theme.Material.Light.NoActionBar" />
+    <style name="Base.Theme.AiChatSample" parent="Theme.Material3.DayNight.NoActionBar">
+        <!-- Customize your light theme here. -->
+        <!-- <item name="colorPrimary">@color/my_light_primary</item> -->
+    </style>
+
+    <style name="Theme.AiChatSample" parent="Base.Theme.AiChatSample" />
 </resources>

examples/llama.android/build.gradle.kts

@@ -1,6 +1,6 @@
 // Top-level build file where you can add configuration options common to all sub-projects/modules.
 plugins {
-    id("com.android.application") version "8.2.0" apply false
-    id("org.jetbrains.kotlin.android") version "1.9.0" apply false
-    id("com.android.library") version "8.2.0" apply false
+    alias(libs.plugins.android.application) apply false
+    alias(libs.plugins.android.library) apply false
+    alias(libs.plugins.jetbrains.kotlin.android) apply false
 }

examples/llama.android/gradle.properties

@@ -21,3 +21,4 @@ kotlin.code.style=official
 # resources declared in the library itself and none from the library's dependencies,
 # thereby reducing the size of the R class for that library
 android.nonTransitiveRClass=true
+android.native.buildOutput=verbose

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

@@ -0,0 +1,53 @@
+[versions]
+
+# Plugins
+agp = "8.13.0"
+kotlin = "2.2.20"
+
+# AndroidX
+activity = "1.11.0"
+appcompat = "1.7.1"
+core-ktx = "1.17.0"
+constraint-layout = "2.2.1"
+datastore-preferences = "1.1.7"
+
+# Material
+material = "1.13.0"
+
+# Testing
+espresso-core = "3.7.0"
+androidx-junit = "1.3.0"
+junit = "4.13.2"
+
+
+[plugins]
+android-application = { id = "com.android.application", version.ref = "agp" }
+android-library = { id = "com.android.library", version.ref = "agp" }
+jetbrains-kotlin-android = { id = "org.jetbrains.kotlin.android", version.ref = "kotlin" }
+
+
+[libraries]
+
+# AndroidX
+androidx-activity = { group = "androidx.activity", name = "activity", version.ref = "activity" }
+androidx-appcompat = { group = "androidx.appcompat", name = "appcompat", version.ref = "appcompat" }
+androidx-constraintlayout = { group = "androidx.constraintlayout", name = "constraintlayout", version.ref = "constraint-layout" }
+androidx-core-ktx = { group = "androidx.core", name = "core-ktx", version.ref = "core-ktx" }
+androidx-datastore-preferences = { group = "androidx.datastore", name = "datastore-preferences", version.ref = "datastore-preferences" }
+
+#Material
+material = { group = "com.google.android.material", name = "material", version.ref = "material" }
+
+# Testing
+androidx-espresso-core = { group = "androidx.test.espresso", name = "espresso-core", version.ref = "espresso-core" }
+androidx-junit = { group = "androidx.test.ext", name = "junit", version.ref = "androidx-junit" }
+junit = { group = "junit", name = "junit", version.ref = "junit" }
+
+[bundles]
+androidx = [
+    "androidx-activity",
+    "androidx-appcompat",
+    "androidx-constraintlayout",
+    "androidx-core-ktx",
+    "androidx-datastore-preferences",
+]

examples/llama.android/gradle/wrapper/gradle-wrapper.properties

@@ -1,6 +1,6 @@
-#Thu Dec 21 14:31:09 AEDT 2023
+#Tue Apr 01 11:15:06 PDT 2025
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-8.2-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.14.3-bin.zip
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists

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

@@ -0,0 +1,78 @@
+plugins {
+    alias(libs.plugins.android.library)
+    alias(libs.plugins.jetbrains.kotlin.android)
+}
+
+android {
+    namespace = "com.arm.aichat"
+    compileSdk = 36
+
+    ndkVersion = "29.0.13113456"
+
+    defaultConfig {
+        minSdk = 33
+
+        testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
+        consumerProguardFiles("consumer-rules.pro")
+
+        ndk {
+             abiFilters += listOf("arm64-v8a", "x86_64")
+        }
+        externalNativeBuild {
+            cmake {
+                arguments += "-DCMAKE_BUILD_TYPE=Release"
+                arguments += "-DCMAKE_MESSAGE_LOG_LEVEL=DEBUG"
+                arguments += "-DCMAKE_VERBOSE_MAKEFILE=ON"
+
+                arguments += "-DBUILD_SHARED_LIBS=ON"
+                arguments += "-DLLAMA_BUILD_COMMON=ON"
+                arguments += "-DLLAMA_CURL=OFF"
+
+                arguments += "-DGGML_NATIVE=OFF"
+                arguments += "-DGGML_BACKEND_DL=ON"
+                arguments += "-DGGML_CPU_ALL_VARIANTS=ON"
+                arguments += "-DGGML_LLAMAFILE=OFF"
+            }
+        }
+        aarMetadata {
+            minCompileSdk = 35
+        }
+    }
+    externalNativeBuild {
+        cmake {
+            path("src/main/cpp/CMakeLists.txt")
+            version = "3.31.6"
+        }
+    }
+    compileOptions {
+        sourceCompatibility = JavaVersion.VERSION_17
+        targetCompatibility = JavaVersion.VERSION_17
+    }
+    kotlin {
+        jvmToolchain(17)
+
+        compileOptions {
+            targetCompatibility = JavaVersion.VERSION_17
+        }
+    }
+
+    packaging {
+        resources {
+            excludes += "/META-INF/{AL2.0,LGPL2.1}"
+        }
+    }
+
+    publishing {
+        singleVariant("release") {
+            withJavadocJar()
+        }
+    }
+}
+
+dependencies {
+    implementation(libs.androidx.core.ktx)
+    implementation(libs.androidx.datastore.preferences)
+
+    testImplementation(libs.junit)
+    androidTestImplementation(libs.androidx.junit)
+}

examples/llama.android/lib/consumer-rules.pro

@@ -0,0 +1,8 @@
+-keep class com.arm.aichat.* { *; }
+-keep class com.arm.aichat.gguf.* { *; }
+
+-keepclasseswithmembernames class * {
+    native <methods>;
+}
+
+-keep class kotlin.Metadata { *; }

examples/llama.android/lib/src/main/cpp/CMakeLists.txt

@@ -0,0 +1,56 @@
+cmake_minimum_required(VERSION 3.31.6)
+
+project("ai-chat" VERSION 1.0.0 LANGUAGES C CXX)
+
+set(CMAKE_C_STANDARD 11)
+set(CMAKE_C_STANDARD_REQUIRED true)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED true)
+
+set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}"   CACHE STRING "" FORCE)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}" CACHE STRING "" FORCE)
+
+# --------------------------------------------------------------------------
+# AI Chat library
+# --------------------------------------------------------------------------
+
+if(DEFINED ANDROID_ABI)
+    message(STATUS "Detected Android ABI: ${ANDROID_ABI}")
+    if(ANDROID_ABI STREQUAL "arm64-v8a")
+        set(GGML_SYSTEM_ARCH "ARM")
+        set(GGML_CPU_KLEIDIAI ON)
+        set(GGML_OPENMP ON)
+    elseif(ANDROID_ABI STREQUAL "x86_64")
+        set(GGML_SYSTEM_ARCH "x86")
+        set(GGML_CPU_KLEIDIAI OFF)
+        set(GGML_OPENMP OFF)
+    else()
+        message(FATAL_ERROR "Unsupported ABI: ${ANDROID_ABI}")
+    endif()
+endif()
+
+set(LLAMA_SRC ${CMAKE_CURRENT_LIST_DIR}/../../../../../../)
+add_subdirectory(${LLAMA_SRC} build-llama)
+
+add_library(${CMAKE_PROJECT_NAME} SHARED
+        ai_chat.cpp)
+
+target_compile_definitions(${CMAKE_PROJECT_NAME} PRIVATE
+        GGML_SYSTEM_ARCH=${GGML_SYSTEM_ARCH}
+        GGML_CPU_KLEIDIAI=$<BOOL:${GGML_CPU_KLEIDIAI}>
+        GGML_OPENMP=$<BOOL:${GGML_OPENMP}>
+)
+
+target_include_directories(${CMAKE_PROJECT_NAME} PRIVATE
+        ${LLAMA_SRC}
+        ${LLAMA_SRC}/common
+        ${LLAMA_SRC}/include
+        ${LLAMA_SRC}/ggml/include
+        ${LLAMA_SRC}/ggml/src)
+
+target_link_libraries(${CMAKE_PROJECT_NAME}
+        llama
+        common
+        android
+        log)

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

@@ -0,0 +1,565 @@
+#include <android/log.h>
+#include <jni.h>
+#include <iomanip>
+#include <cmath>
+#include <string>
+#include <unistd.h>
+#include <sampling.h>
+
+#include "logging.h"
+#include "chat.h"
+#include "common.h"
+#include "llama.h"
+
+template<class T>
+static std::string join(const std::vector<T> &values, const std::string &delim) {
+    std::ostringstream str;
+    for (size_t i = 0; i < values.size(); i++) {
+        str << values[i];
+        if (i < values.size() - 1) { str << delim; }
+    }
+    return str.str();
+}
+
+/**
+ * LLama resources: context, model, batch and sampler
+ */
+constexpr int   N_THREADS_MIN           = 2;
+constexpr int   N_THREADS_MAX           = 4;
+constexpr int   N_THREADS_HEADROOM      = 2;
+
+constexpr int   DEFAULT_CONTEXT_SIZE    = 8192;
+constexpr int   OVERFLOW_HEADROOM       = 4;
+constexpr int   BATCH_SIZE              = 512;
+constexpr float DEFAULT_SAMPLER_TEMP    = 0.3f;
+
+static llama_model                      * g_model;
+static llama_context                    * g_context;
+static llama_batch                        g_batch;
+static common_chat_templates_ptr          g_chat_templates;
+static common_sampler                   * g_sampler;
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_init(JNIEnv *env, jobject /*unused*/, jstring nativeLibDir) {
+    // Set llama log handler to Android
+    llama_log_set(aichat_android_log_callback, nullptr);
+
+    // Loading all CPU backend variants
+    const auto *path_to_backend = env->GetStringUTFChars(nativeLibDir, 0);
+    LOGi("Loading backends from %s", path_to_backend);
+    ggml_backend_load_all_from_path(path_to_backend);
+    env->ReleaseStringUTFChars(nativeLibDir, path_to_backend);
+
+    // Initialize backends
+    llama_backend_init();
+    LOGi("Backend initiated; Log handler set.");
+}
+
+extern "C"
+JNIEXPORT jint JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_load(JNIEnv *env, jobject, jstring jmodel_path) {
+    llama_model_params model_params = llama_model_default_params();
+
+    const auto *model_path = env->GetStringUTFChars(jmodel_path, 0);
+    LOGd("%s: Loading model from: \n%s\n", __func__, model_path);
+
+    auto *model = llama_model_load_from_file(model_path, model_params);
+    env->ReleaseStringUTFChars(jmodel_path, model_path);
+    if (!model) {
+        return 1;
+    }
+    g_model = model;
+    return 0;
+}
+
+static llama_context *init_context(llama_model *model, const int n_ctx = DEFAULT_CONTEXT_SIZE) {
+    if (!model) {
+        LOGe("%s: model cannot be null", __func__);
+        return nullptr;
+    }
+
+    // Multi-threading setup
+    const int n_threads = std::max(N_THREADS_MIN, std::min(N_THREADS_MAX,
+                                                     (int) sysconf(_SC_NPROCESSORS_ONLN) -
+                                                     N_THREADS_HEADROOM));
+    LOGi("%s: Using %d threads", __func__, n_threads);
+
+    // Context parameters setup
+    llama_context_params ctx_params = llama_context_default_params();
+    const int trained_context_size = llama_model_n_ctx_train(model);
+    if (n_ctx > trained_context_size) {
+        LOGw("%s: Model was trained with only %d context size! Enforcing %d context size...",
+             __func__, trained_context_size, n_ctx);
+    }
+    ctx_params.n_ctx = n_ctx;
+    ctx_params.n_batch = BATCH_SIZE;
+    ctx_params.n_ubatch = BATCH_SIZE;
+    ctx_params.n_threads = n_threads;
+    ctx_params.n_threads_batch = n_threads;
+    auto *context = llama_init_from_model(g_model, ctx_params);
+    if (context == nullptr) {
+        LOGe("%s: llama_new_context_with_model() returned null)", __func__);
+    }
+    return context;
+}
+
+static common_sampler *new_sampler(float temp) {
+    common_params_sampling sparams;
+    sparams.temp = temp;
+    return common_sampler_init(g_model, sparams);
+}
+
+extern "C"
+JNIEXPORT jint JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_prepare(JNIEnv * /*env*/, jobject /*unused*/) {
+    auto *context = init_context(g_model);
+    if (!context) { return 1; }
+    g_context = context;
+    g_batch = llama_batch_init(BATCH_SIZE, 0, 1);
+    g_chat_templates = common_chat_templates_init(g_model, "");
+    g_sampler = new_sampler(DEFAULT_SAMPLER_TEMP);
+    return 0;
+}
+
+static std::string get_backend() {
+    std::vector<std::string> backends;
+    for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
+        auto *reg = ggml_backend_reg_get(i);
+        std::string name = ggml_backend_reg_name(reg);
+        if (name != "CPU") {
+            backends.push_back(ggml_backend_reg_name(reg));
+        }
+    }
+    return backends.empty() ? "CPU" : join(backends, ",");
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_systemInfo(JNIEnv *env, jobject /*unused*/) {
+    return env->NewStringUTF(llama_print_system_info());
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_benchModel(JNIEnv *env, jobject /*unused*/, jint pp, jint tg,
+                                                      jint pl, jint nr) {
+    auto *context = init_context(g_model, pp);
+    if (!context) {
+        const auto *const err_msg = "Fail to init_context! Bench aborted.";
+        LOGe(err_msg);
+        return env->NewStringUTF(err_msg);
+    }
+
+    auto pp_avg = 0.0;
+    auto tg_avg = 0.0;
+    auto pp_std = 0.0;
+    auto tg_std = 0.0;
+
+    const uint32_t n_ctx = llama_n_ctx(context);
+    LOGi("n_ctx = %d", n_ctx);
+
+    int i, j;
+    int nri;
+    for (nri = 0; nri < nr; nri++) {
+        LOGi("Benchmark prompt processing (pp = %d)", pp);
+
+        common_batch_clear(g_batch);
+
+        const int n_tokens = pp;
+        for (i = 0; i < n_tokens; i++) {
+            common_batch_add(g_batch, 0, i, {0}, false);
+        }
+
+        g_batch.logits[g_batch.n_tokens - 1] = true;
+        llama_memory_clear(llama_get_memory(context), false);
+
+        const auto t_pp_start = ggml_time_us();
+        if (llama_decode(context, g_batch) != 0) {
+            LOGe("llama_decode() failed during prompt processing");
+        }
+        const auto t_pp_end = ggml_time_us();
+
+        // bench text generation
+
+        LOGi("Benchmark text generation (tg = %d)", tg);
+
+        llama_memory_clear(llama_get_memory(context), false);
+        const auto t_tg_start = ggml_time_us();
+        for (i = 0; i < tg; i++) {
+            common_batch_clear(g_batch);
+            for (j = 0; j < pl; j++) {
+                common_batch_add(g_batch, 0, i, {j}, true);
+            }
+
+            if (llama_decode(context, g_batch) != 0) {
+                LOGe("llama_decode() failed during text generation");
+            }
+        }
+        const auto t_tg_end = ggml_time_us();
+
+        llama_memory_clear(llama_get_memory(context), false);
+
+        const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
+        const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
+
+        const auto speed_pp = double(pp) / t_pp;
+        const auto speed_tg = double(pl * tg) / t_tg;
+
+        pp_avg += speed_pp;
+        tg_avg += speed_tg;
+
+        pp_std += speed_pp * speed_pp;
+        tg_std += speed_tg * speed_tg;
+
+        LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
+    }
+
+    llama_free(context);
+
+    pp_avg /= double(nr);
+    tg_avg /= double(nr);
+
+    if (nr > 1) {
+        pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
+        tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
+    } else {
+        pp_std = 0;
+        tg_std = 0;
+    }
+
+    char model_desc[128];
+    llama_model_desc(g_model, model_desc, sizeof(model_desc));
+
+    const auto model_size = double(llama_model_size(g_model)) / 1024.0 / 1024.0 / 1024.0;
+    const auto model_n_params = double(llama_model_n_params(g_model)) / 1e9;
+
+    const auto backend = get_backend();
+    std::stringstream result;
+    result << std::setprecision(3);
+    result << "| model | size | params | backend | test | t/s |\n";
+    result << "| --- | --- | --- | --- | --- | --- |\n";
+    result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | "
+           << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
+    result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | "
+           << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
+    return env->NewStringUTF(result.str().c_str());
+}
+
+
+/**
+ * Completion loop's long-term states:
+ * - chat management
+ * - position tracking
+ */
+constexpr const char *ROLE_SYSTEM       = "system";
+constexpr const char *ROLE_USER         = "user";
+constexpr const char *ROLE_ASSISTANT    = "assistant";
+
+static std::vector<common_chat_msg> chat_msgs;
+static llama_pos system_prompt_position;
+static llama_pos current_position;
+
+static void reset_long_term_states(const bool clear_kv_cache = true) {
+    chat_msgs.clear();
+    system_prompt_position = 0;
+    current_position = 0;
+
+    if (clear_kv_cache)
+        llama_memory_clear(llama_get_memory(g_context), false);
+}
+
+/**
+ * TODO-hyin: implement sliding-window version as a better alternative
+ *
+ * Context shifting by discarding the older half of the tokens appended after system prompt:
+ * - take the [system_prompt_position] first tokens from the original prompt
+ * - take half of the last (system_prompt_position - system_prompt_position) tokens
+ * - recompute the logits in batches
+ */
+static void shift_context() {
+    const int n_discard = (current_position - system_prompt_position) / 2;
+    LOGi("%s: Discarding %d tokens", __func__, n_discard);
+    llama_memory_seq_rm(llama_get_memory(g_context), 0, system_prompt_position, system_prompt_position + n_discard);
+    llama_memory_seq_add(llama_get_memory(g_context), 0, system_prompt_position + n_discard, current_position, -n_discard);
+    current_position -= n_discard;
+    LOGi("%s: Context shifting done! Current position: %d", __func__, current_position);
+}
+
+static std::string chat_add_and_format(const std::string &role, const std::string &content) {
+    common_chat_msg new_msg;
+    new_msg.role = role;
+    new_msg.content = content;
+    auto formatted = common_chat_format_single(
+            g_chat_templates.get(), chat_msgs, new_msg, role == ROLE_USER, /* use_jinja */ false);
+    chat_msgs.push_back(new_msg);
+    LOGi("%s: Formatted and added %s message: \n%s\n", __func__, role.c_str(), formatted.c_str());
+    return formatted;
+}
+
+/**
+ * Completion loop's short-term states:
+ * - stop generation position
+ * - token chars caching
+ * - current assistant message being generated
+ */
+static llama_pos stop_generation_position;
+static std::string cached_token_chars;
+static std::ostringstream assistant_ss;
+
+static void reset_short_term_states() {
+    stop_generation_position = 0;
+    cached_token_chars.clear();
+    assistant_ss.str("");
+}
+
+static int decode_tokens_in_batches(
+        llama_context *context,
+        llama_batch &batch,
+        const llama_tokens &tokens,
+        const llama_pos start_pos,
+        const bool compute_last_logit = false) {
+    // Process tokens in batches using the global batch
+    LOGd("%s: Decode %d tokens starting at position %d", __func__, (int) tokens.size(), start_pos);
+    for (int i = 0; i < (int) tokens.size(); i += BATCH_SIZE) {
+        const int cur_batch_size = std::min((int) tokens.size() - i, BATCH_SIZE);
+        common_batch_clear(batch);
+        LOGv("%s: Preparing a batch size of %d starting at: %d", __func__, cur_batch_size, i);
+
+        // Shift context if current batch cannot fit into the context
+        if (start_pos + i + cur_batch_size >= DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM) {
+            LOGw("%s: Current batch won't fit into context! Shifting...", __func__);
+            shift_context();
+        }
+
+        // Add tokens to the batch with proper positions
+        for (int j = 0; j < cur_batch_size; j++) {
+            const llama_token token_id = tokens[i + j];
+            const llama_pos position = start_pos + i + j;
+            const bool want_logit = compute_last_logit && (i + j == tokens.size() - 1);
+            common_batch_add(batch, token_id, position, {0}, want_logit);
+        }
+
+        // Decode this batch
+        const int decode_result = llama_decode(context, batch);
+        if (decode_result) {
+            LOGe("%s: llama_decode failed w/ %d", __func__, decode_result);
+            return 1;
+        }
+    }
+    return 0;
+}
+
+extern "C"
+JNIEXPORT jint JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_processSystemPrompt(
+        JNIEnv *env,
+        jobject /*unused*/,
+        jstring jsystem_prompt
+) {
+    // Reset long-term & short-term states
+    reset_long_term_states();
+    reset_short_term_states();
+
+    // Obtain system prompt from JEnv
+    const auto *system_prompt = env->GetStringUTFChars(jsystem_prompt, nullptr);
+    LOGd("%s: System prompt received: \n%s", __func__, system_prompt);
+    std::string formatted_system_prompt(system_prompt);
+    env->ReleaseStringUTFChars(jsystem_prompt, system_prompt);
+
+    // Format system prompt if applicable
+    const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
+    if (has_chat_template) {
+        formatted_system_prompt = chat_add_and_format(ROLE_SYSTEM, system_prompt);
+    }
+
+    // Tokenize system prompt
+    const auto system_tokens = common_tokenize(g_context, formatted_system_prompt,
+                                               has_chat_template, has_chat_template);
+    for (auto id: system_tokens) {
+        LOGv("token: `%s`\t -> `%d`", common_token_to_piece(g_context, id).c_str(), id);
+    }
+
+    // Handle context overflow
+    const int max_batch_size = DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM;
+    if ((int) system_tokens.size() > max_batch_size) {
+        LOGe("%s: System prompt too long for context! %d tokens, max: %d",
+             __func__, (int) system_tokens.size(), max_batch_size);
+        return 1;
+    }
+
+    // Decode system tokens in batches
+    if (decode_tokens_in_batches(g_context, g_batch, system_tokens, current_position)) {
+        LOGe("%s: llama_decode() failed!", __func__);
+        return 2;
+    }
+
+    // Update position
+    system_prompt_position = current_position = (int) system_tokens.size();
+    return 0;
+}
+
+extern "C"
+JNIEXPORT jint JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_processUserPrompt(
+        JNIEnv *env,
+        jobject /*unused*/,
+        jstring juser_prompt,
+        jint n_predict
+) {
+    // Reset short-term states
+    reset_short_term_states();
+
+    // Obtain and tokenize user prompt
+    const auto *const user_prompt = env->GetStringUTFChars(juser_prompt, nullptr);
+    LOGd("%s: User prompt received: \n%s", __func__, user_prompt);
+    std::string formatted_user_prompt(user_prompt);
+    env->ReleaseStringUTFChars(juser_prompt, user_prompt);
+
+    // Format user prompt if applicable
+    const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
+    if (has_chat_template) {
+        formatted_user_prompt = chat_add_and_format(ROLE_USER, user_prompt);
+    }
+
+    // Decode formatted user prompts
+    auto user_tokens = common_tokenize(g_context, formatted_user_prompt, has_chat_template, has_chat_template);
+    for (auto id: user_tokens) {
+        LOGv("token: `%s`\t -> `%d`", common_token_to_piece(g_context, id).c_str(), id);
+    }
+
+    // Ensure user prompt doesn't exceed the context size by truncating if necessary.
+    const int user_prompt_size = (int) user_tokens.size();
+    const int max_batch_size = DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM;
+    if (user_prompt_size > max_batch_size) {
+        const int skipped_tokens = user_prompt_size - max_batch_size;
+        user_tokens.resize(max_batch_size);
+        LOGw("%s: User prompt too long! Skipped %d tokens!", __func__, skipped_tokens);
+    }
+
+    // Decode user tokens in batches
+    if (decode_tokens_in_batches(g_context, g_batch, user_tokens, current_position, true)) {
+        LOGe("%s: llama_decode() failed!", __func__);
+        return 2;
+    }
+
+    // Update position
+    current_position += user_prompt_size;
+    stop_generation_position = current_position + user_prompt_size + n_predict;
+    return 0;
+}
+
+static bool is_valid_utf8(const char *string) {
+    if (!string) { return true; }
+
+    const auto *bytes = (const unsigned char *) string;
+    int num;
+
+    while (*bytes != 0x00) {
+        if ((*bytes & 0x80) == 0x00) {
+            // U+0000 to U+007F
+            num = 1;
+        } else if ((*bytes & 0xE0) == 0xC0) {
+            // U+0080 to U+07FF
+            num = 2;
+        } else if ((*bytes & 0xF0) == 0xE0) {
+            // U+0800 to U+FFFF
+            num = 3;
+        } else if ((*bytes & 0xF8) == 0xF0) {
+            // U+10000 to U+10FFFF
+            num = 4;
+        } else {
+            return false;
+        }
+
+        bytes += 1;
+        for (int i = 1; i < num; ++i) {
+            if ((*bytes & 0xC0) != 0x80) {
+                return false;
+            }
+            bytes += 1;
+        }
+    }
+    return true;
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_generateNextToken(
+        JNIEnv *env,
+        jobject /*unused*/
+) {
+    // Infinite text generation via context shifting
+    if (current_position >= DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM) {
+        LOGw("%s: Context full! Shifting...", __func__);
+        shift_context();
+    }
+
+    // Stop if reaching the marked position
+    if (current_position >= stop_generation_position) {
+        LOGw("%s: STOP: hitting stop position: %d", __func__, stop_generation_position);
+        return nullptr;
+    }
+
+    // Sample next token
+    const auto new_token_id = common_sampler_sample(g_sampler, g_context, -1);
+    common_sampler_accept(g_sampler, new_token_id, true);
+
+    // Populate the batch with new token, then decode
+    common_batch_clear(g_batch);
+    common_batch_add(g_batch, new_token_id, current_position, {0}, true);
+    if (llama_decode(g_context, g_batch) != 0) {
+        LOGe("%s: llama_decode() failed for generated token", __func__);
+        return nullptr;
+    }
+
+    // Update position
+    current_position++;
+
+    // Stop if next token is EOG
+    if (llama_vocab_is_eog(llama_model_get_vocab(g_model), new_token_id)) {
+        LOGd("id: %d,\tIS EOG!\nSTOP.", new_token_id);
+        chat_add_and_format(ROLE_ASSISTANT, assistant_ss.str());
+        return nullptr;
+    }
+
+    // If not EOG, convert to text
+    auto new_token_chars = common_token_to_piece(g_context, new_token_id);
+    cached_token_chars += new_token_chars;
+
+    // Create and return a valid UTF-8 Java string
+    jstring result = nullptr;
+    if (is_valid_utf8(cached_token_chars.c_str())) {
+        result = env->NewStringUTF(cached_token_chars.c_str());
+        LOGv("id: %d,\tcached: `%s`,\tnew: `%s`", new_token_id, cached_token_chars.c_str(), new_token_chars.c_str());
+
+        assistant_ss << cached_token_chars;
+        cached_token_chars.clear();
+    } else {
+        LOGv("id: %d,\tappend to cache", new_token_id);
+        result = env->NewStringUTF("");
+    }
+    return result;
+}
+
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_unload(JNIEnv * /*unused*/, jobject /*unused*/) {
+    // Reset long-term & short-term states
+    reset_long_term_states();
+    reset_short_term_states();
+
+    // Free up resources
+    common_sampler_free(g_sampler);
+    g_chat_templates.reset();
+    llama_batch_free(g_batch);
+    llama_free(g_context);
+    llama_model_free(g_model);
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_arm_aichat_internal_InferenceEngineImpl_shutdown(JNIEnv *env, jobject /*unused*/) {
+    llama_backend_free();
+}

examples/llama.android/lib/src/main/cpp/logging.h

@@ -0,0 +1,61 @@
+//
+// Created by Han Yin on 10/31/25.
+//
+
+#ifndef AICHAT_LOGGING_H
+#define AICHAT_LOGGING_H
+
+#endif //AICHAT_LOGGING_H
+
+#pragma once
+#include <android/log.h>
+
+#ifndef LOG_TAG
+#define LOG_TAG "ai-chat"
+#endif
+
+#ifndef LOG_MIN_LEVEL
+#if defined(NDEBUG)
+#define LOG_MIN_LEVEL ANDROID_LOG_INFO
+#else
+#define LOG_MIN_LEVEL ANDROID_LOG_VERBOSE
+#endif
+#endif
+
+static inline int ai_should_log(int prio) {
+    return __android_log_is_loggable(prio, LOG_TAG, LOG_MIN_LEVEL);
+}
+
+#if LOG_MIN_LEVEL <= ANDROID_LOG_VERBOSE
+#define LOGv(...) do { if (ai_should_log(ANDROID_LOG_VERBOSE)) __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, __VA_ARGS__); } while (0)
+#else
+#define LOGv(...) ((void)0)
+#endif
+
+#if LOG_MIN_LEVEL <= ANDROID_LOG_DEBUG
+#define LOGd(...) do { if (ai_should_log(ANDROID_LOG_DEBUG)) __android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__); } while (0)
+#else
+#define LOGd(...) ((void)0)
+#endif
+
+#define LOGi(...)   do { if (ai_should_log(ANDROID_LOG_INFO )) __android_log_print(ANDROID_LOG_INFO , LOG_TAG, __VA_ARGS__); } while (0)
+#define LOGw(...)   do { if (ai_should_log(ANDROID_LOG_WARN )) __android_log_print(ANDROID_LOG_WARN , LOG_TAG, __VA_ARGS__); } while (0)
+#define LOGe(...)   do { if (ai_should_log(ANDROID_LOG_ERROR)) __android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__); } while (0)
+
+static inline int android_log_prio_from_ggml(enum ggml_log_level level) {
+    switch (level) {
+        case GGML_LOG_LEVEL_ERROR: return ANDROID_LOG_ERROR;
+        case GGML_LOG_LEVEL_WARN:  return ANDROID_LOG_WARN;
+        case GGML_LOG_LEVEL_INFO:  return ANDROID_LOG_INFO;
+        case GGML_LOG_LEVEL_DEBUG: return ANDROID_LOG_DEBUG;
+        default:                   return ANDROID_LOG_DEFAULT;
+    }
+}
+
+static inline void aichat_android_log_callback(enum ggml_log_level level,
+                                              const char* text,
+                                              void* /*user*/) {
+    const int prio = android_log_prio_from_ggml(level);
+    if (!ai_should_log(prio)) return;
+    __android_log_write(prio, LOG_TAG, text);
+}

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

@@ -0,0 +1,14 @@
+package com.arm.aichat
+
+import android.content.Context
+import com.arm.aichat.internal.InferenceEngineImpl
+
+/**
+ * Main entry point for Arm's AI Chat library.
+ */
+object AiChat {
+    /**
+     * Get the inference engine single instance.
+     */
+    fun getInferenceEngine(context: Context) = InferenceEngineImpl.getInstance(context)
+}

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

@@ -0,0 +1,89 @@
+package com.arm.aichat
+
+import com.arm.aichat.InferenceEngine.State
+import kotlinx.coroutines.flow.Flow
+import kotlinx.coroutines.flow.StateFlow
+
+/**
+ * Interface defining the core LLM inference operations.
+ */
+interface InferenceEngine {
+    /**
+     * Current state of the inference engine
+     */
+    val state: StateFlow<State>
+
+    /**
+     * Load a model from the given path.
+     *
+     * @throws UnsupportedArchitectureException if model architecture not supported
+     */
+    suspend fun loadModel(pathToModel: String)
+
+    /**
+     * Sends a system prompt to the loaded model
+     */
+    suspend fun setSystemPrompt(systemPrompt: String)
+
+    /**
+     * Sends a user prompt to the loaded model and returns a Flow of generated tokens.
+     */
+    fun sendUserPrompt(message: String, predictLength: Int = DEFAULT_PREDICT_LENGTH): Flow<String>
+
+    /**
+     * Runs a benchmark with the specified parameters.
+     */
+    suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String
+
+    /**
+     * Unloads the currently loaded model.
+     */
+    suspend fun cleanUp()
+
+    /**
+     * Cleans up resources when the engine is no longer needed.
+     */
+    fun destroy()
+
+    /**
+     * States of the inference engine
+     */
+    sealed class State {
+        object Uninitialized : State()
+        object Initializing : State()
+        object Initialized : State()
+
+        object LoadingModel : State()
+        object UnloadingModel : State()
+        object ModelReady : State()
+
+        object Benchmarking : State()
+        object ProcessingSystemPrompt : State()
+        object ProcessingUserPrompt : State()
+
+        object Generating : State()
+
+        data class Error(val exception: Exception) : State()
+    }
+
+    companion object {
+        const val DEFAULT_PREDICT_LENGTH = 1024
+    }
+}
+
+val State.isUninterruptible
+    get() = this is State.Initializing ||
+        this is State.LoadingModel ||
+        this is State.UnloadingModel ||
+        this is State.Benchmarking ||
+        this is State.ProcessingSystemPrompt ||
+        this is State.ProcessingUserPrompt
+
+val State.isModelLoaded: Boolean
+    get() = this is State.ModelReady ||
+        this is State.Benchmarking ||
+        this is State.ProcessingSystemPrompt ||
+        this is State.ProcessingUserPrompt ||
+        this is State.Generating
+
+class UnsupportedArchitectureException : Exception()

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

@@ -0,0 +1,61 @@
+package com.arm.aichat.gguf
+
+import kotlin.collections.get
+
+
+/**
+ * Numerical codes used by `general.file_type` (see llama.cpp repo's `constants.py`).
+ * The `label` matches what llama‑cli prints.
+ */
+enum class FileType(val code: Int, val label: String) {
+    ALL_F32(0, "all F32"),
+    MOSTLY_F16(1, "F16"),
+    MOSTLY_Q4_0(2, "Q4_0"),
+    MOSTLY_Q4_1(3, "Q4_1"),
+    // 4 removed
+    MOSTLY_Q8_0(7, "Q8_0"),
+    MOSTLY_Q5_0(8, "Q5_0"),
+    MOSTLY_Q5_1(9, "Q5_1"),
+
+    /* K‑quants ------------------------------------------------------------ */
+    MOSTLY_Q2_K      (10, "Q2_K - Medium"),
+    MOSTLY_Q3_K_S    (11, "Q3_K - Small"),
+    MOSTLY_Q3_K_M    (12, "Q3_K - Medium"),
+    MOSTLY_Q3_K_L    (13, "Q3_K - Large"),
+    MOSTLY_Q4_K_S    (14, "Q4_K - Small"),
+    MOSTLY_Q4_K_M    (15, "Q4_K - Medium"),
+    MOSTLY_Q5_K_S    (16, "Q5_K - Small"),
+    MOSTLY_Q5_K_M    (17, "Q5_K - Medium"),
+    MOSTLY_Q6_K      (18, "Q6_K"),
+
+    /* IQ quants ----------------------------------------------------------- */
+    MOSTLY_IQ2_XXS   (19, "IQ2_XXS - 2.06 bpw"),
+    MOSTLY_IQ2_XS    (20, "IQ2_XS - 2.31 bpw"),
+    MOSTLY_Q2_K_S    (21, "Q2_K - Small"),
+    MOSTLY_IQ3_XS    (22, "IQ3_XS - 3.30 bpw"),
+    MOSTLY_IQ3_XXS   (23, "IQ3_XXS - 3.06 bpw"),
+    MOSTLY_IQ1_S     (24, "IQ1_S - 1.56 bpw"),
+    MOSTLY_IQ4_NL    (25, "IQ4_NL - 4.5 bpw"),
+    MOSTLY_IQ3_S     (26, "IQ3_S - 3.44 bpw"),
+    MOSTLY_IQ3_M     (27, "IQ3_M - 3.66 bpw"),
+    MOSTLY_IQ2_S     (28, "IQ2_S - 2.50 bpw"),
+    MOSTLY_IQ2_M     (29, "IQ2_M - 2.70 bpw"),
+    MOSTLY_IQ4_XS    (30, "IQ4_XS - 4.25 bpw"),
+    MOSTLY_IQ1_M     (31, "IQ1_M - 1.75 bpw"),
+
+    /* BF16 & Ternary ------------------------------------------------------ */
+    MOSTLY_BF16      (32, "BF16"),
+    MOSTLY_TQ1_0     (36, "TQ1_0 - 1.69 bpw ternary"),
+    MOSTLY_TQ2_0     (37, "TQ2_0 - 2.06 bpw ternary"),
+
+    /* Special flag -------------------------------------------------------- */
+    GUESSED(1024, "(guessed)"),
+
+    UNKNOWN(-1, "unknown");
+
+    companion object {
+        private val map = entries.associateBy(FileType::code)
+
+        fun fromCode(code: Int?): FileType = map[code] ?: UNKNOWN
+    }
+}

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

@@ -0,0 +1,132 @@
+package com.arm.aichat.gguf
+
+import java.io.IOException
+
+
+/**
+ * Structured metadata of GGUF
+ */
+data class GgufMetadata(
+    // Basic file info
+    val version: GgufVersion,
+    val tensorCount: Long,
+    val kvCount: Long,
+
+    // General info
+    val basic: BasicInfo,
+    val author: AuthorInfo? = null,
+    val additional: AdditionalInfo? = null,
+    val architecture: ArchitectureInfo? = null,
+    val baseModels: List<BaseModelInfo>? = null,
+    val tokenizer: TokenizerInfo? = null,
+
+    // Derivative info
+    val dimensions: DimensionsInfo? = null,
+    val attention: AttentionInfo? = null,
+    val rope: RopeInfo? = null,
+    val experts: ExpertsInfo? = null
+) {
+    enum class GgufVersion(val code: Int, val label: String) {
+        /** First public draft; little‑endian only, no alignment key. */
+        LEGACY_V1(1, "Legacy v1"),
+
+        /** Added split‑file support and some extra metadata keys. */
+        EXTENDED_V2(2, "Extended v2"),
+
+        /** Current spec: endian‑aware, mandatory alignment, fully validated. */
+        VALIDATED_V3(3, "Validated v3");
+
+        companion object {
+            fun fromCode(code: Int): GgufVersion =
+                entries.firstOrNull { it.code == code }
+                    ?: throw IOException("Unknown GGUF version code $code")
+        }
+
+        override fun toString(): String = "$label (code=$code)"
+    }
+
+    data class BasicInfo(
+        val uuid: String? = null,
+        val name: String? = null,
+        val nameLabel: String? = null,
+        val sizeLabel: String? = null,  // Size label like "7B"
+    )
+
+    data class AuthorInfo(
+        val organization: String? = null,
+        val author: String? = null,
+        val doi: String? = null,
+        val url: String? = null,
+        val repoUrl: String? = null,
+        val license: String? = null,
+        val licenseLink: String? = null,
+    )
+
+    data class AdditionalInfo(
+        val type: String? = null,
+        val description: String? = null,
+        val tags: List<String>? = null,
+        val languages: List<String>? = null,
+    )
+
+    data class ArchitectureInfo(
+        val architecture: String? = null,
+        val fileType: Int? = null,
+        val vocabSize: Int? = null,
+        val finetune: String? = null,
+        val quantizationVersion: Int? = null,
+    )
+
+    data class BaseModelInfo(
+        val name: String? = null,
+        val author: String? = null,
+        val version: String? = null,
+        val organization: String? = null,
+        val url: String? = null,
+        val doi: String? = null,
+        val uuid: String? = null,
+        val repoUrl: String? = null,
+    )
+
+    data class TokenizerInfo(
+        val model: String? = null,
+        val bosTokenId: Int? = null,
+        val eosTokenId: Int? = null,
+        val unknownTokenId: Int? = null,
+        val paddingTokenId: Int? = null,
+        val addBosToken: Boolean? = null,
+        val addEosToken: Boolean? = null,
+        val chatTemplate: String? = null,
+    )
+
+    data class DimensionsInfo(
+        val contextLength: Int? = null,
+        val embeddingSize: Int? = null,
+        val blockCount: Int? = null,
+        val feedForwardSize: Int? = null,
+    )
+
+    data class AttentionInfo(
+        val headCount: Int? = null,
+        val headCountKv: Int? = null,
+        val keyLength: Int? = null,
+        val valueLength: Int? = null,
+        val layerNormEpsilon: Float? = null,
+        val layerNormRmsEpsilon: Float? = null,
+    )
+
+    data class RopeInfo(
+        val frequencyBase: Float? = null,
+        val dimensionCount: Int? = null,
+        val scalingType: String? = null,
+        val scalingFactor: Float? = null,
+        val attnFactor: Float? = null,
+        val originalContextLength: Int? = null,
+        val finetuned: Boolean? = null,
+    )
+
+    data class ExpertsInfo(
+        val count: Int? = null,
+        val usedCount: Int? = null,
+    )
+}

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

@@ -0,0 +1,77 @@
+package com.arm.aichat.gguf
+
+import android.content.Context
+import android.net.Uri
+import com.arm.aichat.internal.gguf.GgufMetadataReaderImpl
+import java.io.File
+import java.io.IOException
+import java.io.InputStream
+
+/**
+ * Interface for reading GGUF metadata from model files.
+ * Use `GgufMetadataReader.create()` to get an instance.
+ */
+interface GgufMetadataReader {
+    /**
+     * Reads the magic number from the specified file path.
+     *
+     * @param file Java File to the GGUF file with absolute path
+     * @return true if file is valid GGUF, otherwise false
+     * @throws InvalidFileFormatException if file format is invalid
+     */
+    suspend fun ensureSourceFileFormat(file: File): Boolean
+
+    /**
+     * Reads the magic number from the specified file path.
+     *
+     * @param context Context for obtaining [android.content.ContentProvider]
+     * @param uri Uri to the GGUF file provided by [android.content.ContentProvider]
+     * @return true if file is valid GGUF, otherwise false
+     * @throws InvalidFileFormatException if file format is invalid
+     */
+    suspend fun ensureSourceFileFormat(context: Context, uri: Uri): Boolean
+
+    /**
+     * Reads and parses GGUF metadata from the specified file path.
+     *
+     * @param input the [InputStream] obtained from a readable file or content
+     * @return Structured metadata extracted from the file
+     * @throws IOException if file is damaged or cannot be read
+     * @throws InvalidFileFormatException if file format is invalid
+     */
+    suspend fun readStructuredMetadata(input: InputStream): GgufMetadata
+
+    companion object {
+        private val DEFAULT_SKIP_KEYS = setOf(
+            "tokenizer.chat_template",
+            "tokenizer.ggml.scores",
+            "tokenizer.ggml.tokens",
+            "tokenizer.ggml.token_type"
+        )
+
+        /**
+         * Creates a default GgufMetadataReader instance
+         */
+        fun create(): GgufMetadataReader = GgufMetadataReaderImpl(
+            skipKeys = DEFAULT_SKIP_KEYS,
+            arraySummariseThreshold = 1_000
+        )
+
+        /**
+         * Creates a GgufMetadataReader with custom configuration
+         *
+         * @param skipKeys Keys whose value should be skipped entirely (not kept in the result map)
+         * @param arraySummariseThreshold If ≥0, arrays longer get summarised, not materialised;
+         *                                If -1, never summarise.
+         */
+        fun create(
+            skipKeys: Set<String> = DEFAULT_SKIP_KEYS,
+            arraySummariseThreshold: Int = 1_000
+        ): GgufMetadataReader = GgufMetadataReaderImpl(
+            skipKeys = skipKeys,
+            arraySummariseThreshold = arraySummariseThreshold
+        )
+    }
+}
+
+class InvalidFileFormatException : IOException()

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

@@ -0,0 +1,309 @@
+package com.arm.aichat.internal
+
+import android.content.Context
+import android.util.Log
+import com.arm.aichat.InferenceEngine
+import com.arm.aichat.UnsupportedArchitectureException
+import com.arm.aichat.internal.InferenceEngineImpl.Companion.getInstance
+import dalvik.annotation.optimization.FastNative
+import kotlinx.coroutines.CancellationException
+import kotlinx.coroutines.CoroutineScope
+import kotlinx.coroutines.Dispatchers
+import kotlinx.coroutines.ExperimentalCoroutinesApi
+import kotlinx.coroutines.SupervisorJob
+import kotlinx.coroutines.cancel
+import kotlinx.coroutines.flow.Flow
+import kotlinx.coroutines.flow.MutableStateFlow
+import kotlinx.coroutines.flow.StateFlow
+import kotlinx.coroutines.flow.flow
+import kotlinx.coroutines.flow.flowOn
+import kotlinx.coroutines.launch
+import kotlinx.coroutines.withContext
+import java.io.File
+import java.io.IOException
+
+/**
+ * JNI wrapper for the llama.cpp library providing Android-friendly access to large language models.
+ *
+ * This class implements a singleton pattern for managing the lifecycle of a single LLM instance.
+ * All operations are executed on a dedicated single-threaded dispatcher to ensure thread safety
+ * with the underlying C++ native code.
+ *
+ * The typical usage flow is:
+ * 1. Get instance via [getInstance]
+ * 2. Load a model with [loadModel]
+ * 3. Send prompts with [sendUserPrompt]
+ * 4. Generate responses as token streams
+ * 5. Perform [cleanUp] when done with a model
+ * 6. Properly [destroy] when completely done
+ *
+ * State transitions are managed automatically and validated at each operation.
+ *
+ * @see ai_chat.cpp for the native implementation details
+ */
+internal class InferenceEngineImpl private constructor(
+    private val nativeLibDir: String
+) : InferenceEngine {
+
+    companion object {
+        private val TAG = InferenceEngineImpl::class.java.simpleName
+
+        @Volatile
+        private var instance: InferenceEngine? = null
+
+        /**
+         * Create or obtain [InferenceEngineImpl]'s single instance.
+         *
+         * @param Context for obtaining native library directory
+         * @throws IllegalArgumentException if native library path is invalid
+         * @throws UnsatisfiedLinkError if library failed to load
+         */
+        internal fun getInstance(context: Context) =
+            instance ?: synchronized(this) {
+                val nativeLibDir = context.applicationInfo.nativeLibraryDir
+                require(nativeLibDir.isNotBlank()) { "Expected a valid native library path!" }
+
+                try {
+                    Log.i(TAG, "Instantiating InferenceEngineImpl,,,")
+                    InferenceEngineImpl(nativeLibDir).also { instance = it }
+                } catch (e: UnsatisfiedLinkError) {
+                    Log.e(TAG, "Failed to load native library from $nativeLibDir", e)
+                    throw e
+                }
+            }
+    }
+
+    /**
+     * JNI methods
+     * @see ai_chat.cpp
+     */
+    @FastNative
+    private external fun init(nativeLibDir: String)
+
+    @FastNative
+    private external fun load(modelPath: String): Int
+
+    @FastNative
+    private external fun prepare(): Int
+
+    @FastNative
+    private external fun systemInfo(): String
+
+    @FastNative
+    private external fun benchModel(pp: Int, tg: Int, pl: Int, nr: Int): String
+
+    @FastNative
+    private external fun processSystemPrompt(systemPrompt: String): Int
+
+    @FastNative
+    private external fun processUserPrompt(userPrompt: String, predictLength: Int): Int
+
+    @FastNative
+    private external fun generateNextToken(): String?
+
+    @FastNative
+    private external fun unload()
+
+    @FastNative
+    private external fun shutdown()
+
+    private val _state =
+        MutableStateFlow<InferenceEngine.State>(InferenceEngine.State.Uninitialized)
+    override val state: StateFlow<InferenceEngine.State> = _state
+
+    private var _readyForSystemPrompt = false
+
+    /**
+     * Single-threaded coroutine dispatcher & scope for LLama asynchronous operations
+     */
+    @OptIn(ExperimentalCoroutinesApi::class)
+    private val llamaDispatcher = Dispatchers.IO.limitedParallelism(1)
+    private val llamaScope = CoroutineScope(llamaDispatcher + SupervisorJob())
+
+    init {
+        llamaScope.launch {
+            try {
+                check(_state.value is InferenceEngine.State.Uninitialized) {
+                    "Cannot load native library in ${_state.value.javaClass.simpleName}!"
+                }
+                _state.value = InferenceEngine.State.Initializing
+                Log.i(TAG, "Loading native library...")
+                System.loadLibrary("ai-chat")
+                init(nativeLibDir)
+                _state.value = InferenceEngine.State.Initialized
+                Log.i(TAG, "Native library loaded! System info: \n${systemInfo()}")
+
+            } catch (e: Exception) {
+                Log.e(TAG, "Failed to load native library", e)
+                throw e
+            }
+        }
+    }
+
+    /**
+     * Load the LLM
+     */
+    override suspend fun loadModel(pathToModel: String) =
+        withContext(llamaDispatcher) {
+            check(_state.value is InferenceEngine.State.Initialized) {
+                "Cannot load model in ${_state.value.javaClass.simpleName}!"
+            }
+
+            try {
+                Log.i(TAG, "Checking access to model file... \n$pathToModel")
+                File(pathToModel).let {
+                    require(it.exists()) { "File not found" }
+                    require(it.isFile) { "Not a valid file" }
+                    require(it.canRead()) { "Cannot read file" }
+                }
+
+                Log.i(TAG, "Loading model... \n$pathToModel")
+                _readyForSystemPrompt = false
+                _state.value = InferenceEngine.State.LoadingModel
+                load(pathToModel).let {
+                    // TODO-han.yin: find a better way to pass other error codes
+                    if (it != 0) throw UnsupportedArchitectureException()
+                }
+                prepare().let {
+                    if (it != 0) throw IOException("Failed to prepare resources")
+                }
+                Log.i(TAG, "Model loaded!")
+                _readyForSystemPrompt = true
+                _state.value = InferenceEngine.State.ModelReady
+            } catch (e: Exception) {
+                Log.e(TAG, (e.message ?: "Error loading model") + "\n" + pathToModel, e)
+                _state.value = InferenceEngine.State.Error(e)
+                throw e
+            }
+        }
+
+    /**
+     * Process the plain text system prompt
+     *
+     * TODO-han.yin: return error code if system prompt not correct processed?
+     */
+    override suspend fun setSystemPrompt(prompt: String) =
+        withContext(llamaDispatcher) {
+            require(prompt.isNotBlank()) { "Cannot process empty system prompt!" }
+            check(_readyForSystemPrompt) { "System prompt must be set ** RIGHT AFTER ** model loaded!" }
+            check(_state.value is InferenceEngine.State.ModelReady) {
+                "Cannot process system prompt in ${_state.value.javaClass.simpleName}!"
+            }
+
+            Log.i(TAG, "Sending system prompt...")
+            _readyForSystemPrompt = false
+            _state.value = InferenceEngine.State.ProcessingSystemPrompt
+            processSystemPrompt(prompt).let { result ->
+                if (result != 0) {
+                    RuntimeException("Failed to process system prompt: $result").also {
+                        _state.value = InferenceEngine.State.Error(it)
+                        throw it
+                    }
+                }
+            }
+            Log.i(TAG, "System prompt processed! Awaiting user prompt...")
+            _state.value = InferenceEngine.State.ModelReady
+        }
+
+    /**
+     * Send plain text user prompt to LLM, which starts generating tokens in a [Flow]
+     */
+    override fun sendUserPrompt(
+        message: String,
+        predictLength: Int,
+    ): Flow<String> = flow {
+        require(message.isNotEmpty()) { "User prompt discarded due to being empty!" }
+        check(_state.value is InferenceEngine.State.ModelReady) {
+            "User prompt discarded due to: ${_state.value.javaClass.simpleName}"
+        }
+
+        try {
+            Log.i(TAG, "Sending user prompt...")
+            _readyForSystemPrompt = false
+            _state.value = InferenceEngine.State.ProcessingUserPrompt
+
+            processUserPrompt(message, predictLength).let { result ->
+                if (result != 0) {
+                    Log.e(TAG, "Failed to process user prompt: $result")
+                    return@flow
+                }
+            }
+
+            Log.i(TAG, "User prompt processed. Generating assistant prompt...")
+            _state.value = InferenceEngine.State.Generating
+            while (true) {
+                generateNextToken()?.let { utf8token ->
+                    if (utf8token.isNotEmpty()) emit(utf8token)
+                } ?: break
+            }
+            Log.i(TAG, "Assistant generation complete. Awaiting user prompt...")
+            _state.value = InferenceEngine.State.ModelReady
+        } catch (e: CancellationException) {
+            Log.i(TAG, "Generation cancelled by user.")
+            _state.value = InferenceEngine.State.ModelReady
+            throw e
+        } catch (e: Exception) {
+            Log.e(TAG, "Error during generation!", e)
+            _state.value = InferenceEngine.State.Error(e)
+            throw e
+        }
+    }.flowOn(llamaDispatcher)
+
+    /**
+     * Benchmark the model
+     */
+    override suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int): String =
+        withContext(llamaDispatcher) {
+            check(_state.value is InferenceEngine.State.ModelReady) {
+                "Benchmark request discarded due to: $state"
+            }
+            Log.i(TAG, "Start benchmark (pp: $pp, tg: $tg, pl: $pl, nr: $nr)")
+            _readyForSystemPrompt = false   // Just to be safe
+            _state.value = InferenceEngine.State.Benchmarking
+            benchModel(pp, tg, pl, nr).also {
+                _state.value = InferenceEngine.State.ModelReady
+            }
+        }
+
+    /**
+     * Unloads the model and frees resources, or reset error states
+     */
+    override suspend fun cleanUp() =
+        withContext(llamaDispatcher) {
+            when (val state = _state.value) {
+                is InferenceEngine.State.ModelReady -> {
+                    Log.i(TAG, "Unloading model and free resources...")
+                    _readyForSystemPrompt = false
+                    _state.value = InferenceEngine.State.UnloadingModel
+
+                    unload()
+
+                    _state.value = InferenceEngine.State.Initialized
+                    Log.i(TAG, "Model unloaded!")
+                    Unit
+                }
+
+                is InferenceEngine.State.Error -> {
+                    Log.i(TAG, "Resetting error states...")
+                    _state.value = InferenceEngine.State.Initialized
+                    Log.i(TAG, "States reset!")
+                    Unit
+                }
+
+                else -> throw IllegalStateException("Cannot unload model in ${state.javaClass.simpleName}")
+            }
+        }
+
+    /**
+     * Cancel all ongoing coroutines and free GGML backends
+     */
+    override fun destroy() {
+        _readyForSystemPrompt = false
+        llamaScope.cancel()
+        when(_state.value) {
+            is InferenceEngine.State.Uninitialized -> {}
+            is InferenceEngine.State.Initialized -> shutdown()
+            else -> { unload(); shutdown() }
+        }
+    }
+}

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

@@ -0,0 +1,590 @@
+package com.arm.aichat.internal.gguf
+
+import android.content.Context
+import android.net.Uri
+import com.arm.aichat.gguf.GgufMetadata
+import com.arm.aichat.gguf.GgufMetadataReader
+import com.arm.aichat.gguf.InvalidFileFormatException
+import java.io.File
+import java.io.IOException
+import java.io.InputStream
+
+
+/**
+ * Utility class to read GGUF model files and extract metadata key-value pairs.
+ * This parser reads the header and metadata of a GGUF v3 file (little-endian) and skips tensor data.
+ */
+internal class GgufMetadataReaderImpl(
+    private val skipKeys: Set<String>,
+    private val arraySummariseThreshold: Int,
+) : GgufMetadataReader {
+    companion object {
+        private const val ARCH_LLAMA = "llama"
+    }
+
+    /** Enum corresponding to GGUF metadata value types (for convenience and array element typing). */
+    enum class MetadataType(val code: Int) {
+        UINT8(0), INT8(1), UINT16(2), INT16(3),
+        UINT32(4), INT32(5), FLOAT32(6), BOOL(7),
+        STRING(8), ARRAY(9), UINT64(10), INT64(11), FLOAT64(12);
+        companion object {
+            private val codeMap = entries.associateBy(MetadataType::code)
+            fun fromCode(code: Int): MetadataType = codeMap[code]
+                ?: throw IOException("Unknown metadata value type code: $code")
+        }
+    }
+
+    /** Sealed class hierarchy for metadata values, providing type-safe representations for each GGUF metadata type. */
+    sealed class MetadataValue {
+        data class UInt8(val value: UByte) : MetadataValue()       // 0:  8-bit unsigned int
+        data class Int8(val value: Byte) : MetadataValue()         // 1:  8-bit signed int
+        data class UInt16(val value: UShort) : MetadataValue()     // 2:  16-bit unsigned int (little-endian)
+        data class Int16(val value: Short) : MetadataValue()       // 3:  16-bit signed int (little-endian)
+        data class UInt32(val value: UInt) : MetadataValue()       // 4:  32-bit unsigned int (little-endian)
+        data class Int32(val value: Int) : MetadataValue()         // 5:  32-bit signed int (little-endian)
+        data class Float32(val value: Float) : MetadataValue()     // 6:  32-bit IEEE754 float
+        data class Bool(val value: Boolean) : MetadataValue()      // 7:  Boolean (1-byte, 0=false, 1=true)
+        data class StringVal(val value: String) : MetadataValue()  // 8:  UTF-8 string (length-prefixed)
+        data class ArrayVal(val elementType: MetadataType, val elements: List<MetadataValue>) : MetadataValue()
+        data class UInt64(val value: ULong) : MetadataValue()      // 10: 64-bit unsigned int (little-endian)
+        data class Int64(val value: Long) : MetadataValue()        // 11: 64-bit signed int (little-endian)
+        data class Float64(val value: Double) : MetadataValue()    // 12: 64-bit IEEE754 double
+    }
+
+    /* Convert MetadataValue to plain Kotlin primitives for allMetadata map */
+    private fun MetadataValue.toPrimitive(): Any = when (this) {
+        is MetadataValue.UInt8     -> value
+        is MetadataValue.Int8      -> value
+        is MetadataValue.UInt16    -> value
+        is MetadataValue.Int16     -> value
+        is MetadataValue.UInt32    -> value
+        is MetadataValue.Int32     -> value
+        is MetadataValue.Float32   -> value
+        is MetadataValue.Bool      -> value
+        is MetadataValue.StringVal -> value
+        is MetadataValue.UInt64    -> value
+        is MetadataValue.Int64     -> value
+        is MetadataValue.Float64   -> value
+        is MetadataValue.ArrayVal  -> elements.map { it.toPrimitive() }
+    }
+
+    /**
+     * Reads the magic number from the specified file path.
+     *
+     * @param context Context for obtaining ContentResolver
+     * @param uri Uri to the GGUF file provided by ContentProvider
+     * @return true if file is valid GGUF, otherwise false
+     */
+    override suspend fun ensureSourceFileFormat(file: File): Boolean =
+        file.inputStream().buffered().use { ensureMagic(it) }
+
+    /**
+     * Reads the magic number from the specified file path.
+     *
+     * @param context Context for obtaining ContentResolver
+     * @param uri Uri to the GGUF file provided by ContentProvider
+     * @return true if file is valid GGUF, otherwise false
+     */
+    override suspend fun ensureSourceFileFormat(context: Context, uri: Uri): Boolean =
+        context.contentResolver.openInputStream(uri)?.buffered()?.use { ensureMagic(it) } == true
+
+    /** Reads the 4‑byte magic; throws if magic ≠ "GGUF". */
+    private fun ensureMagic(input: InputStream): Boolean =
+        ByteArray(4).let {
+            if (input.read(it) != 4) throw IOException("Not a valid file!")
+            it.contentEquals(byteArrayOf(0x47, 0x47, 0x55, 0x46)) // "GGUF"
+        }
+
+    /**
+     * High‑level entry point: parses a `.gguf` file on disk and returns the fully
+     * populated [GgufMetadata] tree.
+     *
+     * Steps performed internally:
+     * 1.  Reads and validates the 8‑byte header (`"GGUF"` magic + version).
+     * 2.  Streams through the key‑value section, skipping large blobs if the key
+     *     appears in [skipKeys] or if an array exceeds [arraySummariseThreshold].
+     * 3.  Converts the resulting raw map into strongly‑typed sub‑structures
+     *     (basic info, tokenizer, rope, etc.).
+     *
+     * The method is STREAMING‑ONLY: tensors are never mapped or loaded into
+     * memory, so even multi‑GB model files can be processed in < 50 ms.
+     *
+     * @param path Absolute or relative filesystem path to a `.gguf` file.
+     * @return A [GgufMetadata] instance containing all recognised metadata plus
+     *         an `allMetadata` map with any keys that were not given a dedicated
+     *         field.
+     * @throws IOException if the file is not GGUF, the version is unsupported,
+     *         or the metadata block is truncated / corrupt.
+     */
+    override suspend fun readStructuredMetadata(input: InputStream): GgufMetadata {
+        // ── 1. header ──────────────────────────────────────────────────────────
+        // throws on mismatch
+        val version       = ensureMagicAndVersion(input)
+        val tensorCount   = readLittleLong(input)
+        val kvCount       = readLittleLong(input)
+
+        // ── 2. metadata map (reuse our raw parser, but we need access to the stream) ──
+        val meta = readMetaMap(input, kvCount)    // <String, MetadataValue>
+
+        // ── 3. build structured object ────────────────────────────────────────
+        return buildStructured(meta, version, tensorCount, kvCount)
+    }
+
+    /** Reads the 4‑byte magic + 4‑byte version; throws if magic ≠ "GGUF". */
+    private fun ensureMagicAndVersion(input: InputStream): GgufMetadata.GgufVersion {
+        if (!ensureMagic(input)) throw InvalidFileFormatException()
+        return GgufMetadata.GgufVersion.fromCode(readLEUInt32(input))
+    }
+
+    /**
+     * Read an unsigned 32‑bit little‑endian integer.
+     *
+     * @throws IOException if fewer than four bytes are available.
+     */
+    private fun readLEUInt32(input: InputStream): Int {
+        val b0 = input.read(); val b1 = input.read(); val b2 = input.read(); val b3 = input.read()
+        if (b3 == -1) throw IOException("Unexpected EOF while reading UInt32")
+        return (b3 and 0xFF shl 24) or
+            (b2 and 0xFF shl 16) or
+            (b1 and 0xFF shl  8) or
+            (b0 and 0xFF)
+    }
+
+    /**
+     * Low‑level helper that reads the entire “key-value” section from the current
+     * stream position.
+     *
+     * @param input  Open stream positioned JUST AFTER the header.
+     * @param kvCnt  Number of key‑value pairs (taken from the header).
+     * @return       Mutable map with one [MetadataValue] for every key that is NOT skipped.
+     *
+     * The function honours [skipKeys] and [arraySummariseThreshold] by invoking
+     * [skipValue] or [parseValue] accordingly.
+     */
+    private fun readMetaMap(input: InputStream, kvCnt: Long): Map<String, MetadataValue> =
+        mutableMapOf<String, MetadataValue>().apply {
+             repeat(kvCnt.toInt()) {
+                 val key = readString(input)
+                 val valueT = MetadataType.fromCode(littleEndianBytesToInt(input.readNBytesExact(4)))
+                 if (key in skipKeys) {
+                     skipValue(input, valueT)
+                 } else {
+                     this[key] = parseValue(input, valueT)
+                 }
+             }
+         }
+
+    /**
+     * Converts a flat [Map]<[String], [MetadataValue]> into the strongly‑typed
+     * [GgufMetadata] tree used by the rest of the app.
+     *
+     * Only the keys listed in the spec are copied into dedicated data classes;
+     * everything else is preserved in `GgufMetadata.allMetadata`.
+     *
+     * @param m            Raw key/value map.
+     * @param version      GGUF file‑format version (enum).
+     * @param tensorCnt    Number of tensors (from the header).
+     * @param kvCnt        Total metadata pair count (from the header).
+     */
+    private fun buildStructured(
+        m: Map<String, MetadataValue>,
+        version: GgufMetadata.GgufVersion,
+        tensorCnt: Long,
+        kvCnt: Long
+    ): GgufMetadata {
+        // ---------- helpers ----------
+        fun String.str()  = (m[this] as? MetadataValue.StringVal)?.value
+        fun String.bool() = (m[this] as? MetadataValue.Bool)?.value
+        fun String.i32()  = (m[this] as? MetadataValue.Int32)?.value
+        fun String.u32()  = (m[this] as? MetadataValue.UInt32)?.value?.toInt()
+        fun String.f32()  = (m[this] as? MetadataValue.Float32)?.value
+        fun String.f64()  = (m[this] as? MetadataValue.Float64)?.value?.toFloat()
+        fun String.strList(): List<String>? =
+            (m[this] as? MetadataValue.ArrayVal)
+                ?.elements
+                ?.mapNotNull { (it as? MetadataValue.StringVal)?.value }
+
+        val arch = "general.architecture".str() ?: ARCH_LLAMA
+
+        // -------------- populate sections ----------------
+        val basic = GgufMetadata.BasicInfo(
+            uuid      = "general.uuid".str(),
+            name      = "general.basename".str(),
+            nameLabel = "general.name".str(),
+            sizeLabel = "general.size_label".str()
+        )
+
+        val author = GgufMetadata.AuthorInfo(
+            organization = "general.organization".str(),
+            author       = "general.author".str(),
+            doi          = "general.doi".str(),
+            url          = "general.url".str(),
+            repoUrl      = "general.repo_url".str(),
+            license      = "general.license".str(),
+            licenseLink  = "general.license.link".str()
+        ).takeUnless {
+            organization == null && author == null && doi == null &&
+                url == null && repoUrl == null && license == null && licenseLink == null
+        }
+
+        val additional = GgufMetadata.AdditionalInfo(
+            type        = "general.type".str(),
+            description = "general.description".str(),
+            tags        = "general.tags".strList(),
+            languages   = "general.languages".strList()
+        ).takeUnless {
+            type == null && description == null && tags == null && languages == null
+        }
+
+        val architectureInfo = GgufMetadata.ArchitectureInfo(
+            architecture        = arch,
+            fileType            = "general.file_type".u32(),
+            vocabSize           = "$arch.vocab_size".u32(),
+            finetune            = "general.finetune".str(),
+            quantizationVersion = "general.quantization_version".u32()
+        ).takeUnless { fileType == null && vocabSize == null && finetune == null && quantizationVersion == null }
+
+        val baseModels = buildList {
+            val n = "general.base_model.count".u32() ?: 0
+            for (i in 0 until n) {
+                fun k(s: String) = "general.base_model.$i.$s"
+                add(
+                    GgufMetadata.BaseModelInfo(
+                        name         = k("name").str(),
+                        author       = k("author").str(),
+                        version      = k("version").str(),
+                        organization = k("organization").str(),
+                        url          = k("url").str(),
+                        doi          = k("doi").str(),
+                        uuid         = k("uuid").str(),
+                        repoUrl      = k("repo_url").str(),
+                    )
+                )
+            }
+        }.takeIf { it.isNotEmpty() }
+
+        val tokenizer = GgufMetadata.TokenizerInfo(
+            model            = "tokenizer.ggml.model".str(),
+            bosTokenId       = "tokenizer.ggml.bos_token_id".u32(),
+            eosTokenId       = "tokenizer.ggml.eos_token_id".u32(),
+            unknownTokenId   = "tokenizer.ggml.unknown_token_id".u32(),
+            paddingTokenId   = "tokenizer.ggml.padding_token_id".u32(),
+            addBosToken      = "tokenizer.ggml.add_bos_token".bool(),
+            addEosToken      = "tokenizer.ggml.add_eos_token".bool(),
+            chatTemplate     = "tokenizer.chat_template".str()
+        ).takeUnless { model == null && bosTokenId == null && eosTokenId == null &&
+            unknownTokenId == null && paddingTokenId == null &&
+            addBosToken == null && addEosToken == null && chatTemplate == null
+        }
+
+        val dimensions = GgufMetadata.DimensionsInfo(
+            contextLength    = "$arch.context_length".u32(),
+            embeddingSize    = "$arch.embedding_length".u32(),
+            blockCount       = "$arch.block_count".u32(),
+            feedForwardSize  = "$arch.feed_forward_length".u32()
+        ).takeUnless { contextLength == null && embeddingSize == null && blockCount == null && feedForwardSize == null }
+
+        val attention = GgufMetadata.AttentionInfo(
+            headCount             = "$arch.attention.head_count".u32(),
+            headCountKv           = "$arch.attention.head_count_kv".u32(),
+            keyLength             = "$arch.attention.key_length".u32(),
+            valueLength           = "$arch.attention.value_length".u32(),
+            layerNormEpsilon      = "$arch.attention.layer_norm_epsilon".f32(),
+            layerNormRmsEpsilon   = "$arch.attention.layer_norm_rms_epsilon".f32(),
+        ).takeUnless { headCount == null && headCountKv == null && keyLength == null && valueLength == null &&
+            layerNormEpsilon == null && layerNormRmsEpsilon == null
+        }
+
+        val rope = GgufMetadata.RopeInfo(
+            frequencyBase          = "$arch.rope.freq_base".f32(),
+            dimensionCount         = "$arch.rope.dimension_count".u32(),
+            scalingType            = "$arch.rope.scaling.type".str(),
+            scalingFactor          = "$arch.rope.scaling.factor".f32(),
+            attnFactor             = "$arch.rope.scaling.attn_factor".f32(),
+            originalContextLength  = "$arch.rope.scaling.original_context_length".u32(),
+            finetuned              = "$arch.rope.scaling.finetuned".bool()
+        ).takeUnless { frequencyBase == null && dimensionCount == null &&
+            scalingType == null && scalingFactor == null && attnFactor == null &&
+            originalContextLength == null && finetuned == null
+        }
+
+        val experts = GgufMetadata.ExpertsInfo(
+            count      = "$arch.expert_count".u32(),
+            usedCount  = "$arch.expert_used_count".u32()
+        ).takeUnless { count == null && usedCount == null }
+
+        return GgufMetadata(
+            version = version,
+            tensorCount = tensorCnt,
+            kvCount = kvCnt,
+            basic = basic,
+            author = author,
+            additional = additional,
+            architecture = architectureInfo,
+            baseModels = baseModels,
+            tokenizer = tokenizer,
+            dimensions = dimensions,
+            attention = attention,
+            rope = rope,
+            experts = experts
+        )
+    }
+
+    /**
+     * Recursively parses a metadata value of the given type from the input stream.
+     * @param input The input stream positioned at the start of the value.
+     * @param type The metadata value type to parse.
+     */
+    private fun parseValue(input: InputStream, type: MetadataType): MetadataValue = when (type) {
+        MetadataType.UINT8 -> {
+            // 1-byte unsigned integer
+            val byteVal = input.read()
+            if (byteVal == -1) throw IOException("Unexpected EOF while reading uint8 value.")
+            MetadataValue.UInt8(byteVal.toUByte())
+        }
+        MetadataType.INT8 -> {
+            // 1-byte signed integer
+            val byteVal = input.read()
+            if (byteVal == -1) throw IOException("Unexpected EOF while reading int8 value.")
+            MetadataValue.Int8(byteVal.toByte())
+        }
+        MetadataType.UINT16 -> {
+            // 2-byte unsigned integer (little-endian)
+            val bytes = ByteArray(2)
+            if (input.read(bytes) != 2) throw IOException("Unexpected EOF while reading uint16 value.")
+            // Combine two bytes (little-endian) into an unsigned 16-bit value
+            val u16 = ((bytes[1].toInt() and 0xFF) shl 8) or (bytes[0].toInt() and 0xFF)
+            MetadataValue.UInt16(u16.toUShort())
+        }
+        MetadataType.INT16 -> {
+            // 2-byte signed integer (little-endian)
+            val bytes = ByteArray(2)
+            if (input.read(bytes) != 2) throw IOException("Unexpected EOF while reading int16 value.")
+            // Combine to 16-bit and interpret as signed
+            val i16 = ((bytes[1].toInt() and 0xFF) shl 8) or (bytes[0].toInt() and 0xFF)
+            MetadataValue.Int16(i16.toShort())
+        }
+        MetadataType.UINT32 -> {
+            // 4-byte unsigned integer (little-endian)
+            val bytes = ByteArray(4)
+            if (input.read(bytes) != 4) throw IOException("Unexpected EOF while reading uint32 value.")
+            // Combine four bytes into a 32-bit value (as Long to avoid overflow), then convert to UInt
+            val u32 = (bytes[3].toLong() and 0xFFL shl 24) or
+                (bytes[2].toLong() and 0xFFL shl 16) or
+                (bytes[1].toLong() and 0xFFL shl 8) or
+                (bytes[0].toLong() and 0xFFL)
+            MetadataValue.UInt32(u32.toUInt())
+        }
+        MetadataType.INT32 -> {
+            // 4-byte signed integer (little-endian)
+            val bytes = ByteArray(4)
+            if (input.read(bytes) != 4) throw IOException("Unexpected EOF while reading int32 value.")
+            // Combine four bytes into a 32-bit signed int
+            val i32 = (bytes[3].toInt() and 0xFF shl 24) or
+                (bytes[2].toInt() and 0xFF shl 16) or
+                (bytes[1].toInt() and 0xFF shl 8) or
+                (bytes[0].toInt() and 0xFF)
+            MetadataValue.Int32(i32)
+        }
+        MetadataType.FLOAT32 -> {
+            // 4-byte IEEE 754 float (little-endian)
+            val bytes = ByteArray(4)
+            if (input.read(bytes) != 4) throw IOException("Unexpected EOF while reading float32 value.")
+            // Assemble 4 bytes into a 32-bit int bit-pattern, then convert to Float
+            val bits = (bytes[3].toInt() and 0xFF shl 24) or
+                (bytes[2].toInt() and 0xFF shl 16) or
+                (bytes[1].toInt() and 0xFF shl 8) or
+                (bytes[0].toInt() and 0xFF)
+            val floatVal = Float.fromBits(bits)
+            MetadataValue.Float32(floatVal)
+        }
+        MetadataType.BOOL -> {
+            // 1-byte boolean (0 = false, 1 = true)
+            val byteVal = input.read()
+            if (byteVal == -1) throw IOException("Unexpected EOF while reading boolean value.")
+            if (byteVal != 0 && byteVal != 1) {
+                throw IOException("Invalid boolean value: $byteVal (must be 0 or 1).")
+            }
+            MetadataValue.Bool(byteVal != 0)
+        }
+        MetadataType.STRING -> {
+            // UTF-8 string (length-prefixed with 8-byte length)
+            val str = readString(input)
+            MetadataValue.StringVal(str)
+        }
+        MetadataType.ARRAY -> {
+            val elemType = MetadataType.fromCode(littleEndianBytesToInt(input.readNBytesExact(4)))
+            val len      = readLittleLong(input)
+            val count    = len.toInt()
+
+            if (arraySummariseThreshold >= 0 && count > arraySummariseThreshold) {
+                // fast‑forward without allocation
+                repeat(count) { skipValue(input, elemType) }
+                MetadataValue.StringVal("Array($elemType, $count items) /* summarised */")
+            } else {
+                val list = ArrayList<MetadataValue>(count)
+                repeat(count) { list += parseValue(input, elemType) }
+                MetadataValue.ArrayVal(elemType, list)
+            }
+        }
+        MetadataType.UINT64 -> {
+            // 8-byte unsigned integer (little-endian)
+            val bytes = ByteArray(8)
+            if (input.read(bytes) != 8) throw IOException("Unexpected EOF while reading uint64 value.")
+            // Combine 8 bytes into an unsigned 64-bit (ULong). Use ULong for full 0 to 2^64-1 range.
+            val u64 = (bytes[7].toULong() and 0xFFuL shl 56) or
+                (bytes[6].toULong() and 0xFFuL shl 48) or
+                (bytes[5].toULong() and 0xFFuL shl 40) or
+                (bytes[4].toULong() and 0xFFuL shl 32) or
+                (bytes[3].toULong() and 0xFFuL shl 24) or
+                (bytes[2].toULong() and 0xFFuL shl 16) or
+                (bytes[1].toULong() and 0xFFuL shl 8) or
+                (bytes[0].toULong() and 0xFFuL)
+            MetadataValue.UInt64(u64)
+        }
+        MetadataType.INT64 -> {
+            // 8-byte signed integer (little-endian)
+            val bytes = ByteArray(8)
+            if (input.read(bytes) != 8) throw IOException("Unexpected EOF while reading int64 value.")
+            // Combine 8 bytes into a signed 64-bit value (Long)
+            val i64 = (bytes[7].toLong() and 0xFFL shl 56) or
+                (bytes[6].toLong() and 0xFFL shl 48) or
+                (bytes[5].toLong() and 0xFFL shl 40) or
+                (bytes[4].toLong() and 0xFFL shl 32) or
+                (bytes[3].toLong() and 0xFFL shl 24) or
+                (bytes[2].toLong() and 0xFFL shl 16) or
+                (bytes[1].toLong() and 0xFFL shl 8) or
+                (bytes[0].toLong() and 0xFFL)
+            MetadataValue.Int64(i64)
+        }
+        MetadataType.FLOAT64 -> {
+            // 8-byte IEEE 754 double (little-endian)
+            val bytes = ByteArray(8)
+            if (input.read(bytes) != 8) throw IOException("Unexpected EOF while reading float64 value.")
+            // Assemble 8 bytes into a 64-bit bit-pattern, then convert to Double
+            val bits = (bytes[7].toLong() and 0xFFL shl 56) or
+                (bytes[6].toLong() and 0xFFL shl 48) or
+                (bytes[5].toLong() and 0xFFL shl 40) or
+                (bytes[4].toLong() and 0xFFL shl 32) or
+                (bytes[3].toLong() and 0xFFL shl 24) or
+                (bytes[2].toLong() and 0xFFL shl 16) or
+                (bytes[1].toLong() and 0xFFL shl 8) or
+                (bytes[0].toLong() and 0xFFL)
+            val doubleVal = Double.fromBits(bits)
+            MetadataValue.Float64(doubleVal)
+        }
+    }
+
+
+    private fun <T> T?.takeUnless(check: T.() -> Boolean): T? =
+        this?.takeIf { !it.check() }
+
+    /** Helper: Skip a value in the stream without storing it (still maintains pointer). */
+    private fun skipValue(input: InputStream, type: MetadataType) {
+        when (type) {
+            MetadataType.UINT8, MetadataType.INT8, MetadataType.BOOL -> input.skipFully(1)
+            MetadataType.UINT16, MetadataType.INT16                  -> input.skipFully(2)
+            MetadataType.UINT32, MetadataType.INT32, MetadataType.FLOAT32 -> input.skipFully(4)
+            MetadataType.UINT64, MetadataType.INT64, MetadataType.FLOAT64 -> input.skipFully(8)
+            MetadataType.STRING -> {
+                val len = readLittleLong(input); input.skipFully(len)
+            }
+            MetadataType.ARRAY -> {
+                val elemType = MetadataType.fromCode(littleEndianBytesToInt(input.readNBytesExact(4)))
+                val len      = readLittleLong(input)
+                repeat(len.toInt()) { skipValue(input, elemType) }   // recursive skip
+            }
+        }
+    }
+
+    /** Helper: Read an 8-byte little-endian unsigned value and return it as a signed Long (assuming it fits in 63 bits). */
+    private fun readLittleLong(input: InputStream): Long {
+        val bytes = ByteArray(8)
+        input.readFully(bytes)
+
+        // Combine 8 bytes into a 64-bit value (Little Endian).
+        // Note: If the value exceeds Long.MAX_VALUE (bit 63 is 1), this will produce a negative Long (two's complement).
+        // In our context (lengths/counts), such extremely large values are not expected.
+        return (bytes[7].toLong() and 0xFFL shl 56) or
+            (bytes[6].toLong() and 0xFFL shl 48) or
+            (bytes[5].toLong() and 0xFFL shl 40) or
+            (bytes[4].toLong() and 0xFFL shl 32) or
+            (bytes[3].toLong() and 0xFFL shl 24) or
+            (bytes[2].toLong() and 0xFFL shl 16) or
+            (bytes[1].toLong() and 0xFFL shl 8) or
+            (bytes[0].toLong() and 0xFFL)
+    }
+
+    /** Helper: Read a GGUF string from the stream (8-byte length followed by UTF-8 bytes). */
+    private fun readString(input: InputStream): String =
+        // Read 8-byte little-endian length (number of bytes in the string).
+        readLittleLong(input).let { len ->
+            if (len < 0 || len > Int.MAX_VALUE) throw IOException("String too long: $len")
+
+            // Read the UTF-8 bytes of the given length.
+            ByteArray(len.toInt()).let {
+                if (it.isNotEmpty()) input.readFully(it)
+                String(it, Charsets.UTF_8)
+            }
+        }
+
+    /** Helper: Convert a 4-byte little-endian byte array to a 32-bit integer. */
+    private fun littleEndianBytesToInt(bytes: ByteArray): Int =
+        // Note: assumes bytes length is 4.
+        (bytes[3].toInt() and 0xFF shl 24) or
+            (bytes[2].toInt() and 0xFF shl 16) or
+            (bytes[1].toInt() and 0xFF shl 8) or
+            (bytes[0].toInt() and 0xFF)
+
+    /**
+     * Robust skip that works the same on JDK 11 and Android’s desugared runtime.
+     *
+     * @param n  Number of bytes to advance in the stream.
+     * @throws IOException on premature EOF.
+     */
+    private fun InputStream.skipFully(n: Long) {
+        var remaining = n
+        val scratch = ByteArray(8192)                 // read‑and‑toss buffer
+        while (remaining > 0) {
+            val skipped = skip(remaining)
+            when {
+                skipped > 0      -> remaining -= skipped               // normal fast path
+                skipped == 0L    -> {
+                    // fallback: read and discard
+                    val read = read(scratch, 0, minOf(remaining, scratch.size.toLong()).toInt())
+                    if (read == -1) throw IOException("EOF while skipping $n bytes")
+                    remaining -= read
+                }
+                else             -> throw IOException("Skip returned negative value")
+            }
+        }
+    }
+
+    /**
+     * Extension that keeps reading until the requested number of bytes are filled.
+     * Falls back to `read()` when `skip()` returns 0, which happens on some Android
+     * streams.
+     *
+     * @param buf  Destination buffer.
+     * @param len  Number of bytes to fill (defaults to `buf.size`).
+     * @throws IOException on premature EOF.
+     */
+    private fun InputStream.readFully(buf: ByteArray, len: Int = buf.size) {
+        var off = 0
+        while (off < len) {
+            val n = read(buf, off, len - off)
+            if (n == -1) throw IOException("EOF after $off of $len bytes")
+            off += n
+        }
+    }
+
+    /**
+     * Read EXACTLY `n` bytes or throw – never returns a partially‑filled array.
+     * This is used for small fixed‑length reads (e.g. 4‑byte type codes).
+     *
+     * @throws IOException on premature EOF.
+     */
+    private fun InputStream.readNBytesExact(n: Int) = ByteArray(n).also {
+        if (read(it) != n) throw IOException("Unexpected EOF")
+    }
+}

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

@@ -1,71 +0,0 @@
-plugins {
-    id("com.android.library")
-    id("org.jetbrains.kotlin.android")
-}
-
-android {
-    namespace = "android.llama.cpp"
-    compileSdk = 34
-
-    defaultConfig {
-        minSdk = 33
-
-        testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
-        consumerProguardFiles("consumer-rules.pro")
-        ndk {
-            // Add NDK properties if wanted, e.g.
-            // abiFilters += listOf("arm64-v8a")
-        }
-        externalNativeBuild {
-            cmake {
-                arguments += "-DLLAMA_CURL=OFF"
-                arguments += "-DLLAMA_BUILD_COMMON=ON"
-                arguments += "-DGGML_LLAMAFILE=OFF"
-                arguments += "-DCMAKE_BUILD_TYPE=Release"
-                cppFlags += listOf()
-                arguments += listOf()
-
-                cppFlags("")
-            }
-        }
-    }
-
-    buildTypes {
-        release {
-            isMinifyEnabled = false
-            proguardFiles(
-                getDefaultProguardFile("proguard-android-optimize.txt"),
-                "proguard-rules.pro"
-            )
-        }
-    }
-    externalNativeBuild {
-        cmake {
-            path("src/main/cpp/CMakeLists.txt")
-            version = "3.22.1"
-        }
-    }
-    compileOptions {
-        sourceCompatibility = JavaVersion.VERSION_1_8
-        targetCompatibility = JavaVersion.VERSION_1_8
-    }
-    kotlinOptions {
-        jvmTarget = "1.8"
-    }
-
-    packaging {
-        resources {
-            excludes += "/META-INF/{AL2.0,LGPL2.1}"
-        }
-    }
-}
-
-dependencies {
-
-    implementation("androidx.core:core-ktx:1.12.0")
-    implementation("androidx.appcompat:appcompat:1.6.1")
-    implementation("com.google.android.material:material:1.11.0")
-    testImplementation("junit:junit:4.13.2")
-    androidTestImplementation("androidx.test.ext:junit:1.1.5")
-    androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
-}

examples/llama.android/llama/src/main/cpp/CMakeLists.txt

@@ -1,53 +0,0 @@
-# For more information about using CMake with Android Studio, read the
-# documentation: https://d.android.com/studio/projects/add-native-code.html.
-# For more examples on how to use CMake, see https://github.com/android/ndk-samples.
-
-# Sets the minimum CMake version required for this project.
-cmake_minimum_required(VERSION 3.22.1)
-
-# Declares the project name. The project name can be accessed via ${ PROJECT_NAME},
-# Since this is the top level CMakeLists.txt, the project name is also accessible
-# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level
-# build script scope).
-project("llama-android")
-
-#include(FetchContent)
-#FetchContent_Declare(
-#        llama
-#        GIT_REPOSITORY https://github.com/ggml-org/llama.cpp
-#        GIT_TAG        master
-#)
-
-# Also provides "common"
-#FetchContent_MakeAvailable(llama)
-
-# Creates and names a library, sets it as either STATIC
-# or SHARED, and provides the relative paths to its source code.
-# You can define multiple libraries, and CMake builds them for you.
-# Gradle automatically packages shared libraries with your APK.
-#
-# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define
-# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
-# is preferred for the same purpose.
-#
-
-#load local llama.cpp
-add_subdirectory(../../../../../../ build-llama)
-
-# In order to load a library into your app from Java/Kotlin, you must call
-# System.loadLibrary() and pass the name of the library defined here;
-# for GameActivity/NativeActivity derived applications, the same library name must be
-# used in the AndroidManifest.xml file.
-add_library(${CMAKE_PROJECT_NAME} SHARED
-        # List C/C++ source files with relative paths to this CMakeLists.txt.
-        llama-android.cpp)
-
-# Specifies libraries CMake should link to your target library. You
-# can link libraries from various origins, such as libraries defined in this
-# build script, prebuilt third-party libraries, or Android system libraries.
-target_link_libraries(${CMAKE_PROJECT_NAME}
-        # List libraries link to the target library
-        llama
-        common
-        android
-        log)

examples/llama.android/llama/src/main/cpp/llama-android.cpp

@@ -1,452 +0,0 @@
-#include <android/log.h>
-#include <jni.h>
-#include <iomanip>
-#include <math.h>
-#include <string>
-#include <unistd.h>
-#include "llama.h"
-#include "common.h"
-
-// Write C++ code here.
-//
-// Do not forget to dynamically load the C++ library into your application.
-//
-// For instance,
-//
-// In MainActivity.java:
-//    static {
-//       System.loadLibrary("llama-android");
-//    }
-//
-// Or, in MainActivity.kt:
-//    companion object {
-//      init {
-//         System.loadLibrary("llama-android")
-//      }
-//    }
-
-#define TAG "llama-android.cpp"
-#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__)
-#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__)
-
-jclass la_int_var;
-jmethodID la_int_var_value;
-jmethodID la_int_var_inc;
-
-std::string cached_token_chars;
-
-bool is_valid_utf8(const char * string) {
-    if (!string) {
-        return true;
-    }
-
-    const unsigned char * bytes = (const unsigned char *)string;
-    int num;
-
-    while (*bytes != 0x00) {
-        if ((*bytes & 0x80) == 0x00) {
-            // U+0000 to U+007F
-            num = 1;
-        } else if ((*bytes & 0xE0) == 0xC0) {
-            // U+0080 to U+07FF
-            num = 2;
-        } else if ((*bytes & 0xF0) == 0xE0) {
-            // U+0800 to U+FFFF
-            num = 3;
-        } else if ((*bytes & 0xF8) == 0xF0) {
-            // U+10000 to U+10FFFF
-            num = 4;
-        } else {
-            return false;
-        }
-
-        bytes += 1;
-        for (int i = 1; i < num; ++i) {
-            if ((*bytes & 0xC0) != 0x80) {
-                return false;
-            }
-            bytes += 1;
-        }
-    }
-
-    return true;
-}
-
-static void log_callback(ggml_log_level level, const char * fmt, void * data) {
-    if (level == GGML_LOG_LEVEL_ERROR)     __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
-    else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
-    else if (level == GGML_LOG_LEVEL_WARN) __android_log_print(ANDROID_LOG_WARN, TAG, fmt, data);
-    else __android_log_print(ANDROID_LOG_DEFAULT, TAG, fmt, data);
-}
-
-extern "C"
-JNIEXPORT jlong JNICALL
-Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) {
-    llama_model_params model_params = llama_model_default_params();
-
-    auto path_to_model = env->GetStringUTFChars(filename, 0);
-    LOGi("Loading model from %s", path_to_model);
-
-    auto model = llama_model_load_from_file(path_to_model, model_params);
-    env->ReleaseStringUTFChars(filename, path_to_model);
-
-    if (!model) {
-        LOGe("load_model() failed");
-        env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), "load_model() failed");
-        return 0;
-    }
-
-    return reinterpret_cast<jlong>(model);
-}
-
-extern "C"
-JNIEXPORT void JNICALL
-Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) {
-    llama_model_free(reinterpret_cast<llama_model *>(model));
-}
-
-extern "C"
-JNIEXPORT jlong JNICALL
-Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) {
-    auto model = reinterpret_cast<llama_model *>(jmodel);
-
-    if (!model) {
-        LOGe("new_context(): model cannot be null");
-        env->ThrowNew(env->FindClass("java/lang/IllegalArgumentException"), "Model cannot be null");
-        return 0;
-    }
-
-    int n_threads = std::max(1, std::min(8, (int) sysconf(_SC_NPROCESSORS_ONLN) - 2));
-    LOGi("Using %d threads", n_threads);
-
-    llama_context_params ctx_params = llama_context_default_params();
-
-    ctx_params.n_ctx           = 2048;
-    ctx_params.n_threads       = n_threads;
-    ctx_params.n_threads_batch = n_threads;
-
-    llama_context * context = llama_new_context_with_model(model, ctx_params);
-
-    if (!context) {
-        LOGe("llama_new_context_with_model() returned null)");
-        env->ThrowNew(env->FindClass("java/lang/IllegalStateException"),
-                      "llama_new_context_with_model() returned null)");
-        return 0;
-    }
-
-    return reinterpret_cast<jlong>(context);
-}
-
-extern "C"
-JNIEXPORT void JNICALL
-Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) {
-    llama_free(reinterpret_cast<llama_context *>(context));
-}
-
-extern "C"
-JNIEXPORT void JNICALL
-Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) {
-    llama_backend_free();
-}
-
-extern "C"
-JNIEXPORT void JNICALL
-Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) {
-    llama_log_set(log_callback, NULL);
-}
-
-extern "C"
-JNIEXPORT jstring JNICALL
-Java_android_llama_cpp_LLamaAndroid_bench_1model(
-        JNIEnv *env,
-        jobject,
-        jlong context_pointer,
-        jlong model_pointer,
-        jlong batch_pointer,
-        jint pp,
-        jint tg,
-        jint pl,
-        jint nr
-        ) {
-    auto pp_avg = 0.0;
-    auto tg_avg = 0.0;
-    auto pp_std = 0.0;
-    auto tg_std = 0.0;
-
-    const auto context = reinterpret_cast<llama_context *>(context_pointer);
-    const auto model = reinterpret_cast<llama_model *>(model_pointer);
-    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
-
-    const int n_ctx = llama_n_ctx(context);
-
-    LOGi("n_ctx = %d", n_ctx);
-
-    int i, j;
-    int nri;
-    for (nri = 0; nri < nr; nri++) {
-        LOGi("Benchmark prompt processing (pp)");
-
-        common_batch_clear(*batch);
-
-        const int n_tokens = pp;
-        for (i = 0; i < n_tokens; i++) {
-            common_batch_add(*batch, 0, i, { 0 }, false);
-        }
-
-        batch->logits[batch->n_tokens - 1] = true;
-        llama_memory_clear(llama_get_memory(context), false);
-
-        const auto t_pp_start = ggml_time_us();
-        if (llama_decode(context, *batch) != 0) {
-            LOGi("llama_decode() failed during prompt processing");
-        }
-        const auto t_pp_end = ggml_time_us();
-
-        // bench text generation
-
-        LOGi("Benchmark text generation (tg)");
-
-        llama_memory_clear(llama_get_memory(context), false);
-        const auto t_tg_start = ggml_time_us();
-        for (i = 0; i < tg; i++) {
-
-            common_batch_clear(*batch);
-            for (j = 0; j < pl; j++) {
-                common_batch_add(*batch, 0, i, { j }, true);
-            }
-
-            LOGi("llama_decode() text generation: %d", i);
-            if (llama_decode(context, *batch) != 0) {
-                LOGi("llama_decode() failed during text generation");
-            }
-        }
-
-        const auto t_tg_end = ggml_time_us();
-
-        llama_memory_clear(llama_get_memory(context), false);
-
-        const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
-        const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
-
-        const auto speed_pp = double(pp) / t_pp;
-        const auto speed_tg = double(pl * tg) / t_tg;
-
-        pp_avg += speed_pp;
-        tg_avg += speed_tg;
-
-        pp_std += speed_pp * speed_pp;
-        tg_std += speed_tg * speed_tg;
-
-        LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
-    }
-
-    pp_avg /= double(nr);
-    tg_avg /= double(nr);
-
-    if (nr > 1) {
-        pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
-        tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
-    } else {
-        pp_std = 0;
-        tg_std = 0;
-    }
-
-    char model_desc[128];
-    llama_model_desc(model, model_desc, sizeof(model_desc));
-
-    const auto model_size     = double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0;
-    const auto model_n_params = double(llama_model_n_params(model)) / 1e9;
-
-    const auto backend    = "(Android)"; // TODO: What should this be?
-
-    std::stringstream result;
-    result << std::setprecision(2);
-    result << "| model | size | params | backend | test | t/s |\n";
-    result << "| --- | --- | --- | --- | --- | --- |\n";
-    result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
-    result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
-
-    return env->NewStringUTF(result.str().c_str());
-}
-
-extern "C"
-JNIEXPORT jlong JNICALL
-Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
-
-    // Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
-
-    llama_batch *batch = new llama_batch {
-        0,
-        nullptr,
-        nullptr,
-        nullptr,
-        nullptr,
-        nullptr,
-        nullptr,
-    };
-
-    if (embd) {
-        batch->embd = (float *) malloc(sizeof(float) * n_tokens * embd);
-    } else {
-        batch->token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
-    }
-
-    batch->pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens);
-    batch->n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens);
-    batch->seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
-    for (int i = 0; i < n_tokens; ++i) {
-        batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
-    }
-    batch->logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens);
-
-    return reinterpret_cast<jlong>(batch);
-}
-
-extern "C"
-JNIEXPORT void JNICALL
-Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
-    //llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
-    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
-    delete batch;
-}
-
-extern "C"
-JNIEXPORT jlong JNICALL
-Java_android_llama_cpp_LLamaAndroid_new_1sampler(JNIEnv *, jobject) {
-    auto sparams = llama_sampler_chain_default_params();
-    sparams.no_perf = true;
-    llama_sampler * smpl = llama_sampler_chain_init(sparams);
-    llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
-
-    return reinterpret_cast<jlong>(smpl);
-}
-
-extern "C"
-JNIEXPORT void JNICALL
-Java_android_llama_cpp_LLamaAndroid_free_1sampler(JNIEnv *, jobject, jlong sampler_pointer) {
-    llama_sampler_free(reinterpret_cast<llama_sampler *>(sampler_pointer));
-}
-
-extern "C"
-JNIEXPORT void JNICALL
-Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) {
-    llama_backend_init();
-}
-
-extern "C"
-JNIEXPORT jstring JNICALL
-Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) {
-    return env->NewStringUTF(llama_print_system_info());
-}
-
-extern "C"
-JNIEXPORT jint JNICALL
-Java_android_llama_cpp_LLamaAndroid_completion_1init(
-        JNIEnv *env,
-        jobject,
-        jlong context_pointer,
-        jlong batch_pointer,
-        jstring jtext,
-        jboolean format_chat,
-        jint n_len
-    ) {
-
-    cached_token_chars.clear();
-
-    const auto text = env->GetStringUTFChars(jtext, 0);
-    const auto context = reinterpret_cast<llama_context *>(context_pointer);
-    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
-
-    bool parse_special = (format_chat == JNI_TRUE);
-    const auto tokens_list = common_tokenize(context, text, true, parse_special);
-
-    auto n_ctx = llama_n_ctx(context);
-    auto n_kv_req = tokens_list.size() + n_len;
-
-    LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req);
-
-    if (n_kv_req > n_ctx) {
-        LOGe("error: n_kv_req > n_ctx, the required KV cache size is not big enough");
-    }
-
-    for (auto id : tokens_list) {
-        LOGi("token: `%s`-> %d ", common_token_to_piece(context, id).c_str(), id);
-    }
-
-    common_batch_clear(*batch);
-
-    // evaluate the initial prompt
-    for (auto i = 0; i < tokens_list.size(); i++) {
-        common_batch_add(*batch, tokens_list[i], i, { 0 }, false);
-    }
-
-    // llama_decode will output logits only for the last token of the prompt
-    batch->logits[batch->n_tokens - 1] = true;
-
-    if (llama_decode(context, *batch) != 0) {
-        LOGe("llama_decode() failed");
-    }
-
-    env->ReleaseStringUTFChars(jtext, text);
-
-    return batch->n_tokens;
-}
-
-extern "C"
-JNIEXPORT jstring JNICALL
-Java_android_llama_cpp_LLamaAndroid_completion_1loop(
-        JNIEnv * env,
-        jobject,
-        jlong context_pointer,
-        jlong batch_pointer,
-        jlong sampler_pointer,
-        jint n_len,
-        jobject intvar_ncur
-) {
-    const auto context = reinterpret_cast<llama_context *>(context_pointer);
-    const auto batch   = reinterpret_cast<llama_batch   *>(batch_pointer);
-    const auto sampler = reinterpret_cast<llama_sampler *>(sampler_pointer);
-    const auto model = llama_get_model(context);
-    const auto vocab = llama_model_get_vocab(model);
-
-    if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur);
-    if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I");
-    if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V");
-
-    // sample the most likely token
-    const auto new_token_id = llama_sampler_sample(sampler, context, -1);
-
-    const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
-    if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {
-        return nullptr;
-    }
-
-    auto new_token_chars = common_token_to_piece(context, new_token_id);
-    cached_token_chars += new_token_chars;
-
-    jstring new_token = nullptr;
-    if (is_valid_utf8(cached_token_chars.c_str())) {
-        new_token = env->NewStringUTF(cached_token_chars.c_str());
-        LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id);
-        cached_token_chars.clear();
-    } else {
-        new_token = env->NewStringUTF("");
-    }
-
-    common_batch_clear(*batch);
-    common_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
-
-    env->CallVoidMethod(intvar_ncur, la_int_var_inc);
-
-    if (llama_decode(context, *batch) != 0) {
-        LOGe("llama_decode() returned null");
-    }
-
-    return new_token;
-}
-
-extern "C"
-JNIEXPORT void JNICALL
-Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
-    llama_memory_clear(llama_get_memory(reinterpret_cast<llama_context *>(context)), true);
-}

examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt

@@ -1,180 +0,0 @@
-package android.llama.cpp
-
-import android.util.Log
-import kotlinx.coroutines.CoroutineDispatcher
-import kotlinx.coroutines.asCoroutineDispatcher
-import kotlinx.coroutines.flow.Flow
-import kotlinx.coroutines.flow.flow
-import kotlinx.coroutines.flow.flowOn
-import kotlinx.coroutines.withContext
-import java.util.concurrent.Executors
-import kotlin.concurrent.thread
-
-class LLamaAndroid {
-    private val tag: String? = this::class.simpleName
-
-    private val threadLocalState: ThreadLocal<State> = ThreadLocal.withInitial { State.Idle }
-
-    private val runLoop: CoroutineDispatcher = Executors.newSingleThreadExecutor {
-        thread(start = false, name = "Llm-RunLoop") {
-            Log.d(tag, "Dedicated thread for native code: ${Thread.currentThread().name}")
-
-            // No-op if called more than once.
-            System.loadLibrary("llama-android")
-
-            // Set llama log handler to Android
-            log_to_android()
-            backend_init(false)
-
-            Log.d(tag, system_info())
-
-            it.run()
-        }.apply {
-            uncaughtExceptionHandler = Thread.UncaughtExceptionHandler { _, exception: Throwable ->
-                Log.e(tag, "Unhandled exception", exception)
-            }
-        }
-    }.asCoroutineDispatcher()
-
-    private val nlen: Int = 64
-
-    private external fun log_to_android()
-    private external fun load_model(filename: String): Long
-    private external fun free_model(model: Long)
-    private external fun new_context(model: Long): Long
-    private external fun free_context(context: Long)
-    private external fun backend_init(numa: Boolean)
-    private external fun backend_free()
-    private external fun new_batch(nTokens: Int, embd: Int, nSeqMax: Int): Long
-    private external fun free_batch(batch: Long)
-    private external fun new_sampler(): Long
-    private external fun free_sampler(sampler: Long)
-    private external fun bench_model(
-        context: Long,
-        model: Long,
-        batch: Long,
-        pp: Int,
-        tg: Int,
-        pl: Int,
-        nr: Int
-    ): String
-
-    private external fun system_info(): String
-
-    private external fun completion_init(
-        context: Long,
-        batch: Long,
-        text: String,
-        formatChat: Boolean,
-        nLen: Int
-    ): Int
-
-    private external fun completion_loop(
-        context: Long,
-        batch: Long,
-        sampler: Long,
-        nLen: Int,
-        ncur: IntVar
-    ): String?
-
-    private external fun kv_cache_clear(context: Long)
-
-    suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String {
-        return withContext(runLoop) {
-            when (val state = threadLocalState.get()) {
-                is State.Loaded -> {
-                    Log.d(tag, "bench(): $state")
-                    bench_model(state.context, state.model, state.batch, pp, tg, pl, nr)
-                }
-
-                else -> throw IllegalStateException("No model loaded")
-            }
-        }
-    }
-
-    suspend fun load(pathToModel: String) {
-        withContext(runLoop) {
-            when (threadLocalState.get()) {
-                is State.Idle -> {
-                    val model = load_model(pathToModel)
-                    if (model == 0L)  throw IllegalStateException("load_model() failed")
-
-                    val context = new_context(model)
-                    if (context == 0L) throw IllegalStateException("new_context() failed")
-
-                    val batch = new_batch(512, 0, 1)
-                    if (batch == 0L) throw IllegalStateException("new_batch() failed")
-
-                    val sampler = new_sampler()
-                    if (sampler == 0L) throw IllegalStateException("new_sampler() failed")
-
-                    Log.i(tag, "Loaded model $pathToModel")
-                    threadLocalState.set(State.Loaded(model, context, batch, sampler))
-                }
-                else -> throw IllegalStateException("Model already loaded")
-            }
-        }
-    }
-
-    fun send(message: String, formatChat: Boolean = false): Flow<String> = flow {
-        when (val state = threadLocalState.get()) {
-            is State.Loaded -> {
-                val ncur = IntVar(completion_init(state.context, state.batch, message, formatChat, nlen))
-                while (ncur.value <= nlen) {
-                    val str = completion_loop(state.context, state.batch, state.sampler, nlen, ncur)
-                    if (str == null) {
-                        break
-                    }
-                    emit(str)
-                }
-                kv_cache_clear(state.context)
-            }
-            else -> {}
-        }
-    }.flowOn(runLoop)
-
-    /**
-     * Unloads the model and frees resources.
-     *
-     * This is a no-op if there's no model loaded.
-     */
-    suspend fun unload() {
-        withContext(runLoop) {
-            when (val state = threadLocalState.get()) {
-                is State.Loaded -> {
-                    free_context(state.context)
-                    free_model(state.model)
-                    free_batch(state.batch)
-                    free_sampler(state.sampler);
-
-                    threadLocalState.set(State.Idle)
-                }
-                else -> {}
-            }
-        }
-    }
-
-    companion object {
-        private class IntVar(value: Int) {
-            @Volatile
-            var value: Int = value
-                private set
-
-            fun inc() {
-                synchronized(this) {
-                    value += 1
-                }
-            }
-        }
-
-        private sealed interface State {
-            data object Idle: State
-            data class Loaded(val model: Long, val context: Long, val batch: Long, val sampler: Long): State
-        }
-
-        // Enforce only one instance of Llm.
-        private val _instance: LLamaAndroid = LLamaAndroid()
-
-        fun instance(): LLamaAndroid = _instance
-    }
-}

examples/llama.android/settings.gradle.kts

@@ -8,11 +8,11 @@ pluginManagement {
 dependencyResolutionManagement {
     repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
     repositories {
-        google()
         mavenCentral()
+        google()
     }
 }
 
-rootProject.name = "LlamaAndroid"
+rootProject.name = "AiChat"
 include(":app")
-include(":llama")
+include(":lib")

examples/lookahead/lookahead.cpp

@@ -55,10 +55,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the target model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     auto * mem = llama_get_memory(ctx);
 

examples/lookup/lookup-create.cpp

@@ -18,16 +18,16 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model_ptr & model = llama_init.model;
-    llama_context_ptr & ctx = llama_init.context;
+    auto * model = llama_init->model();
+    auto * ctx = llama_init->context();
 
     GGML_ASSERT(model != nullptr);
 
     // tokenize the prompt
     std::vector<llama_token> inp;
-    inp = common_tokenize(ctx.get(), params.prompt, true, true);
+    inp = common_tokenize(ctx, params.prompt, true, true);
     fprintf(stderr, "%s: tokenization done\n", __func__);
 
     common_ngram_cache ngram_cache;

examples/lookup/lookup-stats.cpp

@@ -28,13 +28,13 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_context_ptr & ctx = llama_init.context;
+    llama_context * ctx = llama_init->context();
 
     // tokenize the prompt
     std::vector<llama_token> inp;
-    inp = common_tokenize(ctx.get(), params.prompt, true, true);
+    inp = common_tokenize(ctx, params.prompt, true, true);
 
     common_ngram_cache ngram_cache_context;
     common_ngram_cache ngram_cache_dynamic;
@@ -65,7 +65,7 @@ int main(int argc, char ** argv){
     }
 
     const int n_input = inp.size();
-    const int n_ctx = llama_n_ctx(ctx.get());
+    const int n_ctx = llama_n_ctx(ctx);
 
     int n_drafted = 0;
     int n_accept  = 0;

examples/lookup/lookup.cpp

@@ -29,10 +29,10 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 

examples/model-conversion/README.md

@@ -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:

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

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

examples/model-conversion/scripts/causal/run-org-model.py

@@ -5,7 +5,7 @@ import os
 import importlib
 from pathlib import Path
 
-from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText, AutoConfig
 import torch
 import numpy as np
 
@@ -116,11 +116,11 @@ def debug_hook(name):
     def fn(_m, input, output):
         if isinstance(input, torch.Tensor):
             summarize(input, name + "_in")
-        elif isinstance(input, (tuple, list)) and isinstance(input[0], torch.Tensor):
+        elif isinstance(input, (tuple, list)) and len(input) > 0 and isinstance(input[0], torch.Tensor):
             summarize(input[0], name + "_in")
         if isinstance(output, torch.Tensor):
             summarize(output, name + "_out")
-        elif isinstance(output, (tuple, list)) and isinstance(output[0], torch.Tensor):
+        elif isinstance(output, (tuple, list)) and len(output) > 0 and isinstance(output[0], torch.Tensor):
             summarize(output[0], name + "_out")
 
     return fn
@@ -130,6 +130,7 @@ unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME")
 
 parser = argparse.ArgumentParser(description="Process model with specified path")
 parser.add_argument("--model-path", "-m", help="Path to the model")
+parser.add_argument("--prompt-file", "-f", help="Optional prompt file", required=False)
 args = parser.parse_args()
 
 model_path = os.environ.get("MODEL_PATH", args.model_path)
@@ -142,8 +143,13 @@ if model_path is None:
 print("Loading model and tokenizer using AutoTokenizer:", model_path)
 tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+multimodal = False
+full_config = config
 
 print("Model type:       ", config.model_type)
+if "vocab_size" not in config and "text_config" in config:
+    config = config.text_config
+    multimodal = True
 print("Vocab size:       ", config.vocab_size)
 print("Hidden size:      ", config.hidden_size)
 print("Number of layers: ", config.num_hidden_layers)
@@ -169,9 +175,14 @@ if unreleased_model_name:
         print(f"Failed to import or load model: {e}")
         exit(1)
 else:
-    model = AutoModelForCausalLM.from_pretrained(
-        model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=config
-    )
+    if multimodal:
+        model = AutoModelForImageTextToText.from_pretrained(
+            model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=full_config
+        )
+    else:
+        model = AutoModelForCausalLM.from_pretrained(
+            model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=config
+        )
 
 for name, module in model.named_modules():
     if len(list(module.children())) == 0:  # only leaf modules
@@ -185,7 +196,10 @@ model_name = os.path.basename(model_path)
 print(f"Model class: {model.__class__.__name__}")
 
 device = next(model.parameters()).device
-if os.getenv("MODEL_TESTING_PROMPT"):
+if args.prompt_file:
+    with open(args.prompt_file, encoding='utf-8') as f:
+        prompt = f.read()
+elif os.getenv("MODEL_TESTING_PROMPT"):
     prompt = os.getenv("MODEL_TESTING_PROMPT")
 else:
     prompt = "Hello, my name is"
@@ -195,12 +209,21 @@ print(f"Input tokens: {input_ids}")
 print(f"Input text: {repr(prompt)}")
 print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
 
+batch_size = 512
+
 with torch.no_grad():
-    outputs = model(input_ids.to(model.device))
-    logits = outputs.logits
+    past = None
+    outputs = None
+    for i in range(0, input_ids.size(1), batch_size):
+        print(f"Processing chunk with tokens {i} to {i + batch_size}")
+        chunk = input_ids[:, i:i + batch_size]
+        outputs = model(chunk.to(model.device), past_key_values=past, use_cache=True)
+        past = outputs.past_key_values
+
+    logits = outputs.logits # type: ignore
 
     # Extract logits for the last token (next token prediction)
-    last_logits = logits[0, -1, :].cpu().numpy()
+    last_logits = logits[0, -1, :].float().cpu().numpy()
 
     print(f"Logits shape: {logits.shape}")
     print(f"Last token logits shape: {last_logits.shape}")

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

@@ -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)

examples/parallel/parallel.cpp

@@ -192,10 +192,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the target model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     auto * mem = llama_get_memory(ctx);
 

examples/retrieval/retrieval.cpp

@@ -149,10 +149,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);

examples/save-load-state/save-load-state.cpp

@@ -34,10 +34,10 @@ int main(int argc, char ** argv) {
     std::string result2;
 
     // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == nullptr || ctx == nullptr) {
         fprintf(stderr, "%s : failed to init\n", __func__);

examples/speculative-simple/speculative-simple.cpp

@@ -40,10 +40,10 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    common_init_result llama_init_tgt = common_init_from_params(params);
+    auto llama_init_tgt = common_init_from_params(params);
 
-    model_tgt = llama_init_tgt.model.get();
-    ctx_tgt   = llama_init_tgt.context.get();
+    model_tgt = llama_init_tgt->model();
+    ctx_tgt   = llama_init_tgt->context();
 
     const llama_vocab * vocab = llama_model_get_vocab(model_tgt);
 
@@ -61,10 +61,10 @@ int main(int argc, char ** argv) {
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
     params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
 
-    common_init_result llama_init_dft = common_init_from_params(params);
+    auto llama_init_dft = common_init_from_params(params);
 
-    //model_dft = llama_init_dft.model.get();
-    ctx_dft   = llama_init_dft.context.get();
+    //model_dft = llama_init_dft->model();
+    ctx_dft   = llama_init_dft->context();
 
     if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
         LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str());
@@ -255,6 +255,8 @@ int main(int argc, char ** argv) {
     LOG_INF("target:\n\n");
     common_perf_print(ctx_tgt, smpl);
 
+    llama_batch_free(batch_tgt);
+
     common_sampler_free(smpl);
     common_speculative_free(spec);
 

examples/speculative/speculative.cpp

@@ -71,10 +71,10 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    common_init_result llama_init_tgt = common_init_from_params(params);
+    auto llama_init_tgt = common_init_from_params(params);
 
-    model_tgt = llama_init_tgt.model.get();
-    ctx_tgt   = llama_init_tgt.context.get();
+    model_tgt = llama_init_tgt->model();
+    ctx_tgt   = llama_init_tgt->context();
 
     // load the draft model
     params.devices = params.speculative.devices;
@@ -87,10 +87,10 @@ int main(int argc, char ** argv) {
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
     params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
 
-    common_init_result llama_init_dft = common_init_from_params(params);
+    auto llama_init_dft = common_init_from_params(params);
 
-    model_dft = llama_init_dft.model.get();
-    ctx_dft   = llama_init_dft.context.get();
+    model_dft = llama_init_dft->model();
+    ctx_dft   = llama_init_dft->context();
 
     const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
     const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);

examples/training/finetune.cpp

@@ -39,9 +39,10 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
     // load the model and apply lora adapter, if any
-    common_init_result   llama_init = common_init_from_params(params);
-    llama_model_ptr    & model      = llama_init.model;
-    llama_context_ptr  & ctx        = llama_init.context;
+    auto llama_init = common_init_from_params(params);
+
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
@@ -54,8 +55,8 @@ int main(int argc, char ** argv) {
         LOG_INF("%s\n", common_params_get_system_info(params).c_str());
     }
 
-    std::vector<llama_token> tokens  = common_tokenize(ctx.get(), params.prompt, true);
-    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get()) / 2);
+    std::vector<llama_token> tokens  = common_tokenize(ctx, params.prompt, true);
+    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx, tokens, llama_n_ctx(ctx) / 2);
 
     struct lr_opt & lr = params.lr;
     LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n",
@@ -70,7 +71,7 @@ int main(int argc, char ** argv) {
         /*get_opt_pars_ud =*/&params.lr,
         /*optimizer_type  =*/params.optimizer,
     };
-    llama_opt_init(ctx.get(), model.get(), lopt_params);
+    llama_opt_init(ctx, model, lopt_params);
 
     const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split);
 
@@ -78,7 +79,7 @@ int main(int argc, char ** argv) {
     ggml_opt_result_t result_eval  = ggml_opt_result_init();
 
     for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) {
-        llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
+        llama_opt_epoch(ctx, dataset, result_train, result_eval, idata_split,
                         ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
         fprintf(stderr, "\n");
 
@@ -88,7 +89,7 @@ int main(int argc, char ** argv) {
     ggml_opt_result_free(result_train);
     ggml_opt_result_free(result_eval);
 
-    llama_model_save_to_file(model.get(), params.out_file.c_str());
+    llama_model_save_to_file(model, params.out_file.c_str());
 
     llama_backend_free();
 

ggml/CMakeLists.txt

@@ -54,6 +54,10 @@ if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     # TODO
 else()
     set(GGML_STANDALONE OFF)
+
+    if (NOT CMAKE_RUNTIME_OUTPUT_DIRECTORY)
+        set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+    endif()
 endif()
 
 if (EMSCRIPTEN)

ggml/include/ggml-alloc.h

@@ -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);