Merge 95d2017d28 into b7ad48ebda

* Work towards removing bitcast

* Move rest of existing types over

* Add timeout back to wait and remove synchronous set_tensor/memset_tensor

* move to unpackf16 for wider compatibility

* cleanup

* Remove deadlock condition in free_bufs

* Start work on removing parameter buffer pools

* Simplify and optimize further

* simplify profile futures

* Fix stride

* Try using a single command buffer per batch

* formatting

.devops/cuda-new.Dockerfile

@@ -1,97 +0,0 @@
-ARG UBUNTU_VERSION=24.04
-# This needs to generally match the container host's environment.
-ARG CUDA_VERSION=13.1.1
-# Target the CUDA build image
-ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
-
-# CUDA architecture to build for (defaults to all supported archs)
-ARG CUDA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y gcc-14 g++-14 build-essential cmake python3 python3-pip git libssl-dev libgomp1
-
-ENV CC=gcc-14 CXX=g++-14 CUDAHOSTCXX=g++-14
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
-    export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so*" -exec cp -P {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ${BASE_CUDA_RUN_CONTAINER} AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    python3-wheel \
-    && pip install --break-system-packages --upgrade setuptools \
-    && pip install --break-system-packages -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/nix/package.nix

@@ -16,7 +16,7 @@
   rocmPackages,
   vulkan-headers,
   vulkan-loader,
-  curl,
+  openssl,
   shaderc,
   useBlas ?
     builtins.all (x: !x) [
@@ -160,7 +160,8 @@ effectiveStdenv.mkDerivation (finalAttrs: {
     ++ optionals useMpi [ mpi ]
     ++ optionals useRocm rocmBuildInputs
     ++ optionals useBlas [ blas ]
-    ++ optionals useVulkan vulkanBuildInputs;
+    ++ optionals useVulkan vulkanBuildInputs
+    ++ [ openssl ];
 
   cmakeFlags =
     [

.devops/rocm.Dockerfile

@@ -1,8 +1,8 @@
 ARG UBUNTU_VERSION=24.04
 
 # This needs to generally match the container host's environment.
-ARG ROCM_VERSION=7.2
-ARG AMDGPU_VERSION=7.2
+ARG ROCM_VERSION=7.2.1
+ARG AMDGPU_VERSION=7.2.1
 
 # Target the ROCm build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
@@ -12,11 +12,11 @@ FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 # This is mostly tied to rocBLAS supported archs.
-# check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.2.0/reference/system-requirements.html
+# check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.2.1/reference/system-requirements.html
 # check https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/docs/compatibility/compatibilityrad/native_linux/native_linux_compatibility.html
 # check https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/docs/compatibility/compatibilityryz/native_linux/native_linux_compatibility.html
 
-ARG ROCM_DOCKER_ARCH='gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1151;gfx1150;gfx1200;gfx1201'
+ARG ROCM_DOCKER_ARCH='gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1151;gfx1150;gfx1200;gfx1201'
 
 # Set ROCm architectures
 ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}

.github/labeler.yml

@@ -27,6 +27,11 @@ IBM zDNN:
         - any-glob-to-any-file:
             - ggml/include/ggml-zdnn.h
             - ggml/src/ggml-zdnn/**
+AMD ZenDNN:
+    - changed-files:
+        - any-glob-to-any-file:
+            - ggml/include/ggml-zendnn.h
+            - ggml/src/ggml-zendnn/**
 documentation:
     - changed-files:
         - any-glob-to-any-file:

.github/workflows/build-self-hosted.yml

@@ -213,6 +213,27 @@ jobs:
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
+  ggml-ci-win-intel-vulkan:
+    runs-on: [self-hosted, Windows, X64, Intel]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        shell: C:\msys64\usr\bin\bash.exe --noprofile --norc -eo pipefail "{0}"
+        env:
+          MSYSTEM: UCRT64
+          CHERE_INVOKING: 1
+          PATH: C:\msys64\ucrt64\bin;C:\msys64\usr\bin;C:\Windows\System32;${{ env.PATH }}
+        run: |
+          vulkaninfo --summary
+          # Skip python related tests with GG_BUILD_LOW_PERF=1 since Windows MSYS2 UCRT64 currently fails to create
+          # a valid python environment for testing
+          LLAMA_FATAL_WARNINGS=OFF GG_BUILD_NINJA=1 GG_BUILD_VULKAN=1 GG_BUILD_LOW_PERF=1 ./ci/run.sh ./results/llama.cpp ./mnt/llama.cpp
+
   ggml-ci-intel-openvino-gpu-low-perf:
     runs-on: [self-hosted, Linux, Intel, OpenVINO]
 

.github/workflows/build.yml

@@ -472,6 +472,7 @@ jobs:
           cmake -B build -S . \
             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
             -DGGML_HIP_ROCWMMA_FATTN=ON \
+            -DGPU_TARGETS="gfx1030" \
             -DGGML_HIP=ON
           cmake --build build --config Release -j $(nproc)
 
@@ -941,7 +942,7 @@ jobs:
       - name: Grab rocWMMA package
         id: grab_rocwmma
         run: |
-          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70200-43~24.04_amd64.deb"
+          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70201-81~24.04_amd64.deb"
           7z x rocwmma.deb
           7z x data.tar
 
@@ -984,12 +985,13 @@ jobs:
           cmake -G "Unix Makefiles" -B build -S . `
             -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
             -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.2.0/include/" `
+            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.2.1/include/" `
             -DCMAKE_BUILD_TYPE=Release `
             -DLLAMA_BUILD_BORINGSSL=ON `
             -DROCM_DIR="${env:HIP_PATH}" `
             -DGGML_HIP=ON `
             -DGGML_HIP_ROCWMMA_FATTN=ON `
+            -DGPU_TARGETS="gfx1100"  `
             -DGGML_RPC=ON
           cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
 

.github/workflows/docker.yml

@@ -73,10 +73,10 @@ jobs:
             { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
             { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-arm" },
             { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x" },
-            { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
-            { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
-            { "tag": "cuda13", "dockerfile": ".devops/cuda-new.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
-            { "tag": "cuda13", "dockerfile": ".devops/cuda-new.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
+            { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.9.1", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
+            { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.9.1", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
+            { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.1.1", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
+            { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.1.1", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
             { "tag": "musa", "dockerfile": ".devops/musa.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
             { "tag": "intel", "dockerfile": ".devops/intel.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
             { "tag": "vulkan", "dockerfile": ".devops/vulkan.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },

.github/workflows/hip-quality-check.yml

@@ -35,7 +35,7 @@ env:
 jobs:
   ubuntu-22-hip-quality-check:
     runs-on: ubuntu-22.04
-    container: rocm/dev-ubuntu-22.04:7.2
+    container: rocm/dev-ubuntu-22.04:7.2.1
     steps:
       - name: Clone
         id: checkout
@@ -59,7 +59,7 @@ jobs:
         run: |
           cmake -B build -S . \
             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-            -DGPU_TARGETS=gfx908 \
+            -DGPU_TARGETS=gfx942 \
             -DGGML_HIP=ON \
             -DGGML_HIP_EXPORT_METRICS=Off \
             -DCMAKE_HIP_FLAGS="-Werror -Wno-tautological-compare" \

.github/workflows/release.yml

@@ -639,8 +639,8 @@ jobs:
     strategy:
       matrix:
         include:
-          - ROCM_VERSION: "7.2"
-            gpu_targets: "gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1151;gfx1150;gfx1200;gfx1201"
+          - ROCM_VERSION: "7.2.1"
+            gpu_targets: "gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1151;gfx1150;gfx1200;gfx1201"
             build: 'x64'
 
     steps:
@@ -662,7 +662,7 @@ jobs:
           sudo apt install -y build-essential git cmake wget
 
       - name: Setup Legacy ROCm
-        if: matrix.ROCM_VERSION == '7.2'
+        if: matrix.ROCM_VERSION == '7.2.1'
         id: legacy_env
         run: |
           sudo mkdir --parents --mode=0755 /etc/apt/keyrings
@@ -683,7 +683,7 @@ jobs:
           sudo apt-get install -y libssl-dev rocm-hip-sdk
 
       - name: Setup TheRock
-        if: matrix.ROCM_VERSION != '7.2'
+        if: matrix.ROCM_VERSION != '7.2.1'
         id: therock_env
         run: |
           wget https://repo.amd.com/rocm/tarball/therock-dist-linux-gfx1151-${{ matrix.ROCM_VERSION }}.tar.gz
@@ -699,7 +699,6 @@ jobs:
         run: |
           cmake -B build -S . \
             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-            -DCMAKE_HIP_FLAGS="-mllvm --amdgpu-unroll-threshold-local=600" \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_BACKEND_DL=ON \
             -DGGML_NATIVE=OFF \
@@ -717,17 +716,20 @@ jobs:
         id: tag
         uses: ./.github/actions/get-tag-name
 
+      - name: Get ROCm short version
+        run: echo "ROCM_VERSION_SHORT=$(echo '${{ matrix.ROCM_VERSION }}' | cut -d '.' -f 1,2)" >> $GITHUB_ENV
+
       - name: Pack artifacts
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
         with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}.tar.gz
-          name: llama-bin-ubuntu-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}.tar.gz
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
+          name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
 
   windows-hip:
     runs-on: windows-2022
@@ -749,7 +751,7 @@ jobs:
       - name: Grab rocWMMA package
         id: grab_rocwmma
         run: |
-          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70200-43~24.04_amd64.deb"
+          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70201-81~24.04_amd64.deb"
           7z x rocwmma.deb
           7z x data.tar
 
@@ -806,7 +808,7 @@ jobs:
           cmake -G "Unix Makefiles" -B build -S . `
             -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
             -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.2.0/include/ -Wno-ignored-attributes -Wno-nested-anon-types" `
+            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.2.1/include/ -Wno-ignored-attributes -Wno-nested-anon-types" `
             -DCMAKE_BUILD_TYPE=Release `
             -DGGML_BACKEND_DL=ON `
             -DGGML_NATIVE=OFF `

ci/run.sh

@@ -119,6 +119,11 @@ if [ ! -z ${GG_BUILD_VULKAN} ]; then
         CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=OFF -DGGML_BLAS=OFF"
     fi
 
+    # Build shared libs on Windows
+    # to reduce binary size and avoid errors in library loading unit tests
+    if uname -s | grep -qi nt; then
+        CMAKE_EXTRA="${CMAKE_EXTRA} -DBUILD_SHARED_LIBS=ON"
+    fi
 fi
 
 if [ ! -z ${GG_BUILD_WEBGPU} ]; then
@@ -221,7 +226,7 @@ function gg_run_ctest_debug {
 
     set -e
 
-    # Check cmake and ctest are installed
+    # Check required binaries are installed
     gg_check_build_requirements
 
     (cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
@@ -252,7 +257,7 @@ function gg_run_ctest_release {
 
     set -e
 
-    # Check cmake and ctest are installed
+    # Check required binaries are installed
     gg_check_build_requirements
 
     (cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
@@ -627,10 +632,38 @@ function gg_sum_rerank_tiny {
 }
 
 function gg_check_build_requirements {
+    if ! command -v git &> /dev/null; then
+        gg_printf 'git not found, please install'
+    fi
+
+    if ! command -v git-lfs &> /dev/null; then
+        gg_printf 'git-lfs not found, please install'
+    fi
+
+    if ! command -v wget &> /dev/null; then
+        gg_printf 'wget not found, please install'
+    fi
+
+    if ! command -v python3 &> /dev/null; then
+        gg_printf 'python3 not found, please install'
+    fi
+
+    if ! command -v pip3 &> /dev/null; then
+        gg_printf 'pip3 not found, please install'
+    fi
+
+    if ! python3 -m ensurepip --help &> /dev/null; then
+        gg_printf 'ensurepip not found, please install python3-venv package'
+    fi
+
     if ! command -v cmake &> /dev/null; then
         gg_printf 'cmake not found, please install'
     fi
 
+    if ! command -v ccache &> /dev/null; then
+        gg_printf 'ccache not found, please consider installing for faster builds'
+    fi
+
     if ! command -v ctest &> /dev/null; then
         gg_printf 'ctest not found, please install'
     fi

common/arg.cpp

@@ -1311,6 +1311,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.kv_unified = value;
         }
     ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_PARALLEL}));
+    add_opt(common_arg(
+        {"--clear-idle"},
+        {"--no-clear-idle"},
+        "save and clear idle slots on new task (default: enabled, requires unified KV and cache-ram)",
+        [](common_params & params, bool value) {
+            params.clear_idle = value;
+        }
+    ).set_env("LLAMA_ARG_CLEAR_IDLE").set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--context-shift"},
         {"--no-context-shift"},
@@ -3891,9 +3899,10 @@ void common_params_add_preset_options(std::vector<common_arg> & args) {
         [](common_params &, int) { /* unused */ }
     ).set_env(COMMON_ARG_PRESET_STOP_TIMEOUT).set_preset_only());
 
-    // args.push_back(common_arg(
-    //     {"pin"},
-    //     "in server router mode, do not unload this model if models_max is exceeded",
-    //     [](common_params &) { /* unused */ }
-    // ).set_preset_only());
+    args.push_back(common_arg(
+        {"pin"},
+        nullptr,
+        "in server router mode, do not unload this model if models_max is exceeded",
+        [](common_params &, const std::string &) { /* unused */ }
+    ).set_env(COMMON_ARG_PRESET_PIN).set_preset_only());
 }

common/arg.h

@@ -11,6 +11,7 @@
 // pseudo-env variable to identify preset-only arguments
 #define COMMON_ARG_PRESET_LOAD_ON_STARTUP "__PRESET_LOAD_ON_STARTUP"
 #define COMMON_ARG_PRESET_STOP_TIMEOUT    "__PRESET_STOP_TIMEOUT"
+#define COMMON_ARG_PRESET_PIN             "__PRESET_PIN"
 
 //
 // CLI argument parsing

common/chat-auto-parser-generator.cpp

@@ -6,12 +6,111 @@
 #include "json-schema-to-grammar.h"
 #include "log.h"
 #include "nlohmann/json.hpp"
+#include "peg-parser.h"
 
+#include <algorithm>
 #include <stdexcept>
 #include <string>
 
 using json = nlohmann::ordered_json;
 
+namespace {
+
+// Gemma4-specific PEG builder extending the standard chat builder.
+// Adds value type parsers that use <|\"|> as string delimiters
+// instead of JSON's double quotes, and disables json-to-schema
+// conversion for these types.
+class common_peg_gemma4_builder {
+    common_chat_peg_builder & p_;
+    static constexpr const char * QUOTE = "<|\"|>";
+
+public:
+    explicit common_peg_gemma4_builder(common_chat_peg_builder & p) : p_(p) {}
+
+    common_peg_parser gemma4_string() {
+        return p_.rule("gemma4-string", [&]() {
+            return p_.literal(QUOTE) + p_.until(QUOTE) + p_.literal(QUOTE);
+        });
+    }
+
+    common_peg_parser gemma4_number() {
+        return p_.rule("gemma4-number", [&]() {
+            auto digit1_9 = p_.chars("[1-9]", 1, 1);
+            auto digits   = p_.chars("[0-9]");
+            auto int_part = p_.choice({p_.literal("0"), p_.sequence({digit1_9, p_.chars("[0-9]", 0, -1)})});
+            auto frac     = p_.sequence({p_.literal("."), digits});
+            auto exp      = p_.sequence({p_.choice({p_.literal("e"), p_.literal("E")}),
+                                         p_.optional(p_.chars("[+-]", 1, 1)), digits});
+            auto not_number_continuation = p_.negate(p_.chars("[0-9.eE+-]", 1, 1));
+            return p_.sequence({p_.optional(p_.literal("-")), int_part, p_.optional(frac),
+                                p_.optional(exp), not_number_continuation});
+        });
+    }
+
+    common_peg_parser gemma4_bool() {
+        return p_.rule("gemma4-bool", [&]() {
+            return p_.choice({p_.literal("true"), p_.literal("false")});
+        });
+    }
+
+    common_peg_parser gemma4_null() {
+        return p_.rule("gemma4-null", [&]() {
+            return p_.literal("null");
+        });
+    }
+
+    common_peg_parser gemma4_dict() {
+        return p_.rule("gemma4-dict", [&]() {
+            auto ws = p_.space();
+            auto key = p_.until(":");
+            auto member = p_.sequence({key, p_.literal(":"), ws, gemma4_value()});
+            auto members = p_.sequence({member, p_.zero_or_more(p_.sequence({p_.literal(","), ws, member}))});
+            return p_.sequence({
+                p_.literal("{"), ws,
+                p_.choice({p_.literal("}"), p_.sequence({members, ws, p_.literal("}")})})
+            });
+        });
+    }
+
+    common_peg_parser gemma4_array() {
+        return p_.rule("gemma4-array", [&]() {
+            auto ws = p_.space();
+            auto elements = p_.sequence({gemma4_value(), p_.zero_or_more(p_.sequence({p_.literal(","), ws, gemma4_value()}))});
+            return p_.sequence({
+                p_.literal("["), ws,
+                p_.choice({p_.literal("]"), p_.sequence({elements, ws, p_.literal("]")})})
+            });
+        });
+    }
+
+    common_peg_parser gemma4_value() {
+        return p_.rule("gemma4-value", [&]() {
+            return p_.choice({gemma4_string(), gemma4_dict(), gemma4_array(),
+                              gemma4_number(), gemma4_bool(), gemma4_null()});
+        });
+    }
+
+    // Select the appropriate value parser based on JSON schema type.
+    // Does NOT use schema() - the gemma4 types are pure PEG without
+    // JSON schema metadata, so GBNF is generated directly from the
+    // PEG structure.
+    common_peg_parser gemma4_value_for_type(const json & schema) {
+        if (!schema.contains("type") || !schema.at("type").is_string()) {
+            return gemma4_value();
+        }
+        std::string type = schema.at("type").get<std::string>();
+        if (type == "string")  { return gemma4_string(); }
+        if (type == "number")  { return gemma4_number(); }
+        if (type == "integer") { return gemma4_number(); }
+        if (type == "boolean") { return gemma4_bool(); }
+        if (type == "object")  { return gemma4_dict(); }
+        if (type == "array")   { return gemma4_array(); }
+        return gemma4_value();
+    }
+};
+
+}  // anonymous namespace
+
 // Helper to iterate over tools/functions
 static void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
     for (const auto & tool : tools) {
@@ -43,7 +142,9 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
     // Create the result structure
     common_chat_params data;
     data.prompt           = common_chat_template_direct_apply(tmpl, inputs);
-    data.format           = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.format           = (autoparser.tools.format.mode == tool_format::TAG_WITH_GEMMA4_DICT)
+                            ? COMMON_CHAT_FORMAT_PEG_GEMMA4
+                            : COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.preserved_tokens = autoparser.preserved_tokens;
 
     auto parser = autoparser.build_parser(inputs);
@@ -92,6 +193,7 @@ common_peg_arena autoparser::build_parser(const generation_params & inputs) cons
 
         ctx.extracting_reasoning = extract_reasoning && reasoning.mode != reasoning_mode::NONE;
         ctx.content              = &content;
+        ctx.reasoning            = &reasoning;
 
         // Build reasoning parser
         ctx.reasoning_parser = reasoning.build_parser(ctx);
@@ -216,6 +318,44 @@ common_peg_parser analyze_tools::build_tool_parser_json_native(parser_build_cont
            p.end();
 }
 
+common_peg_parser analyze_tools::build_func_parser(common_chat_peg_builder & p, const std::string & name,
+                                                    const common_peg_parser & call_id_section, bool have_call_id,
+                                                    const common_peg_parser & args,
+                                                    std::optional<common_peg_parser> atomic_peek) const {
+    auto              open           = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix);
+    bool              matched_atomic = false;
+    common_peg_parser func_parser    = p.eps();
+
+    if (!function.name_suffix.empty()) {
+        func_parser    = open + call_id_section + p.space() + args;
+        matched_atomic = true;
+    } else if (have_call_id) {
+        func_parser    = p.atomic(open + call_id_section) + p.space() + args;
+        matched_atomic = true;
+    } else if (atomic_peek.has_value()) {
+        func_parser    = p.atomic(open + call_id_section + p.space() + *atomic_peek) + args;
+        matched_atomic = true;
+    } else {
+        func_parser = open + call_id_section + p.space() + args;
+    }
+
+    if (!function.close.empty()) {
+        func_parser = func_parser + p.space() + p.tool_close(p.literal(function.close));
+    } else if (!format.per_call_end.empty()) {
+        // When there's no func_close but there is a per_call_end marker, use peek() to ensure
+        // we only emit tool_close when we can actually see the closing marker. This prevents
+        // premature closing during partial parsing when we've seen e.g. "</" which could be
+        // either "</tool_call>" (end) or "<arg_key>" prefix that failed to match.
+        func_parser = func_parser + p.tool_close(p.peek(p.literal(format.per_call_end)));
+    } else {
+        func_parser = func_parser + p.tool_close(p.space());  // force this to process tool closing callbacks in mapper
+    }
+    if (!matched_atomic) {
+        func_parser = p.atomic(func_parser);
+    }
+    return func_parser;
+}
+
 common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context & ctx) const {
     auto &       p           = ctx.p;
     const auto & inputs      = ctx.inputs;
@@ -229,17 +369,27 @@ common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context
         const auto & schema = func.contains("parameters") ? func.at("parameters") : json::object();
 
         // Build call_id parser based on position (if supported)
+        bool have_call_id = false;
         common_peg_parser call_id_section = p.eps();
         if (call_id.pos == call_id_position::BETWEEN_FUNC_AND_ARGS && !call_id.prefix.empty() &&
-            !call_id.suffix.empty()) {
-            call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(call_id.suffix))) + call_id.suffix;
+            (!call_id.suffix.empty() || !arguments.start.empty())) {
+            if (!call_id.suffix.empty()) {
+                call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(call_id.suffix))) + call_id.suffix;
+            } else {
+                call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(arguments.start)));
+            }
+            have_call_id = true;
+        }
+        auto args_parser = p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema));
+        if (!arguments.start.empty()) {
+            args_parser = p.literal(arguments.start) + args_parser;
+        }
+        if (!arguments.end.empty()) {
+            args_parser = args_parser + p.literal(arguments.end);
         }
 
-        auto func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
-                           call_id_section + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema));
-        if (!function.close.empty()) {
-            func_parser = func_parser + function.close;
-        }
+        auto atomic_peek = !arguments.start.empty() ? std::optional(p.peek(p.literal(arguments.start))) : std::nullopt;
+        auto func_parser = build_func_parser(p, name, call_id_section, have_call_id, args_parser, atomic_peek);
         tool_choice |= p.rule("tool-" + name, func_parser);
     });
 
@@ -299,12 +449,34 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
         for (const auto & [param_name, param_schema] : properties.items()) {
             bool        is_required = required.find(param_name) != required.end();
             std::string type        = "object";
-            auto        type_obj    = param_schema.contains("type") ? param_schema.at("type") : json::object();
-            if (type_obj.is_string()) {
-                type_obj.get_to(type);
-            } else if (type_obj.is_object()) {
-                if (type_obj.contains("type") && type_obj.at("type").is_string()) {
-                    type_obj.at("type").get_to(type);
+            if (param_schema.contains("type")) {
+                const auto & type_obj = param_schema.at("type");
+                if (type_obj.is_string()) {
+                    type_obj.get_to(type);
+                } else if (type_obj.is_array()) {
+                    // Handle nullable types like ["string", "null"]
+                    for (const auto & t : type_obj) {
+                        if (t.is_string() && t.get<std::string>() != "null") {
+                            type = t.get<std::string>();
+                            break;
+                        }
+                    }
+                } else if (type_obj.is_object()) {
+                    if (type_obj.contains("type") && type_obj.at("type").is_string()) {
+                        type_obj.at("type").get_to(type);
+                    }
+                }
+            }
+            // Infer string type from enum values when type is unspecified
+            if (type == "object" && param_schema.contains("enum")) {
+                const auto & enum_vals = param_schema.at("enum");
+                if (enum_vals.is_array()) {
+                    for (const auto & v : enum_vals) {
+                        if (v.is_string()) {
+                            type = "string";
+                            break;
+                        }
+                    }
                 }
             }
 
@@ -347,52 +519,31 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
             args_seq = args_seq + p.repeat(p.space() + any_opt, 0, (int) optional_parsers.size());
         }
 
+        if (!arguments.start.empty()) {
+            args_seq = p.literal(arguments.start) + args_seq;
+        }
+        if (!arguments.end.empty()) {
+            args_seq = args_seq + p.literal(arguments.end);
+        }
+
         // Build call_id parser based on position (if supported)
         common_peg_parser call_id_section = p.eps();
         bool have_call_id = false;
         if (call_id.pos == call_id_position::BETWEEN_FUNC_AND_ARGS && !call_id.prefix.empty() &&
-            !call_id.suffix.empty()) {
+            (!call_id.suffix.empty() || !arguments.start.empty())) {
             have_call_id = true;
-            call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(call_id.suffix)) + call_id.suffix);
-        }
-
-        bool matched_atomic = false;
-        common_peg_parser func_parser = p.eps();
-        if (!function.name_suffix.empty()) {
-            func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
-                call_id_section + p.space() + args_seq;
-            matched_atomic = true;
-        } else if (have_call_id) {
-            func_parser = p.atomic(p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
-                call_id_section) + p.space() + args_seq;
-            matched_atomic = true;
-        } else if (!arguments.name_prefix.empty() && !required_parsers.empty()) {
-            // Only peek for an arg tag when there are required args that must follow.
-            // When all args are optional, the model may emit no arg tags at all (#20650).
-            func_parser = p.atomic(p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
-                call_id_section + p.space() + p.peek(p.literal(arguments.name_prefix))) + args_seq;
-            matched_atomic = true;
-        } else {
-            func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
-                call_id_section + p.space() + args_seq;
-        }
-
-        if (!function.close.empty()) {
-            func_parser = func_parser + p.space() + p.tool_close(p.literal(function.close));
-        } else if (!format.per_call_end.empty()) {
-            // When there's no func_close but there is a per_call_end marker, use peek() to ensure
-            // we only emit tool_close when we can actually see the closing marker. This prevents
-            // premature closing during partial parsing when we've seen e.g. "</" which could be
-            // either "</tool_call>" (end) or "<arg_key>" prefix that failed to match.
-            func_parser = func_parser + p.tool_close(p.peek(p.literal(format.per_call_end)));
-        } else {
-            func_parser =
-                func_parser + p.tool_close(p.space());  // force this to process tool closing callbacks in mapper
-        }
-        if (!matched_atomic) {
-            func_parser = p.atomic(func_parser);
+            if (!call_id.suffix.empty()) {
+                call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(call_id.suffix)) + call_id.suffix);
+            } else {
+                call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(arguments.start)));
+            }
         }
 
+        // Only peek for an arg tag when there are required args that must follow.
+        // When all args are optional, the model may emit no arg tags at all (#20650).
+        auto atomic_peek = (!arguments.name_prefix.empty() && !required_parsers.empty()) ?
+            std::optional(p.peek(p.literal(arguments.name_prefix))) : std::nullopt;
+        auto func_parser = build_func_parser(p, name, call_id_section, have_call_id, args_seq, atomic_peek);
         tool_choice |= p.rule("tool-" + name, func_parser);
     });
 
@@ -440,7 +591,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_gemma4_dict(parser_build_
     const auto & inputs      = ctx.inputs;
     bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
 
-    // The Gemma4 string quote token used in place of JSON "
+    common_peg_gemma4_builder g4(p);
     static const std::string QUOTE = "<|\"|>";
 
     common_peg_parser tool_choice = p.choice();
@@ -451,7 +602,6 @@ common_peg_parser analyze_tools::build_tool_parser_tag_gemma4_dict(parser_build_
         const auto & params = func.at("parameters");
 
         if (!params.contains("properties") || !params.at("properties").is_object()) {
-            // No arguments - just match the function name with empty braces
             auto func_parser = p.atomic(
                 p.tool_open(p.literal(function.name_prefix) + p.tool_name(p.literal(name)) + p.literal("{")) +
                 p.tool_args(p.eps()) +
@@ -474,9 +624,33 @@ common_peg_parser analyze_tools::build_tool_parser_tag_gemma4_dict(parser_build_
         std::vector<arg_entry> arg_entries;
 
         for (const auto & [param_name, param_schema] : properties.items()) {
-            std::string type    = "object";
-            auto        type_v  = param_schema.contains("type") ? param_schema.at("type") : json::object();
-            if (type_v.is_string()) type_v.get_to(type);
+            std::string type = "object";
+            if (param_schema.contains("type")) {
+                const auto & type_v = param_schema.at("type");
+                if (type_v.is_string()) {
+                    type_v.get_to(type);
+                } else if (type_v.is_array()) {
+                    // Handle nullable types like ["string", "null"]
+                    for (const auto & t : type_v) {
+                        if (t.is_string() && t.get<std::string>() != "null") {
+                            type = t.get<std::string>();
+                            break;
+                        }
+                    }
+                }
+            }
+            // Infer string type from enum values when type is unspecified
+            if (type == "object" && param_schema.contains("enum")) {
+                const auto & enum_vals = param_schema.at("enum");
+                if (enum_vals.is_array()) {
+                    for (const auto & v : enum_vals) {
+                        if (v.is_string()) {
+                            type = "string";
+                            break;
+                        }
+                    }
+                }
+            }
 
             common_peg_parser value_parser = p.eps();
             if (type == "string") {
@@ -486,9 +660,18 @@ common_peg_parser analyze_tools::build_tool_parser_tag_gemma4_dict(parser_build_
                     p.tool_arg_string_value(p.schema(p.until(QUOTE),
                         "tool-" + name + "-arg-" + param_name + "-schema", param_schema, true)) +
                     p.literal(QUOTE);
+            } else if (type == "number" || type == "integer") {
+                value_parser = p.tool_arg_value(g4.gemma4_number());
+            } else if (type == "boolean") {
+                value_parser = p.tool_arg_value(g4.gemma4_bool());
+            } else if (type == "null") {
+                value_parser = p.tool_arg_value(g4.gemma4_null());
+            } else if (type == "object") {
+                value_parser = p.tool_arg_value(g4.gemma4_dict());
+            } else if (type == "array") {
+                value_parser = p.tool_arg_value(g4.gemma4_array());
             } else {
-                // Numbers, booleans: raw text up to the next comma or closing brace
-                value_parser = p.tool_arg_value(p.until_one_of({",", "}"}));
+                value_parser = p.tool_arg_value(g4.gemma4_value());
             }
 
             auto arg = p.tool_arg(
@@ -538,9 +721,9 @@ common_peg_parser analyze_tools::build_tool_parser_tag_gemma4_dict(parser_build_
         tool_calls = p.optional(tool_calls);
     }
 
-    auto content_before_tools = p.until(format.per_call_start);
+    auto content_before_tools = p.until_one_of({ format.per_call_start, ctx.reasoning->start });
     return ctx.reasoning_parser +
-           (force_tools ? p.eps() : p.optional(p.content(content_before_tools))) +
+           (force_tools ? p.eps() : p.optional(p.content(content_before_tools) + p.optional(ctx.reasoning_parser))) +
            tool_calls + p.end();
 }
 

common/chat-auto-parser-helpers.h

@@ -1,7 +1,7 @@
 #pragma once
 
 #include "chat-auto-parser.h"
-#include "peg-parser.h"
+
 #include <functional>
 #include <optional>
 #include <string>

common/chat-auto-parser.h

@@ -4,6 +4,7 @@
 #include "common.h"
 #include "jinja/caps.h"
 #include "peg-parser.h"
+#include "nlohmann/json.hpp"
 
 #include <chrono>
 #include <optional>
@@ -215,12 +216,14 @@ struct tool_id_analysis {
 // ============================================================================
 
 struct analyze_content;
+struct analyze_reasoning;
 
 struct parser_build_context {
     common_chat_peg_builder & p;
-    const generation_params &          inputs;
+    const generation_params &         inputs;
     common_peg_parser                 reasoning_parser;
     bool                              extracting_reasoning = false;
+    const analyze_reasoning *         reasoning            = nullptr;
     const analyze_content *           content              = nullptr;
 
     parser_build_context(common_chat_peg_builder & p, const generation_params & inputs);
@@ -353,6 +356,13 @@ struct analyze_tools : analyze_base {
     common_peg_parser build_tool_parser_json_native(parser_build_context & ctx) const;
     common_peg_parser build_tool_parser_tag_json(parser_build_context & ctx) const;
     common_peg_parser build_tool_parser_tag_tagged(parser_build_context & ctx) const;
+
+    // Shared helper: builds func_parser from open+call_id+args, handling atomic wrapping and close.
+    // atomic_peek: if present, used as the peek expression in the third atomicity branch.
+    common_peg_parser build_func_parser(common_chat_peg_builder & p, const std::string & name,
+                                        const common_peg_parser & call_id_section, bool have_call_id,
+                                        const common_peg_parser & args,
+                                        std::optional<common_peg_parser> atomic_peek) const;
     common_peg_parser build_tool_parser_tag_gemma4_dict(parser_build_context & ctx) const;
 };
 

common/chat-diff-analyzer.cpp

@@ -25,6 +25,9 @@ static const std::string ARG_SECOND = "BB_ARG_SND_BB";
 static const std::string USER_MSG = "U_USER_MSG Hello END_U";
 static const std::string ASSISTANT_MSG = "A_ASST_MSG I can help END_A";
 static const std::string THINKING_CONTENT = "REASON_PART I am thinking END_R";
+static const std::string CALL_ID_001 = "call00001";
+static const std::string CALL_ID_002 = "call00002";
+static const std::string CALL_ID_999 = "call99999";
 
 static std::vector<std::function<void(const common_chat_template & tmpl, autoparser &)>> workarounds(
     { // Old reasoning Qwen templates - they don't really display reasoning content, but we still want to
@@ -104,10 +107,11 @@ static std::vector<std::function<void(const common_chat_template & tmpl, autopar
               analysis.tools.function.name_suffix  = "";
               analysis.tools.arguments.start       = "{";
               analysis.tools.arguments.end         = "}";
+              analysis.tools.arguments.name_prefix = "";
               analysis.tools.arguments.name_suffix = ":";
               analysis.tools.arguments.separator   = ",";
               analysis.reasoning.mode              = reasoning_mode::TAG_BASED;
-              analysis.reasoning.start             = "<|channel>thought\n";
+              analysis.reasoning.start             = "<|channel>thought";
               analysis.reasoning.end               = "<channel|>";
               analysis.preserved_tokens.clear();
               analysis.preserved_tokens.push_back("<|tool_call>");
@@ -130,6 +134,7 @@ static std::vector<std::function<void(const common_chat_template & tmpl, autopar
               analysis.tools.function.name_prefix  = "<｜tool▁sep｜>";
               analysis.tools.format.per_call_end   = "<｜tool▁call▁end｜>";
               analysis.tools.function.close        = "```";
+              LOG_DBG(ANSI_ORANGE "[Patch: DeepSeek-R1-Distill-Qwen]\n" ANSI_RESET);
           }
       }
     });
@@ -157,7 +162,7 @@ static json user_msg = json{
     { "content", USER_MSG }
 };
 
-static json build_tool_call(const std::string & name, const json & args, const std::string & id = "call00001") {
+static json build_tool_call(const std::string & name, const json & args, const std::string & id = CALL_ID_001) {
     return json{
         { "id",       id                                              },
         { "type",     "function"                                      },
@@ -165,17 +170,17 @@ static json build_tool_call(const std::string & name, const json & args, const s
     };
 }
 
-static json first_tool_call_zero_args         = build_tool_call(FUN_FIRST, json::object(), "call00001");
-static json first_tool_call_one_arg           = build_tool_call(FUN_FIRST, {{ ARG_FIRST, "XXXX" }}, "call00001");
-static json first_tool_call_one_arg_other_val = build_tool_call(FUN_FIRST, {{ ARG_FIRST, "YYYY" }}, "call00001");
-static json first_tool_call_other_arg         = build_tool_call(FUN_FIRST, {{ ARG_SECOND, "YYYY" }}, "call00001");
+static json first_tool_call_zero_args         = build_tool_call(FUN_FIRST, json::object(), CALL_ID_001);
+static json first_tool_call_one_arg           = build_tool_call(FUN_FIRST, {{ ARG_FIRST, "XXXX" }}, CALL_ID_001);
+static json first_tool_call_one_arg_other_val = build_tool_call(FUN_FIRST, {{ ARG_FIRST, "YYYY" }}, CALL_ID_001);
+static json first_tool_call_other_arg         = build_tool_call(FUN_FIRST, {{ ARG_SECOND, "YYYY" }}, CALL_ID_001);
 
 static json first_tool_call =
-    build_tool_call(FUN_FIRST, json{{ ARG_FIRST,  "XXXX" }, { ARG_SECOND, "YYYY" }}, "call00001");
+    build_tool_call(FUN_FIRST, json{{ ARG_FIRST,  "XXXX" }, { ARG_SECOND, "YYYY" }}, CALL_ID_001);
 static json second_tool_call =
-    build_tool_call(FUN_SECOND, json{ { ARG_FIRST,  "XXXX" }, { ARG_SECOND, "YYYY" }}, "call00002");
+    build_tool_call(FUN_SECOND, json{ { ARG_FIRST,  "XXXX" }, { ARG_SECOND, "YYYY" }}, CALL_ID_002);
 static json first_tool_call_alt_id =
-    build_tool_call(FUN_FIRST, json{{ ARG_FIRST,  "XXXX" }, { ARG_SECOND, "YYYY" }}, "call99999");
+    build_tool_call(FUN_FIRST, json{{ ARG_FIRST,  "XXXX" }, { ARG_SECOND, "YYYY" }}, CALL_ID_999);
 
 template <typename T>
 static std::string mode_to_str(T mode) {
@@ -214,6 +219,11 @@ void autoparser::analyze_template(const common_chat_template & tmpl) {
     LOG_DBG("func_name_prefix: '%s'\n", tools.function.name_prefix.c_str());
     LOG_DBG("func_name_suffix: '%s'\n", tools.function.name_suffix.c_str());
     LOG_DBG("func_close: '%s'\n", tools.function.close.c_str());
+    LOG_DBG("call_id_prefix: '%s'\n", tools.call_id.prefix.c_str());
+    LOG_DBG("call_id_suffix: '%s'\n", tools.call_id.suffix.c_str());
+    LOG_DBG("call_id_pos: '%s'\n", mode_to_str(tools.call_id.pos).c_str());
+    LOG_DBG("args_start: '%s'\n", tools.arguments.start.c_str());
+    LOG_DBG("args_end: '%s'\n", tools.arguments.end.c_str());
     LOG_DBG("arg_name_prefix: '%s'\n", tools.arguments.name_prefix.c_str());
     LOG_DBG("arg_name_suffix: '%s'\n", tools.arguments.name_suffix.c_str());
     LOG_DBG("arg_value_prefix: '%s'\n", tools.arguments.value_prefix.c_str());
@@ -582,12 +592,15 @@ analyze_tools::analyze_tools(const common_chat_template & tmpl,
         if (caps.supports_parallel_tool_calls) {
             check_per_call_markers();
         }
+        LOG_DBG(ANSI_ORANGE "Phase 3a: Function call analysis\n" ANSI_RESET);
         extract_function_markers();
+        LOG_DBG(ANSI_ORANGE "Phase 3b: Argument analysis\n" ANSI_RESET);
         if (format.mode == tool_format::TAG_WITH_TAGGED) {
             analyze_arguments();
         }
         extract_argument_separator();
         extract_args_markers();
+        LOG_DBG(ANSI_ORANGE "Phase 3c: Call id analysis\n" ANSI_RESET);
         extract_call_id_markers();
     }
 }
@@ -978,8 +991,6 @@ void analyze_tools::extract_function_markers() {
 }
 
 void analyze_tools::analyze_arguments() {
-    LOG_DBG(ANSI_ORANGE "Phase 4: Argument analysis\n" ANSI_RESET);
-
     extract_argument_name_markers();
     extract_argument_value_markers();
 }
@@ -1188,7 +1199,7 @@ void analyze_tools::extract_args_markers() {
 
     const auto & diff = comparison->diff;
 
-    if (format.mode != tool_format::JSON_NATIVE) {
+    if (format.mode == tool_format::JSON_NATIVE) {
         std::string prefix_marker = !format.section_start.empty() ? format.section_start : format.per_call_start;
         std::string suffix_marker = !format.section_end.empty() ? format.section_end : format.per_call_end;
         // these might happen earlier in the tools section as an example or somewhere else, so we need to find the closest ones
@@ -1210,6 +1221,10 @@ void analyze_tools::extract_args_markers() {
             if (find_fun != std::string::npos) {
                 args_start = args_start.substr(find_fun + FUN_FIRST.size(), args_start.size() - find_fun - FUN_FIRST.size());
             }
+            size_t find_call_id = args_start.find(CALL_ID_001);
+            if (find_call_id != std::string::npos) {
+                args_start = args_start.substr(find_call_id + CALL_ID_001.size(), args_start.size() - find_call_id - CALL_ID_001.size());
+            }
             arguments.start = args_start;
             arguments.end   = args_end;
         }
@@ -1249,8 +1264,8 @@ void analyze_tools::extract_call_id_markers() {
         return;
     }
 
-    std::string id_value_1 = "call00001";
-    std::string id_value_2 = "call99999";
+    std::string id_value_1 = CALL_ID_001;
+    std::string id_value_2 = CALL_ID_999;
 
     size_t common_id_prefix_len = 0;
     for (size_t i = 0; i < std::min(id_value_1.length(), id_value_2.length()); i++) {
@@ -1349,6 +1364,14 @@ void analyze_tools::extract_call_id_markers() {
         call_id.suffix = find_first_marker(before_func);
     }
 
+    if (call_id.prefix == arguments.end) {
+        call_id.prefix = "";
+    }
+
+    if (call_id.suffix == arguments.start) {
+        call_id.suffix = "";
+    }
+
     // When call_id is detected, per_call_end may have been incorrectly set to include
     // the call_id_suffix and sample args. Clear it if it starts with call_id_suffix.
     if (call_id.pos != call_id_position::NONE && !call_id.suffix.empty() &&

common/chat-peg-parser.cpp

@@ -75,6 +75,84 @@ static std::string escape_json_string_inner(const std::string & s) {
     return escaped;
 }
 
+static const std::string GEMMA4_QUOTE = "<|\"|>";
+
+static std::string normalize_gemma4_to_json(const std::string & input) {
+    std::string result;
+    result.reserve(input.size() * 2);
+
+    enum Ctx { DICT, ARRAY };
+    std::vector<Ctx> ctx;
+
+    auto is_ws = [](char c) { return c == ' ' || c == '\t' || c == '\n' || c == '\r'; };
+    auto skip_ws = [&](size_t & pos) {
+        while (pos < input.size() && is_ws(input[pos])) {
+            result += input[pos++];
+        }
+    };
+
+    auto quote_unquoted_key = [&](size_t & pos) {
+        if (pos < input.size() && input[pos] != '"' && input[pos] != '}') {
+            result += '"';
+            while (pos < input.size() && input[pos] != ':' && !is_ws(input[pos])) {
+                result += input[pos++];
+            }
+            result += '"';
+            skip_ws(pos);
+        }
+    };
+
+    size_t i = 0;
+    while (i < input.size()) {
+        if (i + GEMMA4_QUOTE.size() <= input.size() &&
+            input.compare(i, GEMMA4_QUOTE.size(), GEMMA4_QUOTE) == 0) {
+            result += '"';
+            i += GEMMA4_QUOTE.size();
+            continue;
+        }
+
+        char c = input[i];
+
+        if (c == '{') {
+            result += c;
+            ctx.push_back(DICT);
+            ++i;
+            skip_ws(i);
+            quote_unquoted_key(i);
+            continue;
+        }
+        if (c == '}') {
+            result += c;
+            if (!ctx.empty()) ctx.pop_back();
+            ++i;
+            continue;
+        }
+        if (c == '[') {
+            result += c;
+            ctx.push_back(ARRAY);
+            ++i;
+            continue;
+        }
+        if (c == ']') {
+            result += c;
+            if (!ctx.empty()) ctx.pop_back();
+            ++i;
+            continue;
+        }
+        if (c == ',' && !ctx.empty() && ctx.back() == DICT) {
+            result += c;
+            ++i;
+            skip_ws(i);
+            quote_unquoted_key(i);
+            continue;
+        }
+
+        result += c;
+        ++i;
+    }
+    return result;
+}
+
 // Convert Python-style single-quoted strings to JSON double-quoted strings
 // Only converts outer string delimiters, properly handling escape sequences:
 // - {'key': 'value'} -> {"key": "value"}
@@ -214,6 +292,14 @@ std::string & common_chat_peg_mapper::args_target() {
     return (current_tool && !current_tool->name.empty()) ? current_tool->arguments : args_buffer;
 }
 
+std::string common_chat_peg_mapper::normalize_container_value(const std::string & input) {
+    return normalize_quotes_to_json(input);
+}
+
+std::string common_chat_peg_gemma4_mapper::normalize_container_value(const std::string & input) {
+    return normalize_quotes_to_json(normalize_gemma4_to_json(input));
+}
+
 void common_chat_peg_mapper::from_ast(const common_peg_ast_arena &    arena,
                                       const common_peg_parse_result & parse_result_arg) {
     arena.visit(parse_result_arg, [this](const common_peg_ast_node & node) { map(node); });
@@ -352,7 +438,7 @@ void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
             // For potential containers, normalize Python-style single quotes to JSON double quotes
             bool is_potential_container = value_content[0] == '[' || value_content[0] == '{';
             if (is_potential_container) {
-                value_content = normalize_quotes_to_json(value_content);
+                value_content = normalize_container_value(value_content);
             }
 
             // Try to parse as JSON value (number, bool, null, object, array)

common/chat-peg-parser.h

@@ -17,7 +17,9 @@ class common_chat_peg_mapper {
 
     virtual void from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result);
     virtual void map(const common_peg_ast_node & node);
-    private:
+  protected:
+    virtual std::string normalize_container_value(const std::string & input);
+  private:
       // Tool call handling state
       std::optional<common_chat_tool_call> pending_tool_call;  // Tool call waiting for name
       common_chat_tool_call *              current_tool          = nullptr;
@@ -30,6 +32,13 @@ class common_chat_peg_mapper {
       std::string & args_target();
 };
 
+class common_chat_peg_gemma4_mapper : public common_chat_peg_mapper {
+  public:
+    common_chat_peg_gemma4_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {}
+  protected:
+    std::string normalize_container_value(const std::string & input) override;
+};
+
 struct content_structure;
 struct tool_call_structure;
 

common/chat.cpp

@@ -13,6 +13,8 @@
 #include "jinja/caps.h"
 #include "peg-parser.h"
 
+#include "nlohmann/json.hpp"
+
 #include <cstdio>
 #include <cstdlib>
 #include <ctime>
@@ -694,6 +696,8 @@ const char * common_chat_format_name(common_chat_format format) {
             return "peg-simple";
         case COMMON_CHAT_FORMAT_PEG_NATIVE:
             return "peg-native";
+        case COMMON_CHAT_FORMAT_PEG_GEMMA4:
+            return "peg-gemma4";
         default:
             throw std::runtime_error("Unknown chat format");
     }
@@ -760,12 +764,12 @@ static void foreach_parameter(const json &
     }
 }
 
-std::string common_chat_template_direct_apply(
+static std::string common_chat_template_direct_apply_impl(
     const common_chat_template & tmpl,
     const autoparser::generation_params & inputs,
-    const std::optional<json> & messages_override,
-    const std::optional<json> & tools_override,
-    const std::optional<json> & additional_context) {
+    const std::optional<json> & messages_override = std::nullopt,
+    const std::optional<json> & tools_override = std::nullopt,
+    const std::optional<json> & additional_context = std::nullopt) {
     jinja::context ctx(tmpl.source());
 
     nlohmann::ordered_json inp = nlohmann::ordered_json{
@@ -812,6 +816,12 @@ std::string common_chat_template_direct_apply(
     return result;
 }
 
+std::string common_chat_template_direct_apply(
+    const common_chat_template & tmpl,
+    const autoparser::generation_params & inputs) {
+    return common_chat_template_direct_apply_impl(tmpl, inputs, std::nullopt, std::nullopt, std::nullopt);
+}
+
 static common_chat_params common_chat_params_init_ministral_3(const common_chat_template &    tmpl,
                                                               const autoparser::generation_params & inputs) {
     common_chat_params data;
@@ -862,7 +872,7 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
     data.supports_thinking  = true;
     data.thinking_start_tag = "[THINK]";
     data.thinking_end_tag   = "[/THINK]";
-    data.prompt            = common_chat_template_direct_apply(tmpl, inputs, /* messages_override = */ adjusted_messages);
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs, /* messages_override = */ adjusted_messages);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.preserved_tokens  = {
         "[THINK]",
@@ -945,7 +955,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
         adjusted_messages.push_back(msg);
     }
 
-    auto prompt = common_chat_template_direct_apply(tmpl, inputs, /* messages_override= */ adjusted_messages);
+    auto prompt = common_chat_template_direct_apply_impl(tmpl, inputs, /* messages_override= */ adjusted_messages);
 
     // Check if we need to replace the return token with end token during
     // inference and without generation prompt. For more details see:
@@ -1072,7 +1082,7 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
                                                                    const autoparser::generation_params & inputs) {
     common_chat_params data;
 
-    data.prompt           = common_chat_template_direct_apply(tmpl, inputs);
+    data.prompt           = common_chat_template_direct_apply_impl(tmpl, inputs);
     data.format           = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.preserved_tokens = {
         ">>>all",
@@ -1166,7 +1176,7 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
                                                           const autoparser::generation_params & inputs) {
     common_chat_params data;
 
-    data.prompt             = common_chat_template_direct_apply(tmpl, inputs);
+    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
     data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking  = true;
     data.preserved_tokens  = {
@@ -1289,7 +1299,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
                                                        const autoparser::generation_params & inputs) {
     common_chat_params data;
 
-    data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = true;
     data.preserved_tokens  = {
@@ -1368,7 +1378,7 @@ static common_chat_params common_chat_params_init_lfm2_5(const common_chat_templ
                                                          const autoparser::generation_params & inputs) {
     common_chat_params data;
 
-    data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = true;
     data.preserved_tokens  = {
@@ -1439,7 +1449,7 @@ static common_chat_params common_chat_params_init_gigachat_v3(
 
     common_chat_params data;
 
-    data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = false;
     data.preserved_tokens  = {
@@ -1621,7 +1631,7 @@ static json common_chat_extra_context() {
     return ctx;
 }
 
-static std::optional<common_chat_params> try_specialized_template(
+std::optional<common_chat_params> common_chat_try_specialized_template(
         const common_chat_template &          tmpl,
         const std::string &                   src,
         const autoparser::generation_params & params) {
@@ -1722,9 +1732,9 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
     }
 
     params.add_generation_prompt = false;
-    std::string no_gen_prompt    = common_chat_template_direct_apply(tmpl, params);
+    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params);
     params.add_generation_prompt = true;
-    std::string gen_prompt       = common_chat_template_direct_apply(tmpl, params);
+    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params);
     auto        diff             = calculate_diff_split(no_gen_prompt, gen_prompt);
     params.generation_prompt     = diff.right;
 
@@ -1758,7 +1768,7 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         common_chat_params data;
         auto params_copy               = params;
         params_copy.reasoning_format   = COMMON_REASONING_FORMAT_NONE;
-        data.prompt                    = common_chat_template_direct_apply(tmpl, params_copy);
+        data.prompt                    = common_chat_template_direct_apply_impl(tmpl, params_copy);
         data.format                    = COMMON_CHAT_FORMAT_PEG_NATIVE;
         data.generation_prompt         = params.generation_prompt;
         auto parser                    = build_chat_peg_parser([&params](common_chat_peg_builder &p) {
@@ -1768,7 +1778,7 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         return data;
     }
 
-    if (auto result = try_specialized_template(tmpl, src, params)) {
+    if (auto result = common_chat_try_specialized_template(tmpl, src, params)) {
         result->generation_prompt = params.generation_prompt;
         return *result;
     }
@@ -1905,8 +1915,13 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
             // Try to extract any partial results from what was successfully parsed
             common_chat_msg msg;
             msg.role = "assistant";
-            auto mapper = common_chat_peg_mapper(msg);
-            mapper.from_ast(ctx.ast, result);
+            std::unique_ptr<common_chat_peg_mapper> mapper;
+            if (params.format == COMMON_CHAT_FORMAT_PEG_GEMMA4) {
+                mapper = std::make_unique<common_chat_peg_gemma4_mapper>(msg);
+            } else {
+                mapper = std::make_unique<common_chat_peg_mapper>(msg);
+            }
+            mapper->from_ast(ctx.ast, result);
 
             if (ctx.is_debug()) {
                 fprintf(stderr, "\nAST for partial parse (fail):\n%s\n", ctx.ast.dump().c_str());
@@ -1921,8 +1936,13 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
     common_chat_msg msg;
     msg.role = "assistant";
 
-    auto mapper = common_chat_peg_mapper(msg);
-    mapper.from_ast(ctx.ast, result);
+    std::unique_ptr<common_chat_peg_mapper> mapper;
+    if (params.format == COMMON_CHAT_FORMAT_PEG_GEMMA4) {
+        mapper = std::make_unique<common_chat_peg_gemma4_mapper>(msg);
+    } else {
+        mapper = std::make_unique<common_chat_peg_mapper>(msg);
+    }
+    mapper->from_ast(ctx.ast, result);
 
     if (ctx.is_debug()) {
         fprintf(stderr, "\nAST for %s parse:\n%s\n", is_partial ? "partial" : "full", ctx.ast.dump().c_str());

common/chat.h

@@ -3,12 +3,12 @@
 #pragma once
 
 #include "common.h"
-#include "jinja/parser.h"
-#include "nlohmann/json_fwd.hpp"
 #include "peg-parser.h"
+#include "jinja/parser.h"
 #include "jinja/runtime.h"
 #include "jinja/caps.h"
-#include "nlohmann/json.hpp"
+
+#include "nlohmann/json_fwd.hpp"
 
 #include <chrono>
 #include <functional>
@@ -19,8 +19,6 @@
 using chat_template_caps = jinja::caps;
 using json = nlohmann::ordered_json;
 
-#include <nlohmann/json_fwd.hpp>
-
 struct common_chat_templates;
 
 namespace autoparser {
@@ -75,41 +73,9 @@ struct common_chat_template {
     const std::string & bos_token() const { return bos_tok; }
     const std::string & eos_token() const { return eos_tok; }
 
-    // TODO: this is ugly, refactor it somehow
-    json add_system(const json & messages, const std::string & system_prompt) const {
-        GGML_ASSERT(messages.is_array());
-        auto msgs_copy = messages;
-        if (!caps.supports_system_role) {
-            if (msgs_copy.empty()) {
-                msgs_copy.insert(msgs_copy.begin(), json{
-                    {"role", "user"},
-                    {"content", system_prompt}
-                });
-            } else {
-                auto & first_msg = msgs_copy[0];
-                if (!first_msg.contains("content")) {
-                    first_msg["content"] = "";
-                }
-                first_msg["content"] = system_prompt + "\n\n"
-                    + first_msg["content"].get<std::string>();
-            }
-        } else {
-            if (msgs_copy.empty() || msgs_copy[0].at("role") != "system") {
-                msgs_copy.insert(msgs_copy.begin(), json{
-                    {"role", "system"},
-                    {"content", system_prompt}
-                });
-            } else if (msgs_copy[0].at("role") == "system") {
-                msgs_copy[0]["content"] = system_prompt;
-            }
-        }
-        return msgs_copy;
-    }
-
     chat_template_caps original_caps() const {
         return caps;
     }
-
 };
 
 struct common_chat_msg {
@@ -184,6 +150,7 @@ enum common_chat_format {
     // These are intended to be parsed by the PEG parser
     COMMON_CHAT_FORMAT_PEG_SIMPLE,
     COMMON_CHAT_FORMAT_PEG_NATIVE,
+    COMMON_CHAT_FORMAT_PEG_GEMMA4,
 
     COMMON_CHAT_FORMAT_COUNT,  // Not a format, just the # formats
 };
@@ -256,8 +223,8 @@ common_chat_templates_ptr common_chat_templates_init(const struct llama_model *
                                                      const std::string &        bos_token_override = "",
                                                      const std::string &        eos_token_override = "");
 
-bool         common_chat_templates_was_explicit(const struct common_chat_templates * tmpls);
-std::string  common_chat_templates_source(const struct common_chat_templates * tmpls, const std::string & variant = "");
+bool        common_chat_templates_was_explicit(const struct common_chat_templates * tmpls);
+std::string common_chat_templates_source(const struct common_chat_templates * tmpls, const std::string & variant = "");
 
 struct common_chat_params common_chat_templates_apply(const struct common_chat_templates *        tmpls,
                                                       const struct common_chat_templates_inputs & inputs);
@@ -274,9 +241,9 @@ std::string common_chat_format_example(const struct common_chat_templates *
                                        bool                                       use_jinja,
                                        const std::map<std::string, std::string> & chat_template_kwargs);
 
-const char *            common_chat_format_name(common_chat_format format);
-common_chat_msg           common_chat_parse(const std::string & input, bool is_partial, const common_chat_parser_params & params);
-common_chat_msg           common_chat_peg_parse(const common_peg_arena & src_parser, const std::string & input, bool is_partial, const common_chat_parser_params & params);
+const char *    common_chat_format_name(common_chat_format format);
+common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_parser_params & params);
+common_chat_msg common_chat_peg_parse(const common_peg_arena & src_parser, const std::string & input, bool is_partial, const common_chat_parser_params & params);
 
 // used by arg and server
 const char *            common_reasoning_format_name(common_reasoning_format format);
@@ -302,7 +269,9 @@ std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_tem
 
 std::string common_chat_template_direct_apply(
     const common_chat_template & tmpl,
-    const autoparser::generation_params & inputs,
-    const std::optional<json> & messages_override = std::nullopt,
-    const std::optional<json> & tools_override = std::nullopt,
-    const std::optional<json> & additional_context = std::nullopt);
+    const autoparser::generation_params & inputs);
+
+std::optional<common_chat_params> common_chat_try_specialized_template(
+        const common_chat_template &          tmpl,
+        const std::string &                   src,
+        const autoparser::generation_params & params);

common/common.h

@@ -579,8 +579,9 @@ struct common_params {
     int32_t n_threads_http      = -1;    // number of threads to process HTTP requests (TODO: support threadpool)
     int32_t n_cache_reuse       = 0;     // min chunk size to reuse from the cache via KV shifting
     bool    cache_prompt        = true;  // whether to enable prompt caching
-    int32_t n_ctx_checkpoints   = 32;     // max number of context checkpoints per slot
-    int32_t checkpoint_every_nt = 8192;   // make a checkpoint every n tokens during prefill
+    bool    clear_idle          = true;  // save and clear idle slots upon starting a new task
+    int32_t n_ctx_checkpoints   = 32;    // max number of context checkpoints per slot
+    int32_t checkpoint_every_nt = 8192;  // make a checkpoint every n tokens during prefill
     int32_t cache_ram_mib       = 8192;  // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
 
     std::string hostname      = "127.0.0.1";

common/jinja/runtime.cpp

@@ -306,6 +306,19 @@ value filter_expression::execute_impl(context & ctx) {
             filter_id = "strip"; // alias
         }
         JJ_DEBUG("Applying filter '%s' to %s", filter_id.c_str(), input->type().c_str());
+        // TODO: Refactor filters so this coercion can be done automatically
+        if (!input->is_undefined() && !is_val<value_string>(input) && (
+            filter_id == "capitalize" ||
+            filter_id == "lower" ||
+            filter_id == "replace" ||
+            filter_id == "strip" ||
+            filter_id == "title" ||
+            filter_id == "upper" ||
+            filter_id == "wordcount"
+        )) {
+            JJ_DEBUG("Coercing %s to String for '%s' filter", input->type().c_str(), filter_id.c_str());
+            input = mk_val<value_string>(input->as_string());
+        }
         return try_builtin_func(ctx, filter_id, input)->invoke(func_args(ctx));
 
     } else if (is_stmt<call_expression>(filter)) {

common/jinja/value.cpp

@@ -465,8 +465,9 @@ const func_builtins & value_int_t::get_builtins() const {
             double val = static_cast<double>(args.get_pos(0)->as_int());
             return mk_val<value_float>(val);
         }},
-        {"tojson", tojson},
+        {"safe", tojson},
         {"string", tojson},
+        {"tojson", tojson},
     };
     return builtins;
 }
@@ -485,8 +486,9 @@ const func_builtins & value_float_t::get_builtins() const {
             int64_t val = static_cast<int64_t>(args.get_pos(0)->as_float());
             return mk_val<value_int>(val);
         }},
-        {"tojson", tojson},
+        {"safe", tojson},
         {"string", tojson},
+        {"tojson", tojson},
     };
     return builtins;
 }
@@ -771,6 +773,11 @@ const func_builtins & value_string_t::get_builtins() const {
 
 
 const func_builtins & value_bool_t::get_builtins() const {
+    static const func_handler tostring = [](const func_args & args) -> value {
+        args.ensure_vals<value_bool>();
+        bool val = args.get_pos(0)->as_bool();
+        return mk_val<value_string>(val ? "True" : "False");
+    };
     static const func_builtins builtins = {
         {"default", default_value},
         {"int", [](const func_args & args) -> value {
@@ -783,11 +790,8 @@ const func_builtins & value_bool_t::get_builtins() const {
             bool val = args.get_pos(0)->as_bool();
             return mk_val<value_float>(val ? 1.0 : 0.0);
         }},
-        {"string", [](const func_args & args) -> value {
-            args.ensure_vals<value_bool>();
-            bool val = args.get_pos(0)->as_bool();
-            return mk_val<value_string>(val ? "True" : "False");
-        }},
+        {"safe", tostring},
+        {"string", tostring},
         {"tojson", tojson},
     };
     return builtins;
@@ -1100,18 +1104,14 @@ const func_builtins & value_object_t::get_builtins() const {
 }
 
 const func_builtins & value_none_t::get_builtins() const {
+    static const func_handler tostring = [](const func_args &) -> value {
+        return mk_val<value_string>("None");
+    };
     static const func_builtins builtins = {
         {"default", default_value},
         {"tojson", tojson},
-        {"string", [](const func_args &) -> value {
-            return mk_val<value_string>("None");
-        }},
-        {"safe", [](const func_args &) -> value {
-            return mk_val<value_string>("None");
-        }},
-        {"strip", [](const func_args &) -> value {
-            return mk_val<value_string>("None");
-        }},
+        {"string", tostring},
+        {"safe", tostring},
         {"items", empty_value_fn<value_array>},
         {"map", empty_value_fn<value_array>},
         {"reject", empty_value_fn<value_array>},

common/peg-parser.cpp

@@ -1561,7 +1561,23 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
         if (!s.schema) {
             return true;
         }
-        if (s.raw && s.schema->contains("type") && s.schema->at("type").is_string() && s.schema->at("type") == "string") {
+        if (s.raw && s.schema->contains("type")) {
+            const auto & type_val = s.schema->at("type");
+            if (type_val.is_string() && type_val == "string") {
+                return true;
+            }
+            // Handle nullable types like ["string", "null"] - delegate when the
+            // non-null type is string, since the tagged format uses raw text
+            if (type_val.is_array()) {
+                for (const auto & t : type_val) {
+                    if (t.is_string() && t.get<std::string>() != "null") {
+                        return t.get<std::string>() == "string";
+                    }
+                }
+            }
+        }
+        // Delegate for enum schemas in raw mode - enum values are literal strings
+        if (s.raw && !s.schema->contains("type") && s.schema->contains("enum")) {
             return true;
         }
         return false;

convert_hf_to_gguf.py

@@ -7464,9 +7464,6 @@ class Gemma4Model(Gemma3Model):
 
         assert len(tokens) == vocab.vocab_size
 
-        # TODO @ngxson : there are some known (rare) issues with the tokenizer during development
-        # but I don't have time to dive into them right now;
-        # using a dedicated tokenizer name so that we can fix later without re-converting GGUF
         self.gguf_writer.add_tokenizer_model("gemma4")
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_scores(scores)

docs/backend/ZenDNN.md

@@ -57,13 +57,14 @@ ZenDNN is optimized for AMD EPYC™ processors and AMD Ryzen™ processors based
 
 ## Supported Operations
 
-The ZenDNN backend currently accelerates **matrix multiplication (MUL_MAT)** operations only. Other operations are handled by the standard CPU backend.
+The ZenDNN backend accelerates **matrix multiplication (MUL_MAT)** and **expert-based matrix multiplication (MUL_MAT_ID)** operations. Other operations are handled by the standard CPU backend.
 
 | Operation    | Status  | Notes                                          |
 |:-------------|:-------:|:----------------------------------------------:|
 | MUL_MAT      | Support | Accelerated via ZenDNN LowOHA MatMul           |
+| MUL_MAT_ID   | Support | Accelerated via ZenDNN LowOHA MatMul (MoE)     |
 
-*Note:* Since only MUL_MAT is accelerated, models will benefit most from ZenDNN when matrix multiplications dominate the computational workload (which is typical for transformer-based LLMs).
+*Note:* Since MUL_MAT and MUL_MAT_ID are accelerated, models will benefit most from ZenDNN when matrix multiplications dominate the computational workload (which is typical for transformer-based LLMs and Mixture-of-Experts models).
 
 ## DataType Supports
 
@@ -181,7 +182,7 @@ For detailed profiling and logging options, refer to the [ZenDNN Logging Documen
 
 ## Known Issues
 
-- **Limited operation support**: Currently only matrix multiplication (MUL_MAT) is accelerated via ZenDNN. Other operations fall back to the standard CPU backend.
+- **Limited operation support**: Currently matrix multiplication (MUL_MAT) and expert-based matrix multiplication (MUL_MAT_ID) are accelerated via ZenDNN. Other operations fall back to the standard CPU backend. Future updates may expand supported operations.
 - **BF16 support**: BF16 operations require AMD Zen 4 or Zen 5 architecture (EPYC 9004/9005 series). On older CPUs, operations will use FP32.
 - **NUMA awareness**: For multi-socket systems, manual NUMA binding may be required for optimal performance.
 
@@ -216,4 +217,4 @@ Please add the **[ZenDNN]** prefix/tag in issues/PRs titles to help the ZenDNN-t
 
 ## TODO
 
-- Expand operation support beyond MUL_MAT (attention operations, activations, etc.)
+- Expand operation support beyond MUL_MAT and MUL_MAT_ID (attention operations, activations, etc.)

docs/build.md

@@ -389,7 +389,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm
 
 
 The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
-If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
+If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. Note that [`HSA_OVERRIDE_GFX_VERSION`] is [not supported on Windows](https://github.com/ROCm/ROCm/issues/2654)
 
 ### Unified Memory
 

docs/ops.md

@@ -68,7 +68,7 @@ Legend:
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | 🟡 | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |

ggml/src/ggml-rpc/ggml-rpc.cpp

@@ -1009,8 +1009,8 @@ public:
     bool get_device_memory(const rpc_msg_get_device_memory_req & request, rpc_msg_get_device_memory_rsp & response);
 
     struct stored_graph {
-        ggml_context_ptr ctx_ptr;
-        ggml_cgraph *    graph;
+        std::vector<uint8_t>   buffer;
+        ggml_cgraph          * graph;
     };
 
 private:
@@ -1518,10 +1518,12 @@ bool rpc_server::graph_compute(const std::vector<uint8_t> & input) {
     LOG_DBG("[%s] device: %u, n_nodes: %u, n_tensors: %u\n", __func__, device, n_nodes, n_tensors);
 
     size_t buf_size = ggml_tensor_overhead()*(n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false);
-
+    if (stored_graphs[device].buffer.size() < buf_size) {
+        stored_graphs[device].buffer.resize(buf_size);
+    }
     struct ggml_init_params params = {
         /*.mem_size   =*/ buf_size,
-        /*.mem_buffer =*/ NULL,
+        /*.mem_buffer =*/ stored_graphs[device].buffer.data(),
         /*.no_alloc   =*/ true,
     };
     ggml_context_ptr ctx_ptr { ggml_init(params) };
@@ -1551,7 +1553,6 @@ bool rpc_server::graph_compute(const std::vector<uint8_t> & input) {
     }
     ggml_status status = ggml_backend_graph_compute(backends[device], graph);
     GGML_ASSERT(status == GGML_STATUS_SUCCESS && "Unsuccessful graph computations are not supported with RPC");
-    stored_graphs[device].ctx_ptr.swap(ctx_ptr);
     stored_graphs[device].graph = graph;
     return true;
 }

ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp

@@ -437,12 +437,18 @@ inline uint32_t ggml_webgpu_flash_attn_pick_vec_ne(const ggml_webgpu_flash_attn_
 
     // Head-dim specializations used by the tuned vec f16 path.
     switch (key.head_dim_qk) {
-        case 64: return 2u;
-        case 96: return 4u;
-        case 128: return 1u;
-        case 192: return 2u;
-        case 576: return 2u;
-        default: return 1u;
+        case 64:
+            return 2u;
+        case 96:
+            return 4u;
+        case 128:
+            return 1u;
+        case 192:
+            return 2u;
+        case 576:
+            return 2u;
+        default:
+            return 1u;
     }
 }
 
@@ -513,9 +519,9 @@ struct ggml_webgpu_flash_attn_blk_shader_lib_context {
 };
 
 inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_flash_attn_blk_shader(
-    pre_wgsl::Preprocessor &                                    preprocessor,
-    const char *                                                shader_src,
-    const ggml_webgpu_flash_attn_blk_shader_lib_context &       context) {
+    pre_wgsl::Preprocessor &                              preprocessor,
+    const char *                                          shader_src,
+    const ggml_webgpu_flash_attn_blk_shader_lib_context & context) {
     std::vector<std::string> defines;
     std::string              variant = "flash_attn_vec_blk";
 
@@ -1857,9 +1863,8 @@ class ggml_webgpu_shader_lib {
         defines.push_back(std::string("SG_MAT_K=") + std::to_string(context.sg_mat_k));
 
         uint32_t q_tile  = context.sg_mat_m;
-        uint32_t kv_tile =
-            std::min(ggml_webgpu_flash_attn_max_kv_tile(context),
-                     context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES);
+        uint32_t kv_tile = std::min(ggml_webgpu_flash_attn_max_kv_tile(context),
+                                    context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES);
         if (context.key.use_vec) {
             q_tile  = 1;
             kv_tile = std::max(context.sg_mat_n, std::min(32u, ggml_webgpu_flash_attn_max_kv_tile(context)));
@@ -1885,14 +1890,14 @@ class ggml_webgpu_shader_lib {
         }
         defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
 
-        const char * shader_src = context.key.use_vec ? wgsl_flash_attn_vec_split : wgsl_flash_attn;
+        const char *    shader_src = context.key.use_vec ? wgsl_flash_attn_vec_split : wgsl_flash_attn;
         webgpu_pipeline pipeline =
             ggml_webgpu_create_pipeline(device, preprocessor.preprocess(shader_src, defines), variant);
-        auto decisions     = std::make_shared<ggml_webgpu_flash_attn_shader_decisions>();
-        decisions->q_tile  = q_tile;
-        decisions->kv_tile = kv_tile;
-        decisions->wg_size = wg_size;
-        pipeline.context   = decisions;
+        auto decisions                    = std::make_shared<ggml_webgpu_flash_attn_shader_decisions>();
+        decisions->q_tile                 = q_tile;
+        decisions->kv_tile                = kv_tile;
+        decisions->wg_size                = wg_size;
+        pipeline.context                  = decisions;
         flash_attn_pipelines[context.key] = pipeline;
         return flash_attn_pipelines[context.key];
     }
@@ -1905,7 +1910,7 @@ class ggml_webgpu_shader_lib {
 
         ggml_webgpu_processed_shader processed =
             ggml_webgpu_preprocess_flash_attn_blk_shader(preprocessor, wgsl_flash_attn_vec_blk, context);
-        webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed.wgsl, processed.variant);
+        webgpu_pipeline pipeline              = ggml_webgpu_create_pipeline(device, processed.wgsl, processed.variant);
         flash_attn_blk_pipelines[context.key] = pipeline;
         return flash_attn_blk_pipelines[context.key];
     }

ggml/src/ggml-zendnn/CMakeLists.txt

@@ -28,7 +28,7 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
     ExternalProject_Add(
         zendnn
         GIT_REPOSITORY https://github.com/amd/ZenDNN.git
-        GIT_TAG a18adf8c605fb5f5e52cefd7eda08a7b18febbaf    # ZenDNN-2026-WW08
+        GIT_TAG f79f7321a1add65ced6397a6bfab7edba6e3e14e    # ZenDNN-2026-WW13
         PREFIX      ${ZENDNN_PREFIX}
         SOURCE_DIR  ${ZENDNN_SOURCE_DIR}
         BINARY_DIR  ${ZENDNN_BUILD_DIR}

ggml/src/ggml-zendnn/ggml-zendnn.cpp

@@ -190,6 +190,170 @@ static void ggml_zendnn_compute_forward_mul_mat(
     }
 }
 
+struct mmid_row_mapping {
+    int32_t i1;
+    int32_t i2;
+};
+
+static void ggml_zendnn_compute_forward_mul_mat_id(
+    ggml_backend_zendnn_context * ctx,
+    ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];  // expert weights
+    const ggml_tensor * src1 = dst->src[1];  // inputs
+    const ggml_tensor * ids  = dst->src[2];  // expert ids
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    // exit for no tokens to process
+    if (ne2 == 0 || ne11 == 0) {
+        return;
+    }
+
+    ggml_type         const vec_dot_type = src0->type;
+    ggml_from_float_t const from_float = ggml_get_type_traits(vec_dot_type)->from_float_ref;
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == ggml_type_size(src0->type));
+    GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    GGML_ASSERT(ne03 == 1);
+    GGML_ASSERT(ne13 == 1);
+    GGML_ASSERT(ne3  == 1);
+
+    // row groups
+    const int n_ids = ids->ne[0]; // n_expert_used
+    const int n_as  = ne02;       // n_experts
+
+    std::vector<int64_t> matrix_row_counts(n_as, 0);
+    std::vector<std::vector<mmid_row_mapping>> matrix_rows(n_as);
+
+    int64_t max_rows = 0;
+    // group rows by expert (preprocessing step)
+    for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
+        for (int id = 0; id < n_ids; ++id) {
+            const int32_t i02 = *(const int32_t *)((const char *)ids->data + iid1*ids->nb[1] + id*ids->nb[0]);
+
+            GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+            matrix_rows[i02].push_back({id, iid1});
+            matrix_row_counts[i02]++;
+            if (matrix_row_counts[i02] > max_rows) {
+                max_rows = matrix_row_counts[i02];
+            }
+        }
+    }
+
+    if (max_rows == 0) {
+        return; // no rows to process
+    }
+
+    const size_t row_size = ggml_row_size(vec_dot_type, ne10);
+
+    // size for converting src1 rows to vec_dot_type if needed
+    const size_t nbw1 = row_size;
+    const size_t nbw2 = nbw1 * ne11;
+    const size_t nbw3 = nbw2 * ne12;
+    const size_t src1_conv_size = (src1->type != vec_dot_type) ? ne13 * nbw3 : 0;
+
+    // size for MoE gather/scatter buffers
+    const size_t wdata_cur_size = max_rows * row_size;
+    const size_t dst_cur_size = max_rows * ggml_row_size(dst->type, ne01);
+
+    // allocate single buffer for all needs
+    const size_t total_size = src1_conv_size + wdata_cur_size + dst_cur_size;
+    if (ctx->work_size < total_size) {
+        ctx->work_data.reset(new char[total_size]);
+        ctx->work_size = total_size;
+    }
+
+    // partition the buffer
+    char * work_data = ctx->work_data.get();
+    char * wdata_cur = work_data + src1_conv_size;
+    char * dst_cur = wdata_cur + wdata_cur_size;
+
+    if (src1->type != vec_dot_type) {
+        GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+        #pragma omp parallel for collapse(3) num_threads(ctx->n_threads) schedule(static)
+        for (int64_t i13 = 0; i13 < ne13; ++i13) {
+            for (int64_t i12 = 0; i12 < ne12; ++i12) {
+                for (int64_t i11 = 0; i11 < ne11; ++i11) {
+                    const float * src1_f32 = (float *)((char *)src1->data + i11*nb11 + i12*nb12 + i13*nb13);
+                    void * src1_conv = (char *)work_data + i11*nbw1 + i12*nbw2 + i13*nbw3;
+                    from_float(src1_f32, src1_conv, ne10);
+                }
+            }
+        }
+    }
+
+    const void * wdata = src1->type == vec_dot_type ? src1->data : work_data;
+
+    // process each expert with gather -> gemm -> scatter pattern
+    for (int64_t cur_a = 0; cur_a < n_as; ++cur_a) {
+        const int64_t cne1 = matrix_row_counts[cur_a];
+
+        if (cne1 == 0) {
+            continue;
+        }
+
+        const char * src0_cur = (const char *) src0->data + cur_a*nb02;
+
+        // gather input rows for this expert
+        #pragma omp parallel for num_threads(ctx->n_threads) schedule(static)
+        for (int64_t ir1 = 0; ir1 < cne1; ++ir1) {
+            const mmid_row_mapping & row_mapping = matrix_rows[cur_a][ir1];
+            const int64_t id = row_mapping.i1;
+            const int64_t i11 = id % ne11;
+            const int64_t i12 = row_mapping.i2;
+
+            std::memcpy(
+                wdata_cur + ir1 * row_size,
+                (const char *) wdata + (i11 + i12*ne11) * row_size,
+                row_size
+            );
+        }
+
+        // batched gemm for all tokens in this expert
+        if (!ggml_zendnn_sgemm(ctx,
+                              ne01,       // m
+                              cne1,       // n
+                              ne10,       // k
+                              src0_cur,
+                              ne00,       // lda
+                              wdata_cur,
+                              ne10,       // ldb
+                              dst_cur,
+                              ne01,       // ldc
+                              src0->type,
+                              vec_dot_type,
+                              dst->type)) {
+            GGML_ABORT("%s: ZenDNN sgemm failed\n", __func__);
+        }
+
+        // scatter output rows to destination
+        #pragma omp parallel for num_threads(ctx->n_threads) schedule(static)
+        for (int64_t ir1 = 0; ir1 < cne1; ++ir1) {
+            const mmid_row_mapping & row_mapping = matrix_rows[cur_a][ir1];
+            const int64_t id = row_mapping.i1;
+            const int64_t i1 = id;
+            const int64_t i2 = row_mapping.i2;
+
+            std::memcpy(
+                (char *) dst->data + i1*nb1 + i2*nb2,
+                dst_cur + ir1 * ggml_row_size(dst->type, ne01),
+                ggml_row_size(dst->type, ne01)
+            );
+        }
+    }
+}
+
 // backend interface
 
 static const char * ggml_backend_zendnn_get_name(ggml_backend_t backend) {
@@ -218,6 +382,9 @@ static ggml_status ggml_backend_zendnn_graph_compute(ggml_backend_t backend, ggm
             case GGML_OP_MUL_MAT:
                 ggml_zendnn_compute_forward_mul_mat(ctx, node);
                 break;
+            case GGML_OP_MUL_MAT_ID:
+                ggml_zendnn_compute_forward_mul_mat_id(ctx, node);
+                break;
             case GGML_OP_NONE:
             case GGML_OP_RESHAPE:
             case GGML_OP_VIEW:
@@ -361,6 +528,7 @@ static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const
             return true;
 
         case GGML_OP_MUL_MAT:
+        case GGML_OP_MUL_MAT_ID:
         {
             const ggml_tensor * weights = op->src[0];
             const ggml_tensor * inputs = op->src[1];
@@ -374,6 +542,17 @@ static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const
                 ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) {
                     return false;
             }
+            // MUL_MAT_ID performs best with a moderate number of experts due to its
+            // gather + batched matmul + scatter approach. Future versions will leverage
+            // ZenDNN's grouped_gemm for better scalability with larger expert counts:
+            // https://github.com/amd/ZenDNN/blob/main/docs/operator/lowoha_group_gemm_operator.md
+            if (op->op == GGML_OP_MUL_MAT_ID) {
+                const int64_t n_experts = weights->ne[2];
+                const int64_t max_experts = 32;
+                if (n_experts > max_experts) {
+                    return false;
+                }
+            }
             switch (weights->type) {
                 case GGML_TYPE_F32:
                 case GGML_TYPE_BF16:

models/templates/gemma4.jinja

@@ -0,0 +1,266 @@
+{%- macro format_parameters(properties, required) -%}
+    {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
+    {%- set ns = namespace(found_first=false) -%}
+    {%- for key, value in properties | dictsort -%}
+        {%- set add_comma = false -%}
+        {%- if key not in standard_keys -%}
+            {%- if ns.found_first %},{% endif -%}
+            {%- set ns.found_first = true -%}
+            {{ key }}:{
+            {%- if value['description'] -%}
+                description:<|"|>{{ value['description'] }}<|"|>
+                {%- set add_comma = true -%}
+            {%- endif -%}
+            {%- if value['nullable'] %}
+                {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                nullable:true
+            {%- endif -%}
+            {%- if value['type'] | upper == 'STRING' -%}
+                {%- if value['enum'] -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    enum:{{ format_argument(value['enum']) }}
+                {%- endif -%}
+            {%- elif value['type'] | upper == 'OBJECT' -%}
+                ,properties:{
+                {%- if value['properties'] is defined and value['properties'] is mapping -%}
+                    {{- format_parameters(value['properties'], value['required'] | default([])) -}}
+                {%- elif value is mapping -%}
+                    {{- format_parameters(value, value['required'] | default([])) -}}
+                {%- endif -%}
+                }
+                {%- if value['required'] -%}
+                    ,required:[
+                    {%- for item in value['required'] | default([]) -%}
+                        <|"|>{{- item -}}<|"|>
+                        {%- if not loop.last %},{% endif -%}
+                    {%- endfor -%}
+                    ]
+                {%- endif -%}
+            {%- elif value['type'] | upper == 'ARRAY' -%}
+                {%- if value['items'] is mapping and value['items'] -%}
+                    ,items:{
+                    {%- set ns_items = namespace(found_first=false) -%}
+                    {%- for item_key, item_value in value['items'] | dictsort -%}
+                        {%- if item_value is not none -%}
+                            {%- if ns_items.found_first %},{% endif -%}
+                            {%- set ns_items.found_first = true -%}
+                            {%- if item_key == 'properties' -%}
+                                properties:{
+                                {%- if item_value is mapping -%}
+                                    {{- format_parameters(item_value, value['items']['required'] | default([])) -}}
+                                {%- endif -%}
+                                }
+                            {%- elif item_key == 'required' -%}
+                                required:[
+                                {%- for req_item in item_value -%}
+                                    <|"|>{{- req_item -}}<|"|>
+                                    {%- if not loop.last %},{% endif -%}
+                                {%- endfor -%}
+                                ]
+                            {%- elif item_key == 'type' -%}
+                                {%- if item_value is string -%}
+                                    type:{{ format_argument(item_value | upper) }}
+                                {%- else -%}
+                                    type:{{ format_argument(item_value | map('upper') | list) }}
+                                {%- endif -%}
+                            {%- else -%}
+                                {{ item_key }}:{{ format_argument(item_value) }}
+                            {%- endif -%}
+                        {%- endif -%}
+                    {%- endfor -%}
+                    }
+                {%- endif -%}
+            {%- endif -%}
+            {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+            type:<|"|>{{ value['type'] | upper }}<|"|>}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endmacro -%}
+{%- macro format_function_declaration(tool_data) -%}
+    declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|>
+    {%- set params = tool_data['function']['parameters'] -%}
+    {%- if params -%}
+        ,parameters:{
+        {%- if params['properties'] -%}
+            properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
+        {%- endif -%}
+        {%- if params['required'] -%}
+            required:[
+            {%- for item in params['required'] -%}
+                <|"|>{{- item -}}<|"|>
+                {{- ',' if not loop.last -}}
+            {%- endfor -%}
+            ],
+        {%- endif -%}
+        {%- if params['type'] -%}
+            type:<|"|>{{- params['type'] | upper -}}<|"|>}
+        {%- endif -%}
+    {%- endif -%}
+    {%- if 'response' in tool_data['function'] -%}
+        {%- set response_declaration = tool_data['function']['response'] -%}
+        ,response:{
+        {%- if response_declaration['description'] -%}
+            description:<|"|>{{- response_declaration['description'] -}}<|"|>,
+        {%- endif -%}
+        {%- if response_declaration['type'] | upper == 'OBJECT' -%}
+            type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>}
+        {%- endif -%}
+    {%- endif -%}
+    }
+{%- endmacro -%}
+{%- macro format_argument(argument, escape_keys=True) -%}
+    {%- if argument is string -%}
+        {{- '<|"|>' + argument + '<|"|>' -}}
+    {%- elif argument is boolean -%}
+        {{- 'true' if argument else 'false' -}}
+    {%- elif argument is mapping -%}
+        {{- '{' -}}
+        {%- set ns = namespace(found_first=false) -%}
+        {%- for key, value in argument | dictsort -%}
+            {%- if ns.found_first %},{% endif -%}
+            {%- set ns.found_first = true -%}
+            {%- if escape_keys -%}
+                {{- '<|"|>' + key + '<|"|>' -}}
+            {%- else -%}
+                {{- key -}}
+            {%- endif -%}
+            :{{- format_argument(value, escape_keys=escape_keys) -}}
+        {%- endfor -%}
+        {{- '}' -}}
+    {%- elif argument is sequence -%}
+        {{- '[' -}}
+        {%- for item in argument -%}
+            {{- format_argument(item, escape_keys=escape_keys) -}}
+            {%- if not loop.last %},{% endif -%}
+        {%- endfor -%}
+        {{- ']' -}}
+    {%- else -%}
+        {{- argument -}}
+    {%- endif -%}
+{%- endmacro -%}
+{%- macro strip_thinking(text) -%}
+    {%- set ns = namespace(result='') -%}
+    {%- for part in text.split('<channel|>') -%}
+        {%- if '<|channel>' in part -%}
+            {%- set ns.result = ns.result + part.split('<|channel>')[0] -%}
+        {%- else -%}
+            {%- set ns.result = ns.result + part -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {{- ns.result | trim -}}
+{%- endmacro -%}
+
+{%- set ns = namespace(prev_message_type=None) -%}
+{%- set loop_messages = messages -%}
+{{ bos_token }}
+{#- Handle System/Tool Definitions Block -#}
+{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}
+    {{- '<|turn>system\n' -}}
+
+    {#- Inject Thinking token at the very top of the FIRST system turn -#}
+    {%- if enable_thinking is defined and enable_thinking -%}
+        {{- '<|think|>' -}}
+        {%- set ns.prev_message_type = 'think' -%}
+    {%- endif -%}
+
+    {%- if messages[0]['role'] in ['system', 'developer'] -%}
+        {{- messages[0]['content'] | trim -}}
+        {%- set loop_messages = messages[1:] -%}
+    {%- endif -%}
+
+    {%- if tools -%}
+        {%- for tool in tools %}
+            {{- '<|tool>' -}}
+            {{- format_function_declaration(tool) | trim -}}
+            {{- '<tool|>' -}}
+        {%- endfor %}
+        {%- set ns.prev_message_type = 'tool' -%}
+    {%- endif -%}
+
+    {{- '<turn|>\n' -}}
+{%- endif %}
+
+{#- Loop through messages -#}
+{%- for message in loop_messages -%}
+    {%- set ns.prev_message_type = None -%}
+    {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
+        {{- '<|turn>' + role + '\n' }}
+
+            {%- if message['tool_calls'] -%}
+                {%- for tool_call in message['tool_calls'] -%}
+                    {%- set function = tool_call['function'] -%}
+                    {{- '<|tool_call>call:' + function['name'] + '{' -}}
+                    {%- if function['arguments'] is mapping -%}
+                        {%- set ns_args = namespace(found_first=false) -%}
+                        {%- for key, value in function['arguments'] | dictsort -%}
+                            {%- if ns_args.found_first %},{% endif -%}
+                            {%- set ns_args.found_first = true -%}
+                            {{- key -}}:{{- format_argument(value, escape_keys=False) -}}
+                        {%- endfor -%}
+                    {%- elif function['arguments'] is string -%}
+                        {{- function['arguments'] -}}
+                    {%- endif -%}
+                    {{- '}<tool_call|>' -}}
+                {%- endfor -%}
+                {%- set ns.prev_message_type = 'tool_call' -%}
+            {%- endif -%}
+
+            {%- if message['tool_responses'] -%}
+                {#- Tool Response handling -#}
+                {%- for tool_response in message['tool_responses'] -%}
+                    {{- '<|tool_response>' -}}
+                    {%- if tool_response['response'] is mapping -%}
+                        {{- 'response:' + tool_response['name'] | default('unknown') + '{' -}}
+                        {%- for key, value in tool_response['response'] | dictsort -%}
+                            {{- key -}}:{{- format_argument(value, escape_keys=False) -}}
+                            {%- if not loop.last %},{% endif -%}
+                        {%- endfor -%}
+                        {{- '}' -}}
+                    {%- else -%}
+                        {{- 'response:' + tool_response['name'] | default('unknown') + '{value:' + format_argument(tool_response['response'], escape_keys=False) + '}' -}}
+                    {%- endif -%}
+                    {{- '<tool_response|>' -}}
+                {%- endfor -%}
+                {%- set ns.prev_message_type = 'tool_response' -%}
+            {%- endif -%}
+
+            {%- if message['content'] is string -%}
+                {%- if role == 'model' -%}
+                    {{- strip_thinking(message['content']) -}}
+                {%- else -%}
+                    {{- message['content'] | trim -}}
+                {%- endif -%}
+            {%- elif message['content'] is sequence -%}
+                {%- for item in message['content'] -%}
+                    {%- if item['type'] == 'text' -%}
+                        {%- if role == 'model' -%}
+                            {{- strip_thinking(item['text']) -}}
+                        {%- else -%}
+                            {{- item['text'] | trim -}}
+                        {%- endif -%}
+                    {%- elif item['type'] == 'image' -%}
+                        {{- '\n\n<|image|>\n\n' -}}
+                        {%- set ns.prev_message_type = 'image' -%}
+                    {%- elif item['type'] == 'audio' -%}
+                        {{- '<|audio|>' -}}
+                        {%- set ns.prev_message_type = 'audio' -%}
+                    {%- elif item['type'] == 'video' -%}
+                        {{- '\n\n<|video|>\n\n' -}}
+                        {%- set ns.prev_message_type = 'video' -%}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- endif -%}
+
+        {%- if not (message['tool_responses'] and not message['content']) -%}
+            {{- '<turn|>\n' -}}
+        {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {%- if ns.prev_message_type != 'tool_response' -%}
+        {{- '<|turn>model\n' -}}
+    {%- endif -%}
+    {%- if not enable_thinking | default(false) -%}
+        {{- '<|channel>thought\n<channel|>' -}}
+    {%- endif -%}
+{%- endif -%}

src/llama-ext.h

@@ -1,8 +1,8 @@
 #pragma once
 
-#include "llama-context.h"
-#include "ggml.h"
-#include "stdint.h"
+#include "llama.h"
+
+#include <cstdint>
 
 // Reserve a new compute graph. It is valid until the next call to llama_graph_reserve.
 LLAMA_API struct ggml_cgraph * llama_graph_reserve(
@@ -10,3 +10,47 @@ LLAMA_API struct ggml_cgraph * llama_graph_reserve(
         uint32_t n_tokens,
         uint32_t n_seqs,
         uint32_t n_outputs);
+
+// Get the default ggml_type for a given ftype.
+LLAMA_API ggml_type llama_ftype_get_default_type(llama_ftype ftype);
+
+// Quantization state.
+struct quantize_state_impl;
+
+LLAMA_API quantize_state_impl * llama_quant_init(
+        const llama_model * model,
+        const llama_model_quantize_params * params);
+
+LLAMA_API void llama_quant_free(quantize_state_impl * qs);
+
+// Descriptor for constructing a mock model for quantization testing.
+struct llama_quant_model_desc {
+    const char * architecture;
+    uint32_t n_embd;
+    uint32_t n_ff;
+    uint32_t n_layer;
+    uint32_t n_head;
+    uint32_t n_head_kv;
+    uint32_t n_expert;
+    uint32_t n_embd_head_k;
+    uint32_t n_embd_head_v;
+};
+
+// Create a mock model from a metadata descriptor (for testing).
+// The returned model must be freed with llama_model_free().
+LLAMA_API llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * desc);
+
+// Returns true if this tensor should be quantized (based on name, dims, params).
+LLAMA_API bool llama_quant_tensor_allows_quantization(
+        const quantize_state_impl * qs,
+        const ggml_tensor * tensor);
+
+// Compute quantization type assignments for a list of tensors.
+// All tensors should be quantizable (use llama_quant_tensor_allows_quantization to filter).
+// result_types: caller-allocated array of n_tensors elements, filled with assigned types.
+LLAMA_API void llama_quant_compute_types(
+        quantize_state_impl * qs,
+        llama_ftype ftype,
+        ggml_tensor ** tensors,
+        ggml_type * result_types,
+        size_t n_tensors);

src/llama-kv-cache-iswa.cpp

@@ -66,9 +66,8 @@ llama_kv_cache_iswa::llama_kv_cache_iswa(
 
     LLAMA_LOG_INFO("%s: creating     SWA KV cache, size = %u cells\n", __func__, size_swa);
 
-    // note: the SWA cache is never quantized because it is relatively small
     kv_swa = std::make_unique<llama_kv_cache>(
-            model, GGML_TYPE_F16, GGML_TYPE_F16,
+            model, type_k, type_v,
             v_trans, offload, unified, size_swa, n_seq_max, n_pad,
             hparams.n_swa, hparams.swa_type, filter_swa, reuse);
 }

src/llama-quant.cpp

@@ -1,11 +1,11 @@
-#include "llama.h"
 #include "llama-impl.h"
 #include "llama-model.h"
 #include "llama-model-loader.h"
+#include "llama-ext.h"
 
+#include <algorithm>
 #include <cmath>
 #include <cstring>
-#include <string>
 #include <cinttypes>
 #include <fstream>
 #include <mutex>
@@ -197,6 +197,7 @@ struct quantize_state_impl {
 
 // per-tensor metadata, computed in the preliminary loop and used in the main loop
 struct tensor_metadata {
+    std::string     name;
     ggml_type       target_type;
     tensor_category category;
     std::string     remapped_imatrix_name;
@@ -788,7 +789,7 @@ static bool tensor_requires_imatrix(const char * tensor_name, const ggml_type ds
 // given a file type, get the default tensor type
 //
 
-static ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
+ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
     switch (ftype) {
         case LLAMA_FTYPE_MOSTLY_Q4_0: return GGML_TYPE_Q4_0;
         case LLAMA_FTYPE_MOSTLY_Q4_1: return GGML_TYPE_Q4_1;
@@ -827,16 +828,32 @@ static ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_IQ3_S:
         case LLAMA_FTYPE_MOSTLY_IQ3_M:   return GGML_TYPE_IQ3_S;
 
-        default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
+        default: return GGML_TYPE_COUNT;
     }
 }
 
+
+static void init_quantize_state_counters(quantize_state_impl & qs, std::vector<tensor_metadata> & metadata) {
+    for (auto & tm : metadata) {
+        tensor_category cat = tensor_get_category(tm.name);
+        tm.category = cat;
+
+        if (category_is_attn_v(cat)) {
+            ++qs.n_attention_wv;
+        }
+
+        if (cat == tensor_category::OUTPUT) {
+            qs.has_tied_embeddings = false;
+        }
+    }
+    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer;
+}
+
 //
 // main quantization driver
 //
 
 static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
-    ggml_type default_type;
     llama_ftype ftype = params->ftype;
 
     int nthread = params->nthread;
@@ -845,7 +862,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         nthread = std::thread::hardware_concurrency();
     }
 
-    default_type = llama_ftype_get_default_type(ftype);
+    ggml_type default_type = llama_ftype_get_default_type(ftype);
+    if (default_type == GGML_TYPE_COUNT) {
+        throw std::runtime_error(format("invalid output file type %d\n", ftype));
+    }
 
     // mmap consistently increases speed on Linux, and also increases speed on Windows with
     // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
@@ -964,6 +984,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         });
     }
 
+    // compute tensor metadata once and cache it
+    std::vector<tensor_metadata> metadata(tensors.size());
+    for (size_t i = 0; i < tensors.size(); ++i) {
+        metadata[i].name = ggml_get_name(tensors[i]->tensor);
+    }
+
+    // initialize quantization state counters and metadata categories
+    init_quantize_state_counters(qs, metadata);
+
     int idx = 0;
     uint16_t n_split = 1;
 
@@ -976,25 +1005,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
     std::vector<gguf_context_ptr> ctx_outs(n_split);
     ctx_outs[0] = std::move(ctx_out);
 
-    // compute tensor metadata once and cache it
-    std::vector<tensor_metadata> metadata(tensors.size());
-
-    // initialize quantization state before preliminary loop (counters for use_more_bits)
-    {
-        for (size_t i = 0; i < tensors.size(); ++i) {
-            const auto cat = tensor_get_category(tensors[i]->tensor->name);
-            if (category_is_attn_v(cat)) {
-                ++qs.n_attention_wv;
-            }
-            if (cat == tensor_category::OUTPUT) {
-                qs.has_tied_embeddings = false;
-            }
-            metadata[i].category = cat; // save and re-use the category while we're at it
-        }
-        // these also need to be set to n_layer by default
-        qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer;
-    }
-
     // flag for --dry-run
     bool will_require_imatrix = false;
 
@@ -1005,7 +1015,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
     for (size_t i = 0; i < tensors.size(); ++i) {
         const auto * it = tensors[i];
         const struct ggml_tensor * tensor = it->tensor;
-        const std::string name = ggml_get_name(tensor);
 
         uint16_t i_split = params->keep_split ? it->idx : 0;
         if (!ctx_outs[i_split]) {
@@ -1034,7 +1043,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
                                 "        - offending tensor: %s\n"
                                 "        - target type: %s\n"
                                 "============================================================================\n\n",
-                                name.c_str(), ggml_type_name(metadata[i].target_type));
+                                metadata[i].name.c_str(), ggml_type_name(metadata[i].target_type));
                 throw std::runtime_error("this quantization requires an imatrix!");
             }
         }
@@ -1107,7 +1116,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
             new_ofstream(weight.idx);
         }
 
-        const std::string name = ggml_get_name(tensor);
         const size_t tensor_size = ggml_nbytes(tensor);
 
         if (!params->dry_run) {
@@ -1238,9 +1246,9 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
             total_size_new += new_size;
 
             // update the gguf meta data as we go
-            gguf_set_tensor_type(ctx_outs[cur_split].get(), name.c_str(), new_type);
-            GGML_ASSERT(gguf_get_tensor_size(ctx_outs[cur_split].get(), gguf_find_tensor(ctx_outs[cur_split].get(), name.c_str())) == new_size);
-            gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data);
+            gguf_set_tensor_type(ctx_outs[cur_split].get(), metadata[i].name.c_str(), new_type);
+            GGML_ASSERT(gguf_get_tensor_size(ctx_outs[cur_split].get(), gguf_find_tensor(ctx_outs[cur_split].get(), metadata[i].name.c_str())) == new_size);
+            gguf_set_tensor_data(ctx_outs[cur_split].get(), metadata[i].name.c_str(), new_data);
 
             // write tensor data + padding
             fout.write((const char *) new_data, new_size);
@@ -1305,3 +1313,89 @@ uint32_t llama_model_quantize(
 
     return 0;
 }
+
+//
+// Helper functions for external tools exposed in llama-ext.h
+//
+
+quantize_state_impl * llama_quant_init(
+        const llama_model * model,
+        const llama_model_quantize_params * params) {
+    return new quantize_state_impl(*model, params);
+}
+
+void llama_quant_free(quantize_state_impl * qs) {
+    delete qs;
+}
+
+llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * desc) {
+    struct llama_model_params mparams = llama_model_default_params();
+    auto * model = new llama_model(mparams);
+
+    model->arch = llm_arch_from_string(desc->architecture);
+
+    // infer llm_type: only LLM_TYPE_70B matters for quantization logic
+    if (model->arch == LLM_ARCH_LLAMA && desc->n_layer == 80 && desc->n_head != desc->n_head_kv) {
+        model->type = LLM_TYPE_70B;
+    }
+
+    model->hparams.n_embd             = desc->n_embd;
+    model->hparams.n_embd_head_k_full = desc->n_embd_head_k;
+    model->hparams.n_embd_head_v_full = desc->n_embd_head_v;
+    model->hparams.n_layer            = desc->n_layer;
+    model->hparams.n_expert           = desc->n_expert;
+
+    for (uint32_t i = 0; i < desc->n_layer; i++) {
+        model->hparams.n_head_arr[i]    = desc->n_head;
+        model->hparams.n_head_kv_arr[i] = desc->n_head_kv;
+        model->hparams.n_ff_arr[i]      = desc->n_ff;
+    }
+
+    return model;
+}
+
+bool llama_quant_tensor_allows_quantization(
+        const quantize_state_impl * qs,
+        const ggml_tensor * tensor) {
+    return tensor_allows_quantization(qs->params, qs->model.arch, tensor);
+}
+
+void llama_quant_compute_types(
+        quantize_state_impl * qs,
+        llama_ftype ftype,
+        ggml_tensor ** tensors,
+        ggml_type * result_types,
+        size_t n_tensors) {
+    // reset per-computation state
+    qs->n_attention_wv      = 0;
+    qs->n_ffn_down          = 0;
+    qs->n_ffn_gate          = 0;
+    qs->n_ffn_up            = 0;
+    qs->i_attention_wv      = 0;
+    qs->i_ffn_down          = 0;
+    qs->i_ffn_gate          = 0;
+    qs->i_ffn_up            = 0;
+    qs->n_fallback          = 0;
+    qs->has_imatrix         = false;
+    qs->has_tied_embeddings = true;
+
+    // build metadata from tensor names
+    std::vector<tensor_metadata> metadata(n_tensors);
+    for (size_t i = 0; i < n_tensors; i++) {
+        metadata[i].name = ggml_get_name(tensors[i]);
+    }
+
+    // initialize counters and categories
+    init_quantize_state_counters(*qs, metadata);
+
+    // use a local copy of params with the requested ftype
+    llama_model_quantize_params local_params = *qs->params;
+    local_params.ftype = ftype;
+
+    ggml_type default_type = llama_ftype_get_default_type(ftype);
+
+    // compute types
+    for (size_t i = 0; i < n_tensors; i++) {
+        result_types[i] = llama_tensor_get_type(*qs, &local_params, tensors[i], default_type, metadata[i]);
+    }
+}

src/llama-vocab.cpp

@@ -493,6 +493,16 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?(?:\\p{L}\\p{M}*(?: \\p{L}\\p{M}*)*)+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]?|\\s*[\\r\\n]|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_GEMMA4:
+                // Gemma4 uses SPM-style BPE: spaces are replaced with ▁ by the
+                // normalizer, then BPE merges run on the whole text without
+                // word-level pre-splitting. We only need to split on newlines
+                // since BPE merge lookup asserts no newlines in tokens.
+                regex_exprs = {
+                    "[^\\n]+|[\\n]+",
+                };
+                byte_encode = false; // uses raw UTF-8, not GPT-2 byte encoding
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -506,6 +516,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
     }
 
     std::vector<std::string> regex_exprs;
+    bool byte_encode = true; // GPT-2 byte encoding; false for SPM-style BPE (raw UTF-8)
 };
 
 struct llm_tokenizer_bpe_session {
@@ -550,9 +561,10 @@ struct llm_tokenizer_bpe_session {
 
     void tokenize(const std::string & text, std::vector<llama_token> & output) {
         int final_prev_index = -1;
-        const auto word_collection = unicode_regex_split(text, tokenizer.regex_exprs);
+        const auto word_collection = unicode_regex_split(text, tokenizer.regex_exprs, tokenizer.byte_encode);
 
         symbols_final.clear();
+        auto tok_pre = vocab.get_pre_type();
 
         for (const auto & word : word_collection) {
             work_queue = llm_bigram_bpe::queue();
@@ -565,6 +577,13 @@ struct llm_tokenizer_bpe_session {
             if (vocab.get_ignore_merges() && vocab.text_to_token(word) != LLAMA_TOKEN_NULL) {
                 symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
                 offset = word.size();
+            } else if (tok_pre == LLAMA_VOCAB_PRE_TYPE_GEMMA4 && word.find_first_not_of('\n') == std::string::npos) {
+                // fix for gemma 4, ref: https://github.com/ggml-org/llama.cpp/pull/21343
+                auto tok = vocab.text_to_token(word);
+                if (tok != LLAMA_TOKEN_NULL) {
+                    symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
+                    offset = word.size();
+                }
             }
 
             while (offset < word.size()) {
@@ -1864,7 +1883,31 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             special_pad_id = 3;  // <|plamo:pad|>
             special_mask_id = LLAMA_TOKEN_NULL;
         } else if (tokenizer_model == "gemma4") {
-            type = LLAMA_VOCAB_TYPE_SPM;
+            type = LLAMA_VOCAB_TYPE_BPE;
+
+            // read bpe merges and populate bpe ranks
+            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
+            if (merges_keyidx == -1) {
+                throw std::runtime_error("cannot find tokenizer merges in model file\n");
+            }
+            {
+                const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
+                for (int i = 0; i < n_merges; i++) {
+                    const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
+
+                    std::string first;
+                    std::string second;
+
+                    const size_t pos = word.find(' ', 1);
+
+                    if (pos != std::string::npos) {
+                        first  = word.substr(0, pos);
+                        second = word.substr(pos + 1);
+                    }
+
+                    bpe_ranks.emplace(std::make_pair(first, second), i);
+                }
+            }
 
             // default special tokens (to be read from GGUF)
             special_bos_id  = LLAMA_TOKEN_NULL;
@@ -1874,7 +1917,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             special_pad_id  = LLAMA_TOKEN_NULL;
             special_mask_id = LLAMA_TOKEN_NULL;
 
-            tokenizer_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            tokenizer_pre = "gemma4";
         } else {
             throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
         }
@@ -1882,6 +1925,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
         // for now, only BPE models have pre-tokenizers
         if (type == LLAMA_VOCAB_TYPE_BPE) {
             add_space_prefix = false;
+            escape_whitespaces = false;
             clean_spaces = true;
             if (tokenizer_pre.empty()) {
                 LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
@@ -1948,6 +1992,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             } else if (
                     tokenizer_pre == "jais-2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_JAIS2;
+            } else if (
+                    tokenizer_pre == "gemma4") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_GEMMA4;
+                escape_whitespaces = true;
             } else if (
                     tokenizer_pre == "jina-v1-en" ||
                     tokenizer_pre == "jina-v2-code" ||
@@ -3045,6 +3093,10 @@ std::vector<llama_token> llama_vocab::impl::tokenize(
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                         std::string text = fragment.raw_text.substr(fragment.offset, fragment.length);
 
+                        if (escape_whitespaces) {
+                            llama_escape_whitespace(text);
+                        }
+
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", text.length(), fragment.offset, fragment.length, text.c_str());
 #endif
@@ -3224,6 +3276,12 @@ int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t
                     return _try_copy(token_text.data(), token_text.size());
                 }
                 if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
+                    if (escape_whitespaces) {
+                        // SPM-style BPE: tokens contain ▁ for spaces
+                        std::string result = token_text;
+                        llama_unescape_whitespace(result);
+                        return _try_copy(result.data(), result.size());
+                    }
                     std::string result = llama_decode_text(token_text);
                     return _try_copy(result.data(), result.size());
                 }
@@ -3654,9 +3712,7 @@ int llama_vocab::max_token_len() const {
 
 int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
     GGML_ASSERT(token_left.find(' ')   == std::string::npos);
-    GGML_ASSERT(token_left.find('\n')  == std::string::npos);
     GGML_ASSERT(token_right.find(' ')  == std::string::npos);
-    GGML_ASSERT(token_right.find('\n') == std::string::npos);
 
     auto it = pimpl->bpe_ranks.find(std::make_pair(token_left, token_right));
     if (it == pimpl->bpe_ranks.end()) {

src/llama-vocab.h

@@ -58,6 +58,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_TINY_AYA        = 47,
     LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM       = 48,
     LLAMA_VOCAB_PRE_TYPE_JAIS2           = 49,
+    LLAMA_VOCAB_PRE_TYPE_GEMMA4          = 50,
 };
 
 struct LLM_KV;

src/unicode.cpp

@@ -753,6 +753,35 @@ static std::vector<size_t> unicode_regex_split_custom_afmoe(const std::string &
     return bpe_offsets;
 }
 
+// regex: [^\n]+|[\n]+
+// splits text into runs of non-newline characters and runs of newline characters
+static std::vector<size_t> unicode_regex_split_custom_newlines(const std::string & text, const std::vector<size_t> & offsets) {
+    std::vector<size_t> bpe_offsets;
+    bpe_offsets.reserve(offsets.size());
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+
+    size_t start = 0;
+    for (auto offset : offsets) {
+        const size_t offset_ini = start;
+        const size_t offset_end = start + offset;
+        assert(offset_end <= cpts.size());
+        start = offset_end;
+
+        size_t pos = offset_ini;
+        while (pos < offset_end) {
+            const bool is_newline = (cpts[pos] == '\n');
+            const size_t run_start = pos;
+            while (pos < offset_end && (cpts[pos] == '\n') == is_newline) {
+                pos++;
+            }
+            bpe_offsets.push_back(pos - run_start);
+        }
+    }
+
+    return bpe_offsets;
+}
+
 static std::vector<size_t> unicode_regex_split_custom(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
     std::vector<size_t> bpe_offsets;
 
@@ -769,6 +798,8 @@ static std::vector<size_t> unicode_regex_split_custom(const std::string & text,
     } else if (regex_expr == "\\p{AFMoE_digits}") {
         // AFMOE digit pattern - use custom implementation for proper splitting
         bpe_offsets = unicode_regex_split_custom_afmoe(text, offsets);
+    } else if (regex_expr == "[^\\n]+|[\\n]+") {
+        bpe_offsets = unicode_regex_split_custom_newlines(text, offsets);
     } else if (regex_expr == "\\d{1,3}(?=(?:\\d{3})*\\b)") {
         // tiny_aya digit grouping pattern from tokenizer.json:
         //   {"type": "Split", "pattern": {"Regex": "\\d{1,3}(?=(?:\\d{3})*\\b)"}, "behavior": "Isolated"}
@@ -912,7 +943,7 @@ bool unicode_cpt_is_han(uint32_t cpt) {
     return false;
 }
 
-std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs) {
+std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs, bool byte_encode) {
     // unicode categories
     static const std::map<std::string, int> k_ucat_enum = {
         { "\\p{N}", unicode_cpt_flags::NUMBER },
@@ -1099,5 +1130,9 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
         start += offset;
     }
 
-    return unicode_byte_encoding_process(bpe_words);
+    if (byte_encode) {
+        return unicode_byte_encoding_process(bpe_words);
+    }
+
+    return bpe_words;
 }

src/unicode.h

@@ -108,4 +108,4 @@ uint32_t unicode_tolower(uint32_t cpt);
 
 bool unicode_cpt_is_han(uint32_t cpt);
 
-std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs);
+std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs, bool byte_encode = true);

tests/.gitignore

@@ -1,5 +1,6 @@
 *
 !*.*
+!snapshots/
 *.o
 ggml-common.h
 **/*.swp

tests/CMakeLists.txt

@@ -274,6 +274,12 @@ if (TARGET cpp-httplib)
         add_executable(test-gguf-model-data test-gguf-model-data.cpp)
         target_link_libraries(test-gguf-model-data PRIVATE gguf-model-data common)
         llama_test(test-gguf-model-data LABEL "model")
+
+        # test-quant-type-selection requires gguf-model-data for remote model metadata
+        llama_build_and_test(test-quant-type-selection.cpp LABEL "model")
+        target_link_libraries(test-quant-type-selection PRIVATE gguf-model-data)
+        target_compile_definitions(test-quant-type-selection PRIVATE
+            SNAPSHOT_DIR="${CMAKE_CURRENT_SOURCE_DIR}/snapshots")
     endif()
 endif()
 

tests/gguf-model-data.cpp

@@ -125,6 +125,35 @@ static bool gguf_skip_value(gguf_buf_reader & r, int32_t vtype) {
 }
 
 static bool gguf_read_uint32_val(gguf_buf_reader & r, int32_t vtype, uint32_t & out) {
+    // Handle array-valued fields (e.g. per-layer head counts in hybrid models)
+    // by reading the first element as a representative value.
+    if (vtype == GGUF_TYPE_ARRAY) {
+        int32_t elem_type;
+        uint64_t count;
+        if (!r.read_val(elem_type)) {
+            return false;
+        }
+        if (!r.read_val(count)) {
+            return false;
+        }
+        if (count == 0) {
+            return false;
+        }
+        // Read first element, skip the rest
+        if (!gguf_read_uint32_val(r, elem_type, out)) {
+            return false;
+        }
+        for (uint64_t i = 1; i < count; i++) {
+            size_t sz = gguf_val_type_size(elem_type);
+            if (sz == 0) {
+                return false;
+            }
+            if (!r.skip(sz)) {
+                return false;
+            }
+        }
+        return true;
+    }
     if (vtype == GGUF_TYPE_UINT8) {
         uint8_t v;
         if (!r.read_val(v)) {
@@ -487,7 +516,8 @@ static std::string detect_gguf_filename(const std::string & repo, const std::str
 static std::optional<gguf_remote_model> fetch_and_parse(
         const std::string & repo,
         const std::string & filename,
-        const std::string & cache_path) {
+        const std::string & cache_path,
+        bool verbose) {
     std::string url = "https://huggingface.co/" + repo + "/resolve/main/" + filename;
 
     // Progressive download inspired by RangeView.fetchChunk()
@@ -496,7 +526,9 @@ static std::optional<gguf_remote_model> fetch_and_parse(
     const size_t max_chunk = 64 * 1024 * 1024;
 
     while (chunk_size <= max_chunk) {
-        fprintf(stderr, "gguf_fetch: downloading %zu bytes from %s\n", chunk_size, filename.c_str());
+        if (verbose) {
+            fprintf(stderr, "gguf_fetch: downloading %zu bytes from %s\n", chunk_size, filename.c_str());
+        }
 
         char range_buf[64];
         snprintf(range_buf, sizeof(range_buf), "bytes=0-%zu", chunk_size - 1);
@@ -542,7 +574,8 @@ static std::optional<gguf_remote_model> fetch_or_cached(
         const std::string & repo,
         const std::string & filename,
         const std::string & cdir,
-        const std::string & repo_part) {
+        const std::string & repo_part,
+        bool verbose) {
     std::string cache_path = get_cache_file_path(cdir, repo_part, filename);
 
     {
@@ -550,20 +583,23 @@ static std::optional<gguf_remote_model> fetch_or_cached(
         if (std::filesystem::exists(cache_path) && read_file(cache_path, cached)) {
             auto result = gguf_parse_meta(cached);
             if (result.has_value()) {
-                fprintf(stderr, "gguf_fetch: loaded from cache: %s\n", cache_path.c_str());
+                if (verbose) {
+                    fprintf(stderr, "gguf_fetch: loaded from cache: %s\n", cache_path.c_str());
+                }
                 return result;
             }
         }
     }
 
     fs_create_directory_with_parents(cdir);
-    return fetch_and_parse(repo, filename, cache_path);
+    return fetch_and_parse(repo, filename, cache_path, verbose);
 }
 
 std::optional<gguf_remote_model> gguf_fetch_model_meta(
         const std::string & repo,
         const std::string & quant,
-        const std::string & cache_dir) {
+        const std::string & cache_dir,
+        bool verbose) {
     std::string cdir = cache_dir.empty() ? get_default_cache_dir() : cache_dir;
     std::string repo_part = sanitize_for_path(repo);
 
@@ -573,7 +609,7 @@ std::optional<gguf_remote_model> gguf_fetch_model_meta(
         return std::nullopt;
     }
 
-    auto model_opt = fetch_or_cached(repo, filename, cdir, repo_part);
+    auto model_opt = fetch_or_cached(repo, filename, cdir, repo_part, verbose);
     if (!model_opt.has_value()) {
         fprintf(stderr, "gguf_fetch: failed to fetch %s\n", filename.c_str());
         return std::nullopt;
@@ -588,8 +624,10 @@ std::optional<gguf_remote_model> gguf_fetch_model_meta(
             return std::nullopt;
         }
 
-        fprintf(stderr, "gguf_fetch: split model with %u shards, fetching remaining %u...\n",
-                model.n_split, model.n_split - 1);
+        if (verbose) {
+            fprintf(stderr, "gguf_fetch: split model with %u shards, fetching remaining %u...\n",
+                    model.n_split, model.n_split - 1);
+        }
 
         for (int i = 2; i <= model.n_split; i++) {
             char num_buf[6], total_buf[6];
@@ -597,7 +635,7 @@ std::optional<gguf_remote_model> gguf_fetch_model_meta(
             snprintf(total_buf, sizeof(total_buf), "%05d", (int)model.n_split);
             std::string shard_name = split_prefix + "-" + num_buf + "-of-" + total_buf + ".gguf";
 
-            auto shard = fetch_or_cached(repo, shard_name, cdir, repo_part);
+            auto shard = fetch_or_cached(repo, shard_name, cdir, repo_part, verbose);
             if (!shard.has_value()) {
                 fprintf(stderr, "gguf_fetch: failed to fetch shard %d: %s\n", i, shard_name.c_str());
                 return std::nullopt;
@@ -620,7 +658,8 @@ std::optional<gguf_remote_model> gguf_fetch_model_meta(
 gguf_context_ptr gguf_fetch_gguf_ctx(
         const std::string & repo,
         const std::string & quant,
-        const std::string & cache_dir) {
+        const std::string & cache_dir,
+        bool verbose) {
     std::string cdir = cache_dir.empty() ? get_default_cache_dir() : cache_dir;
     std::string repo_part = sanitize_for_path(repo);
 
@@ -631,7 +670,7 @@ gguf_context_ptr gguf_fetch_gguf_ctx(
         return nullptr;
     }
 
-    auto model_opt = fetch_or_cached(repo, filename, cdir, repo_part);
+    auto model_opt = fetch_or_cached(repo, filename, cdir, repo_part, verbose);
     if (!model_opt.has_value()) {
         fprintf(stderr, "gguf_fetch: failed to fetch %s\n", filename.c_str());
         return nullptr;
@@ -659,8 +698,10 @@ gguf_context_ptr gguf_fetch_gguf_ctx(
             return nullptr;
         }
 
-        fprintf(stderr, "gguf_fetch: split model with %u shards, fetching remaining %u...\n",
-                model.n_split, model.n_split - 1);
+        if (verbose) {
+            fprintf(stderr, "gguf_fetch: split model with %u shards, fetching remaining %u...\n",
+                    model.n_split, model.n_split - 1);
+        }
 
         for (int i = 2; i <= model.n_split; i++) {
             char num_buf[6], total_buf[6];
@@ -668,7 +709,7 @@ gguf_context_ptr gguf_fetch_gguf_ctx(
             snprintf(total_buf, sizeof(total_buf), "%05d", (int)model.n_split);
             std::string shard_name = split_prefix + "-" + num_buf + "-of-" + total_buf + ".gguf";
 
-            auto shard = fetch_or_cached(repo, shard_name, cdir, repo_part);
+            auto shard = fetch_or_cached(repo, shard_name, cdir, repo_part, verbose);
             if (!shard.has_value()) {
                 fprintf(stderr, "gguf_fetch: failed to fetch shard %d: %s\n", i, shard_name.c_str());
                 return nullptr;

tests/gguf-model-data.h

@@ -40,9 +40,11 @@ struct gguf_remote_model {
 std::optional<gguf_remote_model> gguf_fetch_model_meta(
     const std::string & repo,
     const std::string & quant = "Q8_0",
-    const std::string & cache_dir = "");  // empty = default
+    const std::string & cache_dir = "",  // empty = default
+    bool verbose = true);
 
 gguf_context_ptr gguf_fetch_gguf_ctx(
     const std::string & repo,
     const std::string & quant = "Q8_0",
-    const std::string & cache_dir = "");
+    const std::string & cache_dir = "",
+    bool verbose = true);

tests/test-chat.cpp

@@ -589,6 +589,51 @@ static common_chat_tool amount_tool{
     })",
 };
 
+static common_chat_tool toggle_tool{
+    /* .name = */ "toggle",
+    /* .description = */ "Toggle a feature",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "enabled": {
+                "type": "boolean",
+                "description": "Whether to enable the feature"
+            }
+        },
+        "required": ["enabled"]
+    })",
+};
+
+static common_chat_tool nullable_tool{
+    /* .name = */ "set_nullable",
+    /* .description = */ "Set a nullable value",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "value": {
+                "type": "null",
+                "description": "A null value"
+            }
+        },
+        "required": ["value"]
+    })",
+};
+
+static common_chat_tool config_tool{
+    /* .name = */ "set_config",
+    /* .description = */ "Set configuration",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "config": {
+                "type": "object",
+                "description": "Configuration dict"
+            }
+        },
+        "required": ["config"]
+    })",
+};
+
 static common_chat_tool imaginary_number_tool{
     /* .name = */ "imaginary_number",
     /* .description = */ "Imaginary number converter",
@@ -612,6 +657,66 @@ static common_chat_tool imaginary_number_tool{
     })",
 };
 
+static common_chat_tool nullable_string_tool{
+    /* .name = */ "set_nullable_str",
+    /* .description = */ "Set a nullable string value",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "name": {
+                "type": ["string", "null"],
+                "description": "A nullable string"
+            }
+        },
+        "required": ["name"]
+    })",
+};
+
+static common_chat_tool nullable_string_null_first_tool{
+    /* .name = */ "set_nullable_str_nf",
+    /* .description = */ "Set a nullable string value with null first in type array",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "name": {
+                "type": ["null", "string"],
+                "description": "A nullable string with null first"
+            }
+        },
+        "required": ["name"]
+    })",
+};
+
+static common_chat_tool nullable_int_tool{
+    /* .name = */ "set_nullable_int",
+    /* .description = */ "Set a nullable integer value",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "count": {
+                "type": ["integer", "null"],
+                "description": "A nullable integer"
+            }
+        },
+        "required": ["count"]
+    })",
+};
+
+static common_chat_tool enum_no_type_tool{
+    /* .name = */ "set_unit",
+    /* .description = */ "Set a temperature unit",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "unit": {
+                "enum": ["celsius", "fahrenheit"],
+                "description": "Temperature unit"
+            }
+        },
+        "required": ["unit"]
+    })",
+};
+
 static common_chat_tool string_param_tool{
     /* .name = */ "string_param",
     /* .description = */ "Tool with string parameter for testing",
@@ -1869,6 +1974,130 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
         tst.test("Line 1\nLine 2\nLine 3").expect(simple_assist_msg("Line 1\nLine 2\nLine 3")).expect_reconstruction().run();
     }
 
+    {
+        // Google Gemma 4 (tool calling with Gemma4 dict format)
+        auto tst = peg_tester("models/templates/gemma4.jinja");
+
+        tst.test("Hello, world!").expect(simple_assist_msg("Hello, world!")).run();
+
+        // Simple tool call with string argument
+        tst.test(
+                "<|tool_call>call:get_time{city:<|\"|>London<|\"|>}<tool_call|>")
+            .tools({ get_time_tool })
+            .expect(message_with_tool_calls("get_time", R"({"city": "London"})"))
+            .run();
+
+        // Tool call with string argument containing special chars
+        tst.test(
+                "<|tool_call>call:get_time{city:<|\"|>San Francisco<|\"|>}<tool_call|>")
+            .tools({ get_time_tool })
+            .expect(message_with_tool_calls("get_time", R"({"city": "San Francisco"})"))
+            .run();
+
+        // Tool call with empty args
+        tst.test(
+                "<|tool_call>call:empty_args{}<tool_call|>")
+            .tools({ empty_args_tool })
+            .expect(message_with_tool_calls("empty_args", "{}"))
+            .run();
+
+        // Tool call with string and content
+        tst.test(
+                "Hello, world!\nWhat's up?<|tool_call>call:get_time{city:<|\"|>Paris<|\"|>}<tool_call|>")
+            .tools({ get_time_tool })
+            .expect(message_with_content_and_tool_call("Hello, world!\nWhat's up?", "get_time", R"({"city": "Paris"})"))
+            .run();
+
+        // Parallel tool calls
+        tst.test(
+                "<|tool_call>call:get_time{city:<|\"|>London<|\"|>}<tool_call|>"
+                "<|tool_call>call:get_weather{city:<|\"|>Paris<|\"|>}<tool_call|>")
+            .tools({ get_time_tool, get_weather_tool })
+            .parallel_tool_calls(true)
+            .expect_tool_calls({
+                { "get_time", R"({"city": "London"})", "" },
+                { "get_weather", R"({"city": "Paris"})", "" },
+            })
+            .run();
+
+        // Tool call with integer argument (number type)
+        tst.test(
+                "<|tool_call>call:special_function{arg1:42}<tool_call|>")
+            .tools({ special_function_tool })
+            .expect(message_with_tool_calls("special_function", R"({"arg1": 42})"))
+            .run();
+
+        // Tool call with negative number argument
+        tst.test(
+                "<|tool_call>call:special_function{arg1:-7}<tool_call|>")
+            .tools({ special_function_tool })
+            .expect(message_with_tool_calls("special_function", R"({"arg1": -7})"))
+            .run();
+
+        // Tool call with decimal number argument
+        tst.test(
+                "<|tool_call>call:amount{orig:3.14}<tool_call|>")
+            .tools({ amount_tool })
+            .expect(message_with_tool_calls("amount", R"({"orig": 3.14})"))
+            .run();
+
+        // Tool call with boolean argument (true)
+        tst.test(
+                "<|tool_call>call:toggle{enabled:true}<tool_call|>")
+            .tools({ toggle_tool })
+            .expect(message_with_tool_calls("toggle", R"({"enabled": true})"))
+            .run();
+
+        // Tool call with boolean argument (false)
+        tst.test(
+                "<|tool_call>call:toggle{enabled:false}<tool_call|>")
+            .tools({ toggle_tool })
+            .expect(message_with_tool_calls("toggle", R"({"enabled": false})"))
+            .run();
+
+        // Tool call with null argument
+        tst.test(
+                "<|tool_call>call:set_nullable{value:null}<tool_call|>")
+            .tools({ nullable_tool })
+            .expect(message_with_tool_calls("set_nullable", R"({"value": null})"))
+            .run();
+
+        // Tool call with array argument (todo list)
+        tst.test(
+                "<|tool_call>call:todo_list{todos:[<|\"|>buy milk<|\"|>,<|\"|>walk dog<|\"|>]}<tool_call|>")
+            .tools({ todo_list })
+            .expect(message_with_tool_calls("todo_list", R"({"todos":["buy milk","walk dog"]})"))
+            .run();
+
+        // Tool call with object/dict argument
+        tst.test(
+                "<|tool_call>call:set_config{config:{theme:<|\"|>dark<|\"|>,count:3}}<tool_call|>")
+            .tools({ config_tool })
+            .expect(message_with_tool_calls("set_config", R"({"config":{"theme":"dark","count":3}})"))
+            .run();
+
+        // Tool call with empty array
+        tst.test(
+                "<|tool_call>call:todo_list{todos:[]}<tool_call|>")
+            .tools({ todo_list })
+            .expect(message_with_tool_calls("todo_list", R"({"todos":[]})"))
+            .run();
+
+        // Tool call with empty dict
+        tst.test(
+                "<|tool_call>call:set_config{config:{}}<tool_call|>")
+            .tools({ config_tool })
+            .expect(message_with_tool_calls("set_config", R"({"config":{}})"))
+            .run();
+
+        // Tool call with scientific notation number
+        tst.test(
+                "<|tool_call>call:amount{orig:1.5e10}<tool_call|>")
+            .tools({ amount_tool })
+            .expect(message_with_tool_calls("amount", R"({"orig": 1.5e10})"))
+            .run();
+    }
+
     {
         // Qwen-QwQ-32B (reasoning model)
         auto tst = peg_tester("models/templates/Qwen-QwQ-32B.jinja");
@@ -2031,6 +2260,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
                 }
             })
             .run();
+
     }
 
     {
@@ -2214,6 +2444,58 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             })
             .expect_reconstruction()
             .run();
+
+        // nullable string type ["string", "null"]
+        tst.test(
+               "<tool_call>\n"
+               "<function=set_nullable_str>\n"
+               "<parameter=name>\nhello world\n</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .tools({ nullable_string_tool })
+            .expect_tool_calls({
+                { "set_nullable_str", R"({"name": "hello world"})", {} },
+            })
+            .run();
+
+        // nullable string with null first in type array ["null", "string"]
+        tst.test(
+               "<tool_call>\n"
+               "<function=set_nullable_str_nf>\n"
+               "<parameter=name>\nhello world\n</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .tools({ nullable_string_null_first_tool })
+            .expect_tool_calls({
+                { "set_nullable_str_nf", R"({"name": "hello world"})", {} },
+            })
+            .run();
+
+        // nullable integer type ["integer", "null"] - should use JSON value path, not string
+        tst.test(
+               "<tool_call>\n"
+               "<function=set_nullable_int>\n"
+               "<parameter=count>\n42\n</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .tools({ nullable_int_tool })
+            .expect_tool_calls({
+                { "set_nullable_int", R"({"count": 42})", {} },
+            })
+            .run();
+
+        // enum without explicit type key - should infer string from enum values
+        tst.test(
+               "<tool_call>\n"
+               "<function=set_unit>\n"
+               "<parameter=unit>\ncelsius\n</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .tools({ enum_no_type_tool })
+            .expect_tool_calls({
+                { "set_unit", R"({"unit": "celsius"})", {} },
+            })
+            .run();
     }
     {
         auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V3.1.jinja", detailed_debug);
@@ -2372,55 +2654,57 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
     // #20424 introduced effective_input = generation_prompt + input, but the throw
     // uses input.substr(result.end) where result.end is in effective_input space.
     {
-        auto tmpls = common_chat_templates_ptr(
-            common_chat_templates_init(nullptr, read_file("models/templates/GLM-4.7-Flash.jinja")));
+        if (!g_template_filter.empty() && std::string("models/templates/GLM-4.7-Flash.jinja").find(g_template_filter) != std::string::npos) {
+            auto tmpls = common_chat_templates_ptr(
+                common_chat_templates_init(nullptr, read_file("models/templates/GLM-4.7-Flash.jinja")));
 
-        static common_chat_tool weather_tool{
-            "get_weather", "Get weather",
-            R"({"type":"object","properties":{"city":{"type":"string"}},"required":["city"]})",
-        };
+            static common_chat_tool weather_tool{
+                "get_weather", "Get weather",
+                R"({"type":"object","properties":{"city":{"type":"string"}},"required":["city"]})",
+            };
 
-        common_chat_templates_inputs inputs;
-        inputs.tools = { weather_tool };
-        inputs.enable_thinking = true;
-        inputs.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
-        inputs.add_generation_prompt = true;
-        inputs.use_jinja = true;
-        common_chat_msg msg;
-        msg.role = "user";
-        msg.content = "get_weather";
-        inputs.messages = { msg };
+            common_chat_templates_inputs inputs;
+            inputs.tools = { weather_tool };
+            inputs.enable_thinking = true;
+            inputs.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            inputs.add_generation_prompt = true;
+            inputs.use_jinja = true;
+            common_chat_msg msg;
+            msg.role = "user";
+            msg.content = "get_weather";
+            inputs.messages = { msg };
 
-        auto params = common_chat_templates_apply(tmpls.get(), inputs);
-        common_peg_arena arena;
-        arena.load(params.parser);
-        common_chat_parser_params pp(params);
+            auto params = common_chat_templates_apply(tmpls.get(), inputs);
+            common_peg_arena arena;
+            arena.load(params.parser);
+            common_chat_parser_params pp(params);
 
-        // generation_prompt is non-empty for thinking models, so result.end
-        // will be offset by generation_prompt.size() into effective_input space.
-        assert(!pp.generation_prompt.empty());
+            // generation_prompt is non-empty for thinking models, so result.end
+            // will be offset by generation_prompt.size() into effective_input space.
+            assert(!pp.generation_prompt.empty());
 
-        std::string bad_input =
-            "Thinking.\n"
-            "</think>"
-            "<tool_call>get_weather"
-            "<arg_key>city</arg_key><arg_value>Tokyo</arg_value>"
-            "</tool_call>\n";
+            std::string bad_input =
+                "Thinking.\n"
+                "</think>"
+                "<tool_call>get_weather"
+                "<arg_key>city</arg_key><arg_value>Tokyo</arg_value>"
+                "</tool_call>\n";
 
-        bool got_runtime_error = false;
-        bool got_out_of_range = false;
-        std::string error_msg;
-        try {
-            common_chat_peg_parse(arena, bad_input, /*is_partial=*/false, pp);
-        } catch (const std::out_of_range & e) {
-            got_out_of_range = true;
-            error_msg = e.what();
-        } catch (const std::runtime_error & e) {
-            got_runtime_error = true;
-            error_msg = e.what();
+            bool got_runtime_error = false;
+            bool got_out_of_range = false;
+            std::string error_msg;
+            try {
+                common_chat_peg_parse(arena, bad_input, /*is_partial=*/false, pp);
+            } catch (const std::out_of_range & e) {
+                got_out_of_range = true;
+                error_msg = e.what();
+            } catch (const std::runtime_error & e) {
+                got_runtime_error = true;
+                error_msg = e.what();
+            }
+            GGML_ASSERT(!got_out_of_range && "throw path crashed with out_of_range (input.substr in effective_input space)");
+            GGML_ASSERT(got_runtime_error  && "throw path should produce std::runtime_error with parse position");
         }
-        GGML_ASSERT(!got_out_of_range && "throw path crashed with out_of_range (input.substr in effective_input space)");
-        GGML_ASSERT(got_runtime_error  && "throw path should produce std::runtime_error with parse position");
     }
 
     // Kimi-K2-Thinking tests - custom parser
@@ -3000,6 +3284,21 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .expect(message_assist_call_id)
             .expect_reconstruction()
             .run();
+
+        tst.test("[TOOL_CALLS]special_function[CALL_ID]000000001[ARGS]{\"arg1\": 1}"
+            "[TOOL_CALLS]special_function_with_opt[CALL_ID]000000002[ARGS]{\"arg1\": 1, \"arg2\": 2}")
+            .parallel_tool_calls(true)
+            .tools({
+                special_function_tool, special_function_tool_with_optional_param
+            })
+            .expect_tool_calls({
+                { "special_function", R"({"arg1": 1})", "000000001" },
+                { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", "000000002" },
+            })
+            .expect_reconstruction()
+            .run();
+
+
     }
     // Devstral
     {

tests/test-gguf-model-data.cpp

@@ -116,6 +116,39 @@ int main() {
     // Verify tensor count
     TEST_ASSERT(model3.tensors.size() == 780, "expected tensor count == 780");
 
+    // Test a hybrid-attention model with array-valued head counts
+    auto result4 = gguf_fetch_model_meta("ggml-org/Step-3.5-Flash-GGUF", "Q4_K");
+    if (!result4.has_value()) {
+        fprintf(stderr, "FAIL: could not fetch Step-3.5-Flash metadata\n");
+        return 1;
+    }
+    const auto & model4 = result4.value();
+
+    fprintf(stderr, "Architecture:  %s\n", model4.architecture.c_str());
+    fprintf(stderr, "n_embd:        %u\n", model4.n_embd);
+    fprintf(stderr, "n_ff:          %u\n", model4.n_ff);
+    fprintf(stderr, "n_vocab:       %u\n", model4.n_vocab);
+    fprintf(stderr, "n_layer:       %u\n", model4.n_layer);
+    fprintf(stderr, "n_head:        %u\n", model4.n_head);
+    fprintf(stderr, "n_head_kv:     %u\n", model4.n_head_kv);
+    fprintf(stderr, "n_expert:      %u\n", model4.n_expert);
+    fprintf(stderr, "n_embd_head_k: %u\n", model4.n_embd_head_k);
+    fprintf(stderr, "n_embd_head_v: %u\n", model4.n_embd_head_v);
+    fprintf(stderr, "tensors:       %zu\n", model4.tensors.size());
+
+    TEST_ASSERT(model4.architecture == "step35", "expected architecture 'step35'");
+
+    TEST_ASSERT(model4.n_layer == 45, "expected n_layer == 45");
+    TEST_ASSERT(model4.n_embd == 4096, "expected n_embd == 4096");
+    TEST_ASSERT(model4.n_ff == 11264, "expected n_ff == 11264");
+    TEST_ASSERT(model4.n_head == 64, "expected n_head == 64 (first element of per-layer array)");
+    TEST_ASSERT(model4.n_head_kv == 8, "expected n_head_kv == 8 (first element of per-layer array)");
+    TEST_ASSERT(model4.n_expert == 288, "expected n_expert == 288");
+    TEST_ASSERT(model4.n_embd_head_k == 128, "expected n_embd_head_k == 128");
+    TEST_ASSERT(model4.n_embd_head_v == 128, "expected n_embd_head_v == 128");
+    TEST_ASSERT(model4.n_vocab == 128896, "expected n_vocab == 128896");
+    TEST_ASSERT(model4.tensors.size() == 754, "expected tensor count == 754");
+
     fprintf(stderr, "=== ALL TESTS PASSED ===\n");
     return 0;
 }

tests/test-jinja.cpp

@@ -523,6 +523,18 @@ static void test_filters(testing & t) {
         "hello"
     );
 
+    test_template(t, "upper array",
+        "{{ items|upper }}",
+        {{"items", json::array({"hello", "world"})}},
+        "['HELLO', 'WORLD']"
+    );
+
+    test_template(t, "upper dict",
+        "{{ items|upper }}",
+        {{"items", {{"hello", "world"}}}},
+        "{'HELLO': 'WORLD'}"
+    );
+
     test_template(t, "capitalize",
         "{{ 'heLlo World'|capitalize }}",
         json::object(),

tests/test-quant-type-selection.cpp

@@ -0,0 +1,520 @@
+#include "../src/llama-ext.h"
+#include "ggml-cpp.h"
+#include "gguf-model-data.h"
+#include "llama.h"
+
+#include <algorithm>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
+
+// ---------------------------------------------------------------------------
+// ftype name <-> enum mapping
+// ---------------------------------------------------------------------------
+
+struct ftype_name_entry {
+    const char * name;
+    llama_ftype  ftype;
+};
+
+static const ftype_name_entry ftype_name_table[] = {
+    { "F32",       LLAMA_FTYPE_ALL_F32          },
+    { "F16",       LLAMA_FTYPE_MOSTLY_F16       },
+    { "BF16",      LLAMA_FTYPE_MOSTLY_BF16      },
+    { "Q4_0",      LLAMA_FTYPE_MOSTLY_Q4_0      },
+    { "Q4_1",      LLAMA_FTYPE_MOSTLY_Q4_1      },
+    { "Q5_0",      LLAMA_FTYPE_MOSTLY_Q5_0      },
+    { "Q5_1",      LLAMA_FTYPE_MOSTLY_Q5_1      },
+    { "Q8_0",      LLAMA_FTYPE_MOSTLY_Q8_0      },
+    { "Q2_K",      LLAMA_FTYPE_MOSTLY_Q2_K      },
+    { "Q2_K_S",    LLAMA_FTYPE_MOSTLY_Q2_K_S    },
+    { "Q3_K_S",    LLAMA_FTYPE_MOSTLY_Q3_K_S    },
+    { "Q3_K_M",    LLAMA_FTYPE_MOSTLY_Q3_K_M    },
+    { "Q3_K_L",    LLAMA_FTYPE_MOSTLY_Q3_K_L    },
+    { "Q4_K_S",    LLAMA_FTYPE_MOSTLY_Q4_K_S    },
+    { "Q4_K_M",    LLAMA_FTYPE_MOSTLY_Q4_K_M    },
+    { "Q5_K_S",    LLAMA_FTYPE_MOSTLY_Q5_K_S    },
+    { "Q5_K_M",    LLAMA_FTYPE_MOSTLY_Q5_K_M    },
+    { "Q6_K",      LLAMA_FTYPE_MOSTLY_Q6_K      },
+    { "IQ1_S",     LLAMA_FTYPE_MOSTLY_IQ1_S     },
+    { "IQ1_M",     LLAMA_FTYPE_MOSTLY_IQ1_M     },
+    { "IQ2_XXS",   LLAMA_FTYPE_MOSTLY_IQ2_XXS   },
+    { "IQ2_XS",    LLAMA_FTYPE_MOSTLY_IQ2_XS    },
+    { "IQ2_S",     LLAMA_FTYPE_MOSTLY_IQ2_S     },
+    { "IQ2_M",     LLAMA_FTYPE_MOSTLY_IQ2_M     },
+    { "IQ3_XXS",   LLAMA_FTYPE_MOSTLY_IQ3_XXS   },
+    { "IQ3_XS",    LLAMA_FTYPE_MOSTLY_IQ3_XS    },
+    { "IQ3_S",     LLAMA_FTYPE_MOSTLY_IQ3_S     },
+    { "IQ3_M",     LLAMA_FTYPE_MOSTLY_IQ3_M     },
+    { "IQ4_NL",    LLAMA_FTYPE_MOSTLY_IQ4_NL    },
+    { "IQ4_XS",    LLAMA_FTYPE_MOSTLY_IQ4_XS    },
+    { "TQ1_0",     LLAMA_FTYPE_MOSTLY_TQ1_0     },
+    { "TQ2_0",     LLAMA_FTYPE_MOSTLY_TQ2_0     },
+    { "MXFP4_MOE", LLAMA_FTYPE_MOSTLY_MXFP4_MOE },
+    { "NVFP4",     LLAMA_FTYPE_MOSTLY_NVFP4     },
+};
+
+static llama_ftype llama_ftype_from_name(const char * name) {
+    for (const auto & e : ftype_name_table) {
+        if (strcmp(name, e.name) == 0) {
+            return e.ftype;
+        }
+    }
+    return (llama_ftype) -1;
+}
+
+static const char * llama_ftype_to_name(llama_ftype ftype) {
+    for (const auto & e : ftype_name_table) {
+        if (e.ftype == ftype) {
+            return e.name;
+        }
+    }
+    return nullptr;
+}
+
+// ---------------------------------------------------------------------------
+// ggml_type name lookup
+// ---------------------------------------------------------------------------
+
+static ggml_type ggml_type_from_name(const std::string & name) {
+    for (int i = 0; i < GGML_TYPE_COUNT; i++) {
+        const char * tname = ggml_type_name((ggml_type) i);
+        if (tname && name == tname) {
+            return (ggml_type) i;
+        }
+    }
+    return GGML_TYPE_COUNT;
+}
+
+// ---------------------------------------------------------------------------
+// File parser for snapshot files (quant type schemas)
+// ---------------------------------------------------------------------------
+
+struct snapshot_section {
+    llama_ftype                                    ftype;
+    ggml_type                                      default_type;
+    std::vector<std::pair<std::string, ggml_type>> overrides;
+};
+
+// This function is pretty ugly, but it's a trade-off of readable snapshot files
+// versus readable parsing code
+static bool parse_snapshot_file(const std::string & path, std::vector<snapshot_section> & sections) {
+    std::ifstream f(path);
+    if (!f.good()) {
+        return false;
+    }
+
+    snapshot_section * cur = nullptr;
+    std::string        line;
+
+    while (std::getline(f, line)) {
+        if (line.empty() || line[0] == '#') {
+            continue;
+        }
+
+        // section header: [FTYPE_NAME] default_type
+        if (line[0] == '[') {
+            auto close = line.find(']');
+            if (close == std::string::npos) {
+                fprintf(stderr, "parse error: missing ] in '%s'\n", line.c_str());
+                return false;
+            }
+            std::string ftype_str = line.substr(1, close - 1);
+            std::string default_str;
+            size_t      pos = close + 1;
+            while (pos < line.size() && line[pos] == ' ') {
+                pos++;
+            }
+            default_str = line.substr(pos);
+
+            llama_ftype ftype = llama_ftype_from_name(ftype_str.c_str());
+            if ((int) ftype < 0) {
+                fprintf(stderr, "parse error: unknown ftype '%s'\n", ftype_str.c_str());
+                return false;
+            }
+
+            ggml_type dtype = ggml_type_from_name(default_str);
+            if (dtype == GGML_TYPE_COUNT) {
+                fprintf(stderr, "parse error: unknown default type '%s'\n", default_str.c_str());
+                return false;
+            }
+
+            sections.push_back({ ftype, dtype, {} });
+            cur = &sections.back();
+            continue;
+        }
+
+        if (!cur) {
+            fprintf(stderr, "parse error: tensor line before any section: '%s'\n", line.c_str());
+            return false;
+        }
+
+        auto sp = line.rfind(' ');
+        if (sp == std::string::npos) {
+            fprintf(stderr, "parse error: no space in tensor line: '%s'\n", line.c_str());
+            return false;
+        }
+
+        std::string tname = line.substr(0, sp);
+        std::string ttype = line.substr(sp + 1);
+
+        ggml_type gt = ggml_type_from_name(ttype);
+        if (gt == GGML_TYPE_COUNT) {
+            fprintf(stderr, "parse error: unknown type '%s' for tensor '%s'\n", ttype.c_str(), tname.c_str());
+            return false;
+        }
+
+        cur->overrides.push_back({ tname, gt });
+    }
+
+    return true;
+}
+
+// ---------------------------------------------------------------------------
+// Remote model support using gguf-model-data.cpp
+// ---------------------------------------------------------------------------
+
+struct remote_model_spec {
+    const char * repo;
+    const char * quant;
+};
+
+// Get model name from repo: strip org prefix, strip -GGUF suffix,
+// and strip anything up to and including first '_' (e.g. "deepseek-ai_DeepSeek-V3.1").
+static std::string model_name_from_repo(const char * repo) {
+    std::string s(repo);
+
+    auto slash = s.find('/');
+    if (slash != std::string::npos) {
+        s = s.substr(slash + 1);
+    }
+
+    const std::string suffix = "-GGUF";
+    if (s.size() >= suffix.size() && s.compare(s.size() - suffix.size(), suffix.size(), suffix) == 0) {
+        s = s.substr(0, s.size() - suffix.size());
+    }
+
+    auto underscore = s.find('_');
+    if (underscore != std::string::npos) {
+        s = s.substr(underscore + 1);
+    }
+
+    return s;
+}
+
+static std::string snapshot_file_from_name(const std::string & name) {
+    std::string lower = name;
+    for (auto & c : lower) {
+        c = std::tolower(c);
+    }
+    return lower;
+}
+
+static const remote_model_spec model_specs[] = {
+    { "ggml-org/Qwen3-0.6B-GGUF",                   "Q8_0"   },
+    { "ggml-org/GLM-4.6V-GGUF",                     "Q8_0"   },
+    { "ggml-org/Step-3.5-Flash-GGUF",               "Q4_K"   },
+    { "ggml-org/Qwen3-Coder-Next-GGUF",             "Q8_0"   },
+    { "ggml-org/Qwen3-14B-GGUF",                    "Q8_0"   },
+    { "ggml-org/Nemotron-Nano-3-30B-A3B-GGUF",      "Q8_0"   },
+    { "ggml-org/gpt-oss-120b-GGUF",                 "mxfp4"  },
+    { "ggml-org/gemma-3-4b-it-GGUF",                "Q8_0"   },
+    { "bartowski/Meta-Llama-3.1-70B-Instruct-GGUF", "Q4_K_M" },
+    { "bartowski/deepseek-ai_DeepSeek-V3.1-GGUF",   "IQ1_M"  },
+    { "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF",      "IQ1_S"  }, // TODO: swap with ggml-org if/when it's released
+    { "bartowski/Qwen_Qwen3.5-27B-GGUF",            "Q8_0"   }, // TODO: swap with ggml-org if/when it's released
+};
+
+static const int n_model_specs = (int) (sizeof(model_specs) / sizeof(model_specs[0]));
+
+static llama_model * build_mock_model_from_remote(const gguf_remote_model & remote) {
+    llama_quant_model_desc desc = {};
+    desc.architecture           = remote.architecture.c_str();
+    desc.n_embd                 = remote.n_embd;
+    desc.n_ff                   = remote.n_ff;
+    desc.n_layer                = remote.n_layer;
+    desc.n_head                 = remote.n_head;
+    desc.n_head_kv              = remote.n_head_kv;
+    desc.n_expert               = remote.n_expert;
+    desc.n_embd_head_k          = remote.n_embd_head_k;
+    desc.n_embd_head_v          = remote.n_embd_head_v;
+    return llama_quant_model_from_metadata(&desc);
+}
+
+// Single ggml context holding all quantizable tensors for a model.
+struct mock_tensors {
+    ggml_context_ptr           ctx;
+    std::vector<ggml_tensor *> tensors;
+};
+
+static mock_tensors build_mock_tensors(const quantize_state_impl * qs, const gguf_remote_model & remote) {
+    const size_t            ctx_size = remote.tensors.size() * ggml_tensor_overhead();
+    struct ggml_init_params params   = { ctx_size, nullptr, true };
+    ggml_context_ptr        ctx(ggml_init(params));
+
+    std::vector<ggml_tensor *> result;
+
+    for (const auto & t : remote.tensors) {
+        ggml_tensor * gt = ggml_new_tensor_4d(ctx.get(), GGML_TYPE_F32, t.ne[0], t.ne[1], t.ne[2], t.ne[3]);
+        ggml_set_name(gt, t.name.c_str());
+        if (llama_quant_tensor_allows_quantization(qs, gt)) {
+            result.push_back(gt);
+        }
+    }
+
+    // sort by layer index then name, matching llama_model_loader::weight_name_comparer
+    std::sort(result.begin(), result.end(), [](const ggml_tensor * a, const ggml_tensor * b) {
+        int a_layer = -1, b_layer = -1;
+        sscanf(a->name, "blk.%d.", &a_layer);
+        sscanf(b->name, "blk.%d.", &b_layer);
+        if (a_layer != b_layer) {
+            return a_layer < b_layer;
+        }
+        return strcmp(a->name, b->name) < 0;
+    });
+
+    return { std::move(ctx), std::move(result) };
+}
+
+// ---------------------------------------------------------------------------
+// Generate mode: regenerate all snapshot files
+// Use this when either adding new models or modifying quants
+// ---------------------------------------------------------------------------
+
+static std::string generate_snapshot(const std::string &       name,
+                                     const gguf_remote_model & remote,
+                                     quantize_state_impl *     qs,
+                                     mock_tensors &            mt) {
+    std::ostringstream out;
+
+    out << "# Model: " << name << "\n";
+    out << "# n_embd=" << remote.n_embd << ", n_ff=" << remote.n_ff << ", n_vocab=" << remote.n_vocab
+        << ", n_layer=" << remote.n_layer << ", n_head=" << remote.n_head << ", n_head_kv=" << remote.n_head_kv;
+    if (remote.n_expert > 0) {
+        out << ", n_expert=" << remote.n_expert;
+    }
+    out << "\n";
+
+    for (int i = 0; i < LLAMA_FTYPE_GUESSED; i++) {
+        llama_ftype ft           = (llama_ftype) i;
+        ggml_type   default_type = llama_ftype_get_default_type(ft);
+        if (default_type == GGML_TYPE_COUNT) {
+            continue;
+        }
+        const char * fname = llama_ftype_to_name(ft);
+        if (!fname) {
+            continue;
+        }
+
+        std::vector<ggml_type> result_types(mt.tensors.size());
+        llama_quant_compute_types(qs, ft, mt.tensors.data(), result_types.data(), mt.tensors.size());
+
+        out << "\n[" << fname << "] " << ggml_type_name(default_type) << "\n";
+        for (size_t j = 0; j < mt.tensors.size(); j++) {
+            if (result_types[j] != default_type) {
+                out << ggml_get_name(mt.tensors[j]) << " " << ggml_type_name(result_types[j]) << "\n";
+            }
+        }
+    }
+
+    return out.str();
+}
+
+static int run_generate(const std::string & snapshot_dir) {
+    fprintf(stderr, "This will overwrite all snapshot files in:\n  %s\n", snapshot_dir.c_str());
+    fprintf(stderr, "Continue? [y/N] ");
+    int ch = fgetc(stdin);
+    if (ch != 'y' && ch != 'Y') {
+        fprintf(stderr, "Aborted.\n");
+        return 1;
+    }
+
+    fprintf(stderr, "\n");
+
+    int n_written = 0;
+
+    for (int m = 0; m < n_model_specs; m++) {
+        const auto & spec = model_specs[m];
+        std::string  name = model_name_from_repo(spec.repo);
+
+        fprintf(stderr, "Fetching model metadata for %s from %s...\n", name.c_str(), spec.repo);
+        auto result = gguf_fetch_model_meta(spec.repo, spec.quant);
+        if (!result.has_value()) {
+            fprintf(stderr, "ERROR: could not fetch model metadata for %s\n", name.c_str());
+            return 1;
+        }
+
+        const auto &                remote  = result.value();
+        llama_model *               model   = build_mock_model_from_remote(remote);
+        llama_model_quantize_params qparams = llama_model_quantize_default_params();
+        quantize_state_impl *       qs      = llama_quant_init(model, &qparams);
+        auto                        mt      = build_mock_tensors(qs, remote);
+
+        std::string content = generate_snapshot(name, remote, qs, mt);
+        std::string path    = snapshot_dir + "/" + snapshot_file_from_name(name) + ".schema";
+
+        std::ofstream f(path);
+        if (!f.good()) {
+            fprintf(stderr, "ERROR: could not write %s\n", path.c_str());
+            llama_quant_free(qs);
+            llama_model_free(model);
+            return 1;
+        }
+        f << content;
+        n_written++;
+        fprintf(stderr, "  wrote %s\n", path.c_str());
+        llama_quant_free(qs);
+        llama_model_free(model);
+    }
+
+    fprintf(stderr, "%d files written\n", n_written);
+    return 0;
+}
+
+// ---------------------------------------------------------------------------
+// Test mode: compare against snapshot files
+// ---------------------------------------------------------------------------
+
+static bool run_test_section(quantize_state_impl * qs, mock_tensors & mt, const snapshot_section & section) {
+    // verify default_type matches what llama_ftype_get_default_type returns
+    ggml_type computed_default = llama_ftype_get_default_type(section.ftype);
+    if (computed_default != section.default_type) {
+        printf("  FAIL  [%s] default type mismatch: file says %s, code says %s\n", llama_ftype_to_name(section.ftype),
+               ggml_type_name(section.default_type), ggml_type_name(computed_default));
+        return false;
+    }
+
+    std::vector<ggml_type> result_types(mt.tensors.size());
+    llama_quant_compute_types(qs, section.ftype, mt.tensors.data(), result_types.data(), mt.tensors.size());
+
+    std::map<std::string, ggml_type> override_map(section.overrides.begin(), section.overrides.end());
+
+    bool all_pass         = true;
+    int  n_override_found = 0;
+
+    for (size_t i = 0; i < mt.tensors.size(); i++) {
+        const char * name = ggml_get_name(mt.tensors[i]);
+        ggml_type    got  = result_types[i];
+
+        ggml_type expected = section.default_type;
+        auto      it       = override_map.find(name);
+        if (it != override_map.end()) {
+            expected = it->second;
+            n_override_found++;
+        }
+
+        if (got != expected) {
+            printf("  FAIL  %-50s %-10s expected %s, got %s\n", name, llama_ftype_to_name(section.ftype),
+                   ggml_type_name(expected), ggml_type_name(got));
+            all_pass = false;
+        }
+    }
+
+    if (n_override_found != (int) section.overrides.size()) {
+        printf("  FAIL  [%s] override count mismatch: listed %d, matched %d\n", llama_ftype_to_name(section.ftype),
+               (int) section.overrides.size(), n_override_found);
+        all_pass = false;
+    }
+
+    return all_pass;
+}
+
+static int run_remote_tests(const std::string & snapshot_dir, const char * argv0) {
+    int total_pass = 0;
+    int total_fail = 0;
+    int total_skip = 0;
+
+    for (int m = 0; m < n_model_specs; m++) {
+        const auto & spec = model_specs[m];
+        std::string  name = model_name_from_repo(spec.repo);
+        printf("=== %s ===\n", name.c_str());
+
+        auto result = gguf_fetch_model_meta(spec.repo, spec.quant, "", false);
+        if (!result.has_value()) {
+            printf("  SKIP  (could not fetch model metadata)\n\n");
+            total_skip++;
+            continue;
+        }
+
+        const auto &                remote  = result.value();
+        llama_model *               model   = build_mock_model_from_remote(remote);
+        llama_model_quantize_params qparams = llama_model_quantize_default_params();
+        quantize_state_impl *       qs      = llama_quant_init(model, &qparams);
+        auto                        mt      = build_mock_tensors(qs, remote);
+
+        std::string                   snapshot_path = snapshot_dir + "/" + snapshot_file_from_name(name) + ".schema";
+        std::vector<snapshot_section> sections;
+        if (!parse_snapshot_file(snapshot_path, sections)) {
+            printf("  SKIP  (could not read snapshot file: %s)\n\n", snapshot_path.c_str());
+            llama_quant_free(qs);
+            llama_model_free(model);
+            total_skip++;
+            continue;
+        }
+
+        int model_pass = 0;
+        int model_fail = 0;
+
+        for (const auto & section : sections) {
+            bool pass = run_test_section(qs, mt, section);
+            if (pass) {
+                model_pass++;
+            } else {
+                model_fail++;
+            }
+        }
+
+        printf("  %s  %s: %d/%d ftype sections passed (%d tensors)\n", model_fail == 0 ? "PASS" : "FAIL", name.c_str(),
+               model_pass, model_pass + model_fail, (int) mt.tensors.size());
+        printf("\n");
+
+        if (model_fail == 0) {
+            total_pass++;
+        } else {
+            total_fail++;
+        }
+
+        llama_quant_free(qs);
+        llama_model_free(model);
+    }
+
+    printf("%d/%d models passed", total_pass, total_pass + total_fail);
+    if (total_skip > 0) {
+        printf(", %d skipped", total_skip);
+    }
+    printf("\n");
+
+    if (total_fail > 0) {
+        printf("\nIf these changes are intentional, regenerate snapshot files with:\n");
+        printf("  %s --generate\n", argv0);
+    }
+
+    return total_fail > 0 ? 1 : 0;
+}
+
+int main(int argc, char ** argv) {
+    std::string snapshot_dir = SNAPSHOT_DIR;
+    bool        generate     = false;
+
+    for (int i = 1; i < argc; i++) {
+        if (strcmp(argv[i], "--generate") == 0) {
+            generate = true;
+        } else if (strcmp(argv[i], "--snapshot-dir") == 0 && i + 1 < argc) {
+            snapshot_dir = argv[++i];
+        }
+    }
+
+    if (generate) {
+        return run_generate(snapshot_dir);
+    }
+
+    // suppress llama log warnings during test (e.g. tensor type fallback messages)
+    llama_log_set([](enum ggml_log_level, const char *, void *) {}, nullptr);
+
+    return run_remote_tests(snapshot_dir, argv[0]);
+}

tools/cli/README.md

@@ -176,8 +176,8 @@
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)<br/>(env: LLAMA_ARG_SKIP_CHAT_PARSING) |
 | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles |
 | `--draft, --draft-n, --draft-max N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_DRAFT_MAX) |

tools/completion/README.md

@@ -255,8 +255,8 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)<br/>(env: LLAMA_ARG_SKIP_CHAT_PARSING) |
 | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles |
 

tools/parser/debug-template-parser.cpp

@@ -5,15 +5,15 @@
 #include "gguf.h"
 #include "jinja/runtime.h"
 #include "log.h"
+#include "nlohmann/json.hpp"
+#include "peg-parser.h"
 
 #include <fstream>
 #include <numeric>
+#include <optional>
 #include <sstream>
 #include <string>
 
-#include "nlohmann/json.hpp"
-#include "peg-parser.h"
-
 using json = nlohmann::ordered_json;
 
 enum class output_mode {
@@ -34,14 +34,14 @@ enum class input_message_type {
 };
 
 struct debug_options {
-    std::string      template_path;
-    bool             with_tools        = true;
-    bool             generation_prompt = true;
-    bool             enable_reasoning  = true;
-    bool             debug_jinja       = false;
-    bool             force_tool_call   = false;
-    output_mode      mode              = output_mode::BOTH;
-    input_message_type input_message   = input_message_type::NONE;
+    std::string        template_path;
+    bool               with_tools        = true;
+    bool               generation_prompt = true;
+    bool               enable_reasoning  = true;
+    bool               debug_jinja       = false;
+    bool               force_tool_call   = false;
+    output_mode        mode              = output_mode::BOTH;
+    input_message_type input_message     = input_message_type::NONE;
 };
 
 static std::string read_file(const std::string & path) {
@@ -274,7 +274,7 @@ static void render_scenario(const common_chat_template & tmpl,
     json final_messages = messages;
     if (add_generation_prompt && !messages.empty() && messages.back().value("role", "") == "assistant") {
         final_messages.push_back(json{
-            { "role",    "user" },
+            { "role",    "user"                                       },
             { "content", "Now please continue with another response." }
         });
     }
@@ -305,7 +305,7 @@ static void render_all_scenarios(const common_chat_template & tmpl,
                                  const json &                 tools,
                                  bool                         add_generation_prompt,
                                  bool                         enable_thinking,
-                                 input_message_type             message_type) {
+                                 input_message_type           message_type) {
     json user_msg = build_user_message();
 
     auto render_if = [&](input_message_type type, const std::string & name, const json & assistant_msg) {
@@ -335,6 +335,24 @@ static void render_all_scenarios(const common_chat_template & tmpl,
     }
 }
 
+static autoparser::generation_params prepare_params(const debug_options & opts, const json & tools) {
+    autoparser::generation_params params;
+    params.messages         = json::array({ build_user_message() });
+    params.reasoning_format = opts.enable_reasoning ? COMMON_REASONING_FORMAT_DEEPSEEK : COMMON_REASONING_FORMAT_NONE;
+    params.enable_thinking  = opts.enable_reasoning;
+    params.add_generation_prompt = opts.generation_prompt;
+
+    if (opts.with_tools) {
+        params.tools       = tools;
+        params.tool_choice = opts.force_tool_call ? COMMON_CHAT_TOOL_CHOICE_REQUIRED : COMMON_CHAT_TOOL_CHOICE_AUTO;
+    } else {
+        params.tools       = json();
+        params.tool_choice = COMMON_CHAT_TOOL_CHOICE_NONE;
+    }
+    params.parallel_tool_calls = false;
+    return params;
+}
+
 int main(int argc, char ** argv) {
     // Set log level to most verbose to capture all debug output
     common_log_set_verbosity_thold(99);
@@ -369,49 +387,41 @@ int main(int argc, char ** argv) {
     try {
         common_chat_template chat_template(template_source, "", "");
 
-        // Build tools definition
         json tools = opts.with_tools ? build_tools_definition() : json();
 
-        // Render template scenarios if requested
-        if (opts.input_message != input_message_type::NONE &&
-            (opts.mode == output_mode::TEMPLATE || opts.mode == output_mode::BOTH)) {
+        autoparser::generation_params params = prepare_params(opts, tools);
+        common_chat_params            parser_data;
+        if (std::optional<common_chat_params> spec_tmpl =
+                common_chat_try_specialized_template(chat_template, template_source, params)) {
             LOG_ERR("\n");
-            LOG_ERR("================================================================================\n");
-            LOG_ERR("                         TEMPLATE RENDERING OUTPUT\n");
-            LOG_ERR("================================================================================\n");
+            LOG_ERR("This template uses a specialized parser, analysis results will not be available.");
+            parser_data = *spec_tmpl;
+        } else {
+            // Render template scenarios if requested
+            if (opts.input_message != input_message_type::NONE &&
+                (opts.mode == output_mode::TEMPLATE || opts.mode == output_mode::BOTH)) {
+                LOG_ERR("\n");
+                LOG_ERR("================================================================================\n");
+                LOG_ERR("                         TEMPLATE RENDERING OUTPUT\n");
+                LOG_ERR("================================================================================\n");
 
-            render_all_scenarios(chat_template, tools, opts.generation_prompt, opts.enable_reasoning,
-                                 opts.input_message);
-        }
-
-        // Output analysis if requested
-        if (opts.mode == output_mode::ANALYSIS || opts.mode == output_mode::BOTH) {
-            LOG_ERR("\n");
-            LOG_ERR("================================================================================\n");
-            LOG_ERR("                           TEMPLATE ANALYSIS\n");
-            LOG_ERR("================================================================================\n");
-
-            autoparser::autoparser analysis;
-            analysis.analyze_template(chat_template);
-
-            // Generate Parser
-            autoparser::generation_params params;
-            params.messages = json::array({ build_user_message() });
-            params.reasoning_format =
-                opts.enable_reasoning ? COMMON_REASONING_FORMAT_DEEPSEEK : COMMON_REASONING_FORMAT_NONE;
-            params.enable_thinking       = opts.enable_reasoning;
-            params.add_generation_prompt = opts.generation_prompt;
-
-            if (opts.with_tools) {
-                params.tools       = tools;
-                params.tool_choice = opts.force_tool_call ? COMMON_CHAT_TOOL_CHOICE_REQUIRED : COMMON_CHAT_TOOL_CHOICE_AUTO;
-            } else {
-                params.tools       = json();
-                params.tool_choice = COMMON_CHAT_TOOL_CHOICE_NONE;
+                render_all_scenarios(chat_template, tools, opts.generation_prompt, opts.enable_reasoning,
+                                     opts.input_message);
             }
-            params.parallel_tool_calls = false;
 
-            auto parser_data = autoparser::peg_generator::generate_parser(chat_template, params, analysis);
+            // Output analysis if requested
+            if (opts.mode == output_mode::ANALYSIS || opts.mode == output_mode::BOTH) {
+                LOG_ERR("\n");
+                LOG_ERR("================================================================================\n");
+                LOG_ERR("                           TEMPLATE ANALYSIS\n");
+                LOG_ERR("================================================================================\n");
+
+                autoparser::autoparser analysis;
+                analysis.analyze_template(chat_template);
+
+                // Generate Parser
+                parser_data = autoparser::peg_generator::generate_parser(chat_template, params, analysis);
+            }
 
             LOG_ERR("\n=== Generated Parser ===\n");
             common_peg_arena arena;

tools/server/README.md

@@ -167,6 +167,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-cpent, --checkpoint-every-n-tokens N` | create a checkpoint every n tokens during prefill (processing), -1 to disable (default: 8192)<br/>(env: LLAMA_ARG_CHECKPOINT_EVERY_NT) |
 | `-cram, --cache-ram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)<br/>(env: LLAMA_ARG_CACHE_RAM) |
 | `-kvu, --kv-unified, -no-kvu, --no-kv-unified` | use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
+| `--clear-idle, --no-clear-idle` | save and clear idle slots on new task (default: enabled, requires unified KV and cache-ram)<br/>(env: LLAMA_ARG_CLEAR_IDLE) |
 | `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
 | `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode |
 | `-sp, --special` | special tokens output enabled (default: false) |
@@ -221,8 +222,8 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)<br/>(env: LLAMA_ARG_SKIP_CHAT_PARSING) |
 | `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled<br/><br/>(env: LLAMA_ARG_PREFILL_ASSISTANT) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled) |
@@ -1573,6 +1574,7 @@ The precedence rule for preset options is as follows:
 We also offer additional options that are exclusive to presets (these aren't treated as command-line arguments):
 - `load-on-startup` (boolean): Controls whether the model loads automatically when the server starts
 - `stop-timeout` (int, seconds): After requested unload, wait for this many seconds before forcing termination (default: 10)
+- `pin` (boolean): Prevents the router from unloading this model when the `models_max` limit is exceeded; the model remains loaded until explicitly unloaded or the server restarts.
 
 ### Routing requests
 

tools/server/server-context.cpp

@@ -605,6 +605,17 @@ private:
         llama_batch_free(batch);
     }
 
+    void slot_save_and_clear(server_slot & slot) {
+        if (slot.prompt.n_tokens() == 0) {
+            return;
+        }
+        SLT_INF(slot, "%s", "saving idle slot to prompt cache\n");
+        SLT_DBG(slot, "%s", "__TEST_TAG_CLEAR_IDLE_SLOT__\n");
+        slot.prompt_save(*prompt_cache);
+        slot.prompt_clear(false);
+        prompt_cache->update();
+    }
+
     void handle_sleeping_state(bool new_state) {
         GGML_ASSERT(sleeping != new_state);
         if (new_state) {
@@ -864,6 +875,19 @@ private:
 
         metrics.init();
 
+        if (params_base.clear_idle) {
+            if (!params_base.kv_unified) {
+                SRV_WRN("%s: --clear-idle requires --kv-unified, disabling\n", __func__);
+                params_base.clear_idle = false;
+            } else if (params_base.cache_ram_mib == 0) {
+                SRV_WRN("%s: --clear-idle requires --cache-ram, disabling\n", __func__);
+                params_base.clear_idle = false;
+            } else {
+                SRV_INF("%s: idle slots will be saved to prompt cache and cleared upon starting a new task\n", __func__);
+                SRV_DBG("%s", "__TEST_TAG_CLEAR_IDLE_ENABLED__\n");
+            }
+        }
+
         // populate webui settings
         {
             if (!params_base.webui_config_json.empty()) {
@@ -1010,15 +1034,15 @@ private:
             // cache prompts only for completion tasks
             update_cache = update_cache && task.type == SERVER_TASK_TYPE_COMPLETION;
 
-            // don't update the cache if the slot's context is empty
-            update_cache = update_cache && tokens.size() > 0;
-
             if (update_cache) {
                 SRV_WRN("%s", "updating prompt cache\n");
 
                 const int64_t t_start = ggml_time_us();
 
-                ret->prompt_save(*prompt_cache);
+                // don't save the slot's state if its context is empty
+                if (tokens.size() > 0) {
+                    ret->prompt_save(*prompt_cache);
+                }
 
                 if (!ret->prompt_load(*prompt_cache, task.tokens)) {
                     ret->prompt_clear(false);
@@ -1692,9 +1716,7 @@ private:
                     const int id_slot = task.id_slot;
                     const int id_task = task.id;
 
-                    server_slot * slot = id_slot != -1
-                                            ? get_slot_by_id(id_slot)
-                                            : get_available_slot(task);
+                    server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task);
 
                     //
                     // slot scheduling logic
@@ -1731,6 +1753,14 @@ private:
                         SRV_ERR("failed to launch slot with task, id_task = %d\n", id_task);
                         break; // drop the task
                     }
+
+                    if (params_base.clear_idle) {
+                        for (auto & s : slots) {
+                            if (!s.is_processing()) {
+                                slot_save_and_clear(s);
+                            }
+                        }
+                    }
                 } break;
             case SERVER_TASK_TYPE_CANCEL:
                 {

tools/server/server-models.cpp

@@ -352,6 +352,14 @@ void server_models::load_models() {
         }
     }
 
+    // handle custom pin option
+    for (auto & [name, inst] : mapping) {
+        std::string val;
+        if (inst.meta.preset.get_option(COMMON_ARG_PRESET_PIN, val)) {
+            inst.meta.pinned = true;
+        }
+    }
+
     // load any autoload models
     std::vector<std::string> models_to_load;
     for (const auto & [name, inst] : mapping) {
@@ -506,7 +514,9 @@ void server_models::unload_lru() {
         for (const auto & m : mapping) {
             if (m.second.meta.is_running()) {
                 count_active++;
-                if (m.second.meta.last_used < lru_last_used) {
+                // If all active models are pinned, this condition never holds and no LRU eviction will occur.
+                // We throw an error instead of allowing the server to exceed models_max.
+                if (!m.second.meta.pinned && m.second.meta.last_used < lru_last_used) {
                     lru_model_name = m.first;
                     lru_last_used = m.second.meta.last_used;
                 }
@@ -523,6 +533,11 @@ void server_models::unload_lru() {
                 return mapping[lru_model_name].meta.status == SERVER_MODEL_STATUS_UNLOADED;
             });
         }
+    } else if (count_active >= (size_t)base_params.models_max) {
+        throw std::runtime_error(string_format(
+            "models_max limit (%d) reached, but no unpinned models available for LRU eviction - cannot load more models",
+            base_params.models_max
+        ));
     }
 }
 

tools/server/server-models.h

@@ -65,6 +65,7 @@ struct server_model_meta {
     std::vector<std::string> args; // args passed to the model instance, will be populated by render_args()
     int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED)
     int stop_timeout = 0; // seconds to wait before force-killing the model instance during shutdown
+    bool pinned = false; // if true, this model will not be unloaded by LRU
 
     bool is_ready() const {
         return status == SERVER_MODEL_STATUS_LOADED;

tools/server/server-task.cpp

@@ -2008,7 +2008,7 @@ server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t
 bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot) {
     const int lcp_best = prompt.tokens.get_common_prefix(tokens_new);
 
-    float f_keep_best = float(lcp_best) / prompt.tokens.size();
+    float f_keep_best = prompt.tokens.size() > 0 ? float(lcp_best) / prompt.tokens.size() : -1.0f; // empty slot: any cache entry wins
     float sim_best    = float(lcp_best) / tokens_new.size();
 
     SRV_WRN(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);

tools/server/tests/unit/test_kv_keep_only_active.py

@@ -0,0 +1,115 @@
+import os
+import tempfile
+import pytest
+from utils import *
+
+server = ServerPreset.tinyllama2()
+
+class LogReader:
+    def __init__(self, path):
+        self.path = path
+        self.pos = 0
+    def drain(self):
+        with open(self.path) as f:
+            f.seek(self.pos)
+            content = f.read()
+            self.pos = f.tell()
+        return content
+
+@pytest.fixture(autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+    server.n_slots = 2
+    server.n_predict = 4
+    server.temperature = 0.0
+    server.server_slots = True
+    server.cache_ram = 100
+    server.kv_unified = True
+    server.debug = True
+    fd, server.log_path = tempfile.mkstemp(suffix='.log')
+    os.close(fd)
+    yield
+
+
+LONG_PROMPT = (
+    "Once upon a time in a land far away, there lived a brave knight "
+    "who traveled across mountains and rivers to find the legendary "
+    "golden sword hidden deep within the enchanted forest of whispers. "
+    "He met many creatures along the way including dragons and fairies "
+    "and wizards who helped him on his noble quest to save the kingdom."
+)
+
+
+# idle slot cleared on launch should restore from cache-ram
+def test_clear_and_restore():
+    global server
+    server.start()
+    log = LogReader(server.log_path)
+
+    # verify feature is enabled
+    assert "__TEST_TAG_CLEAR_IDLE_ENABLED__" in log.drain()
+
+    res = server.make_request("POST", "/completion", data={
+        "prompt": LONG_PROMPT,
+        "id_slot": 0,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    original_prompt_n = res.body["timings"]["prompt_n"]
+
+    # Slot 0 is the only slot with KV — should NOT be cleared
+    assert "__TEST_TAG_CLEAR_IDLE_SLOT__" not in log.drain()
+
+    # Launching slot 1 clears idle slot 0
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "The quick brown fox",
+        "id_slot": 1,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert "__TEST_TAG_CLEAR_IDLE_SLOT__" in log.drain()
+
+    # Re-send same prompt — should restore from cache-ram
+    res = server.make_request("POST", "/completion", data={
+        "prompt": LONG_PROMPT,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert "updating prompt cache" in log.drain()
+    assert res.body["timings"]["cache_n"] > 0
+    assert res.body["timings"]["prompt_n"] < original_prompt_n
+
+    # Follow-up — slot 0 kept its KV, no clearing needed
+    res = server.make_request("POST", "/completion", data={
+        "prompt": LONG_PROMPT + " The knight finally reached the castle gates.",
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert "__TEST_TAG_CLEAR_IDLE_SLOT__" not in log.drain()
+
+
+def test_disabled_with_flag():
+    global server
+    server.no_clear_idle = True
+    server.start()
+    log = LogReader(server.log_path)
+
+    # Feature should not be enabled
+    assert "__TEST_TAG_CLEAR_IDLE_ENABLED__" not in log.drain()
+
+    res = server.make_request("POST", "/completion", data={
+        "prompt": LONG_PROMPT,
+        "id_slot": 0,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+
+    # Request on different slot — should NOT trigger clearing
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "The quick brown fox",
+        "id_slot": 1,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert "__TEST_TAG_CLEAR_IDLE_SLOT__" not in log.drain()

tools/server/tests/utils.py

@@ -102,6 +102,9 @@ class ServerProcess:
     mmproj_url: str | None = None
     media_path: str | None = None
     sleep_idle_seconds: int | None = None
+    cache_ram: int | None = None
+    no_clear_idle: bool = False
+    log_path: str | None = None
     webui_mcp_proxy: bool = False
 
     # session variables
@@ -237,6 +240,10 @@ class ServerProcess:
             server_args.extend(["--media-path", self.media_path])
         if self.sleep_idle_seconds is not None:
             server_args.extend(["--sleep-idle-seconds", self.sleep_idle_seconds])
+        if self.cache_ram is not None:
+            server_args.extend(["--cache-ram", self.cache_ram])
+        if self.no_clear_idle:
+            server_args.append("--no-clear-idle")
         if self.webui_mcp_proxy:
             server_args.append("--webui-mcp-proxy")
 
@@ -249,11 +256,16 @@ class ServerProcess:
             flags |= subprocess.CREATE_NEW_PROCESS_GROUP
             flags |= subprocess.CREATE_NO_WINDOW
 
+        if self.log_path:
+            self._log = open(self.log_path, "w")
+        else:
+            self._log = sys.stdout
+
         self.process = subprocess.Popen(
             [str(arg) for arg in [server_path, *server_args]],
             creationflags=flags,
-            stdout=sys.stdout,
-            stderr=sys.stdout,
+            stdout=self._log,
+            stderr=self._log if self._log != sys.stdout else sys.stdout,
             env={**os.environ, "LLAMA_CACHE": "tmp"} if "LLAMA_CACHE" not in os.environ else None,
         )
         server_instances.add(self)
@@ -298,6 +310,8 @@ class ServerProcess:
             except Exception as e:
                 print(f"Error waiting for server: {e}")
             self.process = None
+        if hasattr(self, '_log') and self._log != sys.stdout:
+            self._log.close()
 
     def make_request(
         self,