server : apply checkpoint min-step eviction only when the checkpoint list is full (#28302 )

The spacing eviction in create_checkpoint() keeps the oldest checkpoint and erases every later one within checkpoint_min_step of it. For prompts shorter than checkpoint_min_step this drops the checkpoint at n_tokens - 4 that the next request resumes from, so hybrid/recurrent models re-prefill from the previous checkpoint instead. Apply the spacing rule only once the list is at n_ctx_checkpoints, and replace an existing checkpoint at the same n_tokens instead of appending a duplicate.
metal : fix idle threads in mul_mv_iq3_xxs for ne00 < 1024 (#28086 )
2026-09-09 17:17:09 +02:00 · 2026-09-08 16:01:03 +03:00 · 2026-09-08 15:54:42 +03:00 · 2026-09-08 20:44:33 +08:00 · 2026-09-08 14:06:15 +02:00 · 2026-09-08 13:36:03 +02:00
974 changed files with 90958 additions and 28019 deletions
@@ -31,7 +31,7 @@
    ]
    && blas.meta.available,
  useCuda ? config.cudaSupport,
-  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin,
+  useMetalKit ? stdenv.hostPlatform.isAarch64 && stdenv.hostPlatform.isDarwin,
  # Increases the runtime closure size by ~700M
  useMpi ? false,
  useRocm ? config.rocmSupport,
@@ -92,7 +92,7 @@ let

  cudaBuildInputs = with cudaPackages; [
    cuda_cudart
-    cuda_cccl # <nv/target>
+    cccl # <nv/target>
    libcublas
  ];

@@ -166,7 +166,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
  # `xcrun` is used find the path of the Metal compiler, which is varible
  # and not on $PATH
  # see https://github.com/ggml-org/llama.cpp/pull/6118 for discussion
-  __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders;
+  __noChroot = effectiveStdenv.hostPlatform.isDarwin && useMetalKit && precompileMetalShaders;

  nativeBuildInputs =
    [
@@ -181,10 +181,10 @@ effectiveStdenv.mkDerivation (finalAttrs: {
      autoAddDriverRunpath
    ]
    ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [ glibc.static ]
-    ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [ xcrunHost ];
+    ++ optionals (effectiveStdenv.hostPlatform.isDarwin && useMetalKit && precompileMetalShaders) [ xcrunHost ];

  buildInputs =
-    optionals effectiveStdenv.isDarwin darwinBuildInputs
+    optionals effectiveStdenv.hostPlatform.isDarwin darwinBuildInputs
    ++ optionals useCuda cudaBuildInputs
    ++ optionals useMpi [ mpi ]
    ++ optionals useRocm rocmBuildInputs
@@ -245,7 +245,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {

    # Configurations that are known to result in build failures. Can be
    # overridden by importing Nixpkgs with `allowBroken = true`.
-    broken = (useMetalKit && !effectiveStdenv.isDarwin);
+    broken = (useMetalKit && !effectiveStdenv.hostPlatform.isDarwin);

    description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}";
    homepage = "https://github.com/ggml-org/llama.cpp/";
@@ -1,12 +1,12 @@
-ARG OPENVINO_VERSION_MAJOR=2026.3
-ARG OPENVINO_VERSION_FULL=2026.3.0.22451.bd8d6542e3c
+ARG OPENVINO_VERSION_MAJOR=2026.3.1
+ARG OPENVINO_VERSION_FULL=2026.3.1.22476.56d9685302d
 ARG UBUNTU_VERSION=24.04

 # Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
-ARG IGC_VERSION=v2.38.2
-ARG IGC_VERSION_FULL=2_2.38.2+22051
-ARG COMPUTE_RUNTIME_VERSION=26.27.39122.11
-ARG COMPUTE_RUNTIME_VERSION_FULL=26.27.39122.11-0
+ARG IGC_VERSION=v2.40.13
+ARG IGC_VERSION_FULL=2_2.40.13+22418
+ARG COMPUTE_RUNTIME_VERSION=26.31.39395.13
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.31.39395.13-0
 ARG IGDGMM_VERSION=22.10.0

 # Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
@@ -90,6 +90,9 @@ RUN bash -c "source ${OpenVINO_DIR}/setupvars.sh && \
    cmake -B build/ReleaseOV -G Ninja \
        -DCMAKE_BUILD_TYPE=Release \
        -DLLAMA_BUILD_TESTS=OFF \
+        -DGGML_NATIVE=OFF \
+        -DGGML_BACKEND_DL=ON \
+        -DGGML_CPU_ALL_VARIANTS=ON \
        -DGGML_OPENVINO=ON && \
    cmake --build build/ReleaseOV --parallel "

@@ -1,4 +1,4 @@
-blank_issues_enabled: true
+blank_issues_enabled: false
 contact_links:
  - name: Got an idea?
    url: https://github.com/ggml-org/llama.cpp/discussions/categories/ideas
@@ -0,0 +1,95 @@
+name: "ccache-buckets"
+description: "Save/restore latest GitHub Actions ccache matching a key prefix to/from HF buckets"
+inputs:
+  key:
+    description: "Cache key prefix to match and load"
+    required: true
+  folder:
+    description: "Bucket folder containing ccache files"
+    required: true
+  evict-old-files:
+    description: "Corresponds to the ccache --evict-older-than AGE option, where AGE is the number of seconds or days followed by the 's' or 'd' suffix respectively."
+    default: ''
+  save:
+    description: "Save ccache"
+    required: false
+    default: false
+    type: boolean
+  hf_bucket:
+    description: 'Hugging Face buckets path'
+    required: true
+
+runs:
+  using: "composite"
+  steps:
+    - name: Install Hugging Face Hub CLI
+      shell: bash
+      run: |
+        python3 -m venv .venv-hf
+        .venv-hf/bin/pip install -U huggingface_hub==1.28.0
+
+    - name: Restore ccache from buckets
+      if: ${{ inputs.save != 'true' }}
+      shell: bash
+      run: |
+        set +e -uo pipefail
+        source .venv-hf/bin/activate
+        CCACHE_DIR=$(ccache -k cache_dir)
+        if [[ -d "$CCACHE_DIR" ]]; then
+            CACHE_PATH=$(hf buckets list "hf://buckets/${{ inputs.hf_bucket }}/${{ inputs.folder }}" --json | jq -r '[.[] | select(.type == "file") | select(.path | startswith("${{ inputs.folder }}/${{ inputs.key }}") and endswith(".tar.gz"))] | sort_by(.path) | last | .path // ""')
+            if [[ -n "$CACHE_PATH" ]]; then
+                echo "Restoring ccache from '$CACHE_PATH'."
+                hf buckets cp "hf://buckets/${{ inputs.hf_bucket }}/$CACHE_PATH" ccache_bucket.tar.gz
+                mkdir -p ccache_bucket
+                if tar -xzf ccache_bucket.tar.gz -C ccache_bucket; then
+                    rm -rf "$CCACHE_DIR"
+                    mv ccache_bucket "$CCACHE_DIR"
+                    ccache -z
+                fi
+                rm ccache_bucket.tar.gz
+            else
+                echo "No ccache found."
+            fi
+        else
+            echo "'$CCACHE_DIR' not found."
+        fi
+
+    - name: Save ccache to buckets
+      if: ${{ inputs.save == 'true' }}
+      shell: bash
+      run: |
+        if [[ -n "$HF_TOKEN" ]]; then
+            set +e -uo pipefail
+            source .venv-hf/bin/activate
+            CCACHE_DIR=$(ccache -k cache_dir)
+            if [[ -d "$CCACHE_DIR" ]]; then
+                ccache -s
+                if [[ -n "${{ inputs.evict-old-files }}" ]]; then
+                    ccache --evict-older-than "${{ inputs.evict-old-files }}"
+                fi
+                DATESTAMP=$(date -u +'%Y-%m-%dT%H:%M:%SZ')
+                CACHEFILE="${{ inputs.key }}-$DATESTAMP.tar.gz"
+                if tar -czf ccache_bucket.tar.gz -C "$CCACHE_DIR" .; then
+                    hf buckets cp ccache_bucket.tar.gz "hf://buckets/${{ inputs.hf_bucket }}/${{ inputs.folder }}/$CACHEFILE"
+                fi
+                rm ccache_bucket.tar.gz
+            else
+                echo "'$CCACHE_DIR' not found."
+            fi
+        fi
+
+    - name: Remove old ccache files from buckets
+      if: ${{ inputs.save == 'true' }}
+      shell: bash
+      run: |
+        if [[ -n "$HF_TOKEN" ]]; then
+            set +e -uo pipefail
+            source .venv-hf/bin/activate
+            CACHE_FILES=$(hf buckets list "hf://buckets/${{ inputs.hf_bucket }}/${{ inputs.folder }}" --json | jq -r '[.[] | select(.type == "file") | select((.uploaded_at | .[:19]+"Z" | fromdateiso8601) < (now - 5 * 60)) | select(.path | startswith("${{ inputs.folder }}/${{ inputs.key }}") and endswith(".tar.gz"))] | sort_by(.path)[:-1] | .[] | [.path // ""] | @tsv')
+            if [[ -n "$CACHE_FILES" ]]; then
+                echo "Removing old ccache files..."
+                while IFS=$'\t' read -r CACHE_PATH; do
+                    hf buckets rm "hf://buckets/${{ inputs.hf_bucket }}/$CACHE_PATH" -y
+                done <<< "$CACHE_FILES"
+            fi
+        fi
@@ -21,68 +21,30 @@ inputs:
 runs:
  using: "composite"
  steps:
+    - name: Install GitHub CLI if missing
+      shell: bash
+      run: |
+        # e.g. in container jobs, where it is not preinstalled
+        if ! command -v gh >/dev/null 2>&1; then
+          echo "GitHub CLI not found, installing..."
+          if ! command -v curl >/dev/null 2>&1; then
+            apt-get update >/dev/null 2>&1 || true
+            apt-get install -y curl >/dev/null 2>&1 || true
+          fi
+          mkdir -p -m 755 /etc/apt/keyrings
+          curl -fsSL https://cli.github.com/packages/githubcli-archive-keyring.gpg | tee /etc/apt/keyrings/githubcli-archive-keyring.gpg >/dev/null
+          chmod go+r /etc/apt/keyrings/githubcli-archive-keyring.gpg
+          echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" > /etc/apt/sources.list.d/github-cli.list
+          apt-get update >/dev/null 2>&1 || true
+          apt-get install -y gh || { echo "Failed to install GitHub CLI (gh)" >&2; exit 1; }
+        fi
+        command -v gh >/dev/null 2>&1 || { echo "GitHub CLI (gh) is required but could not be installed" >&2; exit 1; }
+
    - name: Clear caches
      shell: bash
-      env:
-        CLEAR_KEY: ${{ inputs.key }}
-        CLEAR_OLDER: ${{ inputs.older }}
-        CLEAR_MIN: ${{ inputs.min }}
-        CLEAR_DRY_RUN: ${{ inputs.dry-run }}
      run: |
-        # Convert a duration (e.g. 90m, 1h, 1d, plain seconds) to seconds
-        to_seconds() {
-          local val="$1"
-          [[ "$val" =~ ^[0-9]+$ ]] && { echo "$val"; return 0; }
-          local num="${val%?}" unit="${val: -1}" mult
-          [[ "$num" =~ ^[0-9]+$ ]] || return 1
-          case "$unit" in
-            s) mult=1 ;;
-            m) mult=60 ;;
-            h) mult=3600 ;;
-            d) mult=86400 ;;
-            *) return 1 ;;
-          esac
-          echo $((num * mult))
-        }
-
-        [[ "$CLEAR_MIN" =~ ^[0-9]+$ ]] || { echo "Invalid min value: $CLEAR_MIN" >&2; exit 1; }
-        [[ "$CLEAR_DRY_RUN" =~ ^(true|false)$ ]] || { echo "Invalid dry-run value: $CLEAR_DRY_RUN" >&2; exit 1; }
-
-        CACHES=$(gh cache list --key "ccache-$CLEAR_KEY" --json id,key,createdAt --jq '.[] | [.createdAt, .id, .key] | @tsv' 2>/dev/null | LC_ALL=C sort)
-        if [ -z "$CACHES" ]; then
-          echo "No caches found with key prefix: $CLEAR_KEY"
-          exit 0
-        fi
-
-        TOTAL=$(( $(wc -l <<< "$CACHES") ))
-
-        echo "Found $TOTAL cache(s) with key prefix: $CLEAR_KEY (oldest first):"
-        while IFS=$'\t' read -r CREATED ID KEY; do
-          printf '  %s  %s  %s\n' "$CREATED" "$ID" "$KEY"
-        done <<< "$CACHES"
-
-        CUTOFF=""
-        if [ -n "$CLEAR_OLDER" ]; then
-          OLDER_SECONDS=$(to_seconds "$CLEAR_OLDER") || { echo "Invalid older value: $CLEAR_OLDER (expected e.g. 90m, 1h, 1d)" >&2; exit 1; }
-          CUTOFF=$(( $(date +%s) - OLDER_SECONDS ))
-        fi
-
-        # Caches are sorted oldest first
-        DELETED=0
-        while IFS=$'\t' read -r CREATED ID KEY; do
-          if [ -n "$CUTOFF" ] && [ "$(date -d "$CREATED" +%s)" -ge "$CUTOFF" ]; then
-            echo "Rest are not older than $CLEAR_OLDER, stopping"
-            break
-          fi
-          if [ $((TOTAL - DELETED - 1)) -lt "$CLEAR_MIN" ]; then
-            echo "Keeping at least $CLEAR_MIN cache(s), stopping"
-            break
-          fi
-          if [ "$CLEAR_DRY_RUN" = "true" ]; then
-            echo "Would delete cache: $ID ($KEY)"
-          else
-            echo "Deleting cache: $ID ($KEY)"
-            gh cache delete "$ID"
-          fi
-          DELETED=$((DELETED + 1))
-        done <<< "$CACHES"
+        bash scripts/ccache-clear.sh \
+          --key "${{ inputs.key }}" \
+          --older "${{ inputs.older }}" \
+          --min "${{ inputs.min }}" \
+          ${{ inputs.dry-run == 'true' && '--dry-run' || '' }}
@@ -24,7 +24,7 @@ runs:

        write-host "Installing ROCm wheels for multi-arch support"
        # Install ROCm wheels for multi-arch support (this may take several minutes)
-        python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ inputs.version }}"
+        python -m pip install --index-url https://stable.repo.amd.com/rocm/whl-next/ "rocm[libraries,devel]==${{ inputs.version }}"

        # Pre-expand the devel tree so it is included in the cache
        write-host "Initializing ROCm devel tree"
@@ -110,7 +110,7 @@ jobs:
      #   cache on:  https://github.com/ggerganov/tmp2/actions/runs/26534713799/job/78224189394
      #
      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.21
+      #  uses: ggml-org/ccache-action@v1.2.24
      #  with:
      #    key: android-ubuntu-arm64
      #    evict-old-files: 1d
@@ -22,7 +22,8 @@ on:
    types: [opened, synchronize, reopened]
    paths: [
      '.github/workflows/build-apple.yml',
-      'ggml/src/ggml-metal/**'
+      'ggml/src/ggml-metal/**',
+      'ggml/src/ggml-rpc/**'
    ]

 concurrency:
@@ -46,11 +47,19 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: apple-arm64
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: apple-arm64
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build
        id: cmake_build
@@ -65,7 +74,25 @@ jobs:
            -DGGML_RPC=ON \
            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
-          leaks -atExit -- ./build/bin/test-thread-safety -hf ggml-org/gemma-3-270m-qat-GGUF -ngl 99 -p "$(printf 'hello %.0s' {1..128})" -n 16 -c 512 -ub 32 -np 2 -t 2 -lv 1
+
+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: apple-arm64
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
+      - name: Check for leaks
+        run: |
+          cmd=(./build/bin/test-thread-safety -hf ggml-org/gemma-3-270m-qat-GGUF -ngl 99 -p "$(printf 'hello %.0s' {1..128})" -n 16 -c 512 -ub 32 -np 2 -t 2 -lv 1)
+          leaks -atExit -- "${cmd[@]}"
+          # Graphics devices are leaked by Metal in Apple code sometimes, so we ignore those leaks
+          OBJC_DEBUG_MISSING_POOLS=YES "${cmd[@]}" 2>&1 | awk '{ print } index($0, "autoreleased with no pool in place") && !/class [a-zA-Z0-9]+Device autoreleased/ { found = 1 } END { exit found }'

      - name: Test
        id: cmake_test
@@ -82,11 +109,19 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: apple-x64
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: apple-x64
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build
        id: cmake_build
@@ -103,6 +138,18 @@ jobs:
            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: apple-x64
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
      - name: Test
        id: cmake_test
        run: |
@@ -163,14 +210,6 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      # TODO: this likely does not do anything - if yes, remove it
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: apple-tvos
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
      - name: Build
        id: cmake_build
        run: |
@@ -196,14 +235,6 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      # TODO: this likely does not do anything - if yes, remove it
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: apple-visionos
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
      - name: Build
        id: cmake_build
        run: |
@@ -234,14 +265,6 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      # TODO: this likely does not do anything - if yes, remove it
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: apple-swift
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
      - name: Download xcframework artifact
        uses: actions/download-artifact@v7
        with:
@@ -41,8 +41,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -69,8 +69,8 @@ jobs:

    env:
      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -62,11 +62,10 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: cpu-${{ matrix.os }}
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false

      - name: Build Dependencies
        id: build_depends
@@ -91,6 +90,15 @@ jobs:
          python3 -m pip install --upgrade pip setuptools
          pip3 install ./gguf-py

+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: cpu-${{ matrix.os }}
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache
+
      - name: Build
        id: cmake_build
        run: |
@@ -100,6 +108,18 @@ jobs:
            -DGGML_RPC=ON
          time cmake --build build --config Release -j $(nproc)

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: cpu-${{ matrix.os }}
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
      - name: Test
        id: cmake_test
        run: |
@@ -117,18 +137,6 @@ jobs:
          ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
          ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256

-      # note: real deletion only on push to master (same condition as the ccache save),
-      #       dry-run otherwise (the token is read-only on PRs from forks)
-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        env:
-          GH_TOKEN: ${{ github.token }}
-        with:
-          key: cpu-${{ matrix.os }}
-          older: 1h
-          min: 1
-          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
-
  windows:
    name: windows / ${{ matrix.build }}
    runs-on: windows-2025
@@ -156,7 +164,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: cpu-windows-2025-${{ matrix.build }}
          variant: ccache
@@ -215,3 +223,13 @@ jobs:
      #     cd build
      #     $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1
      #     & $sde -future -- ctest -L main -C Release --verbose --timeout 900
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: cpu-windows-2025-${{ matrix.build }}
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -50,14 +50,22 @@ jobs:
          DEBIAN_FRONTEND: noninteractive
        run: |
          apt update
-          apt install -y cmake build-essential ninja-build libgomp1 git libssl-dev
+          apt install -y cmake build-essential ninja-build libgomp1 git libssl-dev jq python3 python3-venv python3-pip

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: cuda-ubuntu-24.04-cuda
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: cuda-ubuntu-24.04-cuda
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build with CMake
        # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
@@ -72,6 +80,18 @@ jobs:
            -DGGML_CUDA_CUB_3DOT2=ON
          cmake --build build

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: cuda-ubuntu-24.04-cuda
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
  hip:
    runs-on: ubuntu-22.04
    container: rocm/dev-ubuntu-22.04:6.1.2
@@ -85,14 +105,22 @@ jobs:
        id: depends
        run: |
          sudo apt-get update
-          sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libssl-dev rocwmma-dev
+          sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libssl-dev rocwmma-dev jq python3-venv

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: cuda-ubuntu-22.04-hip
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: cuda-ubuntu-22.04-hip
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build with native CMake HIP support
        id: cmake_build
@@ -103,6 +131,18 @@ jobs:
            -DGGML_HIP=ON
          cmake --build build --config Release -j $(nproc)

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: cuda-ubuntu-22.04-hip
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
  musa:
    runs-on: ubuntu-22.04
    container: mthreads/musa:rc4.3.0-devel-ubuntu22.04-amd64
@@ -116,14 +156,22 @@ jobs:
        id: depends
        run: |
          apt-get update
-          apt-get install -y build-essential git cmake libssl-dev
+          apt-get install -y build-essential git cmake libssl-dev jq

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: cuda-ubuntu-22.04-musa
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: cuda-ubuntu-22.04-musa
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build with native CMake MUSA support
        id: cmake_build
@@ -131,3 +179,15 @@ jobs:
          cmake -B build -S . \
            -DGGML_MUSA=ON
          time cmake --build build --config Release -j $(nproc)
+
+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: cuda-ubuntu-22.04-musa
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
@@ -47,7 +47,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}

@@ -152,7 +152,7 @@ jobs:
          & "${env:HIP_PATH}\lib\llvm\bin\clang.exe" --version

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          # TODO: this build does not match the build in release.yml, so we use a different cache key
          #       ideally, the builds should match, similar to the CUDA build above so that we would be able
@@ -35,7 +35,7 @@ jobs:
        uses: actions/checkout@v6

      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.16
+      #  uses: ggml-org/ccache-action@v1.2.24
      #  with:
      #    key: msys-windows-2025-x64
      #    variant: ccache
@@ -44,7 +44,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: opencl-windows-2025-x64
          variant: ccache
@@ -80,3 +80,13 @@ jobs:
        run: |
          cmake -S . -B build -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON -DLLAMA_BUILD_BORINGSSL=ON
          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: opencl-windows-2025-x64
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -32,6 +32,8 @@ env:
  LLAMA_ARG_LOG_COLORS: 1
  LLAMA_ARG_LOG_PREFIX: 1
  LLAMA_ARG_LOG_TIMESTAMPS: 1
+  # TODO: fix failing tests on OpenVINO backend
+  CTEST_EXCLUDE: "test-llama-archs|^test-recurrent-state-|test-backend-ops|test-save-load-state"

 jobs:
  ubuntu-24-openvino:
@@ -39,8 +41,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -78,26 +80,24 @@ jobs:

      - name: Test (CPU)
        id: cmake_test_cpu
-        # TODO: fix and re-enable the `test-llama-archs` test below
        run: |
          cd ${{ github.workspace }}
-          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" --verbose --timeout 2000
+          ctest --test-dir build/ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" --verbose --timeout 3000

      - name: Test (GPU)
        id: cmake_test_gpu
-        # TODO: fix and re-enable the `test-llama-archs` test below
        run: |
          cd ${{ github.workspace }}
          export GGML_OPENVINO_DEVICE=GPU
-          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" --verbose --timeout 3000
+          ctest --test-dir build/ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" --verbose --timeout 3000

  openvino-windows-2022:
    runs-on: windows-2022

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -105,7 +105,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: openvino-windows-2022
          variant: ccache
@@ -159,11 +159,20 @@ jobs:
      - name: Test (CPU)
        id: cmake_test_cpu
        shell: cmd
-        # TODO: fix and re-enable the `test-llama-archs` test below
        run: |
          REM Find extracted OpenVINO folder dynamically
          for /d %%i in (openvino_toolkit\*) do set OPENVINO_ROOT=%%i
          call "%OPENVINO_ROOT%\setupvars.bat"

          cd build
-          ctest --test-dir ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" -C Release --verbose --timeout 3000
+          ctest --test-dir ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" -C Release --verbose --timeout 3000
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: openvino-windows-2022
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -67,7 +67,7 @@ jobs:

      # note: sparing some ccache since these jobs run on dedicated runners that are not part of the organitzation
      #- name: ccache
-      #  uses: ggml-org/ccache-action@afde29e5b5422e5da23cb1f639e8baecadeadfc3 # https://github.com/ggml-org/ccache-action/pull/1
+      #  uses: ggml-org/ccache-action@v1.2.24
      #  with:
      #    key: riscv-ubuntu-native
      #    evict-old-files: 1d
@@ -137,7 +137,7 @@ jobs:

      # note: sparing some ccache since these jobs run on dedicated runners that are not part of the organitzation
      #- name: ccache
-      #  uses: ggml-org/ccache-action@afde29e5b5422e5da23cb1f639e8baecadeadfc3 # https://github.com/ggml-org/ccache-action/pull/1
+      #  uses: ggml-org/ccache-action@v1.2.24
      #  with:
      #    key: riscv-ubuntu-native-sanitizer-${{ matrix.sanitizer }}-${{ matrix.build_type }}
      #    evict-old-files: 1d
@@ -55,7 +55,7 @@ jobs:
        uses: actions/checkout@v6

      # - name: ccache
-      #   uses: ggml-org/ccache-action@v1.2.21
+      #   uses: ggml-org/ccache-action@v1.2.24
      #   if: ${{ matrix.sanitizer != 'UNDEFINED' }}
      #   with:
      #     key: ctest-${{ matrix.sanitizer }}-ubuntu-24.04
@@ -288,8 +288,8 @@ jobs:

    env:
      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -75,11 +75,19 @@ jobs:
          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: sycl-ubuntu-24-${{ matrix.build }}
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: sycl-ubuntu-24-${{ matrix.build }}
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build
        id: cmake_build
@@ -96,6 +104,18 @@ jobs:
            -DGGML_SYCL_F16=${{ matrix.fp16 }}
          time cmake --build build --config Release -j $(nproc)

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: sycl-ubuntu-24-${{ matrix.build }}
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
  windows-latest-sycl:
    runs-on: windows-2022

@@ -127,7 +147,7 @@ jobs:
          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: sycl-windows-latest
          variant: ccache
@@ -139,3 +159,13 @@ jobs:
      - name: Build
        id: cmake_build
        run:  examples/sycl/win-build-sycl.bat
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: sycl-windows-latest
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -53,12 +53,20 @@ jobs:
          echo "CXX=g++-14" >> "$GITHUB_ENV"

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
-          key: vulkan-ubuntu-24.04-arm-new
+          key: vulkan-ubuntu-24.04-arm
          variant: ccache
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: vulkan-ubuntu-24.04-arm
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Configure
        id: cmake_configure
@@ -73,6 +81,18 @@ jobs:
        run: |
          time cmake --build build -j $(nproc)

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: vulkan-ubuntu-24.04-arm
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
  ubuntu-llvmpipe:
    runs-on: ubuntu-24.04

@@ -102,11 +122,19 @@ jobs:
          strip: 1

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: vulkan-ubuntu-24.04-llvmpipe
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: vulkan-ubuntu-24.04-llvmpipe
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build
        id: cmake_build
@@ -117,6 +145,18 @@ jobs:
            -DGGML_VULKAN=ON
          cmake --build build --config Release -j $(nproc)

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: vulkan-ubuntu-24.04-llvmpipe
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
      - name: Test
        id: cmake_test
        run: |
@@ -140,7 +180,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: cpu-windows-2025-x64-vulkan
          variant: ccache
@@ -180,3 +220,13 @@ jobs:
        run: |
          cd build
          ctest -L main -C Release --verbose --timeout 900
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: cpu-windows-2025-x64-vulkan
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -54,11 +54,10 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: webgpu-ubuntu-24.04-arm-wasm
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false

      - name: Install Emscripten
        run: |
@@ -76,6 +75,15 @@ jobs:
            "https://github.com/google/dawn/releases/download/${DAWN_TAG}/${EMDAWN_PKG}"
          unzip emdawn.zip

+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: webgpu-ubuntu-24.04-arm-wasm
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache
+
      - name: Build WASM WebGPU
        run: |
          source emsdk/emsdk_env.sh
@@ -88,3 +96,15 @@ jobs:
            -DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg

          time cmake --build build-wasm --config Release --target test-backend-ops -j $(nproc)
+
+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: webgpu-ubuntu-24.04-arm-wasm
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
@@ -69,11 +69,10 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: webgpu-macos-latest
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false

      - name: Dawn Dependency
        id: dawn-depends
@@ -88,6 +87,15 @@ jobs:
          mkdir dawn
          tar -xvf artifact.tar.gz -C dawn --strip-components=1

+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: webgpu-macos-latest
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache
+
      - name: Build
        id: cmake_build
        run: |
@@ -95,6 +103,18 @@ jobs:
          cmake -B build -G "Ninja" -DCMAKE_BUILD_TYPE=Release -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF
          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: webgpu-macos-latest
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
      - name: Test
        id: cmake_test
        run: |
@@ -110,11 +130,10 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: webgpu-ubuntu-24.04
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false

      - name: Dependencies
        id: depends
@@ -138,6 +157,15 @@ jobs:
          mkdir dawn
          tar -xvf artifact.tar.gz -C dawn --strip-components=1

+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: webgpu-ubuntu-24.04
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache
+
      - name: Build
        id: cmake_build
        run: |
@@ -146,6 +174,18 @@ jobs:
            -DGGML_WEBGPU=ON
          time cmake --build build --config Release -j $(nproc)

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: webgpu-ubuntu-24.04
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
      - name: Test
        id: cmake_test
        run: |
@@ -29,7 +29,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: copilot-setup-steps
          evict-old-files: 1d
@@ -64,7 +64,7 @@ jobs:
    needs: create_tag
    uses: ./.github/workflows/ui-build.yml
    with:
-      hf_ui_version: ${{ needs.create_tag.outputs.source_tag }}
+      ui_version: ${{ needs.create_tag.outputs.source_tag }}

  prepare_matrices:
    name: Prepare Docker matrices
@@ -162,7 +162,7 @@ jobs:
        if: ${{ matrix.config.prebuilt_ui == true }}
        uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: tools/ui/dist

      - name: Set up QEMU
@@ -49,14 +49,22 @@ jobs:
        id: depends
        run: |
          sudo apt-get update
-          sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libssl-dev python3
+          sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libssl-dev python3 python3-venv python3-pip jq

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: hip-quality-check-ubuntu-22.04
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: hip-quality-check-ubuntu-22.04
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build with Werror
        id: cmake_build
@@ -84,3 +92,15 @@ jobs:
          cd build
          make -j $(nproc) 2>&1 | tee metrics.log | grep -v 'Rpass-analysis=kernel-resource-usage\|remark:\|^$'
          python3 ../scripts/hip/gcn-cdna-vgpr-check.py metrics.log
+
+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: hip-quality-check-ubuntu-22.04
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
@@ -19,6 +19,7 @@ env:

 permissions:
  contents: write
+  packages: write

 jobs:
  make-release:
@@ -84,11 +85,13 @@ jobs:

            New version has been released.

+            ## Assets
+
            ${{ steps.desc.outputs.nightly }}

-            **Web UI:** the `nightly-tag.txt` asset contains the tag of the corresponding nightly release
+            ## More info

-            **More info:** [dist : releases and versioning of ggml-org projects](https://github.com/ggml-org/ggml/discussions/1579)
+            - [Releases and versioning of `ggml-org` projects](https://github.com/ggml-org/ggml/discussions/1579)

            ## ${{ steps.desc.outputs.changelog_title }}

@@ -111,6 +114,29 @@ jobs:
              data: await fs.readFileSync('./nightly-tag.txt')
            });

+      - name: Re-tag container images with release version
+        if: ${{ github.event.inputs.dry_run == 'false' && steps.desc.outputs.nightly_tag != '' }}
+        env:
+          GITHUB_REPOSITORY_OWNER: ${{ github.repository_owner }}
+        run: |
+          VERSION="${{ steps.checks.outputs.version }}"
+          NIGHTLY_TAG="${{ steps.desc.outputs.nightly_tag }}"
+          REPO_OWNER="${GITHUB_REPOSITORY_OWNER,,}"
+          IMAGE_REPO="ghcr.io/${REPO_OWNER}/${{ github.event.repository.name }}"
+
+          echo "${{ secrets.GITHUB_TOKEN }}" | docker login ghcr.io -u "${{ github.actor }}" --password-stdin
+
+          VARIANTS=("" "-cuda" "-cuda13" "-vulkan" "-rocm" "-intel" "-musa" "-openvino")
+          TYPES=("full" "light" "server")
+          for type in "${TYPES[@]}"; do
+            for variant in "${VARIANTS[@]}"; do
+              src="${IMAGE_REPO}:${type}${variant}-${NIGHTLY_TAG}"
+              dst="${IMAGE_REPO}:${type}${variant}-${VERSION}"
+              echo "Tagging ${src} -> ${dst}"
+              docker buildx imagetools create --tag "${dst}" "${src}"
+            done
+          done
+
      - name: Dry run summary
        if: ${{ github.event.inputs.dry_run == 'true' }}
        run: |
@@ -31,7 +31,7 @@ jobs:
        uses: actions/setup-python@v6
        with:
          python-version: "3.11"
-          pip-install: -r requirements/requirements-all.txt ty==0.0.35
+          pip-install: -r requirements/requirements-all.txt ty==0.0.78
      # - name: Type-check with Pyright
      #   uses: jakebailey/pyright-action@v2
      #   with:
@@ -40,4 +40,4 @@ jobs:
      #     warnings: true
      - name: Type-check with ty
        run: |
-            ty check --output-format=github
+            ty check --exit-zero-on-warning --output-format=github
@@ -61,31 +61,8 @@ jobs:
            echo "should_release=false" >> $GITHUB_OUTPUT
          fi

-  get-version:
-    runs-on: ubuntu-slim
-    outputs:
-      ui_version: ${{ steps.version.outputs.ui_version }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-      - id: version
-        run: |
-          # Resolve UI version: BUILD_NUMBER from cmake/build-info.cmake > git hash + epoch > fallback
-          version=""
-          if grep -q "BUILD_NUMBER" cmake/build-info.cmake; then
-            build_number=$(grep "set(BUILD_NUMBER" cmake/build-info.cmake | grep -oP '\d+')
-            if [ -n "$build_number" ] && [ "$build_number" -gt 0 ]; then
-              version="b${build_number}"
-            fi
-          fi
-          if [ -z "$version" ]; then
-            version=$(git rev-parse --short HEAD)-$(date +%s)
-          fi
-          echo "ui_version=${version}" >> $GITHUB_OUTPUT
-
  macos-cpu:
-    needs: [check-release, get-version]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
    strategy:
      matrix:
@@ -119,15 +96,14 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      - name: Download UI build
+        uses: actions/download-artifact@v7
        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-${{ matrix.os }}-${{ matrix.arch }}

@@ -141,7 +117,6 @@ jobs:
            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
            -DLLAMA_FATAL_WARNINGS=ON \
            -DLLAMA_BUILD_BORINGSSL=ON \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

@@ -167,7 +142,7 @@ jobs:
          key: release-${{ matrix.os }}-${{ matrix.arch }}

  ubuntu-cpu:
-    needs: [check-release, get-version]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
    strategy:
      matrix:
@@ -191,12 +166,11 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      - name: Download UI build
+        uses: actions/download-artifact@v7
        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: Dependencies
        id: depends
@@ -213,7 +187,7 @@ jobs:

      - name: ccache
        if: ${{ matrix.build != 's390x' }}
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-${{ matrix.os }}-cpu

@@ -227,7 +201,6 @@ jobs:
            -DGGML_NATIVE=OFF \
            -DGGML_CPU_ALL_VARIANTS=ON \
            -DLLAMA_FATAL_WARNINGS=ON \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

@@ -254,7 +227,7 @@ jobs:
          key: release-${{ matrix.os }}-cpu

  ubuntu-vulkan:
-    needs: [check-release, get-version]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    strategy:
@@ -277,12 +250,11 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      - name: Download UI build
+        uses: actions/download-artifact@v7
        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: Dependencies
        id: depends
@@ -300,7 +272,7 @@ jobs:
          fi

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-${{ matrix.os }}-vulkan

@@ -314,7 +286,6 @@ jobs:
            -DGGML_NATIVE=OFF \
            -DGGML_CPU_ALL_VARIANTS=ON \
            -DGGML_VULKAN=ON \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

@@ -340,7 +311,7 @@ jobs:
          key: release-${{ matrix.os }}-vulkan

  android-arm64:
-    needs: [check-release, get-version]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: ubuntu-latest
@@ -358,12 +329,11 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      - name: Download UI build
+        uses: actions/download-artifact@v7
        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: Set up JDK
        uses: actions/setup-java@v5
@@ -388,7 +358,7 @@ jobs:
      #   cache on:  https://github.com/ggerganov/tmp2/actions/runs/26534713799/job/78224189394
      #
      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.21
+      #  uses: ggml-org/ccache-action@v1.2.24
      #  with:
      #    key: release-android-arm64

@@ -407,7 +377,6 @@ jobs:
            -DLLAMA_FATAL_WARNINGS=ON \
            -DGGML_OPENMP=OFF \
            -DLLAMA_BUILD_BORINGSSL=ON \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

@@ -433,7 +402,7 @@ jobs:
          name: llama-bin-android-arm64.tar.gz

  ubuntu-24-openvino:
-    needs: [check-release, get-version]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: ubuntu-24.04
@@ -446,8 +415,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Set OpenVINO version output
@@ -460,15 +429,14 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      - name: Download UI build
+        uses: actions/download-artifact@v7
        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-ubuntu-24.04-openvino-release-no-preset-v1

@@ -508,7 +476,6 @@ jobs:
            -DGGML_OPENVINO=ON \
            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
            ${{ env.CMAKE_ARGS }}
          cmake --build build/ReleaseOV --config Release --parallel

@@ -552,7 +519,7 @@ jobs:
          key: release-ubuntu-24.04-openvino-release-no-preset-v1

  windows-openvino:
-    needs: [check-release]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: windows-2022
@@ -562,8 +529,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Set OpenVINO version output
@@ -577,15 +544,14 @@ jobs:
        with:
            fetch-depth: 0

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      - name: Download UI build
+        uses: actions/download-artifact@v7
        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-windows-2022-openvino
          variant: ccache
@@ -682,7 +648,7 @@ jobs:

  windows-cpu:
    name: windows-cpu / ${{ matrix.arch }}
-    needs: [check-release]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: windows-2025-vs2026
@@ -702,19 +668,18 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      - name: Download UI build
+        uses: actions/download-artifact@v7
        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: Install Ninja
        run: |
          choco install ninja

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu

@@ -749,6 +714,8 @@ jobs:
        with:
          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu

+  # note: builds only the ggml-hip backend - llama-server is injected from the
+  #       windows-cpu zip during the release "Merge artifacts" step
  windows-rocm:
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -758,7 +725,7 @@ jobs:
    strategy:
      matrix:
        include:
-          - ROCM_VERSION: "7.14.0"
+          - ROCM_VERSION: "10.0.0"
            gpu_targets: "gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201"
            build: x64

@@ -769,11 +736,16 @@ jobs:
        with:
          fetch-depth: 0

+      - name: Install Ninja
+        run: |
+          choco install ninja
+
      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
          evict-old-files: 1d
+          max-size: "1G"

      # - name: Cache ROCm Installation
      #   id: cache-rocm
@@ -821,33 +793,28 @@ jobs:

      - name: Build
        run: |
-          mkdir build
-          cd build
-          cmake .. `
-            -G "Unix Makefiles" `
+          cmake -S . -B build `
+            -G "Ninja Multi-Config" `
            -DCMAKE_PREFIX_PATH="${env:HIP_PATH}" `
-            -DCMAKE_BUILD_TYPE=Release `
            -DGGML_BACKEND_DL=ON `
            -DGGML_NATIVE=OFF `
-            -DGGML_CPU=ON `
-            -DGGML_CPU_ALL_VARIANTS=ON `
+            -DGGML_CPU=OFF `
            -DGGML_HIP=ON `
            -DCMAKE_C_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang++.exe" `
            -DCMAKE_C_FLAGS="-Wno-error=incompatible-pointer-types" `
            -DCMAKE_HIP_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
            -DHIP_PATH="${env:HIP_PATH}" `
-            -DGGML_HIP_ROCWMMA_FATTN=ON `
            -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}"
-          cmake --build . --config Release --parallel ${env:NUMBER_OF_PROCESSORS}
+          cmake --build build --config Release --parallel ${env:NUMBER_OF_PROCESSORS} --target ggml-hip

      - name: Verify HIP backend was built
        run: |
-          $hipDll = Get-ChildItem -Path build\bin -Filter "ggml-hip*.dll" -ErrorAction SilentlyContinue
+          $hipDll = Get-ChildItem -Path build\bin\Release -Filter "ggml-hip*.dll" -ErrorAction SilentlyContinue
          if (-not $hipDll) {
            Write-Host "##[error]ggml-hip*.dll was NOT produced. The HIP backend silently failed to build."
-            Write-Host "Contents of build\bin:"
-            Get-ChildItem build\bin | Format-Table -AutoSize
+            Write-Host "Contents of build\bin\Release:"
+            Get-ChildItem build\bin\Release | Format-Table -AutoSize
            exit 1
          }
          Write-Host "HIP backend artifact found:"
@@ -862,10 +829,40 @@ jobs:
          $rocmVersionShort = ('${{ matrix.ROCM_VERSION }}'.Split('.')[0..1] -join '.')
          echo "ROCM_VERSION_SHORT=$rocmVersionShort" >> $env:GITHUB_ENV

+      - name: Bundle HIP runtime DLLs (amdhip64_7.dll, rocm_kpack.dll, amd_comgr.dll)
+        run: |
+          $ErrorActionPreference = "Stop"
+          # See issue https://github.com/ggml-org/llama.cpp/issues/26929.
+          # ggml-hip.dll loads amdhip64_7.dll at run time. The Adrenalin driver
+          # ships an amdhip64_7.dll in System32, which the loader searches before PATH,
+          # so a matching DLL from PATH cannot win. Copy amdhip64 next to the
+          # binaries (exe directory is searched before System32) so the correct
+          # runtime is used. rocm_kpack.dll is amdhip64_7's direct dependency, so
+          # copy the matching version too. amd_comgr is copied as well to keep it
+          # in sync with the bundled amdhip64, avoiding a version mismatch with a
+          # amd_comgr from System32.
+          # rocblas/hipblaslt kernels resolve fine via PATH and are not copied.
+          $binPath = (rocm-sdk path --bin).Trim()
+          if (-not $binPath) { throw "rocm-sdk path --bin returned empty" }
+          write-host "ROCm bin path: $binPath"
+
+          $patterns = @("amdhip64_7.dll", "rocm_kpack.dll", "amd_comgr.dll")
+          foreach ($pattern in $patterns) {
+            $files = Get-ChildItem -Path $binPath -Filter $pattern -ErrorAction SilentlyContinue
+            if (-not $files) { throw "no match for $pattern in $binPath" }
+            foreach ($f in $files) {
+              Copy-Item $f.FullName -Destination build\bin\Release -Force
+              write-host "  copied $($f.Name)"
+            }
+          }
+
      - name: Pack artifacts
        run: |
-          cp "LICENSE" "build\bin\"
-          7z a -snl llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip .\build\bin\*
+          7z a -snl llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip `
+            .\build\bin\Release\ggml-hip.dll `
+            .\build\bin\Release\amdhip64_7.dll `
+            .\build\bin\Release\rocm_kpack.dll `
+            .\build\bin\Release\amd_comgr.dll

      - name: Upload artifacts
        uses: actions/upload-artifact@v6
@@ -878,6 +875,8 @@ jobs:
        with:
          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}

+  # note: builds only the backend library - llama-server (with the embedded UI)
+  #       is injected from the windows-cpu zip during the release "Merge artifacts" step
  windows:
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -908,13 +907,6 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
-
      - name: Install Vulkan SDK
        id: get_vulkan
        if: ${{ matrix.backend == 'vulkan' }}
@@ -931,7 +923,7 @@ jobs:

      # TODO: these jobs need to use llvm toolchain in order to utilize the ccache
      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.21
+      #  uses: ggml-org/ccache-action@v1.2.24
      #  with:
      #    key: release-windows-2025-${{ matrix.arch }}-${{ matrix.backend }}

@@ -977,6 +969,8 @@ jobs:
          path: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip
          name: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip

+  # note: builds only the ggml-cuda backend - llama-server is injected from the
+  #       windows-cpu zip during the release "Merge artifacts" step
  windows-cuda:
    name: windows-cuda (${{ matrix.cuda }}, ${{ matrix.arch }})
    needs: [check-release]
@@ -1005,13 +999,6 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
-
      - name: Install Cuda Toolkit
        uses: ./.github/actions/windows-setup-cuda
        with:
@@ -1024,7 +1011,7 @@ jobs:
          choco install ninja

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}

@@ -1083,6 +1070,8 @@ jobs:
        with:
          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}

+  # note: builds only the ggml-sycl backend - llama-server is injected from the
+  #       windows-cpu zip during the release "Merge artifacts" step
  windows-sycl:
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -1117,15 +1106,8 @@ jobs:
          Expand-Archive -Path "level-zero-win-sdk.zip" -DestinationPath "C:/level-zero-sdk" -Force
          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
-
      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-windows-2022-x64-sycl

@@ -1194,7 +1176,7 @@ jobs:
          key: release-windows-2022-x64-sycl

  ubuntu-24-sycl:
-    needs: [check-release]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    strategy:
@@ -1236,15 +1218,14 @@ jobs:
          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      - name: Download UI build
+        uses: actions/download-artifact@v7
        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-ubuntu-24.04-sycl-${{ matrix.build }}

@@ -1286,126 +1267,135 @@ jobs:
        with:
          key: release-ubuntu-24.04-sycl-${{ matrix.build }}

-#   ubuntu-22-rocm:
-#     needs: [check-release, get-version]
-#     if: ${{ needs.check-release.outputs.should_release == 'true' }}
+  ubuntu-24-rocm:
+    needs: [check-release, ui-build]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

-#     runs-on: ubuntu-22.04
+    runs-on: ubuntu-24.04

-#     permissions:
-#       actions: write
+    permissions:
+      actions: write

-#     strategy:
-#       matrix:
-#         include:
-#           - ROCM_VERSION: "7.14.0"
-#             gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
-#             build: 'x64'
+    strategy:
+      matrix:
+        include:
+          - ROCM_VERSION: "10.0.0"
+            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
+            build: 'x64'

-#     steps:
-#       - name: Clone
-#         id: checkout
-#         uses: actions/checkout@v6
-#         with:
-#           fetch-depth: 0
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0

-#       - name: Setup Node.js
-#         uses: actions/setup-node@v6
-#         with:
-#           node-version: "24"
-#           cache: "npm"
-#           cache-dependency-path: "tools/ui/package-lock.json"
+      - name: Download UI build
+        uses: actions/download-artifact@v7
+        with:
+          name: llama-ui.zip
+          path: tools/ui/dist

-#       - name: Free up disk space
-#         uses: ggml-org/free-disk-space@v1.3.1
-#         with:
-#           tool-cache: true
+      - name: Free up disk space
+        uses: ggml-org/free-disk-space@v1.3.1
+        with:
+          tool-cache: true

-#       # - name: ccache
-#       #   uses: ggml-org/ccache-action@v1.2.21
-#       #   with:
-#       #     key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.24
+        with:
+          key: release-ubuntu-24.04-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
+          evict-old-files: 1d
+          max-size: "1G"

-#       - name: Dependencies
-#         id: depends
-#         run: |
-#           sudo apt install -y build-essential git cmake wget
+      - name: Tune ccache for reinstalled ROCm toolchain
+        run: |
+          # ROCm is pip-installed fresh each run, so the clang binary's mtime
+          # changes every time. With the default compiler_check=mtime that
+          # invalidates the cache; hash compiler contents instead so warm
+          # builds hit.
+          ccache --set-config=compiler_check=content
+          ccache --set-config=sloppiness=time_macros,include_file_mtime,include_file_ctime

-#       - name: Setup TheRock with Wheels
-#         id: therock_env
-#         run: |
-#           # Create Python virtual environment
-#           python3 -m venv .venv
-#           source .venv/bin/activate
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt install -y build-essential git cmake wget

-#           # Install ROCm wheels for build
-#           # libraries = HIP runtime and CMake configs needed for linking
-#           # devel = compilers, headers, static libs
-#           python -m pip install --upgrade pip
-#           python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"
+      - name: Setup TheRock with Wheels
+        id: therock_env
+        run: |
+          # Create Python virtual environment
+          python3 -m venv .venv
+          source .venv/bin/activate

-#           # Get ROCm installation paths using the rocm-sdk CLI tool
-#           ROCM_PATH=$(rocm-sdk path --root)
-#           CMAKE_PATH=$(rocm-sdk path --cmake)
-#           BIN_PATH=$(rocm-sdk path --bin)
-#           echo "ROCM_PATH=$ROCM_PATH"
-#           echo "CMAKE_PATH=$CMAKE_PATH"
-#           echo "BIN_PATH=$BIN_PATH"
+          # Install ROCm wheels for build
+          # libraries = HIP runtime and CMake configs needed for linking
+          # devel = compilers, headers, static libs
+          python -m pip install --upgrade pip
+          python -m pip install --index-url https://stable.repo.amd.com/rocm/whl-next/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"

-#           # Set environment variables
-#           echo "ROCM_PATH=$ROCM_PATH" >> $GITHUB_ENV
-#           echo "CMAKE_PREFIX_PATH=$CMAKE_PATH" >> $GITHUB_ENV
-#           echo "HIP_PATH=$ROCM_PATH" >> $GITHUB_ENV
-#           echo "PATH=$BIN_PATH:${PATH}" >> $GITHUB_ENV
-#           echo "LD_LIBRARY_PATH=$ROCM_PATH/lib:${LD_LIBRARY_PATH:-}" >> $GITHUB_ENV
+          # Get ROCm installation paths using the rocm-sdk CLI tool
+          ROCM_PATH=$(rocm-sdk path --root)
+          CMAKE_PATH=$(rocm-sdk path --cmake)
+          BIN_PATH=$(rocm-sdk path --bin)
+          echo "ROCM_PATH=$ROCM_PATH"
+          echo "CMAKE_PATH=$CMAKE_PATH"
+          echo "BIN_PATH=$BIN_PATH"

-#           # Keep venv activated for subsequent steps
-#           echo "$(pwd)/.venv/bin" >> $GITHUB_PATH
+          # Set environment variables
+          echo "ROCM_PATH=$ROCM_PATH" >> $GITHUB_ENV
+          echo "CMAKE_PREFIX_PATH=$CMAKE_PATH" >> $GITHUB_ENV
+          echo "HIP_PATH=$ROCM_PATH" >> $GITHUB_ENV
+          echo "PATH=$BIN_PATH:${PATH}" >> $GITHUB_ENV
+          echo "LD_LIBRARY_PATH=$ROCM_PATH/lib:${LD_LIBRARY_PATH:-}" >> $GITHUB_ENV

-#       - name: Build with native CMake HIP support
-#         id: cmake_build
-#         run: |
-#           cmake -B build -S . \
-#             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-#             -DCMAKE_BUILD_TYPE=Release \
-#             -DGGML_BACKEND_DL=ON \
-#             -DGGML_NATIVE=OFF \
-#             -DCMAKE_INSTALL_RPATH='$ORIGIN' \
-#             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
-#             -DGGML_CPU_ALL_VARIANTS=ON \
-#             -DGPU_TARGETS="${{ matrix.gpu_targets }}" \
-#             -DGGML_HIP=ON \
-#             -DHIP_PLATFORM=amd \
-#             -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
-#             ${{ env.CMAKE_ARGS }}
-#           cmake --build build --config Release -j $(nproc)
+          # Keep venv activated for subsequent steps
+          echo "$(pwd)/.venv/bin" >> $GITHUB_PATH

-#       - name: Determine tag name
-#         id: tag
-#         uses: ./.github/actions/get-tag-name
+      - name: Build with native CMake HIP support
+        id: cmake_build
+        run: |
+          cmake -B build -S . \
+            -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_BACKEND_DL=ON \
+            -DGGML_NATIVE=OFF \
+            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DGGML_CPU_ALL_VARIANTS=ON \
+            -DGPU_TARGETS="${{ matrix.gpu_targets }}" \
+            -DGGML_HIP=ON \
+            -DHIP_PLATFORM=amd \
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build --config Release -j $(nproc)

-#       - name: Get ROCm short version
-#         run: echo "ROCM_VERSION_SHORT=$(echo '${{ matrix.ROCM_VERSION }}' | cut -d '.' -f 1,2)" >> $GITHUB_ENV
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name

-#       - name: Pack artifacts
-#         id: pack_artifacts
-#         run: |
-#           cp LICENSE ./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: Get ROCm short version
+        run: echo "ROCM_VERSION_SHORT=$(echo '${{ matrix.ROCM_VERSION }}' | cut -d '.' -f 1,2)" >> $GITHUB_ENV

-#       - name: Upload artifacts
-#         uses: actions/upload-artifact@v6
-#         with:
-#           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 }}-${{ matrix.build }}.tar.gz
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          tar -czvf llama-${{ 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: ccache-clear
-#       #   uses: ./.github/actions/ccache-clear
-#       #   with:
-#       #     key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v6
+        with:
+          path: llama-${{ 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
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-ubuntu-24.04-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}

  ios-xcode:
-    needs: [check-release, get-version]
+    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
    runs-on: macos-26

@@ -1433,8 +1423,7 @@ jobs:
            -DLLAMA_BUILD_SERVER=OFF \
            -DCMAKE_SYSTEM_NAME=iOS \
            -DCMAKE_OSX_DEPLOYMENT_TARGET=16.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }}
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO

      - name: xcodebuild for swift package
@@ -1557,11 +1546,9 @@ jobs:
 #          name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz

  ui-build:
-    needs: [check-release, get-version]
+    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
    uses: ./.github/workflows/ui-build.yml
-    with:
-      hf_ui_version: ${{ needs.get-version.outputs.ui_version }}

  release:
    if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -1576,14 +1563,13 @@ jobs:
    runs-on: ubuntu-slim

    needs:
-      - get-version
      - windows
      - windows-cpu
      - windows-cuda
      - windows-sycl
      - windows-rocm
      - windows-openvino
-      #- ubuntu-22-rocm
+      - ubuntu-24-rocm
      - ubuntu-cpu
      - ubuntu-vulkan
      - ubuntu-24-openvino
@@ -1616,24 +1602,27 @@ jobs:
          path: ./artifact
          merge-multiple: true

-      - name: Move artifacts
+      - name: Merge artifacts
        id: move_artifacts
        run: |
          mkdir -p release

-          echo "Adding CPU backend files to existing zips..."
+          # the windows-cpu zip contains the full toolset (llama-server with the embedded
+          # UI, ggml-cpu) - inject it into the other windows zips so that every archive
+          # ships the same binaries, only with a different backend library on top
+          echo "Injecting windows-cpu binaries (llama-server + CPU backend) into the backend zips..."
          for arch in x64 arm64; do
            cpu_zip="artifact/llama-bin-win-cpu-${arch}.zip"
            temp_dir=$(mktemp -d)
-            echo "Extracting CPU backend for $arch..."
+            echo "Extracting windows-cpu-${arch} package..."
            unzip "$cpu_zip" -d "$temp_dir"

-            echo "Adding CPU files to $arch zips..."
+            echo "Merging into $arch zips..."
            for target_zip in artifact/llama-bin-win-*-${arch}.zip; do
              if [[ "$target_zip" == "$cpu_zip" ]]; then
                continue
              fi
-              echo "Adding CPU backend to $(basename "$target_zip")"
+              echo "Injecting into $(basename "$target_zip")"
              realpath_target_zip=$(realpath "$target_zip")
              (cd "$temp_dir" && zip -r "$realpath_target_zip" .)
            done
@@ -1657,7 +1646,7 @@ jobs:
        id: download_ui
        uses: actions/download-artifact@v7
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: ./ui-dist

      - name: Package UI
@@ -1714,7 +1703,7 @@ jobs:
            - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz)
            - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
            - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
-            - Ubuntu x64 (ROCm 7.14)[DISABLED](https://github.com/ggml-org/llama.cpp/pull/26969)
+            - [Ubuntu x64 (ROCm 10.0)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-10.0-x64.tar.gz)
            - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ needs.ubuntu-24-openvino.outputs.openvino_version }}-x64.tar.gz)
            - [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp32-x64.tar.gz)
            - [Ubuntu x64 (SYCL FP16)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp16-x64.tar.gz)
@@ -1732,7 +1721,7 @@ jobs:
            - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
            - [Windows x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ needs.windows-openvino.outputs.openvino_version }}-x64.zip)
            - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
-            - [Windows x64 (ROCm 7.14)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-rocm-7.14-x64.zip)
+            - [Windows x64 (ROCm 10.0)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-rocm-10.0-x64.zip)

            **openEuler:**
            - [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23705)
@@ -73,13 +73,6 @@ jobs:
          fetch-depth: 0
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
-
      - name: Build
        id: cmake_build
        run: |
@@ -110,7 +103,7 @@ jobs:
          source .venv/bin/activate
          cd tools/server/tests
          export ${{ matrix.extra_args }}
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

      - name: Slow tests
        id: server_integration_tests_slow
@@ -119,4 +112,4 @@ jobs:
          source .venv/bin/activate
          cd tools/server/tests
          export ${{ matrix.extra_args }}
-          SLOW_TESTS=1 ./tests.sh
+          PYTEST_WORKERS=1 SLOW_TESTS=1 ./tests.sh
@@ -72,7 +72,7 @@ jobs:
        run: |
          cd tools/server/tests
          source venv/bin/activate
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

      - name: Tests (GPUx1, backend-sampling)
        id: server_integration_tests_backend_sampling
@@ -81,7 +81,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export LLAMA_ARG_BACKEND_SAMPLING=1
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

      - name: Tests (GPUx2)
        id: server_integration_tests_gpu2
@@ -90,7 +90,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export GGML_METAL_DEVICES=2
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

      - name: Tests (GPUx2, backend-sampling)
        id: server_integration_tests_gpu2_backend_sampling
@@ -99,10 +99,10 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export GGML_METAL_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

  server-cuda:
-    runs-on: [self-hosted, llama-server, Linux, NVIDIA]
+    runs-on: "hf-jobs-t4-small:cuda13"

    steps:
      - name: Clone
@@ -112,12 +112,42 @@ jobs:
          fetch-depth: 0
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y cmake libssl-dev python3 python3-venv python3-pip
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.24
+        with:
+          restore: false
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        with:
+          key: self-hosted-server-cuda
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache
+
      - name: Build
        id: cmake_build
        run: |
-          cmake -B build -DGGML_CUDA=ON -DGGML_SCHED_NO_REALLOC=ON
+          cmake -B build -DGGML_CUDA=ON -DGGML_SCHED_NO_REALLOC=ON -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc
          cmake --build build --config Release -j $(nproc) --target llama-server

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: self-hosted-server-cuda
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
      - name: Python setup
        id: setup_python
        run: |
@@ -132,7 +162,7 @@ jobs:
        run: |
          cd tools/server/tests
          source venv/bin/activate
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

      - name: Tests (GPUx1, backend-sampling)
        id: server_integration_tests_backend_sampling
@@ -141,7 +171,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export LLAMA_ARG_BACKEND_SAMPLING=1
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

      - name: Tests (GPUx2)
        id: server_integration_tests_gpu2
@@ -150,7 +180,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export GGML_CUDA_DEVICES=2
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

      - name: Tests (GPUx2, backend-sampling)
        id: server_integration_tests_gpu2_backend_sampling
@@ -159,7 +189,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export GGML_CUDA_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1
-          ./tests.sh
+          PYTEST_WORKERS=1 ./tests.sh

  server-kleidiai:
    runs-on: ah-ubuntu_22_04-c8g_8x
@@ -80,11 +80,19 @@ jobs:
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: server-ubuntu-24.04-arm
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+        with:
+          key: server-ubuntu-24.04-arm
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache

      - name: Build
        id: cmake_build
@@ -93,6 +101,18 @@ jobs:
            -DGGML_SCHED_NO_REALLOC=ON
          cmake --build build --config Release -j $(nproc) --target llama-server

+      - name: ccache-buckets-save
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+        uses: ./.github/actions/ccache-buckets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: server-ubuntu-24.04-arm
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
      - name: Python setup
        id: setup_python
        uses: actions/setup-python@v6
@@ -140,7 +160,7 @@ jobs:
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: server-windows-2025-x64
          evict-old-files: 1d
@@ -181,3 +201,13 @@ jobs:
          cd tools/server/tests
          export SLOW_TESTS="1"
          ./tests.sh
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: server-windows-2025-x64
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -17,8 +17,9 @@ jobs:
        uses: actions/setup-node@v6
        with:
          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          # cache: "npm"
+          # cache-dependency-path: "tools/ui/package-lock.json"
+          package-manager-cache: false

      - name: Install dependencies
        run: npm ci
@@ -31,6 +32,6 @@ jobs:
      - name: Upload built UI
        uses: actions/upload-artifact@v6
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: tools/ui/dist/
          retention-days: 1
@@ -3,8 +3,8 @@ name: UI Build
 on:
  workflow_call:
    inputs:
-      hf_ui_version:
-        description: 'Version string for version.json (e.g. 12345)'
+      ui_version:
+        description: 'Version string embedded in build.json (e.g. b1234); defaults to b<commit-count>'
        required: false
        type: string

@@ -17,13 +17,25 @@ jobs:
    steps:
      - name: Checkout code
        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: Resolve UI version
+        id: version
+        run: |
+          version="${{ inputs.ui_version }}"
+          if [ -z "$version" ]; then
+            version="b$(git rev-list --count HEAD)"
+          fi
+          echo "ui_version=${version}" >> $GITHUB_OUTPUT

      - name: Setup Node.js
        uses: actions/setup-node@v6
        with:
          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          # cache: "npm"
+          # cache-dependency-path: "tools/ui/package-lock.json"
+          package-manager-cache: false

      - name: Install dependencies
        run: npm ci
@@ -31,8 +43,7 @@ jobs:

      - name: Build application
        env:
-          HF_UI_VERSION: ${{ inputs.hf_ui_version || '' }}
-          LLAMA_BUILD_NUMBER: ${{ inputs.hf_ui_version || 'b0000' }}
+          LLAMA_BUILD_NUMBER: ${{ steps.version.outputs.ui_version }}
        run: npm run build
        working-directory: tools/ui

@@ -43,6 +54,6 @@ jobs:
      - name: Upload built UI
        uses: actions/upload-artifact@v6
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: tools/ui/dist/
          retention-days: 1
@@ -37,7 +37,7 @@ jobs:
      - name: Download UI build artifact
        uses: actions/download-artifact@v7
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: tools/ui/dist/

      - name: Create distribution archive
@@ -64,7 +64,7 @@ jobs:
      - name: Download built UI artifacts
        uses: actions/download-artifact@v6
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: tools/ui/dist/

      - name: Run type checking
@@ -106,7 +106,7 @@ jobs:
      - name: Download built UI artifacts
        uses: actions/download-artifact@v6
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: tools/ui/dist/

      - name: Build Storybook
@@ -57,13 +57,14 @@ jobs:
        uses: actions/setup-node@v6
        with:
          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          # cache: "npm"
+          # cache-dependency-path: "tools/ui/package-lock.json"
+          package-manager-cache: false

      - name: Download built UI artifacts
        uses: actions/download-artifact@v6
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: tools/ui/dist/

      - name: Install dependencies
@@ -114,8 +115,9 @@ jobs:
        uses: actions/setup-node@v6
        with:
          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+          # cache: "npm"
+          # cache-dependency-path: "tools/ui/package-lock.json"
+          package-manager-cache: false

      - name: Install dependencies
        id: setup
@@ -126,7 +128,7 @@ jobs:
      - name: Download built UI artifacts (reuses ui-build)
        uses: actions/download-artifact@v6
        with:
-          name: ui-build
+          name: llama-ui.zip
          path: tools/ui/dist/

      - name: Install Playwright browsers
@@ -84,6 +84,7 @@ These points are extremely important - failing to follow them won't necessarily
 Common mistakes that AI agents usually make:
 - Write comments first then write code: this usually leads to extensive redundant comments. Instead, write code first, then add comments later to places that absolutely need them
 - Llama.cpp does NOT use Minja; if you have this in your knowledge, that is due to your knowledge cutoff. Llama.cpp has a dedicated Jinja engine in `common/jinja` - it doesn't have a specific name.
+- Do NOT add a new file in `tests/*` without maintainers' approval. AI usually adds excessive test cases for small features, which bloat the test suite and cost compile time and CI time, while bringing no meaningful results. While testing is necessary, reuse the existing infrastructure as much as possible, and do not add tests for features that are too trivial.

 ### Prohibited Actions

@@ -1,4 +1,4 @@
-# date: Tue Aug 18 14:32:43 EEST 2026
+# date: Fri Sep  4 10:06:46 EEST 2026
 # this file is auto-generated by scripts/gen-authors.sh

 Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>
@@ -46,6 +46,7 @@ Abhijit Ramesh <abhijitramesh2k@gmail.com>
 abhijitb11 <113058133+abhijitb11@users.noreply.github.com>
 Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
 Abhinay Krishna <abhinaykrishna60@gmail.com>
+Abhiram <78226909+geckguy@users.noreply.github.com>
 Abhishek Gopinath K <31348521+overtunned@users.noreply.github.com>
 abotsis <github@bots.is>
 Abraham Gonzalez <theabecaster0@gmail.com>
@@ -87,6 +88,7 @@ akleine <alb.kleine@gmx.de>
 Al G <toasting@gmail.com>
 Al Mochkin <14274697+amochkin@users.noreply.github.com>
 Alan Gray <agray3@users.noreply.github.com>
+Alan Tseng <alanhc.tseng1999@gmail.com>
 Alawode Oluwandabira <dabiraalawode@yahoo.com>
 Albert Jin <albert.jin@gmail.com>
 Alberto <57916483+albbus-stack@users.noreply.github.com>
@@ -136,7 +138,9 @@ alonfaraj <alonfaraj@gmail.com>
 AlpinDale <52078762+AlpinDale@users.noreply.github.com>
 alwqx <kenan3015@gmail.com>
 Aman <amangupta052@gmail.com>
+Aman Chadha(IVIXMMI) <79802170+ac-mmi@users.noreply.github.com>
 Aman Gupta <amangupta052@gmail.com>
+Aman Karki <itsamankarki@gmail.com>
 amd-dwang <dong.wang@amd.com>
 amd-lalithnc <lalithnc@amd.com>
 Amir <amir_zia@outlook.com>
@@ -187,6 +191,7 @@ Anton Mitkov <anton.mitkov@codeplay.com>
 Antonis Makropoulos <benuix@gmail.com>
 Anudit Nagar <nagaranudit@gmail.com>
 Anuj Attri <anujattri01@gmail.com>
+anujj <ajalota@nvidia.com>
 anzz1 <anzz1@live.com>
 Aparna M P <aparmp@qti.qualcomm.com>
 Aparna M P <quic_aparmp@quicinc.com>
@@ -196,6 +201,7 @@ arch-btw <57669023+arch-btw@users.noreply.github.com>
 arcrank <arcrank@gmail.com>
 ardfork <134447697+ardfork@users.noreply.github.com>
 Arik Poznanski <arikpoz@users.noreply.github.com>
+Aritro Bandyopadhyay <71339004+AriBandyo@users.noreply.github.com>
 arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com>
 Armen Kaleshian <kriation@users.noreply.github.com>
 Arsen Arutunan <58118221+limloop@users.noreply.github.com>
@@ -230,6 +236,7 @@ bandoti <141645996+bandoti@users.noreply.github.com>
 Bar Haim <barvhaim@gmail.com>
 BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com>
 Bart Louwers <bart.louwers@gmail.com>
+Bartosz Taudul <wolf@nereid.pl>
 Bartowski <3266127+bartowski1182@users.noreply.github.com>
 Bartowski <ckealty1182@gmail.com>
 Bas Nijholt <basnijholt@gmail.com>
@@ -277,6 +284,7 @@ Bono Lv <lvscar@users.noreply.github.com>
 Borislav Stanimirov <b.stanimirov@abv.bg>
 Borislav Stanimirov <b@ibob.bg>
 Bowen Han <fancycode@gmail.com>
+Brad Smith <1472326+infinitewarp@users.noreply.github.com>
 Branden Butler <bwtbutler@hotmail.com>
 Brandon Squizzato <35474886+bsquizz@users.noreply.github.com>
 Brian <mofosyne@gmail.com>
@@ -287,6 +295,7 @@ Bryan Honof <bryanhonof@gmail.com>
 bryanSwk <93190252+bryanSwk@users.noreply.github.com>
 bsilvereagle <bsilvereagle@users.noreply.github.com>
 bssrdf <merlintiger@hotmail.com>
+Buğra Özgürsoy <13810383+ozgursoy@users.noreply.github.com>
 byte-6174 <88070277+byte-6174@users.noreply.github.com>
 Caleb DeLeeuw <143902425+SolshineCode@users.noreply.github.com>
 Calvin Laurenson <calvin@laurenson.dev>
@@ -326,6 +335,7 @@ Chenguang Li <757486878@qq.com>
 Chenguang Li <87689256+noemotiovon@users.noreply.github.com>
 Chipmunk <101038159+CHIPMUNK-T0T@users.noreply.github.com>
 chiranko <96988916+chiranko@users.noreply.github.com>
+Chris Danis <cdanis@gmail.com>
 Chris Elrod <elrodc@gmail.com>
 Chris Kuehl <ckuehl@ckuehl.me>
 Chris Lee <clee@mg8.org>
@@ -356,6 +366,7 @@ clyang <clyang@clyang.net>
 cmdr2 <secondary.cmdr2@gmail.com>
 cmdr2 <shashank.shekhar.global@gmail.com>
 cocktailpeanut <121128867+cocktailpeanut@users.noreply.github.com>
+codemonkey <441345965@qq.com>
 codezjx <code.zjx@gmail.com>
 coezbek <c.oezbek@gmail.com>
 comex <comexk@gmail.com>
@@ -367,6 +378,8 @@ Copilot <198982749+Copilot@users.noreply.github.com>
 Corentin REGAL <corentin.regal@gmail.com>
 cphlipot <9103367+cphlipot@users.noreply.github.com>
 cpumaxx <163466046+cpumaxx@users.noreply.github.com>
+cqderek <cqderek@gmail.com>
+cqderek <cqiang@qti.qualcomm.com>
 crasm <crasm@git.vczf.net>
 crasm <crasm@git.vczf.us>
 crat0z <11581854+crat0z@users.noreply.github.com>
@@ -427,6 +440,7 @@ DavidKorczynski <david@adalogics.com>
 davidrhodus <david@vacovideo.com>
 Dawid Potocki <github@dawidpotocki.com>
 Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com>
+Daya Adianto <addianto@users.noreply.github.com>
 ddh0 <chemist-mulches-39@icloud.com>
 ddh0 <dylanhalladay02@icloud.com>
 ddpasa <112642920+ddpasa@users.noreply.github.com>
@@ -463,6 +477,7 @@ Dmytro Romanov <casteldazur@gmail.com>
 Dobri Danchev <12420863+danchev@users.noreply.github.com>
 DocShotgun <126566557+DocShotgun@users.noreply.github.com>
 Doctor Shotgun <126566557+DocShotgun@users.noreply.github.com>
+Dominik Pantaleoni <95251853+dpantaleoni@users.noreply.github.com>
 Don Mahurin <dmahurin@users.noreply.github.com>
 Dong Won Kim <63934649+ddwkim@users.noreply.github.com>
 Donghyeon Jeong <54725479+djeong20@users.noreply.github.com>
@@ -504,6 +519,7 @@ Emmanuel Ferdman <emmanuelferdman@gmail.com>
 Emreerdog <34742675+Emreerdog@users.noreply.github.com>
 Engininja2 <139037756+Engininja2@users.noreply.github.com>
 Equim <sayaka@ekyu.moe>
+Eric A Stalee <87948564+Eric-A-Stalee@users.noreply.github.com>
 Eric Curtin <ecurtin@redhat.com>
 Eric Curtin <eric.curtin@docker.com>
 Eric Curtin <ericcurtin17@gmail.com>
@@ -519,6 +535,7 @@ Esko Toivonen <eskot98@gmail.com>
 Ethan Turner <eturner64@gmail.com>
 Ettore Di Giacinto <mudler@users.noreply.github.com>
 EugeoSynthesisThirtyTwo <gabriel.dhimoila@gmail.com>
+Eurekatic <eurekatic@eurekatic.eu>
 Evan Huus <eapache@gmail.com>
 Evan Jones <evan.q.jones@gmail.com>
 Evan Miller <emmiller@gmail.com>
@@ -677,6 +694,7 @@ HimariO <dsfhe49854@gmail.com>
 hipudding <huafengchun@gmail.com>
 Hitesh Chopra <34310832+hiteshchopra11@users.noreply.github.com>
 hksdpc255 <43977088+hksdpc255@users.noreply.github.com>
+hmirin <hmirin@users.noreply.github.com>
 hmscider <201289679+hmscider@users.noreply.github.com>
 Hoang Nguyen <hugo53@users.noreply.github.com>
 hoangmit <hoangmit@users.noreply.github.com>
@@ -701,6 +719,7 @@ Huawei Lin <huaweilin.cs@gmail.com>
 Hugo <hugo@whynothugo.nl>
 Hugo Roussel <hugo.rous@gmail.com>
 Huifeng Ou <79071290+ho2103@users.noreply.github.com>
+HumerousGorgon <31957201+HumerousGorgon@users.noreply.github.com>
 hutli <6594598+hutli@users.noreply.github.com>
 hutli <hutli@hutli.hu>
 hutli <jensstaermose@hotmail.com>
@@ -738,12 +757,15 @@ intelmatt <61025942+intelmatt@users.noreply.github.com>
 iohub <rickyang.pro@gmail.com>
 Ionoclast Laboratories <brigham@ionoclast.com>
 iron <lizhenneng@gmail.com>
+Isaac <34376531+init-22@users.noreply.github.com>
 Isaac McFadyen <isaac@imcf.me>
 IsaacDynamo <61521674+IsaacDynamo@users.noreply.github.com>
 Ishaan Gandhi <Ishaangandhi@gmail.com>
 iSma <ismail.senhaji@gmail.com>
 Ismail <115064057+AlrIsmail@users.noreply.github.com>
 issixx <46835150+issixx@users.noreply.github.com>
+itsnotoger <19309683+itsnotoger@users.noreply.github.com>
+itterative <190138728+itterative@users.noreply.github.com>
 Ivan <nekotekina@gmail.com>
 Ivan Chikish <nekotekina@gmail.com>
 Ivan Filipov <159561759+vanaka11@users.noreply.github.com>
@@ -768,6 +790,7 @@ Jakkala Mahesh <155058658+MaheshJakkala@users.noreply.github.com>
 Jakub N <jakubniemczyk97@gmail.com>
 JamePeng <jame_peng@sina.com>
 James A Capozzoli <157492257+jac-jim@users.noreply.github.com>
+James Francis <6763899+JamesFranc@users.noreply.github.com>
 James O'Leary <65884233+jpohhhh@users.noreply.github.com>
 James Reynolds <magnusviri@users.noreply.github.com>
 jameswu2014 <545426914@qq.com>
@@ -798,6 +821,7 @@ Jed Fox <git@jedfox.com>
 Jeff Bolz <jbolz@nvidia.com>
 Jeffrey Morgan <jmorganca@gmail.com>
 Jeffrey Quesnelle <emozilla@nousresearch.com>
+Jeremie Miller <jeremie.miller@gmail.com>
 Jeremy Demeule <jdemeule@users.noreply.github.com>
 Jeremy Rand <244188+JeremyRand@users.noreply.github.com>
 Jeroen Mostert <jeroen.mostert@cm.com>
@@ -809,6 +833,7 @@ Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
 Jesse LaRose <jesse@taey.ai>
 Jesse Posner <jesse.posner@gmail.com>
 Jesus Talavera <145992175+jesus-talavera-ibm@users.noreply.github.com>
+Jetson Tan <tanzongyouyi@outlook.com>
 Jett Janiak <jettjaniak@gmail.com>
 Jeximo <jeximo@gmail.com>
 JFLFY2255 <JFLFY2255@163.com>
@@ -825,6 +850,7 @@ Jie Fu (傅杰) <jiefu@tencent.com>
 jiez <373447296@qq.com>
 Jillis ter Hove <j.terhove@gmail.com>
 Jim Wu <jimw567@users.noreply.github.com>
+Jingxin (Philip) Li <philipaslee@gmail.com>
 Jinwoo Jeong <33892306+williamjeong2@users.noreply.github.com>
 Jinyang He <hejinyang@loongson.cn>
 jinzihao <jinzihao1996@gmail.com>
@@ -850,11 +876,13 @@ John Balis <phobossystems@gmail.com>
 John Bean <113509988+johnbean393@users.noreply.github.com>
 John Eismeier <42679190+jeis4wpi@users.noreply.github.com>
 John Smith <67539080+kingsidelee@users.noreply.github.com>
+John-Henry Lim <42513874+Interpause@users.noreply.github.com>
 Johnathan Craig Maudlin <13183098+jcmdln@users.noreply.github.com>
 JohnnyB <jboero@users.noreply.github.com>
 johnson442 <56517414+johnson442@users.noreply.github.com>
 jojorne <jojorne@users.noreply.github.com>
 jon-chuang <9093549+jon-chuang@users.noreply.github.com>
+Jonas J <111707981+John-194@users.noreply.github.com>
 Jonas Jankaitis <111707981+John-194@users.noreply.github.com>
 Jonas Wunderlich <32615971+jonas-w@users.noreply.github.com>
 Jonathan <47618606+jbuchananr@users.noreply.github.com>
@@ -924,6 +952,7 @@ Karsten Weiss <knweiss@gmail.com>
 Karthick <j.karthic2004@gmail.com>
 Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com>
 Karthik Sethuraman <k.seth1993@gmail.com>
+Kartik Gulia <kgulia@nvidia.com>
 Kartik Sirohi <99896785+sirohikartik@users.noreply.github.com>
 Kashif Rasul <kashif.rasul@gmail.com>
 KASR <karim.asrih@gmail.com>
@@ -931,6 +960,7 @@ Kasumi <90275229+kasumi-1@users.noreply.github.com>
 Katostrofik <georgiopapairo@gmail.com>
 katsu560 <118887472+katsu560@users.noreply.github.com>
 Kawrakow <48489457+ikawrakow@users.noreply.github.com>
+kbenkhaled <khalilbenkhaled01@gmail.com>
 kchro3 <62481661+kchro3@users.noreply.github.com>
 kdkd <2569413+kdkd@users.noreply.github.com>
 Keiichi Tabata <keiichi.tabata@outlook.com>
@@ -939,6 +969,7 @@ Kenvix ⭐ <kenvixzure@live.com>
 Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
 Kevin Gibbons <bakkot@gmail.com>
 Kevin Hannon <kehannon@redhat.com>
+Kevin Hopper <93635715+kh0pper@users.noreply.github.com>
 Kevin Ji <1146876+kevinji@users.noreply.github.com>
 Kevin Kwok <antimatter15@gmail.com>
 Kevin Liu <4396kevinliu@gmail.com>
@@ -964,12 +995,14 @@ Konstantin Herud <konstantin.herud@denkbares.com>
 Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
 Krishna Sridhar <99914379+srikris-sridhar@users.noreply.github.com>
 krystiancha <krystian@krystianch.com>
+krzsztf <krzysztof@witkowscy.org>
 kubawoo <k-wach@o2.pl>
 kumaal <44551860+kumaal@users.noreply.github.com>
 kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
 kunnis <kunnis@users.noreply.github.com>
 Kunshang Ji <kunshang.ji@intel.com>
 kuronekosaiko <EvanChanJ@163.com>
+kurquhar <kurquhar@qti.qualcomm.com>
 Kusha Gharahi <3326002+kushagharahi@users.noreply.github.com>
 kustaaya <58045274+kustaaya@users.noreply.github.com>
 kuvaus <22169537+kuvaus@users.noreply.github.com>
@@ -981,6 +1014,7 @@ Kyle Liang <liangmanlai@gmail.com>
 Kyle Mistele <kyle@mistele.com>
 KyleHagy <59183061+KyleHagy@users.noreply.github.com>
 Kylin <56434533+KyL0N@users.noreply.github.com>
+Kyozzz <1147385157@qq.com>
 l-austenfeld <53152202+l-austenfeld@users.noreply.github.com>
 l3utterfly <gc.pthzfoldr@gmail.com>
 l8bloom <l8bloomapi@gmail.com>
@@ -992,6 +1026,7 @@ Lars Sonchocky-Helldorf <lars.sonchocky-helldorf@hamburg.de>
 las7 <98077186+las7@users.noreply.github.com>
 Lasse Lauwerys <65569591+Iemand005@users.noreply.github.com>
 Laura <Tijntje_7@msn.com>
+Laurent Zuijdwijk <laurent.zuijdwijk@gmail.com>
 Law Po Ying <30721578+yingying0906@users.noreply.github.com>
 lcy <lcy0321@users.noreply.github.com>
 ldwang <ftgreat@163.com>
@@ -1039,6 +1074,8 @@ Ludovic Henry <git@ludovic.dev>
 Ludovic Henry <ludovic@rivosinc.com>
 Lukas Straub <lukasstraub2@web.de>
 Łukasz Ślusarczyk <112692748+lslusarczyk@users.noreply.github.com>
+Lukasz Stolcman <4583553+lstolcman@users.noreply.github.com>
+LunalFresh <165352784+LunalFresh@users.noreply.github.com>
 Luo Tian <lt@basecity.com>
 luoyu-intel <yu.luo@intel.com>
 luyhcsu <110711054+luyhcsu@users.noreply.github.com>
@@ -1054,6 +1091,7 @@ Maarten ter Huurne <maarten@treewalker.org>
 Maciej Lisowski <39798354+MaciejDromin@users.noreply.github.com>
 Mack Straight <eiz@users.noreply.github.com>
 maddes8cht <55592906+maddes8cht@users.noreply.github.com>
+Mads Marquart <mads@marquart.dk>
 Maël Kerbiriou <m431.kerbiriou@gmail.com>
 MaggotHATE <clay1326@gmail.com>
 MagicExists <106458387+gugugiyu@users.noreply.github.com>
@@ -1215,6 +1253,8 @@ Naco Siren <naco-siren@users.noreply.github.com>
 Nam D. Tran <42194884+namtranase@users.noreply.github.com>
 nanahi <130121847+na-na-hi@users.noreply.github.com>
 Nathan Epstein <nate2@umbc.edu>
+Nathan Wilson <67372905+Nathanw1014@users.noreply.github.com>
+Nathanw1014 <67372905+Nathanw1014@users.noreply.github.com>
 Natsu <chino@hotococoa.moe>
 Nauful Shaikh <nauful@gmail.com>
 NawafAlansari <72708095+NawafAlansari@users.noreply.github.com>
@@ -1237,6 +1277,7 @@ niansa/tuxifan <tuxifan@posteo.de>
 Nicholai Tukanov <nicholaitukanov@gmail.com>
 Nicholas Sparks <157740354+nisparks@users.noreply.github.com>
 Nick <0x0b4ac@gmail.com>
+Nick Farrell <nick.farrell@aiven.io>
 nick huang <nickhuang99@hotmail.com>
 Nick Lafleur <55208706+nicklafleur@users.noreply.github.com>
 Nick Towle <ntowle@gmail.com>
@@ -1259,6 +1300,7 @@ NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com>
 Nikolaos Pothitos <pothitos@di.uoa.gr>
 Nikolas <127742645+nneubacher@users.noreply.github.com>
 Nikolay Popov <131475237+npopov-vst@users.noreply.github.com>
+Nils Gladitz <nilsgladitz@gmail.com>
 Nindaleth <Nindaleth@users.noreply.github.com>
 ningshanwutuobang <ningshanwutuobang@gmail.com>
 Noah <99681487+NoahOksuz@users.noreply.github.com>
@@ -1355,6 +1397,7 @@ Pop Flamingo <trevor.annedenise@icloud.com>
 postmasters <namnguyen@google.com>
 Pouya <PooyaGhahramanian@Gmail.com>
 pqnet <119850+pqnet@users.noreply.github.com>
+Prabhsimran Singh <pskrunner14@gmail.com>
 Prabod <prabod@maincode.com>
 Prajwal B Mehendarkar <prajwal.b.mehendarkar@ibm.com>
 Pranav Dhinakar <pdhinaka@qti.qualcomm.com>
@@ -1378,6 +1421,7 @@ qouoq <qouoq@fastmail.com>
 Qu Zongfu <43257352+yancaoweidaode@users.noreply.github.com>
 quei <56998528+quei4r@users.noreply.github.com>
 Quentin Bramas <quentin.bramas@gmail.com>
+QuintinShaw <github@xyt.email>
 QuintinShaw <yx6f20@soton.ac.uk>
 qunash <anzoria@gmail.com>
 quyentonndbs <raynaedgar8677@outlook.com>
@@ -1462,6 +1506,7 @@ robertomeroni <150194833+robertomeroni@users.noreply.github.com>
 Robey Holderith <robey@flaminglunchbox.net>
 Robin Davidsson <40024429+R-Dson@users.noreply.github.com>
 Robyn <robyngraf@users.noreply.github.com>
+Rock Chen <rockchen.tw@gmail.com>
 Rőczey Barnabás <31726601+An0nie@users.noreply.github.com>
 RodriMora <bullerwins@gmail.com>
 Roger Chen <chenrui@gmail.com>
@@ -1499,17 +1544,21 @@ runfuture <runfuture@users.noreply.github.com>
 RunningLeon <maningsheng@sensetime.com>
 RunningLeon <mnsheng@yeah.net>
 Russyyds <161207317+Russyyds@users.noreply.github.com>
+Ryan C <ryan5rdx@users.noreply.github.com>
 Ryan Goulden <percontation@gmail.com>
 Ryan Landay <rlanday@gmail.com>
 Ryan Mangeno <160974989+ryan-mangeno@users.noreply.github.com>
 Ryder Wishart <ryderwishart@gmail.com>
 Ryuei <louixs@users.noreply.github.com>
 s-goto-11 <206795233+s-goto-11@users.noreply.github.com>
+s0mecode <213953308+s0mecode@users.noreply.github.com>
 s8322 <s0527684199@gmail.com>
+Saad Ali <NIXKnight@users.noreply.github.com>
 Saba Fallah <10401143+sfallah@users.noreply.github.com>
 Saba Fallah <sabafallah@gmail.com>
 Sachin Desai <smdesai@gmail.com>
 Sachin Sharma <sachin@zettabolt.com>
+Safi Ullah <safiullah.3915@gmail.com>
 safranowith <bsh155762@gmail.com>
 SakuraUmi <yukinon244@gmail.com>
 Salvador E. Tropea <stropea@inti.gob.ar>
@@ -1552,6 +1601,7 @@ Sergey Alirzaev <l29ah@riseup.net>
 Sergey Alirzaev <zl29ah@gmail.com>
 Sergey Fedorov <vital.had@gmail.com>
 Sergey Malinin <sergmalinin@gmail.com>
+Sergey Sklyarov <sergey.sklyarov@gmail.com>
 Sergio López <slp@redhat.com>
 Sergio López <slp@sinrega.org>
 Sergiu <8598216+mzsergiu@users.noreply.github.com>
@@ -1582,11 +1632,13 @@ Shawn Gu <shawngu@qti.qualcomm.com>
 Shawn yang <137684499+Yangxiaoz@users.noreply.github.com>
 Shelby Jenkins <47464908+ShelbyJenkins@users.noreply.github.com>
 Sheldon Robinson <sheldon.robinson@live.com>
+Shenghan Yang <ysharke@sjtu.edu.cn>
 shibe2 <shibe@tuta.io>
 Shijie <821898965@qq.com>
 Shin-myoung-serp <relent95@naver.com>
 Shintarou Okada <kokuzen@gmail.com>
 shivamkumard-ctrl <shivamkumard@nvidia.com>
+Shobhit <sobhit.me@gmail.com>
 Shouyu <65317431+joeldushouyu@users.noreply.github.com>
 Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com>
 Shouzheng Liu <lshzh.hi@gmail.com>
@@ -1607,6 +1659,7 @@ Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
 Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
 simevo <github@simevo.com>
 Simon Redman <simon@ergotech.com>
+Simon Teixidor <simon@flaskpost.me>
 Simon Willison <swillison@gmail.com>
 simon886212 <37953122+simon886212@users.noreply.github.com>
 Simranjeet Singh <105192966+simrnsingh@users.noreply.github.com>
@@ -1663,6 +1716,7 @@ stevenkuang <stevenkuang@tencent.com>
 Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
 StrangeBytesDev <141275258+StrangeBytesDev@users.noreply.github.com>
 strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com>
+Strongtut <Strongtut@users.noreply.github.com>
 Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com>
 sudhiarm <sudhi.sathyavathy@arm.com>
 Sukriti Sharma <Ssukriti@users.noreply.github.com>
@@ -1687,6 +1741,7 @@ Tamar <Tamar0812@outlook.co.il>
 tamarPal <tamarp3385@gmail.com>
 Tameem <113388789+AhmadTameem@users.noreply.github.com>
 Tamotsu Takahashi <ttakah+github@gmail.com>
+Tanner Bruhn <66120666+tannerbruhn@users.noreply.github.com>
 tarcey <cey.tarik@gmail.com>
 Tarek Dakhran <t.dakhran@gmail.com>
 Tarek Dakhran <tarek@liquid.ai>
@@ -1696,6 +1751,7 @@ Taylor <quantumtraveling@gmail.com>
 tc-mb <157115220+tc-mb@users.noreply.github.com>
 TecJesh <qdvm5gl@163.com>
 Tei Home <taiteitonghome@proton.me>
+Tekin Ertekin <tekin.ertekin@gmail.com>
 Tekin Ertekin <tekinertekin@gmail.com>
 tempstudio <49735574+tempstudio@users.noreply.github.com>
 teo <TeoZosa@users.noreply.github.com>
@@ -1737,6 +1793,7 @@ Ting Lou <louting@189.cn>
 Ting Lou <ting.lou@gmail.com>
 Ting Sun <suntcrick@gmail.com>
 Titaniumtown <titaniumtown@proton.me>
+Tiwei Bie <tiwei.btw@antgroup.com>
 tjohnman <tjohnman@users.noreply.github.com>
 Tobias Lütke <tobi@shopify.com>
 Toby <25832191+aetherbird@users.noreply.github.com>
@@ -1813,6 +1870,7 @@ Vishal Agarwal <vishalagarwal.jss@gmail.com>
 Vishal Singh <vishal@zettabolt.com>
 Vitali Lovich <vlovich+github@gmail.com>
 Vivian <vynride@gmail.com>
+vk <89937361+itsvedantkumar@users.noreply.github.com>
 Vlad <spitfireage@gmail.com>
 Vladimir <bogdad@gmail.com>
 Vladimir Malyutin <first-leon@yandex.ru>
@@ -1897,6 +1955,7 @@ Yaiko <elyaiko@hotmail.com>
 Yakine Tahtah <96926916+ReinforcedKnowledge@users.noreply.github.com>
 YangLe <smilingpoplar@gmail.com>
 yangli2 <yangli2@gmail.com>
+Yaniss Amazouz <yaniss91600@gmail.com>
 Yann Follet <131855179+YannFollet@users.noreply.github.com>
 Yanzhao Wang <yanzhaow@qti.qualcomm.com>
 Yarden Tal <yardent@qti.qualcomm.com>
@@ -4,7 +4,7 @@ include(CheckIncludeFileCXX)

 ### llama.cpp version
 set(LLAMA_VERSION_MAJOR 0)
-set(LLAMA_VERSION_MINOR 2)
+set(LLAMA_VERSION_MINOR 4)
 set(LLAMA_VERSION_PATCH 0)
 set(LLAMA_VERSION_BASE "${LLAMA_VERSION_MAJOR}.${LLAMA_VERSION_MINOR}.${LLAMA_VERSION_PATCH}")

@@ -134,8 +134,8 @@ option(LLAMA_BUILD_TOOLS     "llama: build tools"
 option(LLAMA_BUILD_EXAMPLES  "llama: build examples"                                                             ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER    "llama: build server example"                                                       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_APP       "llama: build the unified binary"                                                   ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_UI        "llama: build the embedded Web UI for server"                                       ON)
-option(LLAMA_USE_PREBUILT_UI "llama: use prebuilt UI from HF Bucket when available (requires LLAMA_BUILD_UI=ON)" ON)
+option(LLAMA_BUILD_UI        "llama: build the embedded Web UI for server"                                       OFF)
+option(LLAMA_USE_PREBUILT_UI "llama: use prebuilt UI from HF Bucket when available" ON)

 option(LLAMA_TOOLS_INSTALL "llama: install tools" ${LLAMA_TOOLS_INSTALL_DEFAULT})
 option(LLAMA_TESTS_INSTALL "llama: install tests" ON)
@@ -57,6 +57,7 @@
 /ggml/src/ggml-cann/                    @ggml-org/ggml-cann
 /ggml/src/ggml-common.h                 @ggerganov
 /ggml/src/ggml-cpu/                     @ggerganov
+/ggml/src/ggml-cpu/iqp.*                @bartowski1182
 /ggml/src/ggml-cpu/spacemit/            @alex-spacemit
 /ggml/src/ggml-cuda/                    @ggml-org/ggml-cuda
 /ggml/src/ggml-cuda/vendors/hip.h       @IMbackK
@@ -74,6 +74,7 @@ For more info, please refer to the [AGENTS.md](AGENTS.md) file.
 - If a PR does not warrant a new release, add `[no release]` in the squashed commit to spare CI resources
 - Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)
 - Add the ["merge ready"](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+is%3Aopen+draft%3Ano+sort%3Aupdated-desc+label%3A%22merge+ready%22+) label to a PR to indicate when a PR can be fast-merged without waiting for 2 independent reviews. [(more info)](https://github.com/ggml-org/llama.cpp/pull/26178)
+- Wait for CI results before merging

 Maintainers reserve the right to decline review or close pull requests for any reason, without any questions, particularly under any of the following conditions:
 - The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone.
@@ -13,7 +13,7 @@
 [![Docker](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/docker.yml?label=Docker)](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
 [![Winget](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/winget.yml?label=Winget)](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)

-[manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Abartowski1182%20OR%20author%3Ahipudding%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3A0cc4m%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [compile times](https://github.com/ggml-org/llama.cpp-dev/blob/master/README-compile-times.md) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)
+[ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Ajhen0409%20OR%20author%3Abartowski1182%20OR%20author%3Anikwen%20OR%20author%3Ahipudding%20OR%20author%3Aravi9%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3Amarty1885%20OR%20author%3A0cc4m%20OR%20author%3ATitaniumtown%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Awine99%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [dev stats](https://github.com/ggml-org/llama.cpp-dev) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)

 </div>

@@ -74,7 +74,7 @@ The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-or
 | [CANN](docs/build.md#cann) | Ascend NPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |
-| [Hexagon [In Progress]](docs/backend/snapdragon/README.md) | Snapdragon |
+| [Hexagon](docs/backend/snapdragon/README.md) | Snapdragon |
 | [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
 | [MUSA](docs/build.md#musa) | Moore Threads GPU |
 | [Metal](docs/build.md#metal-build) | Apple Silicon |
@@ -80,7 +80,7 @@ static const command cmds[] = {
 #undef UPDATE_HIDDEN

 static int version(int /*argc*/, char ** /*argv*/) {
-    llama_print_build_info(llama_version());
+    llama_print_build_info(llama_version(), stdout);
    return 0;
 }

@@ -18,7 +18,7 @@ LLAMA_BUILD_TESTS=OFF
 LLAMA_BUILD_SERVER=OFF
 LLAMA_BUILD_MTMD=ON
 GGML_METAL=ON
-GGML_METAL_EMBED_LIBRARY=ON
+GGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY:-ON}
 GGML_BLAS_DEFAULT=ON
 GGML_OPENMP=OFF

@@ -169,6 +169,14 @@ setup_framework_structure() {
    cp tools/mtmd/mtmd.h           ${header_path}
    cp tools/mtmd/mtmd-helper.h    ${header_path}

+    if [[ "$GGML_METAL_EMBED_LIBRARY" == "OFF" ]]; then
+        if [[ "$platform" == "macos" ]]; then
+            cp ${build_dir}/bin/*.metallib ${build_dir}/framework/${framework_name}.framework/Versions/A/Resources/
+        else
+            cp ${build_dir}/bin/*.metallib ${build_dir}/framework/${framework_name}.framework/
+        fi
+    fi
+
    # Create module map (common for all platforms)
    cat > ${module_path}module.modulemap << EOF
 framework module llama {
@@ -450,6 +458,7 @@ build_ios_sim() {
        -DIOS=ON \
        -DCMAKE_SYSTEM_NAME=iOS \
        -DCMAKE_OSX_SYSROOT=iphonesimulator \
+        -DGGML_METAL_TARGET_OS=ios \
        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \
        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
@@ -467,6 +476,7 @@ build_ios_device() {
        -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
        -DCMAKE_SYSTEM_NAME=iOS \
        -DCMAKE_OSX_SYSROOT=iphoneos \
+        -DGGML_METAL_TARGET_OS=ios \
        -DCMAKE_OSX_ARCHITECTURES="arm64" \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \
        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
@@ -498,6 +508,7 @@ build_visionos() {
        -DCMAKE_OSX_ARCHITECTURES="arm64" \
        -DCMAKE_SYSTEM_NAME=visionOS \
        -DCMAKE_OSX_SYSROOT=xros \
+        -DGGML_METAL_TARGET_OS=xros \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \
        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
@@ -516,6 +527,7 @@ build_visionos_sim() {
        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
        -DCMAKE_SYSTEM_NAME=visionOS \
        -DCMAKE_OSX_SYSROOT=xrsimulator \
+        -DGGML_METAL_TARGET_OS=xros \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \
        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
@@ -534,6 +546,7 @@ build_tvos_sim() {
        -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
        -DCMAKE_SYSTEM_NAME=tvOS \
        -DCMAKE_OSX_SYSROOT=appletvsimulator \
+        -DGGML_METAL_TARGET_OS=tvos \
        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
        -DGGML_METAL=ON \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \
@@ -552,6 +565,7 @@ build_tvos_device() {
        -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
        -DCMAKE_SYSTEM_NAME=tvOS \
        -DCMAKE_OSX_SYSROOT=appletvos \
+        -DGGML_METAL_TARGET_OS=tvos \
        -DCMAKE_OSX_ARCHITECTURES="arm64" \
        -DGGML_METAL=ON \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \
@@ -189,8 +189,8 @@ if [ ! -z ${GG_BUILD_OPENVINO} ]; then
    fi
    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_OPENVINO=ON"

-    # TODO: fix and re-enable the `test-llama-archs` test below
-    CTEST_EXTRA="-E test-llama-archs|test-recurrent-state-rollback-nemotron-h"
+    # TODO: fix failing tests on OpenVINO backend
+    CTEST_EXTRA="-E test-llama-archs|^test-recurrent-state-|test-backend-ops|test-save-load-state"
 fi

 ## helpers
@@ -300,6 +300,40 @@ function gg_sum_ctest_release {
    gg_printf '```\n'
 }

+# test_llama_archs_tensor_split
+
+function gg_run_test_llama_archs_tensor_split {
+    cd ${SRC}
+
+    set -e
+
+    if [ ! -z ${GG_BUILD_CUDA} ]; then
+        GGML_CUDA_DEVICES=1 ./build-ci-release/bin/test-llama-archs -s 1 2>&1
+        GGML_CUDA_DEVICES=2 ./build-ci-release/bin/test-llama-archs -s 1 2>&1
+        GGML_CUDA_DEVICES=3 ./build-ci-release/bin/test-llama-archs -s 1 2>&1
+        GGML_CUDA_DEVICES=4 ./build-ci-release/bin/test-llama-archs -s 1 2>&1
+    fi
+
+    if [ ! -z ${GG_BUILD_METAL} ]; then
+        GGML_METAL_DEVICES=1 ./build-ci-release/bin/test-llama-archs -s 1 2>&1
+        GGML_METAL_DEVICES=2 ./build-ci-release/bin/test-llama-archs -s 1 2>&1
+        GGML_METAL_DEVICES=3 ./build-ci-release/bin/test-llama-archs -s 1 2>&1
+        GGML_METAL_DEVICES=4 ./build-ci-release/bin/test-llama-archs -s 1 2>&1
+    fi
+
+    set +e
+}
+
+function gg_sum_test_llama_archs_tensor_split {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs test-llama-archs with 1 to 4 devices\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}.log)"
+    gg_printf '```\n'
+}
+
 # test_scripts

 function gg_run_test_scripts {
@@ -698,6 +732,11 @@ function gg_check_build_requirements {
        gg_printf 'ctest not found, please install\n'
        exit 1
    fi
+
+    if ! command -v unzip &> /dev/null; then
+        gg_printf 'unzip not found, please install\n'
+        exit 1
+    fi
 }

 function gg_run_test_backend_ops_cpu {
@@ -751,6 +790,8 @@ ret=0
 test $ret -eq 0 && gg_run ctest_debug
 test $ret -eq 0 && gg_run ctest_release

+test $ret -eq 0 && gg_run test_llama_archs_tensor_split
+
 if [ ! -z ${GG_BUILD_HIGH_PERF} ]; then
    test $ret -eq 0 && gg_run test_backend_ops_cpu
 fi
@@ -53,7 +53,10 @@ endif()

 set(TARGET llama-common)

+include(parsers/sources.cmake)
+
 add_library(${TARGET}
+    ${LLAMA_CHAT_PARSERS_SOURCES}
    arg.cpp
    arg.h
    base64.hpp
@@ -81,6 +84,8 @@ add_library(${TARGET}
    imatrix-loader.cpp
    imatrix-loader.h
    json-schema-to-grammar.cpp
+    json.cpp
+    json.h
    llguidance.cpp
    log.cpp
    log.h
@@ -5,6 +5,7 @@
 #include "common.h"
 #include "download.h"
 #include "json-schema-to-grammar.h"
+#include "json.h"
 #include "llama.h"
 #include "log.h"
 #include "sampling.h"
@@ -21,9 +22,6 @@
 #include <shellapi.h>
 #endif

-#define JSON_ASSERT GGML_ASSERT
-#include <nlohmann/json.hpp>
-
 #include <algorithm>
 #include <cinttypes>
 #include <climits>
@@ -32,6 +30,7 @@
 #include <filesystem>
 #include <fstream>
 #include <list>
+#include <numeric>
 #include <regex>
 #include <set>
 #include <string>
@@ -55,7 +54,7 @@

 #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083

-using json = nlohmann::ordered_json;
+using json = common_json;
 using namespace common_arg_utils;

 static std::initializer_list<enum llama_example> mmproj_examples = {
@@ -895,6 +894,12 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
    postprocess_cpu_params(params.speculative.draft.cpuparams,       &params.cpuparams);
    postprocess_cpu_params(params.speculative.draft.cpuparams_batch, &params.cpuparams_batch);

+    // default the mmproj device to the global device selection if not set explicitly with -mmdev
+    if (params.mmproj_use_gpu && params.mmproj_device == nullptr && !params.devices.empty()) {
+        params.mmproj_device = params.devices.front();
+        params.mmproj_use_gpu = params.mmproj_device != nullptr;
+    }
+
    if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
    }
@@ -961,6 +966,11 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        ));
    }

+    // if the preserve_reasoning kwarg was not specified explicitly, enable it by default
+    if (!params.default_template_kwargs.count("preserve_reasoning")) {
+        params.default_template_kwargs["preserve_reasoning"] = "true";
+    }
+
    return true;
 }

@@ -1644,6 +1654,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
        }
    ).set_env("LLAMA_ARG_CTX_SIZE"));
+    add_opt(common_arg(
+        { "--kv-unified-per-slot" }, "N",
+        "context limit per parallel slot (default: unset, behavior unchanged).\n"
+        "when set without -c/--ctx-size, the shared KV pool is sized to n_parallel*N",
+        [](common_params & params, int value) {
+            params.kv_unified_per_slot = value;
+        }
+    ).set_env("LLAMA_ARG_KV_UNIFIED_PER_SLOT").set_examples({ LLAMA_EXAMPLE_SERVER }));
    add_opt(common_arg(
        {"-n", "--predict", "--n-predict"}, "N",
        string_format(
@@ -1898,7 +1916,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params, bool value) {
            params.conversation_mode = value ? COMMON_CONVERSATION_MODE_ENABLED : COMMON_CONVERSATION_MODE_DISABLED;
        }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
    add_opt(common_arg(
        {"-st", "--single-turn"},
        "run conversation for a single turn only, then exit when done\n"
@@ -2598,7 +2616,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    add_opt(common_arg(
        // note: "-mmdev" must sort after "--rpc" in the preset map, else RPC devices are not registered yet
        {"-mmdev", "--mmproj-device"}, "DEVICE",
-        "device to use for multimodal projector (none = don't offload, default: auto)\n"
+        "device to use for multimodal projector (none = don't offload, default: follows --device)\n"
        "use --list-devices to see a list of available devices",
        [](common_params & params, const std::string & value) {
            if (value == "none") {
@@ -2645,6 +2663,27 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.mtmd_batch_max_tokens = value;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MTMD_BATCH_MAX_TOKENS"));
+    add_opt(common_arg(
+        {"--video-fps"}, "N",
+        string_format("target video frame rate (default: %.1f)", params.video_fps),
+        [](common_params & params, const std::string & value) {
+            params.video_fps = std::stof(value);
+        }
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_VIDEO_FPS"));
+    add_opt(common_arg(
+        {"--video-timestamp-interval"}, "N",
+        string_format("interval in milliseconds between text timestamps (default: %" PRId64 ")", params.video_timestamp_interval_ms),
+        [](common_params & params, int value) {
+            params.video_timestamp_interval_ms = value;
+        }
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_VIDEO_TIMESTAMP_INTERVAL"));
+    add_opt(common_arg(
+        {"--video-ffmpeg-dir"}, "DIR",
+        "path to the directory containing ffmpeg and ffprobe (default: search in PATH)",
+        [](common_params & params, const std::string & value) {
+            params.video_ffmpeg_bin_dir = value;
+        }
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_VIDEO_FFMPEG_DIR"));
    if (params.is_gen_docs || llama_supports_rpc()) {
        add_opt(common_arg(
            {"--rpc"}, "SERVERS",
@@ -2700,6 +2739,19 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            else { throw std::invalid_argument("invalid value"); }
        }
    ).set_env("LLAMA_ARG_LOAD_MODE"));
+    add_opt(common_arg(
+        {"-lzm", "--lazy-mode"}, "MODE",
+        "on-demand reading of certain tensors, for example per-layer embeddings (default: auto)\n"
+        "- on: read the rows of such tensors from disk on demand instead of keeping them resident (requires mmap)\n"
+        "- auto: on, but only for tensors larger than 4 GiB\n"
+        "- off: always keep them resident",
+        [](common_params & params, const std::string & value) {
+            /**/ if (value == "on")   { params.lazy_mode = LLAMA_LAZY_MODE_ON;   }
+            else if (value == "auto") { params.lazy_mode = LLAMA_LAZY_MODE_AUTO; }
+            else if (value == "off")  { params.lazy_mode = LLAMA_LAZY_MODE_OFF;  }
+            else { throw std::invalid_argument("invalid value"); }
+        }
+    ).set_env("LLAMA_ARG_LAZY_MODE"));
    add_opt(common_arg(
        {"--numa"}, "TYPE",
        "attempt optimizations that help on some NUMA systems\n"
@@ -2751,14 +2803,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            if (value < 0) {
                throw std::invalid_argument("invalid value");
            }
-            for (int i = 0; i < value; ++i) {
-                // keep strings alive and avoid leaking memory by storing them in a static vector
-                static std::list<std::string> buft_overrides;
-                buft_overrides.push_back(llm_ffn_exps_block_regex(i));
-                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
-            }
+            llm_add_n_cpu_ffn_overrides(value, LLM_FFN_EXPS_REGEX, params.tensor_buft_overrides);
        }
    ).set_env("LLAMA_ARG_N_CPU_MOE"));
+    add_opt(common_arg(
+        {"-ncffn", "--n-cpu-ffn"}, "N",
+        "keep the dense FFN weights of the first N layers in the CPU\n"
+        "(dense models; for MoE expert weights use --n-cpu-moe)",
+        [](common_params & params, int value) {
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
+            llm_add_n_cpu_ffn_overrides(value, LLM_FFN_DENSE_REGEX, params.tensor_buft_overrides);
+        }
+    ).set_env("LLAMA_ARG_N_CPU_FFN"));
    GGML_ASSERT(params.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
    add_opt(common_arg(
        {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
@@ -3506,6 +3564,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                    LOG_WRN("Setting 'enable_thinking' via --chat-template-kwargs is deprecated. "
                            "Use --reasoning on / --reasoning off instead.\n");
                }
+                if (item.key() == "preserve_reasoning") {
+                    LOG_WRN("Setting 'preserve_reasoning' via --chat-template-kwargs is deprecated. "
+                            "Use --reasoning-preserve / --no-reasoning-preserve instead.\n");
+                }
                params.default_template_kwargs[item.key()] = item.value().dump();
            }
        }
@@ -3696,7 +3758,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    add_opt(common_arg(
        {"--reasoning-preserve"},
        {"--no-reasoning-preserve"},
-        "preserve reasoning trace in the full history, not just the last assistant message (default: template default)\n"
+        "preserve reasoning trace in the full history, not just the last assistant message (default: enabled)\n"
        "compatible with certain templates having 'supports_preserve_reasoning' capability\n"
        "example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking",
        [](common_params & params, bool value) {
@@ -3705,6 +3767,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            } else {
                params.default_template_kwargs["preserve_reasoning"] = "false";
            }
+            params.preserve_reasoning_specified = true;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING_PRESERVE"));
    add_opt(common_arg(
@@ -3844,6 +3907,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            common_log_set_file(common_log_main(), value.c_str());
        }
    ).set_env("LLAMA_ARG_LOG_FILE"));
+    add_opt(common_arg(
+        {"--log-jsonl"},
+        {"--no-log-jsonl"},
+        "Log as JSONL (one JSON object per line) to stdout, this also disables colored logging (default: disabled)",
+        [](common_params &, bool value) {
+            common_log_set_jsonl(common_log_main(), value);
+        }
+    ).set_env("LLAMA_ARG_LOG_JSONL"));
    add_opt(common_arg(
        {"--log-prompts-dir"}, "PATH",
        "Log prompts to directory (auto-created if not present; only used for debugging, default: disabled)",
@@ -4085,11 +4156,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            if (value < 0) {
                throw std::invalid_argument("invalid value");
            }
-            for (int i = 0; i < value; ++i) {
-                static std::list<std::string> buft_overrides_draft;
-                buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i));
-                params.speculative.draft.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
-            }
+            llm_add_n_cpu_ffn_overrides(value, LLM_FFN_EXPS_REGEX, params.speculative.draft.tensor_buft_overrides);
        }
    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE"));

@@ -4110,6 +4177,38 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.speculative.draft.n_min = value;
        }
    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_MIN"));
+    add_opt(common_arg(
+        {"--spec-synth-len"}, "L",
+        "target mean synthetic acceptance length, including the target token (benchmarking only)",
+        [](common_params & params, const std::string & value) {
+            const std::string text = string_strip(value);
+            size_t pos = 0;
+            const double length = std::stod(text, &pos);
+            if (pos != text.size() || length == -1.0) {
+                throw std::invalid_argument("invalid value");
+            }
+            params.speculative.synth_len = length;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_SYNTH_LEN"));
+    add_opt(common_arg(
+        {"--spec-synth-rates"}, "P0,P1,...",
+        "comma-separated unconditional per-position synthetic acceptance probabilities (benchmarking only)",
+        [](common_params & params, const std::string & value) {
+            const auto values = string_split<std::string>(value, ',');
+            std::vector<double> rates;
+            rates.reserve(values.size());
+            for (const auto & raw : values) {
+                const std::string text = string_strip(raw);
+                size_t pos = 0;
+                const double rate = std::stod(text, &pos);
+                if (pos != text.size()) {
+                    throw std::invalid_argument("invalid value");
+                }
+                rates.push_back(rate);
+            }
+            params.speculative.synth_rates = std::move(rates);
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_SYNTH_RATES"));

    add_opt(common_arg(
        {"--spec-draft-p-split", "--draft-p-split"}, "P",
@@ -4136,7 +4235,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_BACKEND_SAMPLING"));
    add_opt(common_arg(
        {"--spec-draft-device", "-devd", "--device-draft"}, "<dev1,dev2,..>",
-        "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
+        "comma-separated list of devices to use for offloading the draft model (none = don't offload, default: follows --device)\n"
        "use --list-devices to see a list of available devices",
        [](common_params & params, const std::string & value) {
            params.speculative.draft.devices = parse_device_list(value);
@@ -29,7 +29,7 @@ const char * llama_build_info(void) {
    return s.c_str();
 }

-void llama_print_build_info(const char * llama_version) {
-    fprintf(stderr, "version: %s (build %d, commit %s)\n", llama_version, llama_build_number(), llama_commit());
-    fprintf(stderr, "built with %s for %s\n", llama_compiler(), llama_build_target());
+void llama_print_build_info(const char * llama_version, FILE * stream) {
+    fprintf(stream, "version: %s (build %d, commit %s)\n", llama_version, llama_build_number(), llama_commit());
+    fprintf(stream, "built with %s for %s\n", llama_compiler(), llama_build_target());
 }
@@ -1,5 +1,7 @@
 #pragma once

+#include <cstdio>
+
 int llama_build_number(void);

 const char * llama_commit(void);
@@ -8,4 +10,4 @@ const char * llama_compiler(void);
 const char * llama_build_target(void);
 const char * llama_build_info(void);

-void llama_print_build_info(const char *);
+void llama_print_build_info(const char *, FILE * = stderr);
@@ -5,13 +5,12 @@
 #include "common.h"
 #include "json-schema-to-grammar.h"
 #include "log.h"
-#include "nlohmann/json.hpp"
 #include "peg-parser.h"

 #include <stdexcept>
 #include <string>

-using json = nlohmann::ordered_json;
+using json = common_json;

 // Helper to iterate over tools/functions
 static void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
@@ -391,7 +390,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte

        std::set<std::string> required;
        if (params.contains("required")) {
-            params.at("required").get_to(required);
+            required = params.at("required").get<std::set<std::string>>();
        }

        auto schema_info = common_schema_info();
@@ -4,14 +4,11 @@
 #include "chat-peg-parser.h"
 #include "chat.h"
 #include "log.h"
-#include "nlohmann/json.hpp"
 #include "peg-parser.h"

 #include <cctype>
 #include <numeric>

-using json = nlohmann::ordered_json;
-
 std::string trim_whitespace(const std::string & str) {
    size_t start = 0;
    while (start < str.length() && std::isspace(static_cast<unsigned char>(str[start]))) {
@@ -4,7 +4,7 @@
 #include "common.h"
 #include "jinja/caps.h"
 #include "peg-parser.h"
-#include "nlohmann/json.hpp"
+#include "json.h"

 #include <chrono>
 #include <optional>
@@ -12,7 +12,7 @@
 #include <utility>
 #include <vector>

-using json = nlohmann::ordered_json;
+using json = common_json;

 class common_chat_peg_builder;

@@ -4,11 +4,11 @@
 #include "chat.h"
 #include "common.h"
 #include "log.h"
-#include "nlohmann/json.hpp"
 #include "peg-parser.h"

 #include <algorithm>
 #include <cctype>
+#include <numeric>
 #include <ostream>
 #include <sstream>

@@ -17,7 +17,7 @@
 #define ANSI_ORANGE "\033[1m\x1b[38;5;214m"
 #define ANSI_RED    "\033[1m\x1b[38;5;196m"

-using json = nlohmann::ordered_json;
+using json = common_json;

 namespace autoparser {

@@ -929,7 +929,7 @@ void analyze_tools::analyze_tool_call_format_json_native(const std::string & cle
    int  json_end       = clean_haystack.find_last_of('}');
    std::string cut     = clean_haystack.substr(json_start, json_end - json_start + 1);
    json call_struct    = json::parse(cut);
-    auto register_field = [&](const std::string & prefix, const nlohmann::detail::iteration_proxy_value<json::iterator> & subel) {
+    auto register_field = [&](const std::string & prefix, const common_json_entry & subel) {
        if (subel.value().is_string() && std::string(subel.value()).find("call0000") != std::string::npos) {
            format.id_field = !prefix.empty() ? prefix + "." + subel.key() : subel.key();
        } else if (subel.value().is_string() && std::string(subel.value()) == fun_name_needle) {
@@ -4,12 +4,10 @@
 #include "ggml.h"
 #include "peg-parser.h"

-#include <nlohmann/json.hpp>
-
 #include <cstdint>
 #include <functional>

-using ordered_json = nlohmann::ordered_json;
+using ordered_json = common_json;

 static std::string_view trim_trailing_space(std::string_view sv, int max = -1) {
    int count = 0;
@@ -128,7 +128,7 @@ class common_chat_peg_builder : public common_peg_parser_builder {
    // parameters_order: order in which JSON fields should be parsed
    common_peg_parser standard_json_tools(const std::string &              section_start,
                                          const std::string &              section_end,
-                                          const nlohmann::ordered_json &   tools,
+                                          const common_json &   tools,
                                          bool                             parallel_tool_calls,
                                          bool                             force_tool_calls,
                                          const std::string &              name_key = "",
@@ -143,13 +143,13 @@ class common_chat_peg_builder : public common_peg_parser_builder {
    // Legacy-compatible helper for building XML/tagged style tool calls
    // Used by tests and manual parsers
    common_peg_parser standard_constructed_tools(const std::map<std::string, std::string> & markers,
-                                                 const nlohmann::ordered_json &             tools,
+                                                 const common_json &             tools,
                                                 bool                                       parallel_tool_calls,
                                                 bool                                       force_tool_calls);

    // Helper for Python-style function call format: name(arg1="value1", arg2=123)
    // Used by LFM2 and similar templates
-    common_peg_parser python_style_tool_calls(const nlohmann::ordered_json & tools,
+    common_peg_parser python_style_tool_calls(const common_json & tools,
                                              bool                           parallel_tool_calls,
                                              bool                           allow_json_literals);

@@ -158,19 +158,19 @@ class common_chat_peg_builder : public common_peg_parser_builder {
    common_peg_parser python_or_json_value();

    // Implementation helpers for standard_json_tools — one per JSON tool call layout mode
-    common_peg_parser build_json_tools_function_is_key(const nlohmann::ordered_json & tools,
+    common_peg_parser build_json_tools_function_is_key(const common_json & tools,
                                                       const std::string &            args_key,
                                                       const std::string &            effective_args_key,
                                                       const std::string &            call_id_key,
                                                       const std::string &            gen_call_id_key);

-    common_peg_parser build_json_tools_nested_keys(const nlohmann::ordered_json & tools,
+    common_peg_parser build_json_tools_nested_keys(const common_json & tools,
                                                   const std::string &            effective_name_key,
                                                   const std::string &            effective_args_key,
                                                   const std::string &            call_id_key,
                                                   const std::string &            gen_call_id_key);

-    common_peg_parser build_json_tools_flat_keys(const nlohmann::ordered_json &   tools,
+    common_peg_parser build_json_tools_flat_keys(const common_json &   tools,
                                                 const std::string &              effective_name_key,
                                                 const std::string &              effective_args_key,
                                                 const std::string &              call_id_key,
@@ -8,7 +8,7 @@
 #include "jinja/runtime.h"
 #include "jinja/caps.h"

-#include "nlohmann/json_fwd.hpp"
+#include "json.h"

 #include <chrono>
 #include <functional>
@@ -17,7 +17,6 @@
 #include <vector>

 using chat_template_caps = jinja::caps;
-using json = nlohmann::ordered_json;

 struct common_chat_templates;

@@ -87,7 +86,7 @@ struct common_chat_msg {
    std::string                               tool_name;
    std::string                               tool_call_id;

-    nlohmann::ordered_json to_json_oaicompat(bool concat_typed_text = false) const;
+    common_json to_json_oaicompat(bool concat_typed_text = false) const;

    std::string render_content(const std::string & delimiter = "\n\n") const;

@@ -211,7 +210,7 @@ struct common_chat_msg_delimiters {
    // split tokens into message spans. skips maps a start index to a length of a region to jump over without matching
    common_chat_msg_spans split(const llama_tokens & tokens, const std::map<size_t, size_t> & skips = {}) const;

-    nlohmann::ordered_json to_json() const;
+    common_json to_json() const;
 };

 struct common_chat_tool {
@@ -350,16 +349,16 @@ common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::strin
 bool common_chat_templates_support_enable_thinking(const common_chat_templates * chat_templates);

 // Parses a JSON array of messages in OpenAI's chat completion API format.
-std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const nlohmann::ordered_json & messages);
+std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const common_json & messages);

-std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);
+std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const common_json & tools);

-common_chat_continuation common_chat_continuation_parse(const nlohmann::ordered_json & value);
+common_chat_continuation common_chat_continuation_parse(const common_json & value);

 // DEPRECATED: only used in tests
-nlohmann::ordered_json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);
+common_json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);

-nlohmann::ordered_json common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools);
+common_json common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools);

 // get template caps, useful for reporting to server /props endpoint
 std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_templates * chat_templates);
@@ -386,4 +385,4 @@ struct common_chat_prompt_preset {

 common_chat_prompt_preset common_chat_get_asr_prompt(const common_chat_templates * chat_templates);

-common_chat_msg_delimiters common_chat_msg_delimiters_parse(const nlohmann::ordered_json & delimiters);
+common_chat_msg_delimiters common_chat_msg_delimiters_parse(const common_json & delimiters);
@@ -402,10 +402,11 @@ void common_params_print_info(const common_params & params, bool print_devices)
 #endif
    COM_TRC("%s: build %d (%s) with %s for %s%s\n", __func__, llama_build_number(), llama_commit(), llama_compiler(), llama_build_target(), build_type);

-    COM_INF("%s: verbosity = %d (adjust with the `-lv N` CLI arg)\n", __func__, common_log_get_verbosity_thold());
+    const int verbosity = common_log_get_verbosity_thold();
+    COM_INF("%s: verbosity = %d (adjust with the `-lv N` CLI arg)\n", __func__, verbosity);

    // device enumeration creates a primary context on CUDA backends, skip it when the caller does not own any device
-    if (print_devices) {
+    if (print_devices && verbosity >= LOG_LEVEL_TRACE) {
        COM_TRC("%s", "device_info:\n");
        for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
            auto * dev = ggml_backend_dev_get(i);
@@ -1294,11 +1295,34 @@ common_init_result::common_init_result(common_params & params, bool model_only)
    if (params.fit_params) {
        COM_TRC("%s", "fitting params to device memory ...\n");
        COM_TRC("%s", "(for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)\n");
+
+        // the draft context is created from the same base params and follows the main context, fit both together
+        const bool has_draft = params.speculative.has_dft();
+        const bool spec_mtp  = std::find(params.speculative.types.begin(), params.speculative.types.end(),
+            COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
+
+        common_params params_dft = common_base_params_to_speculative(params);
+
+        auto mparams_dft = common_model_params_to_llama(params_dft);
+        auto cparams_dft = common_context_params_to_llama(params_dft);
+        if (spec_mtp) {
+            cparams_dft.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
+        }
+        cparams_dft.n_rs_seq = 0;
+
+        const common_fit_extra_model extra = {
+            /*.path_model   =*/ params_dft.model.path.c_str(),
+            /*.mparams      =*/ &mparams_dft,
+            /*.cparams      =*/ &cparams_dft,
+            /*.shares_model =*/ !has_draft, // an MTP context runs on the weights of the main model
+        };
+
        common_fit_params(params.model.path.c_str(), &mparams, &cparams,
            params.tensor_split,
            params.tensor_buft_overrides.data(),
            params.fit_params_target.data(),
            params.fit_params_min_ctx,
+            has_draft || spec_mtp ? &extra : nullptr,
            params.verbosity >= LOG_LEVEL_DEBUG ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
    }

@@ -1664,6 +1688,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
    mparams.main_gpu        = params.main_gpu;
    mparams.split_mode      = params.split_mode;
    mparams.load_mode       = params.load_mode;
+    mparams.lazy_mode = params.lazy_mode;
    mparams.tensor_split    = params.tensor_split;
    mparams.check_tensors   = params.check_tensors;
    mparams.use_extra_bufts = !params.no_extra_bufts;
@@ -8,6 +8,7 @@
 #include "ggml.h"
 #include "llama.h"

+#include <list>
 #include <set>
 #include <sstream>
 #include <string>
@@ -269,7 +270,7 @@ struct common_params_sampling {
        COMMON_SAMPLER_TYPE_TEMPERATURE,
    };

-    common_grammar              grammar;      // optional grammar constraint (user / output-format / tool-calls)
+    common_grammar                      grammar;          // optional grammar constraint (user / output-format / tool-calls)
    bool                                grammar_lazy = false;
    std::vector<common_grammar_trigger> grammar_triggers; // optional triggers (for lazy grammars)
    std::set<llama_token>               preserved_tokens;
@@ -369,6 +370,9 @@ struct common_params_speculative_ngram_cache {
 struct common_params_speculative {
    std::vector<enum common_speculative_type> types = { COMMON_SPECULATIVE_TYPE_NONE };

+    double synth_len = -1.0;
+    std::vector<double> synth_rates;
+
    // used by Simple, MTP, Eagle3, etc. - all methods that require some kind of draft model
    common_params_speculative_draft draft;

@@ -383,6 +387,10 @@ struct common_params_speculative {
        return !draft.mparams.empty();
    }

+    bool has_synth() const {
+        return synth_len != -1.0 || !synth_rates.empty();
+    }
+
    uint32_t need_n_rs_seq() const {
        bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH || t == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK;
@@ -475,6 +483,8 @@ struct common_params {
    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
    enum llama_load_mode  load_mode  = LLAMA_LOAD_MODE_AUTO; // how to load the model

+    enum llama_lazy_mode lazy_mode = LLAMA_LAZY_MODE_AUTO; // on-demand reading of tensors marked by the arch
+
    common_cpu_params cpuparams;
    common_cpu_params cpuparams_batch;

@@ -589,6 +599,11 @@ struct common_params {
    int image_max_tokens = -1;
    int mtmd_batch_max_tokens = 1024;

+    // for video input
+    float       video_fps                   = 4.0f;
+    int64_t     video_timestamp_interval_ms = 5000;
+    std::string video_ffmpeg_bin_dir        = "";
+
    // finetune
    struct lr_opt lr;
    enum ggml_opt_optimizer_type optimizer = GGML_OPT_OPTIMIZER_TYPE_ADAMW;
@@ -612,6 +627,7 @@ struct common_params {
    bool    cache_prompt        = true;  // whether to enable prompt caching
    bool    cache_idle_slots    = 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 kv_unified_per_slot = 0;     // max context per parallel slot; 0 = unset
    int32_t checkpoint_min_step = 8192;  // minimum spacing between context checkpoints
    int32_t cache_ram_mib       = 8192;  // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.

@@ -641,6 +657,7 @@ struct common_params {
    std::string ssl_file_cert = "";                                                                         // NOLINT

    std::map<std::string, std::string> default_template_kwargs;
+    bool preserve_reasoning_specified = false;

    // CLI params
    std::string server_base; // if set, connect to this server instead of starting a new one
@@ -1108,19 +1125,30 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 }

 //
-// MoE utils
+// FFN offload utils
 //

 const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate|gate_up)_(ch|)exps";

-inline std::string llm_ffn_exps_block_regex(int idx) {
-    return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);
+const char * const LLM_FFN_DENSE_REGEX = "\\.ffn_(up|down|gate)\\.";
+
+inline std::string llm_ffn_block_regex(int idx, const char * ffn_regex) {
+    return string_format("blk\\.%d%s", idx, ffn_regex);
 }

 inline llama_model_tensor_buft_override llm_ffn_exps_cpu_override() {
    return { LLM_FFN_EXPS_REGEX, ggml_backend_cpu_buffer_type() };
 }

+inline void llm_add_n_cpu_ffn_overrides(int n, const char * ffn_regex, std::vector<llama_model_tensor_buft_override> & overrides) {
+    // keep strings alive and avoid leaking memory by storing them in a static list
+    static std::list<std::string> buft_override_strings;
+    for (int i = 0; i < n; ++i) {
+        buft_override_strings.push_back(llm_ffn_block_regex(i, ffn_regex));
+        overrides.push_back({buft_override_strings.back().c_str(), ggml_backend_cpu_buffer_type()});
+    }
+}
+
 //
 // training utils
 //
@@ -5,9 +5,7 @@
 #include "log.h"
 #include "download.h"
 #include "hf-cache.h"
-
-#define JSON_ASSERT GGML_ASSERT
-#include <nlohmann/json.hpp>
+#include "json.h"

 #include <algorithm>
 #include <filesystem>
@@ -44,8 +42,6 @@
 #include <unistd.h>
 #endif

-using json = nlohmann::ordered_json;
-
 //
 // downloader
 //
@@ -856,8 +852,8 @@ static std::string common_docker_get_token(const std::string & repo) {
        throw std::runtime_error("Failed to get Docker registry token, HTTP code: " + std::to_string(res.first));
    }

-    std::string            response_str(res.second.begin(), res.second.end());
-    nlohmann::ordered_json response = nlohmann::ordered_json::parse(response_str);
+    std::string response_str(res.second.begin(), res.second.end());
+    common_json response = common_json::parse(response_str);

    if (!response.contains("token")) {
        throw std::runtime_error("Docker registry token response missing 'token' field");
@@ -919,9 +915,9 @@ std::string common_docker_resolve_model(const std::string & docker) {
            throw std::runtime_error("Failed to get Docker manifest, HTTP code: " + std::to_string(manifest_res.first));
        }

-        std::string            manifest_str(manifest_res.second.begin(), manifest_res.second.end());
-        nlohmann::ordered_json manifest = nlohmann::ordered_json::parse(manifest_str);
-        std::string            gguf_digest;  // Find the GGUF layer
+        std::string manifest_str(manifest_res.second.begin(), manifest_res.second.end());
+        common_json manifest = common_json::parse(manifest_str);
+        std::string gguf_digest;  // Find the GGUF layer
        if (manifest.contains("layers")) {
            for (const auto & layer : manifest["layers"]) {
                if (layer.contains("mediaType")) {
@@ -178,7 +178,7 @@ common_device_memory_data_vec common_get_device_memory_data(
 static void common_params_fit_impl(
        const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
        float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
-        size_t * margins_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
+        size_t * margins_s, uint32_t n_ctx_min, const common_fit_extra_model * extra, enum ggml_log_level log_level) {
    if (mparams->split_mode == LLAMA_SPLIT_MODE_TENSOR) {
        throw common_params_fit_exception("llama_params_fit is not implemented for SPLIT_MODE_TENSOR, abort");
    }
@@ -191,10 +191,92 @@ static void common_params_fit_impl(
    uint32_t hp_nct = 0; // hparams.n_ctx_train
    uint32_t hp_nex = 0; // hparams.n_expert

+    // with non-unified kv, we need to take into account n_streams
+    // for example, if memory can hold more than model's trained context size, we must extend the n_ctx to hold enough n_streams
+    const uint32_t n_streams  = cparams->kv_unified ? 1 : std::max<uint32_t>(1, cparams->n_seq_max);
+    const bool     n_ctx_auto = cparams->n_ctx == 0;
+
+    dmds_t   dmds_extra;       // memory of the extra model, laid out on the devices of the main model
+    uint32_t n_ctx_extra = 0;  // context that memory was measured at
+
+    // the extra model competes for the same memory as the main model, add it to every measurement
+    // its memory is measured again whenever the context it follows changes
+    auto add_extra_memory = [&](dmds_t & dmds) {
+        if (extra == nullptr) {
+            return;
+        }
+
+        if (dmds_extra.empty() || n_ctx_extra != cparams->n_ctx) {
+            std::vector<ggml_backend_dev_t> devs_extra;
+            uint32_t ngl_extra = 0;
+            uint32_t nct_extra = 0;
+            uint32_t nex_extra = 0;
+
+            extra->cparams->n_ctx = cparams->n_ctx;
+
+            LOG_TRC("%s: getting device memory data for the extra model at a context size of %" PRIu32 ":\n",
+                __func__, cparams->n_ctx);
+
+            dmds_t measured;
+            try {
+                measured = common_get_device_memory_data_impl(
+                    extra->path_model, extra->mparams, extra->cparams, devs_extra, ngl_extra, nct_extra, nex_extra, log_level);
+            } catch (const std::runtime_error & e) {
+                // the extra model is optional, fit the main model alone rather than giving up
+                LOG_WRN("%s: failed to measure the memory of the extra model, fitting without it: %s\n", __func__, e.what());
+                dmds_extra = dmds_t(devs.size() + 1);
+                n_ctx_extra = cparams->n_ctx;
+                return;
+            }
+
+            dmds_extra = dmds_t(devs.size() + 1);
+            dmds_extra.back().mb = measured.back().mb;
+            for (size_t je = 0; je < devs_extra.size(); je++) {
+                for (size_t id = 0; id < devs.size(); id++) {
+                    if (devs_extra[je] == devs[id]) {
+                        dmds_extra[id].mb.model   += measured[je].mb.model;
+                        dmds_extra[id].mb.context += measured[je].mb.context;
+                        dmds_extra[id].mb.compute += measured[je].mb.compute;
+                        break;
+                    }
+                }
+            }
+            if (extra->shares_model) {
+                for (llama_device_memory_data & dmd : dmds_extra) {
+                    dmd.mb.model = 0;
+                }
+            }
+
+            n_ctx_extra = cparams->n_ctx;
+        }
+
+        for (size_t id = 0; id < dmds.size(); id++) {
+            dmds[id].mb.model   += dmds_extra[id].mb.model;
+            dmds[id].mb.context += dmds_extra[id].mb.context;
+            dmds[id].mb.compute += dmds_extra[id].mb.compute;
+        }
+    };
+
    // step 1: get data for default parameters and check whether any changes are necessary in the first place

    LOG_TRC("%s: getting device memory data for initial parameters:\n", __func__);
-    const dmds_t dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+    dmds_t dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+
+    // saturate instead of overflowing, this also preserves the UINT32_MAX sentinel of n_ctx_min:
+    const uint32_t n_ctx_max       = (uint32_t) std::min<uint64_t>(uint64_t(hp_nct)    * n_streams, UINT32_MAX);
+    const uint32_t n_ctx_min_total = (uint32_t) std::min<uint64_t>(uint64_t(n_ctx_min) * n_streams, UINT32_MAX);
+
+    // llama_context would use only hp_nct in total for n_ctx == 0, resolve the context before measuring anything else:
+    if (n_ctx_auto) {
+        cparams->n_ctx = n_ctx_max;
+        if (n_streams > 1) {
+            LOG_TRC("%s: context size unset and KV cache not unified -> using %" PRIu32 " for %" PRIu32 " sequences:\n",
+                __func__, n_ctx_max, n_streams);
+            dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+        }
+    }
+    add_extra_memory(dmds_full);
+
    const size_t nd = devs.size(); // number of devices

    std::vector<int64_t> margins; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
@@ -307,8 +389,8 @@ static void common_params_fit_impl(
                    "%s: cannot meet free memory targets on all devices, need to use %" PRId64 " MiB less in total\n",
                    __func__, -global_surplus/MiB);
            }
-            if (cparams->n_ctx == 0) {
-                if (hp_nct > n_ctx_min) {
+            if (n_ctx_auto) {
+                if (n_ctx_max > n_ctx_min_total) {
                    int64_t sum_used_target = sum_free;
                    if (nd == 0) {
                        sum_used_target -= margins[0];
@@ -328,8 +410,9 @@ static void common_params_fit_impl(
                    }

                    int64_t sum_projected_used_min_ctx = 0;
-                    cparams->n_ctx = n_ctx_min;
-                    const dmds_t dmds_min_ctx = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+                    cparams->n_ctx = n_ctx_min_total;
+                    dmds_t dmds_min_ctx = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+                    add_extra_memory(dmds_min_ctx);
                    if (nd == 0) {
                        sum_projected_used_min_ctx = dmds_min_ctx.back().mb.total();
                    } else {
@@ -339,14 +422,16 @@ static void common_params_fit_impl(
                    }
                    if (sum_used_target > sum_projected_used_min_ctx) {
                        // linear interpolation between minimum and maximum context size:
-                        cparams->n_ctx += (hp_nct - n_ctx_min) * (sum_used_target - sum_projected_used_min_ctx)
+                        cparams->n_ctx += (n_ctx_max - n_ctx_min_total) * (sum_used_target - sum_projected_used_min_ctx)
                            / (sum_projected_used - sum_projected_used_min_ctx);
-                        cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend
+                        // round down context for CUDA backend, keep it divisible by the number of streams:
+                        const uint32_t align = 256 * n_streams;
+                        cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % align, n_ctx_min_total);

-                        const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (hp_nct - n_ctx_min);
-                        const int64_t memory_reduction = (hp_nct - cparams->n_ctx) * bytes_per_ctx;
+                        const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (n_ctx_max - n_ctx_min_total);
+                        const int64_t memory_reduction = (n_ctx_max - cparams->n_ctx) * bytes_per_ctx;
                        LOG_TRC("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
-                            __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
+                            __func__, n_ctx_max, cparams->n_ctx, memory_reduction/MiB);
                        if (nd <= 1) {
                            LOG_TRC("%s: entire model can be fit by reducing context\n", __func__);
                            return;
@@ -355,14 +440,14 @@ static void common_params_fit_impl(
                    } else {
                        const int64_t memory_reduction = sum_projected_used - sum_projected_used_min_ctx;
                        LOG_TRC("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
-                            __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
+                            __func__, n_ctx_max, cparams->n_ctx, memory_reduction/MiB);
                    }
                } else {
                    if (n_ctx_min == UINT32_MAX) {
-                        LOG_TRC("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct);
+                        LOG_TRC("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, n_ctx_max);
                    } else {
                        LOG_TRC("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
-                            __func__, hp_nct, n_ctx_min);
+                            __func__, n_ctx_max, n_ctx_min_total);
                    }
                }
            } else {
@@ -507,8 +592,9 @@ static void common_params_fit_impl(
        llama_model_params mparams_copy = *mparams;
        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy);

-        const dmds_t dmd_nl = common_get_device_memory_data_impl(
+        dmds_t dmd_nl = common_get_device_memory_data_impl(
            path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+        add_extra_memory(dmd_nl);

        LOG_TRC("%s: memory for test allocation by device:\n", func_name);
        for (size_t id = 0; id < nd; id++) {
@@ -535,8 +621,9 @@ static void common_params_fit_impl(
        mparams->tensor_buft_overrides = tensor_buft_overrides;

        LOG_TRC("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
-        const dmds_t dmds_cpu_moe = common_get_device_memory_data_impl(
+        dmds_t dmds_cpu_moe = common_get_device_memory_data_impl(
            path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+        add_extra_memory(dmds_cpu_moe);

        for (size_t id = 0; id < nd; id++) {
            global_surplus_cpu_moe += dmds_cpu_moe[id].free;
@@ -796,11 +883,12 @@ enum common_params_fit_status common_fit_params(
        llama_model_tensor_buft_override * tensor_buft_overrides,
        size_t * margins,
        uint32_t n_ctx_min,
+        const common_fit_extra_model * extra,
        ggml_log_level log_level) {
    const int64_t t0_us = llama_time_us();
    common_params_fit_status status = COMMON_PARAMS_FIT_STATUS_SUCCESS;
    try {
-        common_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margins, n_ctx_min, log_level);
+        common_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margins, n_ctx_min, extra, log_level);
        LOG_TRC("%s: successfully fit params to free device memory\n", __func__);
    } catch (const common_params_fit_exception & e) {
        LOG_WRN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
@@ -11,6 +11,16 @@ enum common_params_fit_status {
    COMMON_PARAMS_FIT_STATUS_ERROR   = 2, // a hard error occurred, e.g. because no model could be found at the specified path
 };

+// a second model that shares the devices of the main model, e.g. a draft model
+//   - its context follows the context of the main model, so its memory is measured again whenever that context changes
+//   - shares_model tells the fit that the weights are already counted in the main model, as for an MTP context
+struct common_fit_extra_model {
+    const char * path_model;
+    llama_model_params * mparams;
+    llama_context_params * cparams;
+    bool shares_model;
+};
+
 // fits mparams and cparams to free device memory (assumes system memory is unlimited)
 //   - returns true if the parameters could be successfully modified to fit device memory
 //   - this function is NOT thread safe because it modifies the global llama logger state
@@ -24,6 +34,7 @@ common_params_fit_status common_fit_params(
   llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
                             size_t * margins,               // margins of memory to leave per device in bytes
                           uint32_t   n_ctx_min,             // minimum context size to set when trying to reduce memory use
+      const common_fit_extra_model * extra,                  // model to fit alongside the main one, nullptr if there is none
                     ggml_log_level   log_level);            // minimum log level to print during fitting, lower levels go to debug log

 // print estimated memory to stdout
@@ -4,9 +4,7 @@
 #include "common.h"
 #include "log.h"
 #include "http.h"
-
-#define JSON_ASSERT GGML_ASSERT
-#include <nlohmann/json.hpp>
+#include "json.h"

 #include <filesystem>
 #include <fstream>
@@ -15,8 +13,6 @@
 #include <string_view>
 #include <stdexcept>

-namespace nl = nlohmann;
-
 #if defined(_WIN32)
 #define WIN32_LEAN_AND_MEAN
 #ifndef NOMINMAX
@@ -195,8 +191,8 @@ static void safe_write_file(const fs::path & path, const std::string & data) {
    }
 }

-static nl::json api_get(const std::string & url,
-                        const std::string & token) {
+static common_json api_get(const std::string & url,
+                           const std::string & token) {
    auto [cli, parts] = common_http_client(url);

    httplib::Headers headers = {
@@ -214,10 +210,10 @@ static nl::json api_get(const std::string & url,
        auto body = res->body;

        if (res->status == 200) {
-            return nl::json::parse(res->body);
+            return common_json::parse(res->body);
        }
        try {
-            body = nl::json::parse(res->body)["error"].get<std::string>();
+            body = common_json::parse(res->body)["error"].get<std::string>();
        } catch (...) { }

        throw std::runtime_error("GET failed (" + std::to_string(res->status) + "): " + body);
@@ -280,7 +276,7 @@ static std::string get_repo_commit(const std::string & repo_id,
        safe_write_file(refs_path / name, commit);
        return commit;

-    } catch (const nl::json::exception & e) {
+    } catch (const common_json_error & e) {
        LOG_ERR("%s: JSON error: %s\n", __func__, e.what());
    } catch (const std::exception & e) {
        LOG_ERR("%s: error: %s\n", __func__, e.what());
@@ -358,7 +354,7 @@ hf_files get_repo_files(const std::string & repo_id,

            files.push_back(file);
        }
-    } catch (const nl::json::exception & e) {
+    } catch (const common_json_error & e) {
        LOG_ERR("%s: JSON error: %s\n", __func__, e.what());
    } catch (const std::exception & e) {
        LOG_ERR("%s: error: %s\n", __func__, e.what());
@@ -7,7 +7,7 @@ The implementation can be found in the `common/jinja` directory.
 ## Key Features

 - Input marking: security against special token injection
- Decoupled from `nlohmann::json`: this dependency is only used for JSON-to-internal type translation and is completely optional
+- Decoupled from the JSON library: `common_json` is only used for JSON-to-internal type translation and is completely optional
 - Minimal primitive types: int, float, bool, string, array, object, none, undefined
 - Detailed logging: allow source tracing on error
 - Clean architecture: workarounds are applied to input data before entering the runtime (see `common/chat.cpp`)
@@ -4,14 +4,14 @@

 // note: the json dependency is only for defining input in a convenient way
 // we can remove it in the future when we figure out a better way to define inputs using jinja::value
-#include <nlohmann/json.hpp>
+#include "json.h"

 #include <functional>
 #include <sstream>

 #define FILENAME "jinja-caps"

-using json = nlohmann::ordered_json;
+using json = common_json;

 namespace jinja {

@@ -117,6 +117,7 @@ caps caps_get(jinja::program & prog) {

    JJ_DEBUG("%s\n", ">>> Running capability check: typed content");

+    bool checks_for_string = false;
    static const std::string content_marker = "STRING_MARKER";

    // case: typed content support
@@ -136,6 +137,10 @@ caps caps_get(jinja::program & prog) {
        [&](context &, bool success, value & messages, value &, const std::string & rendered) {
            auto & content = messages->at(0)->at("content");
            caps_print_stats(content, "messages[0].content");
+            if (has_op(content, "test_is_string")) {
+                // checked if content is string
+                checks_for_string = true;
+            }
            bool used_as_array = has_op(content, "selectattr") || has_op(content, "array_access");
            if (used_as_array) {
                // accessed as an array
@@ -151,6 +156,33 @@ caps caps_get(jinja::program & prog) {
        }
    );

+    if (checks_for_string) {
+        caps_try_execute(
+            prog,
+            [&]() {
+                // messages
+                return json::array({
+                    {
+                        {"role", "user"},
+                        {"content", json::array({
+                        })}
+                    }
+                });
+            },
+            nullptr, // ctx_fn
+            nullptr, // tools_fn
+            [&](context &, bool success, value & messages, value &, const std::string &) {
+                auto & content = messages->at(0)->at("content");
+                caps_print_stats(content, "messages[0].content");
+                bool used_as_array = has_op(content, "selectattr") || has_op(content, "array_access");
+                if (used_as_array && success) {
+                    // accessed as an array
+                    result.supports_typed_content = true;
+                }
+            }
+        );
+    }
+
    JJ_DEBUG("%s\n", ">>> Running capability check: system prompt");

    // case: system prompt support
@@ -412,12 +412,18 @@ value test_expression::execute_impl(context & ctx) {
        throw std::runtime_error("Invalid test expression");
    }

-    auto it = builtins.find("test_is_" + test_id);
-    JJ_DEBUG("Test expression %s '%s' %s (using function 'test_is_%s')", operand->type().c_str(), test_id.c_str(), negate ? "(negate)" : "", test_id.c_str());
+    const std::string test_name = "test_is_" + test_id;
+    auto it = builtins.find(test_name);
+    JJ_DEBUG("Test expression %s '%s' %s (using function '%s')", operand->type().c_str(), test_id.c_str(), negate ? "(negate)" : "", test_name.c_str());
    if (it == builtins.end()) {
        throw std::runtime_error("Unknown test '" + test_id + "'");
    }

+    if (ctx.is_get_stats) {
+        value_t::stats_t::mark_used(input);
+        input->stats.ops.insert(test_name);
+    }
+
    auto res = it->second(args);

    if (negate) {
@@ -3,7 +3,7 @@
 #include "value.h"

 // for converting from JSON to jinja values
-#include <nlohmann/json.hpp>
+#include "json.h"

 #include <sstream>
 #include <string>
@@ -1355,7 +1355,7 @@ const func_builtins & value_undefined_t::get_builtins() const {
 //////////////////////////////////


-static value from_json(const nlohmann::ordered_json & j, bool mark_input) {
+static value from_json(const common_json & j, bool mark_input) {
    if (j.is_null()) {
        return mk_val<value_none>();
    } else if (j.is_boolean()) {
@@ -1452,7 +1452,7 @@ bool value_compare(const value & a, const value & b, value_compare_op op) {
 }

 template<>
-void global_from_json(context & ctx, const nlohmann::ordered_json & json_obj, bool mark_input) {
+void global_from_json(context & ctx, const common_json & json_obj, bool mark_input) {
    // printf("global_from_json: %s\n" , json_obj.dump(2).c_str());
    if (json_obj.is_null() || !json_obj.is_object()) {
        throw std::runtime_error("global_from_json: input JSON value must be an object");
@@ -86,7 +86,7 @@ struct context; // forward declaration
 // marking input can be useful for tracking data provenance
 // and preventing template injection attacks
 //
-// Note: T_JSON can be nlohmann::ordered_json
+// Note: T_JSON can be common_json
 template<typename T_JSON>
 void global_from_json(context & ctx, const T_JSON & json_obj, bool mark_input);

@@ -1,9 +1,8 @@
 #include "json-schema-to-grammar.h"
 #include "common.h"

-#include <nlohmann/json.hpp>
-
 #include <algorithm>
+#include <limits>
 #include <map>
 #include <regex>
 #include <sstream>
@@ -12,7 +11,7 @@
 #include <unordered_set>
 #include <vector>

-using json = nlohmann::ordered_json;
+using json = common_json;

 static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
    auto has_max = max_items != std::numeric_limits<int>::max();
@@ -749,6 +748,10 @@ private:
            optional_props.push_back("*");
        }

+        if (required_props.empty() && optional_props.empty()) {
+            return "\"{\" space \"}\"";
+        }
+
        std::string rule = "\"{\" space ";
        for (size_t i = 0; i < required_props.size(); i++) {
            if (i > 0) {
@@ -917,7 +920,11 @@ public:
            return _add_rule(rule_name, _resolve_ref(schema["$ref"]));
        }
        if (schema.contains("oneOf") || schema.contains("anyOf")) {
-            std::vector<json> alt_schemas = schema.contains("oneOf") ? schema["oneOf"].get<std::vector<json>>() : schema["anyOf"].get<std::vector<json>>();
+            const json & alts = schema.contains("oneOf") ? schema.at("oneOf") : schema.at("anyOf");
+            std::vector<json> alt_schemas;
+            for (const auto & alt : alts) {
+                alt_schemas.push_back(alt);
+            }
            return _add_rule(rule_name, _generate_union_rule(name, alt_schemas));
        }
        if (schema_type.is_array()) {
@@ -1111,7 +1118,7 @@ common_schema_info::~common_schema_info() = default;
 common_schema_info::common_schema_info(common_schema_info &&) noexcept = default;
 common_schema_info & common_schema_info::operator=(common_schema_info &&) noexcept = default;

-void common_schema_info::resolve_refs(nlohmann::ordered_json & schema) {
+void common_schema_info::resolve_refs(common_json & schema) {
    impl_->resolve_refs(schema, "");
 }

@@ -1119,7 +1126,7 @@ void common_schema_info::resolve_refs(nlohmann::ordered_json & schema) {
 // Some models emit raw string values rather than JSON-encoded strings for string parameters.
 // If any branch of the schema (via oneOf, anyOf, $ref, etc.) permits a string, this returns
 // true, allowing callers to handle the value as a raw string for simplicity.
-bool common_schema_info::resolves_to_string(const nlohmann::ordered_json & schema) {
+bool common_schema_info::resolves_to_string(const common_json & schema) {
    std::unordered_set<std::string> visited_refs;

    std::function<bool(const json &)> check = [&](const json & s) -> bool {
@@ -1227,7 +1234,7 @@ bool common_schema_info::resolves_to_string(const nlohmann::ordered_json & schem
    return check(schema);
 }

-std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
+std::string json_schema_to_grammar(const common_json & schema, bool force_gbnf) {
 #ifdef LLAMA_USE_LLGUIDANCE
    if (!force_gbnf) {
        return "%llguidance {}\nstart: %json " + schema.dump();
@@ -1248,10 +1255,10 @@ std::string build_grammar(const std::function<void(const common_grammar_builder
        /* .add_rule = */ [&](const std::string & name, const std::string & rule) {
            return converter._add_rule(name, rule);
        },
-        /* .add_schema = */ [&](const std::string & name, const nlohmann::ordered_json & schema) {
+        /* .add_schema = */ [&](const std::string & name, const common_json & schema) {
            return converter.visit(schema, name == "root" ? "" : name);
        },
-        /* .resolve_refs = */ [&](nlohmann::ordered_json & schema) {
+        /* .resolve_refs = */ [&](common_json & schema) {
            converter.resolve_refs(schema, "");
        }
    };
@@ -1,12 +1,12 @@
 #pragma once

-#include <nlohmann/json_fwd.hpp>
+#include "json.h"

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

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

 class common_schema_converter;
@@ -24,14 +24,14 @@ class common_schema_info {
    common_schema_info(common_schema_info &&) noexcept;
    common_schema_info & operator=(common_schema_info &&) noexcept;

-    void resolve_refs(nlohmann::ordered_json & schema);
-    bool resolves_to_string(const nlohmann::ordered_json & schema);
+    void resolve_refs(common_json & schema);
+    bool resolves_to_string(const common_json & schema);
 };

 struct common_grammar_builder {
    std::function<std::string(const std::string &, const std::string &)> add_rule;
-    std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
-    std::function<void(nlohmann::ordered_json &)> resolve_refs;
+    std::function<std::string(const std::string &, const common_json &)> add_schema;
+    std::function<void(common_json &)> resolve_refs;
 };

 struct common_grammar_options {
@@ -0,0 +1,433 @@
+#include "json.h"
+
+#include "ggml.h"
+
+#define JSON_ASSERT GGML_ASSERT
+#include <nlohmann/json.hpp>
+
+#include <iterator>
+#include <new>
+#include <set>
+#include <unordered_map>
+#include <vector>
+
+using nlohmann::ordered_json;
+
+// a common_json is the backing value, so any value of a tree can be used as a common_json
+static_assert(sizeof(ordered_json)  <= sizeof(common_json),  "common_json storage is too small");
+static_assert(alignof(ordered_json) <= alignof(common_json), "common_json alignment is too weak");
+
+// runs fn and gives every error of the backing library as a common_json_error
+template <typename F>
+static decltype(auto) guard(F && fn) {
+    try {
+        return fn();
+    } catch (const ordered_json::exception & e) {
+        throw common_json_error(e.what());
+    }
+}
+
+static ordered_json & as_json(common_json * self) {
+    return *reinterpret_cast<ordered_json *>(self);
+}
+
+static const ordered_json & as_json(const common_json * self) {
+    return *reinterpret_cast<const ordered_json *>(self);
+}
+
+static common_json & as_common(ordered_json & json) {
+    return *reinterpret_cast<common_json *>(&json);
+}
+
+static const common_json & as_common(const ordered_json & json) {
+    return *reinterpret_cast<const common_json *>(&json);
+}
+
+static ordered_json to_json(const common_json_value & val) {
+    switch (val.type) {
+        case common_json_value::VAL_NULL:   return nullptr;
+        case common_json_value::VAL_BOOL:   return val.val_bool;
+        case common_json_value::VAL_INT:    return val.val_int;
+        case common_json_value::VAL_UINT:   return val.val_uint;
+        case common_json_value::VAL_DOUBLE: return val.val_double;
+        case common_json_value::VAL_STRING: return val.val_string;
+        case common_json_value::VAL_JSON:
+            // one owner means no one else can see this tree, so it is safe to move it out
+            // note: this makes a value single use, same as the json_ref of the backing library
+            if (val.val_json.use_count() == 1) {
+                return std::move(as_json(val.val_json.get()));
+            }
+            return as_json(val.val_json.get());
+    }
+
+    return nullptr;
+}
+
+common_json_value::common_json_value(const char * val) {
+    if (val) {
+        type       = VAL_STRING;
+        val_string = val;
+    } else {
+        type = VAL_NULL;
+    }
+}
+
+common_json_value::common_json_value(const common_json & val) :
+    type(VAL_JSON), val_json(std::make_shared<common_json>(val)) {}
+
+common_json_value::common_json_value(common_json && val) :
+    type(VAL_JSON), val_json(std::make_shared<common_json>(std::move(val))) {}
+
+// the ctors and get<T>() below are explicit specializations, giving strong symbols
+// an explicit instantiation is a weak symbol, dropped by some LTO builds (clang-cl)
+template <typename T>
+static std::shared_ptr<common_json> set_json(const std::set<T> & vals) {
+    common_json out = common_json::array();
+
+    for (const auto & val : vals) {
+        out.push_back(val);
+    }
+
+    return std::make_shared<common_json>(std::move(out));
+}
+
+// a set value is usable only for the types below
+#define COMMON_JSON_SET(...) template <> common_json_value::common_json_value(const std::set<__VA_ARGS__> & vals) : type(VAL_JSON), val_json(set_json(vals)) {}
+
+COMMON_JSON_SET(int)
+COMMON_JSON_SET(std::string)
+
+#undef COMMON_JSON_SET
+
+template <typename T>
+static std::shared_ptr<common_json> map_json(const T & vals) {
+    common_json out = common_json::object();
+
+    for (const auto & val : vals) {
+        out.set({ val.first, val.second });
+    }
+
+    return std::make_shared<common_json>(std::move(out));
+}
+
+// a map value is usable only for the types below
+#define COMMON_JSON_MAP(...) template <> common_json_value::common_json_value(const std::map<std::string, __VA_ARGS__> & vals) : type(VAL_JSON), val_json(map_json(vals)) {}
+
+COMMON_JSON_MAP(bool)
+COMMON_JSON_MAP(std::string)
+
+#undef COMMON_JSON_MAP
+
+// an unordered map value is usable only for the types below
+#define COMMON_JSON_UMAP(...) template <> common_json_value::common_json_value(const std::unordered_map<std::string, __VA_ARGS__> & vals) : type(VAL_JSON), val_json(map_json(vals)) {}
+
+COMMON_JSON_UMAP(size_t)
+
+#undef COMMON_JSON_UMAP
+
+template <typename T>
+static std::shared_ptr<common_json> vec_json(const std::vector<T> & vals) {
+    common_json out = common_json::array();
+
+    for (const auto & val : vals) {
+        out.push_back(val);
+    }
+
+    return std::make_shared<common_json>(std::move(out));
+}
+
+// a vector value is usable only for the types below
+// note: std::vector<bool> is not here, its proxy reference does not convert
+#define COMMON_JSON_VEC(...) template <> common_json_value::common_json_value(const std::vector<__VA_ARGS__> & vals) : type(VAL_JSON), val_json(vec_json(vals)) {}
+
+COMMON_JSON_VEC(int)
+COMMON_JSON_VEC(unsigned char)
+COMMON_JSON_VEC(unsigned int)
+COMMON_JSON_VEC(long)
+COMMON_JSON_VEC(unsigned long)
+COMMON_JSON_VEC(long long)
+COMMON_JSON_VEC(unsigned long long)
+COMMON_JSON_VEC(float)
+COMMON_JSON_VEC(double)
+COMMON_JSON_VEC(std::string)
+COMMON_JSON_VEC(std::vector<float>)
+COMMON_JSON_VEC(common_json)
+
+#undef COMMON_JSON_VEC
+
+common_json_value::common_json_value(std::initializer_list<common_json_item> items) :
+    type(VAL_JSON), val_json(std::make_shared<common_json>(items)) {}
+
+// null, same as the backing library
+// operator[] turns it into an object, push_back() into an array
+common_json::common_json() {
+    new (storage) ordered_json();
+}
+
+common_json::common_json(const common_json & other) {
+    new (storage) ordered_json(as_json(&other));
+}
+
+common_json::common_json(common_json && other) noexcept {
+    new (storage) ordered_json(std::move(as_json(&other)));
+}
+
+common_json::common_json(std::initializer_list<common_json_item> items) {
+    new (storage) ordered_json(ordered_json::object());
+
+    for (const auto & item : items) {
+        set(item);
+    }
+}
+
+common_json::common_json(const common_json_value & val) {
+    new (storage) ordered_json(to_json(val));
+}
+
+common_json::common_json(std::nullptr_t) {
+    new (storage) ordered_json(nullptr);
+}
+
+common_json & common_json::operator=(common_json other) noexcept {
+    as_json(this).swap(as_json(&other));
+
+    return *this;
+}
+
+common_json::~common_json() {
+    as_json(this).~basic_json();
+}
+
+common_json common_json::parse(const std::string & text) {
+    try {
+        // the assignment moves the parsed tree in, it does not copy
+        common_json out;
+        as_json(&out) = ordered_json::parse(text);
+        return out;
+    } catch (const std::exception & e) {
+        throw common_json_error(e.what());
+    }
+}
+
+common_json common_json::parse_no_throw(const std::string & text) {
+    common_json out;
+    as_json(&out) = ordered_json::parse(text, nullptr, false);
+    return out;
+}
+
+bool common_json::is_discarded() const {
+    return as_json(this).is_discarded();
+}
+
+common_json common_json::array() {
+    common_json out;
+    as_json(&out) = ordered_json::array();
+    return out;
+}
+
+common_json common_json::array(std::initializer_list<common_json_value> vals) {
+    common_json out;
+    ordered_json & arr = as_json(&out);
+    arr = ordered_json::array();
+
+    for (const auto & val : vals) {
+        arr.push_back(to_json(val));
+    }
+
+    return out;
+}
+
+common_json common_json::object() {
+    common_json out;
+    as_json(&out) = ordered_json::object();
+    return out;
+}
+
+common_json common_json::object(std::initializer_list<common_json_item> items) {
+    return common_json(items);
+}
+
+common_json common_json::make(const common_json_value & val) {
+    return common_json(val);
+}
+
+bool common_json::is_null()           const { return as_json(this).is_null(); }
+bool common_json::is_object()         const { return as_json(this).is_object(); }
+bool common_json::is_array()          const { return as_json(this).is_array(); }
+bool common_json::is_string()         const { return as_json(this).is_string(); }
+bool common_json::is_boolean()        const { return as_json(this).is_boolean(); }
+bool common_json::is_number()         const { return as_json(this).is_number(); }
+bool common_json::is_number_integer() const { return as_json(this).is_number_integer(); }
+bool common_json::is_number_float()   const { return as_json(this).is_number_float(); }
+
+bool   common_json::empty() const { return as_json(this).empty(); }
+size_t common_json::size()  const { return as_json(this).size(); }
+
+bool common_json::contains(const std::string & key) const {
+    return as_json(this).contains(key);
+}
+
+bool common_json::operator==(const common_json_value & val) const {
+    // compare a tree in place, to_json() would copy it
+    if (val.type == common_json_value::VAL_JSON) {
+        return as_json(this) == as_json(val.val_json.get());
+    }
+    return as_json(this) == to_json(val);
+}
+
+bool common_json::operator!=(const common_json_value & val) const {
+    return !(*this == val);
+}
+
+common_json       & common_json::at(const std::string & key)       { return guard([&]() -> common_json       & { return as_common(as_json(this).at(key)); }); }
+const common_json & common_json::at(const std::string & key) const { return guard([&]() -> const common_json & { return as_common(as_json(this).at(key)); }); }
+common_json       & common_json::at(size_t idx)                    { return guard([&]() -> common_json       & { return as_common(as_json(this).at(idx)); }); }
+const common_json & common_json::at(size_t idx)              const { return guard([&]() -> const common_json & { return as_common(as_json(this).at(idx)); }); }
+
+common_json       & common_json::operator[](const std::string & key)       { return guard([&]() -> common_json       & { return as_common(as_json(this)[key]); }); }
+const common_json & common_json::operator[](const std::string & key) const { return guard([&]() -> const common_json & { return as_common(as_json(this).at(key)); }); }
+common_json       & common_json::operator[](size_t idx)                    { return guard([&]() -> common_json       & { return as_common(as_json(this)[idx]); }); }
+const common_json & common_json::operator[](size_t idx)              const { return guard([&]() -> const common_json & { return as_common(as_json(this).at(idx)); }); }
+
+common_json       & common_json::front()       { return as_common(as_json(this).front()); }
+const common_json & common_json::front() const { return as_common(as_json(this).front()); }
+common_json       & common_json::back()        { return as_common(as_json(this).back()); }
+const common_json & common_json::back()  const { return as_common(as_json(this).back()); }
+
+void common_json::clear() {
+    as_json(this).clear();
+}
+
+void common_json::erase(const std::string & key) {
+    guard([&] { as_json(this).erase(key); });
+}
+
+void common_json::erase(size_t idx) {
+    guard([&] { as_json(this).erase(idx); });
+}
+
+void common_json::assign(const common_json_value & val) {
+    as_json(this) = to_json(val);
+}
+
+void common_json::set(const common_json_item & item) {
+    guard([&] { as_json(this)[item.key] = to_json(item.val); });
+}
+
+void common_json::push_back(const common_json_value & val) {
+    guard([&] { as_json(this).push_back(to_json(val)); });
+}
+
+void common_json::push_back(std::initializer_list<common_json_item> items) {
+    common_json val(items);
+
+    guard([&] { as_json(this).push_back(std::move(as_json(&val))); });
+}
+
+size_t common_json::count(const std::string & key) const {
+    return as_json(this).count(key);
+}
+
+void common_json::insert(const common_json & vals) {
+    guard([&] {
+        ordered_json & self = as_json(this);
+
+        self.insert(self.end(), as_json(&vals).begin(), as_json(&vals).end());
+    });
+}
+
+std::string common_json::dump(int indent) const {
+    return guard([&] { return as_json(this).dump(indent); });
+}
+
+std::string common_json::dump_safe(int indent) const {
+    return as_json(this).dump(indent, ' ', false, ordered_json::error_handler_t::replace);
+}
+
+// an array is indexed directly, an object needs a walk from the start
+common_json & common_json::iterator::operator*() const {
+    return guard([&]() -> common_json & {
+        ordered_json & j = as_json(node);
+
+        if (j.is_object()) {
+            return as_common(std::next(j.begin(), idx).value());
+        }
+        if (j.is_array()) {
+            return as_common(j[idx]);
+        }
+
+        // a plain value gives itself once, same as the backing library
+        return *node;
+    });
+}
+
+std::string common_json::iterator::key() const {
+    return guard([&] { return std::next(as_json(node).begin(), idx).key(); });
+}
+
+common_json::iterator common_json::begin() const {
+    return iterator(const_cast<common_json *>(this), 0);
+}
+
+common_json::iterator common_json::end() const {
+    return iterator(const_cast<common_json *>(this), size());
+}
+
+// the keys follow the backing library: the index for an array, "" for a plain value
+common_json::items_view::entry common_json::items_view::iterator::operator*() const {
+    return guard([&]() -> entry {
+        ordered_json & j = as_json(node);
+
+        if (j.is_object()) {
+            auto it = std::next(j.begin(), idx);
+
+            return { it.key(), as_common(it.value()) };
+        }
+        if (j.is_array()) {
+            return { std::to_string(idx), as_common(j[idx]) };
+        }
+
+        return { std::string(), *node };
+    });
+}
+
+common_json::items_view common_json::items() const {
+    return items_view(const_cast<common_json *>(this), size());
+}
+
+// the backing library cannot build a common_json, so this one is just a copy
+template <> common_json common_json::get<common_json>() const {
+    return *this;
+}
+
+// get<T>() is usable only for the types below
+
+#define COMMON_JSON_GET(...) template <> __VA_ARGS__ common_json::get<__VA_ARGS__>() const { return guard([&] { return as_json(this).get<__VA_ARGS__>(); }); }
+
+COMMON_JSON_GET(bool)
+COMMON_JSON_GET(int)
+COMMON_JSON_GET(unsigned int)
+COMMON_JSON_GET(long)
+COMMON_JSON_GET(unsigned long)
+COMMON_JSON_GET(long long)
+COMMON_JSON_GET(unsigned long long)
+COMMON_JSON_GET(float)
+COMMON_JSON_GET(double)
+COMMON_JSON_GET(std::string)
+COMMON_JSON_GET(std::vector<float>)
+COMMON_JSON_GET(std::vector<std::string>)
+COMMON_JSON_GET(std::set<std::string>)
+COMMON_JSON_GET(std::vector<int>)
+COMMON_JSON_GET(std::vector<size_t>)
+COMMON_JSON_GET(std::unordered_map<std::string, size_t>)
+
+#undef COMMON_JSON_GET
+
+// must stay below the get<std::string> specialization
+common_json::operator std::string() const {
+    return get<std::string>();
+}
+
+std::string common_json::value(const std::string & key, const char * def) const {
+    return contains(key) ? at(key).get<std::string>() : std::string(def);
+}
@@ -0,0 +1,352 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+#include <initializer_list>
+#include <iterator>
+#include <map>
+#include <memory>
+#include <set>
+#include <stdexcept>
+#include <string>
+#include <string_view>
+#include <type_traits>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+// common_json, a thin wrapper around vendor json library
+// the underlay library is pimpl, we are using nlohmann::json for now
+//
+// many features of the library are deliberately left out, to keep this interface small and generic and to keep compile time down
+//
+// some main differences compared to nlohmann::json :
+// - object keys keep the order in which they are added
+// - errors are always throw as common_json_error
+// - obj.push_back({key, val}) is intentionally unsupported to avoid confusion with push_back on a vector; write it as obj[key] = val for clarity
+// - a braced pair in value position does not build, e.g. {"key", {"a", "b"}}; write array({"a", "b"}) where nlohmann made an array
+//
+// in doubt, search the code base for an existing usage example; do not add anything to this header unless absolutely necessary
+
+class common_json;
+
+// common_json_value holds a list of these, and each of them holds a value, so one must come first
+struct common_json_item;
+
+struct common_json_error : std::runtime_error {
+    using std::runtime_error::runtime_error;
+};
+
+// one value, tagged so that this header stays free of the backing library
+// note: a value that holds a tree is single use, the second use gives null
+struct common_json_value {
+    enum value_type {
+        VAL_NULL,
+        VAL_BOOL,
+        VAL_INT,
+        VAL_UINT,
+        VAL_DOUBLE,
+        VAL_STRING,
+        VAL_JSON,
+    };
+
+    value_type type = VAL_NULL;
+
+    union {
+        bool     val_bool;
+        int64_t  val_int;
+        uint64_t val_uint = 0;
+        double   val_double;
+    };
+
+    std::string                  val_string;
+    std::shared_ptr<common_json> val_json;
+
+    common_json_value(std::nullptr_t = nullptr) : type(VAL_NULL) {}
+    common_json_value(bool val) : type(VAL_BOOL), val_bool(val) {}
+    common_json_value(std::string val) : type(VAL_STRING), val_string(std::move(val)) {}
+    // without this a string_view lands on the common_json ctor below and recurses
+    common_json_value(std::string_view val) : type(VAL_STRING), val_string(val) {}
+    common_json_value(const char * val);
+    common_json_value(const common_json & val);
+    common_json_value(common_json && val);
+    // only for the types instantiated in json.cpp, the rest fails at link time
+    template <typename T> common_json_value(const std::vector<T> & vals);
+    // a set becomes an array, in the set's own order
+    template <typename T> common_json_value(const std::set<T> & vals);
+    // a map becomes an object, keyed in the map's own order
+    template <typename T> common_json_value(const std::map<std::string, T> & vals);
+    template <typename T> common_json_value(const std::unordered_map<std::string, T> & vals);
+
+    // nested object, e.g. {"fn", {{"name", "x"}}}
+    // note: a nested pair {"a", "b"} does not build, use common_json::array({"a", "b"}) for an array
+    common_json_value(std::initializer_list<common_json_item> items);
+
+    template <typename T, typename std::enable_if<std::is_integral<T>::value && !std::is_same<T, bool>::value, int>::type = 0>
+    common_json_value(T val) : type(std::is_signed<T>::value ? VAL_INT : VAL_UINT) {
+        if (std::is_signed<T>::value) {
+            val_int = (int64_t) val;
+        } else {
+            val_uint = (uint64_t) val;
+        }
+    }
+
+    template <typename T, typename std::enable_if<std::is_floating_point<T>::value, int>::type = 0>
+    common_json_value(T val) : type(VAL_DOUBLE), val_double((double) val) {}
+};
+
+struct common_json_item {
+    std::string       key;
+    common_json_value val;
+
+    template <typename T>
+    common_json_item(std::string key, T && val) :
+        key(std::move(key)), val(std::forward<T>(val)) {}
+
+    // a braced list cannot deduce T, so it needs its own overload
+    common_json_item(std::string key, std::initializer_list<common_json_item> items) :
+        key(std::move(key)), val(items) {}
+};
+
+// the types common_json_value holds on its own
+// anything else reaches its common_json ctor and recurses forever
+template <typename T> struct common_json_is_value : std::integral_constant<bool,
+    std::is_arithmetic<T>::value ||
+    std::is_same<T, std::nullptr_t>::value ||
+    std::is_same<T, std::string>::value ||
+    std::is_same<T, std::string_view>::value ||
+    std::is_same<T, char *>::value ||
+    std::is_same<T, const char *>::value ||
+    std::is_same<T, common_json>::value> {};
+
+template <typename T, typename A>
+struct common_json_is_value<std::vector<T, A>> : std::true_type {};
+
+template <typename T, typename C, typename A>
+struct common_json_is_value<std::set<T, C, A>> : std::true_type {};
+
+template <typename V, typename C, typename A>
+struct common_json_is_value<std::map<std::string, V, C, A>> : std::true_type {};
+
+template <typename V, typename H, typename E, typename A>
+struct common_json_is_value<std::unordered_map<std::string, V, H, E, A>> : std::true_type {};
+
+class common_json {
+  public:
+    common_json();
+    common_json(const common_json & other);
+    common_json(common_json && other) noexcept;
+    common_json(std::initializer_list<common_json_item> items);
+    common_json(const common_json_value & val);
+
+    // direct, a value would need two conversions in a row
+    common_json(std::nullptr_t);
+
+    // one step, so that "abc" or a vector can go straight into a common_json
+    template <typename T, typename std::enable_if<!std::is_same<typename std::decay<T>::type, common_json>::value &&
+                                                  !std::is_same<typename std::decay<T>::type, common_json_value>::value, int>::type = 0>
+    common_json(T && val) : common_json(common_json_value(std::forward<T>(val))) {
+        static_assert(common_json_is_value<typename std::decay<T>::type>::value,
+                      "no common_json_value ctor holds this type, add one instead of letting it recurse");
+    }
+
+    // by value, same as the backing library
+    // the right side is copied before the left side can invalidate it, e.g. msg["a"] = msg.at("b")
+    common_json & operator=(common_json other) noexcept;
+
+    ~common_json();
+
+    // throws common_json_error if the text is not valid JSON
+    static common_json parse(const std::string & text);
+
+    // gives a discarded value instead of throwing, check it with is_discarded()
+    static common_json parse_no_throw(const std::string & text);
+
+    bool is_discarded() const;
+
+    static common_json array();
+    static common_json array(std::initializer_list<common_json_value> vals);
+    static common_json object();
+    static common_json object(std::initializer_list<common_json_item> items);
+
+    // holds a single value, e.g. make("abc").dump() gives "\"abc\""
+    static common_json make(const common_json_value & val);
+
+    bool is_null()    const;
+    bool is_object()  const;
+    bool is_array()   const;
+    bool is_string()  const;
+    bool is_boolean() const;
+    bool is_number()  const;
+    bool is_number_integer() const;
+    bool is_number_float()   const;
+
+    bool   empty() const;
+    size_t size()  const;
+
+    bool contains(const std::string & key) const;
+
+    bool operator==(const common_json_value & val) const;
+    bool operator!=(const common_json_value & val) const;
+
+    // at() throws common_json_error if the key is missing, operator[] adds a null value instead
+    // note: a const operator[] cannot add, it throws like at()
+    common_json       & at(const std::string & key);
+    const common_json & at(const std::string & key) const;
+    common_json       & at(size_t idx);
+    const common_json & at(size_t idx) const;
+
+    common_json       & operator[](const std::string & key);
+    const common_json & operator[](const std::string & key) const;
+    common_json       & operator[](const char * key)       { return (*this)[std::string(key)]; }
+    const common_json & operator[](const char * key) const { return (*this)[std::string(key)]; }
+    common_json       & operator[](int idx)       { return (*this)[to_idx(idx)]; }
+    const common_json & operator[](int idx) const { return (*this)[to_idx(idx)]; }
+    common_json       & operator[](size_t idx);
+    const common_json & operator[](size_t idx) const;
+
+    common_json       & front();
+    const common_json & front() const;
+    common_json       & back();
+    const common_json & back()  const;
+
+    void clear();
+
+    void erase(const std::string & key);
+    void erase(size_t idx);
+
+    // only for the types instantiated in json.cpp, the rest fails at link time
+    template <typename T> T get() const;
+
+    // implicit get<T>() for plain values, so they can be assigned to their C++ type directly
+    // note: kept to this short list on purpose, a wider one makes j["key"] ambiguous
+    // note: a numeric one would make "str = json;" ambiguous, a number converts to char too
+    operator std::string() const;
+
+    template <typename T>
+    T value(const std::string & key, T def) const {
+        return contains(key) ? at(key).get<T>() : def;
+    }
+
+    std::string value(const std::string & key, const char * def) const;
+
+    // a JSON default needs no get<T>(), it is already the right type
+    common_json value(const std::string & key, const common_json & def) const {
+        return contains(key) ? at(key) : def;
+    }
+
+    void assign(const common_json_value & val);
+    void set(const common_json_item & item);
+    void push_back(const common_json_value & val);
+
+    // appends one object, e.g. push_back({{"a", 1}})
+    void push_back(std::initializer_list<common_json_item> items);
+
+    // 1 if the key is there, 0 if not
+    size_t count(const std::string & key) const;
+
+    // appends every value of another array; inserting an array into itself throws
+    void insert(const common_json & vals);
+
+    // a common_json goes through the copy assignment above, everything else becomes a value
+    template <typename T, typename std::enable_if<!std::is_same<typename std::decay<T>::type, common_json>::value, int>::type = 0>
+    common_json & operator=(T && val) {
+        assign(common_json_value(std::forward<T>(val)));
+        return *this;
+    }
+
+    std::string dump(int indent = -1) const;
+
+    // same as dump(), but bad UTF-8 gets replaced instead of throwing
+    std::string dump_safe(int indent = -1) const;
+
+    // walks an array by index, or an object in insertion order
+    // a plain value gives itself once, same as the backing library
+    class iterator {
+      public:
+        using iterator_category = std::forward_iterator_tag;
+        using value_type        = common_json;
+        using difference_type   = std::ptrdiff_t;
+        using pointer           = common_json *;
+        using reference         = common_json &;
+
+        iterator(common_json * node, size_t idx) : node(node), idx(idx) {}
+
+        common_json & operator*() const;
+        common_json & value()     const { return **this; }
+        std::string   key()       const;
+
+        iterator & operator++() {
+            idx++;
+            return *this;
+        }
+
+        bool operator!=(const iterator & other) const { return idx != other.idx; }
+        bool operator==(const iterator & other) const { return idx == other.idx; }
+
+      private:
+        common_json * node;
+        size_t        idx;
+    };
+
+    iterator begin() const;
+    iterator end()   const;
+
+    // allows: for (const auto & [key, val] : obj.items())
+    class items_view {
+      public:
+        // the members are public, so an entry also works with structured bindings
+        struct entry {
+            std::string   k;
+            common_json & v;
+
+            const std::string & key()   const { return k; }
+            common_json &       value() const { return v; }
+        };
+
+        items_view(common_json * node, size_t n) : node(node), n(n) {}
+
+        class iterator {
+          public:
+            iterator(common_json * node, size_t idx) : node(node), idx(idx) {}
+
+            entry operator*() const;
+
+            iterator & operator++() {
+                idx++;
+                return *this;
+            }
+
+            bool operator!=(const iterator & other) const { return idx != other.idx; }
+
+          private:
+            common_json * node;
+            size_t        idx;
+        };
+
+        iterator begin() const { return iterator(node, 0); }
+        iterator end()   const { return iterator(node, n); }
+
+      private:
+        common_json * node;
+        size_t        n;
+    };
+
+    items_view items() const;
+
+  private:
+    // a negative index must not turn into a huge size_t
+    static size_t to_idx(int idx) {
+        if (idx < 0) {
+            throw common_json_error("negative array index");
+        }
+        return (size_t) idx;
+    }
+
+    // the backing value is built here, json.cpp checks that it fits
+    // it cannot be a pointer: a value inside a tree would then not be a common_json
+    // at() could then only give back a copy instead of a real reference
+    alignas(8) unsigned char storage[32];
+};
+
+using common_json_entry = common_json::items_view::entry;
@@ -1,5 +1,6 @@
 #include "common.h"
 #include "log.h"
+#include "json.h"

 #include <chrono>
 #include <condition_variable>
@@ -66,6 +67,17 @@ static const char* g_col[] = {
    "",
 };

+static const char * level_str(enum ggml_log_level level) {
+    switch (level) {
+        case GGML_LOG_LEVEL_DEBUG: return "debug";
+        case GGML_LOG_LEVEL_INFO:  return "info";
+        case GGML_LOG_LEVEL_WARN:  return "warn";
+        case GGML_LOG_LEVEL_ERROR: return "error";
+        case GGML_LOG_LEVEL_CONT:  return "cont";
+        default:                   return "none";
+    }
+}
+
 struct common_log_entry {
    enum ggml_log_level level {GGML_LOG_LEVEL_INFO};

@@ -74,6 +86,7 @@ struct common_log_entry {
    int64_t timestamp { 0 };
    bool is_end       { false }; // signals the worker thread to stop
    bool prefix       { false };
+    bool jsonl        { false };

    common_log_entry(size_t size = 256) : msg(size) { }

@@ -88,11 +101,23 @@ struct common_log_entry {

            fcur = stdout;

-            if (level != GGML_LOG_LEVEL_NONE) {
+            if (level != GGML_LOG_LEVEL_NONE && !jsonl) {
                fcur = stderr;
            }
        }

+        if (jsonl) {
+            common_json obj = {
+                {"type",  "log"},
+                {"time",  timestamp},
+                {"level", level_str(level)},
+                {"msg",   msg.data()},
+            };
+            fprintf(fcur, "%s\n", obj.dump_safe().c_str());
+            fflush(fcur);
+            return;
+        }
+
        if (level != GGML_LOG_LEVEL_NONE && level != GGML_LOG_LEVEL_CONT && prefix) {
            if (timestamp) {
                // [M.s.ms.us]
@@ -131,6 +156,7 @@ struct common_log {
        file       = nullptr;
        prefix     = false;
        timestamps = false;
+        jsonl      = false;
        running    = false;
        t_start    = t_us();

@@ -158,6 +184,7 @@ private:

    bool prefix;
    bool timestamps;
+    bool jsonl;
    bool running;

    int64_t t_start;
@@ -246,6 +273,7 @@ public:
        entry.is_end    = false;
        entry.level     = level;
        entry.prefix    = prefix;
+        entry.jsonl     = jsonl;
        entry.timestamp = 0;
        if (timestamps) {
            entry.timestamp = t_us() - t_start;
@@ -360,6 +388,12 @@ public:

        this->timestamps = timestamps;
    }
+
+    void set_jsonl(bool jsonl) {
+        std::lock_guard<std::mutex> lock(mtx);
+
+        this->jsonl = jsonl;
+    }
 };

 //
@@ -433,12 +467,16 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps) {
    log->set_timestamps(timestamps);
 }

+void common_log_set_jsonl(struct common_log * log, bool jsonl) {
+    log->set_jsonl(jsonl);
+}
+
 void common_log_flush(struct common_log * log) {
    log->pause();
    log->resume();
 }

-static int common_get_verbosity(enum ggml_log_level level) {
+int common_log_get_verbosity(enum ggml_log_level level) {
    switch (level) {
        case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG;
        case GGML_LOG_LEVEL_INFO:  return LOG_LEVEL_TRACE;
@@ -452,7 +490,7 @@ static int common_get_verbosity(enum ggml_log_level level) {
 }

 void common_log_default_callback(enum ggml_log_level level, const char * text, void * /*user_data*/) {
-    auto verbosity = common_get_verbosity(level);
+    auto verbosity = common_log_get_verbosity(level);
    if (verbosity <= common_log_verbosity_thold) {
        common_log_add(common_log_main(), level, "%s", text);
    }
@@ -43,6 +43,8 @@ int  common_log_get_verbosity_thold(void);

 void common_log_set_verbosity_thold(int verbosity); // not thread-safe

+int common_log_get_verbosity(enum ggml_log_level level);
+
 void common_log_default_callback(enum ggml_log_level level, const char * text, void * user_data);

 // the common_log uses an internal worker thread to print/write log messages
@@ -89,6 +91,7 @@ void common_log_set_file      (struct common_log * log, const char * file); // n
 void common_log_set_colors    (struct common_log * log, log_colors colors); // not thread-safe
 void common_log_set_prefix    (struct common_log * log, bool prefix);       // whether to output prefix to each log
 void common_log_set_timestamps(struct common_log * log, bool timestamps);   // whether to output timestamps in the prefix
+void common_log_set_jsonl     (struct common_log * log, bool jsonl);        // print each log as a JSON object on one line, not thread-safe
 void common_log_flush         (struct common_log * log);                    // flush all pending log messages

 // helper macros for logging
@@ -0,0 +1,150 @@
+#include "parsers.h"
+
+// Cohere2 MoE (a.k.a. "North Code") parser.
+//
+// The assistant turn is fully marker-wrapped:
+//   <|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
+//     <|START_THINKING|>{reasoning}<|END_THINKING|>
+//     then EITHER content:    <|START_TEXT|>{content}<|END_TEXT|>
+//          OR     tool calls: <|START_ACTION|>[
+//                                 {"tool_call_id": "0", "tool_name": "f", "parameters": {...}}, ...
+//                             ]<|END_ACTION|>
+//   <|END_OF_TURN_TOKEN|>
+//
+// The generation prompt forces a leading <|START_THINKING|> (when reasoning is enabled, which is
+// the template default), so the model's output continues from *inside* the thinking block. The
+// parser literal therefore only covers the stable <|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> prefix
+// and the reasoning rule consumes the <|START_THINKING|> ... <|END_THINKING|> markers itself,
+// regardless of whether they came from the generation prompt or the generated text.
+common_chat_params common_chat_params_init_cohere2moe(const common_chat_template &          tmpl,
+                                                              const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    const std::string TURN_START    = "<|START_OF_TURN_TOKEN|>";
+    const std::string TURN_END      = "<|END_OF_TURN_TOKEN|>";
+    const std::string CHATBOT       = "<|CHATBOT_TOKEN|>";
+    const std::string USER          = "<|USER_TOKEN|>";
+    const std::string SYSTEM        = "<|SYSTEM_TOKEN|>";
+    const std::string THINK_START   = "<|START_THINKING|>";
+    const std::string THINK_END     = "<|END_THINKING|>";
+    const std::string TEXT_START    = "<|START_TEXT|>";
+    const std::string TEXT_END      = "<|END_TEXT|>";
+    const std::string ACTION_START  = "<|START_ACTION|>";
+    const std::string ACTION_END    = "<|END_ACTION|>";
+    const std::string RESULT_START  = "<|START_TOOL_RESULT|>";
+    const std::string RESULT_END    = "<|END_TOOL_RESULT|>";
+
+    // Stable prefix of the generation prompt that precedes the (forced) <|START_THINKING|> marker.
+    const std::string GEN_PREFIX = TURN_START + CHATBOT;
+
+    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking  = true;
+    data.thinking_start_tag = THINK_START;
+    data.thinking_end_tags  = {THINK_END};
+    data.preserved_tokens   = {
+        TURN_START, TURN_END, CHATBOT, USER, SYSTEM,
+        THINK_START, THINK_END,
+        TEXT_START, TEXT_END,
+        ACTION_START, ACTION_END,
+        RESULT_START, RESULT_END,
+    };
+
+    // Declare per-role message delimiters. Tool results are rendered with the
+    // system token followed by <|START_TOOL_RESULT|>, so the "tool" delimiter must be listed before
+    // the plain "system" one (it is a strict superset, and the role split tries delimiters in order).
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, GEN_PREFIX },
+        { COMMON_CHAT_ROLE_USER,      TURN_START + USER },
+        { COMMON_CHAT_ROLE_TOOL,      TURN_START + SYSTEM + RESULT_START },
+        { COMMON_CHAT_ROLE_SYSTEM,    TURN_START + SYSTEM },
+    };
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PREFIX + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + TEXT_START + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.literal(GEN_PREFIX);
+        auto end               = p.end();
+
+        // The thinking block is always present (the generation prompt forces <|START_THINKING|>).
+        // When extracting reasoning, capture its body; otherwise keep the whole block (markers
+        // included) inline as content, matching reasoning_format=NONE conventions.
+        common_peg_parser reasoning = p.eps();
+        if (extract_reasoning) {
+            reasoning = p.optional(p.literal(THINK_START) +
+                                   p.reasoning(p.until_one_of({ THINK_END, TEXT_START, ACTION_START })) +
+                                   p.optional(p.literal(THINK_END)));
+        } else {
+            reasoning = p.optional(p.content(p.literal(THINK_START) +
+                                             p.until_one_of({ THINK_END, TEXT_START, ACTION_START }) +
+                                             p.optional(p.literal(THINK_END))));
+        }
+
+        auto text_content = has_response_format
+            ? p.literal(TEXT_START) +
+                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
+                p.optional(p.literal(TEXT_END))
+            : p.literal(TEXT_START) + p.content(p.until(TEXT_END)) + p.optional(p.literal(TEXT_END));
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return generation_prompt + reasoning + text_content + p.optional(p.literal(TURN_END)) + end;
+        }
+
+        auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+
+        // <|START_ACTION|>[ {"tool_call_id": "0", "tool_name": "f", "parameters": {...}}, ... ]<|END_ACTION|>
+        auto tool_calls = p.standard_json_tools(ACTION_START, ACTION_END, inputs.tools, inputs.parallel_tool_calls,
+                                                /* force_tool_calls = */ true,
+                                                /* name_key         = */ "tool_name",
+                                                /* args_key         = */ "parameters",
+                                                /* array_wrapped    = */ true,
+                                                /* function_is_key  = */ false,
+                                                /* call_id_key      = */ "",
+                                                /* gen_call_id_key  = */ "tool_call_id",
+                                                /* parameters_order = */ { "tool_call_id", "tool_name", "parameters" });
+
+        // Content and tool calls are mutually exclusive in this format.
+        common_peg_parser body = require_tools ? tool_calls : p.choice({ tool_calls, text_content });
+
+        return generation_prompt + reasoning + body + p.optional(p.literal(TURN_END)) + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = !has_response_format && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, ACTION_START }
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,287 @@
+#include "parsers.h"
+
+// The DeepSeek V4 reference implementation renders consecutive tool results into a single
+// user block, ordered by the tool call order of the preceding assistant message (matched
+// by tool call id) rather than by the order they appear in the conversation.
+static json deepseek_v4_sort_tool_results(const json & messages) {
+    json adjusted = messages;
+    std::map<std::string, size_t> call_order;
+
+    for (size_t i = 0; i < adjusted.size();) {
+        const auto & msg  = adjusted[i];
+        const auto   role = msg.value("role", "");
+
+        if (role == "assistant" && msg.contains("tool_calls") &&
+                msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty()) {
+            call_order.clear();
+            const auto & tool_calls = msg.at("tool_calls");
+            for (size_t idx = 0; idx < tool_calls.size(); idx++) {
+                auto id = tool_calls[idx].value("id", "");
+                if (!id.empty()) {
+                    call_order[id] = idx;
+                }
+            }
+            i++;
+            continue;
+        }
+
+        if (role != "user" && role != "tool") {
+            i++;
+            continue;
+        }
+
+        // collect a maximal run of user/tool messages - they render into one user block
+        std::vector<size_t> tool_positions;
+        size_t run_end = i;
+        for (; run_end < adjusted.size(); run_end++) {
+            const auto r = adjusted[run_end].value("role", "");
+            if (r == "tool") {
+                tool_positions.push_back(run_end);
+            } else if (r != "user") {
+                break;
+            }
+        }
+
+        if (tool_positions.size() > 1 && !call_order.empty()) {
+            std::vector<json> results;
+            results.reserve(tool_positions.size());
+            for (auto pos : tool_positions) {
+                results.push_back(adjusted[pos]);
+            }
+            std::stable_sort(results.begin(), results.end(), [&](const json & a, const json & b) {
+                const auto order = [&](const json & m) {
+                    auto it = call_order.find(m.value("tool_call_id", ""));
+                    return it == call_order.end() ? (size_t) 0 : it->second;
+                };
+                return order(a) < order(b);
+            });
+            for (size_t k = 0; k < tool_positions.size(); k++) {
+                adjusted[tool_positions[k]] = std::move(results[k]);
+            }
+        }
+
+        i = run_end;
+    }
+
+    return adjusted;
+}
+
+common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template &    tmpl,
+                                                                 const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    // V4 uses the same DSML markup as V3.2, but names the tool call block "tool_calls"
+    // instead of "function_calls", renders tool results in tool call order and its
+    // non-thinking generation prompt ends with a bare </think> instead of an empty
+    // <think></think> pair.
+    const bool is_v4 = tmpl.source().find("function_calls") == std::string::npos;
+
+    std::optional<json> adjusted_messages;
+    if (is_v4) {
+        adjusted_messages = deepseek_v4_sort_tool_results(inputs.messages);
+    }
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    std::optional<json> additional_context;
+    if (is_v4 && has_response_format) {
+        additional_context = json{ { "response_format", inputs.json_schema } };
+    }
+
+    const std::string DSML         = "｜DSML｜";
+    const std::string THINK_START  = "<think>";
+    const std::string THINK_END    = "</think>";
+    const std::string TC_BLOCK     = is_v4 ? "tool_calls" : "function_calls";
+    const std::string FC_START     = "<" + DSML + TC_BLOCK + ">";
+    const std::string FC_END       = "</" + DSML + TC_BLOCK + ">";
+    const std::string INVOKE_START = "<" + DSML + "invoke";
+    const std::string INVOKE_END   = "</" + DSML + "invoke>";
+    const std::string PARAM_START  = "<" + DSML + "parameter";
+    const std::string PARAM_END    = "</" + DSML + "parameter>";
+    const std::string GEN_PROMPT   = "<｜Assistant｜>";
+    const std::string TC_SEPARATOR = "\n\n";
+
+    data.prompt = common_chat_template_direct_apply_impl(
+        tmpl, inputs, adjusted_messages, std::nullopt, additional_context);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(
+        tmpl, inputs, adjusted_messages, std::nullopt, additional_context);
+    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking  = true;
+    data.thinking_start_tag = THINK_START;
+    data.thinking_end_tags  = {THINK_END, FC_START};
+    data.preserved_tokens   = {
+        DSML,
+        THINK_START,
+        THINK_END,
+    };
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        if (is_v4 && msg.reasoning_content.empty()) {
+            data.generation_prompt = GEN_PROMPT + THINK_END;
+            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+                data.generation_prompt += msg.render_content();
+            }
+        } else {
+            data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+                data.generation_prompt += THINK_END + msg.render_content();
+            }
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    bool require_tools   = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+    bool has_tool_calls = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.literal(GEN_PROMPT);
+        auto end               = p.end();
+
+        // build tool call section first since we might need it in reasoning
+        auto tool_choice = p.choice();
+        if (has_tool_calls) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string  name     = function.at("name");
+                auto         params   = function.contains("parameters") ? function.at("parameters") : json::object();
+                const auto & props    = params.contains("properties") ? params.at("properties") : json::object();
+
+                std::set<std::string> required;
+                if (params.contains("required")) {
+                    required = params.at("required").get<std::set<std::string>>();
+                }
+
+                auto schema_info = common_schema_info();
+                schema_info.resolve_refs(params);
+
+                std::vector<common_peg_parser> required_parsers;
+                std::vector<common_peg_parser> optional_parsers;
+                for (const auto & [param_name, param_schema] : props.items()) {
+                    bool is_required = required.find(param_name) != required.end();
+                    bool is_string   = schema_info.resolves_to_string(param_schema);
+
+                    auto arg = p.tool_arg(
+                        p.tool_arg_open(p.literal(PARAM_START + " name=\"") + p.tool_arg_name(p.literal(param_name)) +
+                                        p.literal("\" string=\"" + std::string(is_string ? "true" : "false") + "\">")) +
+                        (is_string ?
+                             p.tool_arg_string_value(p.until(PARAM_END)) :
+                             p.tool_arg_json_value(p.schema(p.json(), "tool-" + name + "-arg-" + param_name + "-schema",
+                                                            param_schema, false))) +
+                        p.tool_arg_close(p.literal(PARAM_END)));
+
+                    auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
+                    if (is_required) {
+                        required_parsers.push_back(named_arg);
+                    } else {
+                        optional_parsers.push_back(named_arg);
+                    }
+                }
+
+                common_peg_parser args_seq = p.eps();
+                for (size_t i = 0; i < required_parsers.size(); i++) {
+                    if (i > 0) {
+                        args_seq = args_seq + p.space();
+                    }
+                    args_seq = args_seq + required_parsers[i];
+                }
+
+                if (!optional_parsers.empty()) {
+                    common_peg_parser any_opt = p.choice();
+                    for (const auto & opt : optional_parsers) {
+                        any_opt |= opt;
+                    }
+                    args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1);
+                }
+
+                common_peg_parser invoke_body = args_seq;
+                auto              func_parser = p.tool(p.tool_open(p.literal(INVOKE_START + " name=\"") +
+                                                                   p.tool_name(p.literal(name)) + p.literal("\">\n")) +
+                                                       invoke_body + p.space() + p.tool_close(p.literal(INVOKE_END)));
+
+                tool_choice |= p.rule("tool-" + name, func_parser);
+            });
+        }
+
+        common_peg_parser tool_calls = p.eps();
+        if (inputs.parallel_tool_calls) {
+            tool_calls = p.trigger_rule("tool-call",
+                p.literal(FC_START) + p.space() + tool_choice +
+                p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
+        } else {
+            tool_calls = p.trigger_rule("tool-call",
+                p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
+        }
+
+        auto reasoning = p.eps();
+        auto reasoning_with_tc = p.eps();
+        auto obligatory_tool_calls = tool_calls;
+        bool allow_reasoning_with_tc = false;
+
+        if (!require_tools) {
+            tool_calls = p.optional(tool_calls);
+        }
+
+        if (extract_reasoning && inputs.enable_thinking) {
+            reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
+            reasoning_with_tc = THINK_START +
+                p.reasoning(p.until_one_of({ TC_SEPARATOR + FC_START, FC_START, THINK_END })) +
+                p.space() + obligatory_tool_calls;
+            allow_reasoning_with_tc = true;
+        } else if (extract_reasoning) {
+            // Thinking disabled but reasoning extraction requested: the generation prompt
+            // contains an empty <think></think> pair (V3.2) or a bare </think> (V4) that
+            // must still be consumed.
+            reasoning = is_v4
+                ? p.optional(p.literal(THINK_END))
+                : p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END));
+        }
+
+        if (has_response_format) {
+            auto response_format = p.rule("response-format",
+                p.literal("```json") + p.space() +
+                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
+                p.space() + p.literal("```"));
+            return generation_prompt + reasoning + response_format + end;
+        }
+
+        if (!has_tool_calls) {
+            return generation_prompt + reasoning + p.content(p.rest()) + end;
+        }
+
+        auto content_before_tools = p.negate(p.literal(THINK_START)) +
+            p.content(p.until_one_of({ TC_SEPARATOR + FC_START, FC_START })) +
+            p.space();
+        return allow_reasoning_with_tc ? generation_prompt + (reasoning_with_tc | (reasoning + content_before_tools + tool_calls)) + end :
+            generation_prompt + reasoning + content_before_tools + tool_calls + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && !require_tools;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, FC_START },
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,101 @@
+#include "parsers.h"
+
+// Functionary v3.2 - uses recipient-based format: >>>recipient\n{content}
+common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template &    tmpl,
+                                                                   const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.preserved_tokens  = {
+        ">>>all",
+    };
+
+    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+        data.generation_prompt = "<|start_header_id|>assistant<|end_header_id|>\n\n>>>all\n" + msg.render_content();
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        // Functionary v3.2 format:
+        // - Normal content: >>>all\n{content}
+        // - Tool calls: >>>function_name\n{json_args}
+        // Generation prompt ends with ">>>" so model outputs recipient immediately
+
+        // Build content parser for >>>all\n{content}
+        // When tools are present, content stops before the next ">>>" (tool call)
+        // When no tools, content goes until end
+        auto content_until_tool = p.literal("all\n") + p.content(p.until(">>>"));
+        auto content_until_end  = p.literal("all\n") + p.content(p.rest());
+        auto generation_prompt  = p.literal("<|start_header_id|>assistant<|end_header_id|>\n\n>>>");
+
+        // If no tools or tool_choice is NONE, just parse content
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            // When no tools, just match the prefix and capture everything after
+            return generation_prompt + content_until_end + p.end();
+        }
+
+        // Build tool call parsers for each available function
+        auto tool_choice = p.choice();
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool.at("function");
+            std::string  name     = function.at("name");
+            const auto & schema   = function.at("parameters");
+
+            // Tool format: >>>function_name\n{json_args}
+            auto tool_parser = p.tool(
+                p.tool_open(p.tool_name(p.literal(name)) + p.literal("\n")) +
+                p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))
+            );
+
+            tool_choice |= p.rule("tool-" + name, tool_parser);
+        });
+
+        auto content_only = content_until_end;
+        auto tools_only = p.trigger_rule("tools", p.one_or_more(tool_choice));
+        auto content_and_tools = content_until_tool + tools_only;
+
+        auto ret = p.eps();
+        if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
+            if (inputs.parallel_tool_calls) {
+                ret = p.choice({ content_and_tools, tools_only }) + p.end();
+            } else {
+                ret = p.choice({ content_until_tool + tool_choice, tools_only }) + p.end();
+            }
+        } else if (inputs.parallel_tool_calls) {
+            ret = p.choice({ content_and_tools, content_only, tools_only }) + p.end();
+        } else {
+            auto content_and_tool = content_until_tool + tool_choice;
+            ret = p.choice({ content_and_tool, content_only, tool_choice }) + p.end();
+        }
+        return generation_prompt + ret;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        // Grammar trigger for when the model starts outputting a tool call
+        // (after the initial ">>>" in the generation prompt but recipient other than "all")
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, ">>>(?!all)" }
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,312 @@
+#include "parsers.h"
+
+namespace workaround {
+
+// Gemma4 uses a custom tool_responses field instead of role:tool messages.
+//
+// This will transform a sequence of messages:
+//   assistant(tool_call+) -> tool+ -> assistant(content)
+//
+// Into a single assistant message containing a tool_responses field:
+//   assistant(content + tool_call + tool_responses)
+//
+// This is necessary for the Gemma4 chat template to properly format the prompt.
+// See https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4
+struct gemma4_model_turn_builder {
+    json & messages;
+    size_t pos;
+    json tool_calls = json::array();
+    json tool_responses = json::array();
+    json content;
+    json reasoning_content;
+
+    gemma4_model_turn_builder(json & msgs, size_t pos) : messages(msgs), pos(pos) {}
+
+    void collect() {
+        // Collect the first assistant message
+        auto & msg = messages[pos];
+        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
+            // According to the prompt formatting guide, we need to preserve reasoning_content
+            // between function calls. The current chat templates do not support this, but we will do it anyway.
+            reasoning_content = msg.at("reasoning_content");
+        }
+        for (auto & tc : msg.at("tool_calls")) {
+            tool_calls.push_back(tc);
+        }
+        pos++;
+
+        // Collect tool call results
+        while (pos < messages.size() && messages[pos].value("role", "") == "tool") {
+            collect_result(messages[pos]);
+            pos++;
+        }
+
+        // Check if the next assistant message is the final message
+        if (pos < messages.size() && messages[pos].value("role", "") == "assistant") {
+            auto & next = messages[pos];
+            if (!has_tool_calls(next) && has_content(next)) {
+                content = next.at("content");
+                pos++;
+            }
+        }
+    }
+
+    void collect_result(const json & curr) {
+        json response;
+        if (curr.contains("content")) {
+            const auto & content = curr.at("content");
+            if (content.is_string()) {
+                // Try to parse the content as JSON; fall back to raw string
+                try {
+                    response = json::parse(content.get<std::string>());
+                } catch (...) {
+                    response = content;
+                }
+            } else {
+                response = content;
+            }
+        }
+
+        std::string name;
+
+        // Match name with corresponding tool call
+        size_t idx = tool_responses.size();
+        if (idx < tool_calls.size()) {
+            auto & tc = tool_calls[idx];
+            if (tc.contains("function")) {
+                name = tc.at("function").value("name", "");
+            }
+        }
+
+        // Fallback to the tool call id
+        if (name.empty()) {
+            name = curr.value("tool_call_id", "");
+        }
+
+        tool_responses.push_back({{"name", name}, {"response", response}});
+    }
+
+    json build() {
+        collect();
+
+        json msg = {
+            {"role", "assistant"},
+            {"tool_calls", tool_calls},
+        };
+        if (!tool_responses.empty()) {
+            msg["tool_responses"] = tool_responses;
+        }
+        if (!content.is_null()) {
+            msg["content"] = content;
+        }
+        if (!reasoning_content.is_null()) {
+            msg["reasoning_content"] = reasoning_content;
+        }
+        return msg;
+    }
+
+    static bool has_content(const json & msg) {
+        if (!msg.contains("content") || msg.at("content").is_null()) {
+            return false;
+        }
+        const auto & content = msg.at("content");
+        if (content.is_string() && !content.get<std::string>().empty()) {
+            return true;
+        }
+        if (content.is_array() && !content.empty()) {
+            return true;
+        }
+        return false;
+    }
+
+    static bool has_tool_calls(const json & msg) {
+        return msg.contains("tool_calls") && msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty();
+    }
+};
+
+void convert_tool_responses_gemma4(json & messages) {
+    json result = json::array();
+    size_t i = 0;
+
+    while (i < messages.size()) {
+        auto & msg = messages[i];
+
+        if (msg.value("role", "") != "assistant" || !msg.contains("tool_calls") ||
+            !msg.at("tool_calls").is_array() || msg.at("tool_calls").empty()) {
+            result.push_back(msg);
+            i++;
+            continue;
+        }
+
+        gemma4_model_turn_builder builder(messages, i);
+        result.push_back(builder.build());
+        i = builder.pos;
+    }
+
+    messages = result;
+}
+
+}
+
+common_chat_params common_chat_params_init_gemma4(const common_chat_template &    tmpl,
+                                                         const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+
+    if (inputs.add_generation_prompt && string_ends_with(data.prompt, "<turn|>\n")) {
+        // This may happen if the model generates content + tool_call, the
+        // template does not add the model's next turn and confuses the model
+        // from emitting its proper reasoning token sequence.
+        data.generation_prompt = "<|turn>model\n";
+        data.prompt += data.generation_prompt;
+    }
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_USER,      "<|turn>user"  },
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|turn>model" },
+    };
+
+    data.format            = COMMON_CHAT_FORMAT_PEG_GEMMA4;
+    data.supports_thinking  = true;
+    data.thinking_start_tag = "<|channel>thought";
+    data.thinking_end_tags  = {"<channel|>"};
+
+    data.preserved_tokens = {
+        "<|channel>",
+        "<channel|>",
+        "<|tool_call>",
+        "<tool_call|>",
+        "<|turn>",
+    };
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = string_ends_with(data.prompt, "<turn|>\n") ? "<|turn>model\n" : "";
+        data.generation_prompt += "<|channel>thought\n" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "<channel|>" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto start = p.rule("start", p.optional(p.literal("<|turn>model\n")));
+
+        if (extract_reasoning) {
+            p.rule("thought", p.literal("<|channel>thought") + p.space() + p.reasoning(p.until("<channel|>")) + p.literal("<channel|>"));
+        } else {
+            p.rule("thought", p.content(p.literal("<|channel>thought") + p.space() + p.until("<channel|>") + p.literal("<channel|>")));
+        }
+
+        auto consume_empty_channels = p.gbnf(p.zero_or_more(p.literal("<|channel>") + p.negate(p.literal("thought"))), "");
+        auto thought = (p.peek(p.literal("<|channel>")) + consume_empty_channels + p.ref("thought")) | p.negate(p.literal("<|channel>"));
+
+        if (has_response_format) {
+            auto response_format = p.literal("```json") <<
+                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) <<
+                p.literal("```");
+            return start + p.optional(thought) + response_format;
+        }
+
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            // Gemma4 tool calling syntax
+            // Rules should match traversal logic in gemma4_to_json()
+            p.rule("gemma4-string-content", p.until("<|\"|>"));
+            p.rule("gemma4-string", p.literal("<|\"|>") + p.ref("gemma4-string-content") + p.literal("<|\"|>"));
+            p.rule("gemma4-bool", p.json_bool());
+            p.rule("gemma4-null", p.json_null());
+            p.rule("gemma4-number", p.json_number());
+            p.rule("gemma4-dict-key", p.rule("gemma4-dict-key-name", p.chars("[^:}]", 1, -1)) + p.literal(":"));
+            p.rule("gemma4-dict-kv", p.ref("gemma4-dict-key") + p.space() + p.ref("gemma4-value"));
+            p.rule("gemma4-dict", [&]() {
+                auto ws = p.space();
+                auto member = p.ref("gemma4-dict-kv");
+                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("}")})})
+                });
+            });
+            p.rule("gemma4-array", [&]() {
+                auto ws = p.space();
+                auto value = p.ref("gemma4-value");
+                auto elements = p.sequence({value, p.zero_or_more(p.sequence({p.literal(","), ws, value}))});
+                return p.sequence({
+                    p.literal("["), ws,
+                    p.choice({p.literal("]"), p.sequence({elements, ws, p.literal("]")})})
+                });
+            });
+            p.rule("gemma4-value", [&]() {
+                return p.choice({
+                    p.ref("gemma4-string"), p.ref("gemma4-dict"), p.ref("gemma4-array"),
+                    p.ref("gemma4-number"), p.ref("gemma4-bool"), p.ref("gemma4-null")
+                });
+            });
+
+            auto tool_choice = p.choice();
+
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string  name     = function.at("name");
+                // TODO @aldehir : need to extend json-schema-to-grammar to produce more than JSON rules
+                // const auto & params   = function.at("parameters");
+
+                tool_choice |= p.rule("tool-" + name, p.tool(p.sequence({
+                    p.tool_open(p.tool_name(p.literal(name)) + p.peek(p.literal("{"))),
+                    p.tool_args(p.ref("gemma4-dict")),
+                })));
+            });
+
+            auto tool_call = p.trigger_rule("tool-call", p.repeat(
+                "<|tool_call>call:" + tool_choice + "<tool_call|>",
+                /* min = */ inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0,
+                /* max = */ inputs.parallel_tool_calls ? -1 : 1
+            ));
+
+            auto scan_to_toolcall = p.rule("scan-to-toolcall", p.until("<|tool_call>"));
+            auto content = p.rule("content", p.content(p.until_one_of({"<|channel>", "<channel|>", "<|tool_call>"})));
+            auto message = p.rule("message", thought + content);
+            return start + p.zero_or_more(message) + scan_to_toolcall + tool_call;
+        }
+
+        // Gemma 4 may emit an extra <|channel>thought\n<channel|> at the end of the content. It may
+        // also emit a single trailing <channel|> token. Consume all complete reasoning blocks and
+        // then stop at the first unmatched <channel|> token.
+        auto content = p.rule("content", p.content(p.until_one_of({"<|channel>", "<channel|>"})));
+        auto message = p.rule("message", thought + content);
+        return start + p.one_or_more(message);
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|tool_call>" },
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,81 @@
+#include "parsers.h"
+
+common_chat_params common_chat_params_init_gigachat_v3(
+        const common_chat_template & tmpl,
+        const autoparser::generation_params & inputs) {
+
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = false;
+    data.preserved_tokens  = {
+        "<|message_sep|>\n\n",
+        "<|role_sep|>\n",
+    };
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+        data.generation_prompt = "assistant<|role_sep|>\n" + msg.render_content();
+        data.prompt += data.generation_prompt;
+    }
+
+    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+    const auto *tool_call_start_prefix = "<|message_sep|>\n\nfunction call<|role_sep|>\n";
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto ret = p.eps();
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            // Build a choice of all available tools
+            auto tool_choice = p.choice();
+            for (const auto & tool : inputs.tools) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                const auto & schema = function.at("parameters");
+
+                auto tool_name = p.json_member("name", "\"" + p.tool_name(p.literal(name)) + "\"");
+                auto tool_args = p.json_member("arguments", p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)));
+
+                auto tool_open = p.tool_open(p.literal("{") << tool_name);
+
+                tool_choice |= p.rule("tool-" + name, tool_open << "," << tool_args << "}");
+            }
+
+            // Define the tool call structure
+            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+            auto max_calls = 1; // parallel toolcalls are not supported
+            auto tool_call = p.rule("tool-call", p.literal(tool_call_start_prefix) + tool_choice);
+            auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(tool_call, /* min = */ min_calls, /* max = */ max_calls));
+
+            ret = p.content(p.until("<|message_sep|>\n\n")) << tool_calls;
+        } else {
+            // Content only parser
+            include_grammar = false;
+            ret = p.content(p.rest());
+        }
+
+        return p.literal("assistant<|role_sep|>\n") + ret;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto schema = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, tool_call_start_prefix}
+        };
+    }
+    return data;
+}
@@ -0,0 +1,167 @@
+#include "parsers.h"
+
+common_chat_params common_chat_params_init_gpt_oss(const common_chat_template &    tmpl,
+                                                          const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    // Copy reasoning to the "thinking" field as expected by the gpt-oss template
+    auto adjusted_messages = json::array();
+    for (auto msg : inputs.messages) {
+        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
+            msg["thinking"] = msg.at("reasoning_content");
+            if (msg.contains("tool_calls") && msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty()) {
+                msg.erase("content");
+            }
+        }
+        adjusted_messages.push_back(msg);
+    }
+
+    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:
+    // https://github.com/ggml-org/llama.cpp/issues/15417
+    if (inputs.is_inference && !inputs.add_generation_prompt) {
+        static constexpr std::string_view return_token = "<|return|>";
+        static constexpr std::string_view end_token    = "<|end|>";
+        if (size_t pos = prompt.rfind(return_token); pos != std::string::npos) {
+            prompt.replace(pos, return_token.length(), end_token);
+        }
+    }
+
+    data.prompt            = prompt;
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override= */ adjusted_messages);
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
+        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>developer" },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
+        { COMMON_CHAT_ROLE_TOOL,      "<|start|>functions" },
+    };
+
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+
+    data.thinking_start_tag = "<|channel|>analysis<|message|>";
+    data.thinking_end_tags  = {"<|end|>"};
+
+    // These special tokens are required to parse properly, so we include them
+    // even if parse_tool_calls is false.
+    data.preserved_tokens = {
+        "<|channel|>", "<|constrain|>", "<|message|>", "<|start|>", "<|end|>",
+    };
+
+    // Adjust prompt for continuation
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "<|start|>assistant<|channel|>analysis<|message|>" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "<|end|><|start|>assistant<|channel|>final<|message|>" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto start           = p.rule("start", p.literal("<|start|>assistant"));
+        auto end             = p.rule("end", p.literal("<|end|>"));
+        auto content         = p.rule("message-content", p.until("<|end|>"));
+        auto channel         = p.literal("<|channel|>") + (p.literal("commentary") | p.literal("analysis"));
+        auto constrain_type  = p.chars("[A-Za-z0-9_-]", 1, -1);
+
+        // Occasionally, gpt-oss-20b will prefix channels with this commentary
+        auto stray_commentary = p.optional(p.literal("<|channel|>commentary") + p.optional(p.literal(" to=assistant")));
+        auto start_analysis = stray_commentary + p.literal("<|channel|>analysis<|message|>");
+
+        if (extract_reasoning) {
+            p.rule("analysis", start_analysis + p.reasoning(content) + end);
+        } else {
+            p.rule("analysis", p.content(start_analysis + content + end));
+        }
+
+        auto analysis = p.ref("analysis");
+        auto preamble = p.rule("preamble", p.literal("<|channel|>commentary<|message|>") + p.content(content) + end);
+        auto final_msg = p.rule("final", stray_commentary + p.literal("<|channel|>final<|message|>") + p.content(content));
+
+        // Consume any unsolicited tool calls, e.g. builtin functions
+        auto unsolicited = p.rule("unsolicited", p.atomic(p.optional(channel) + p.literal(" to=") + content + end));
+
+        auto any = p.rule("any", preamble | analysis);
+
+        if (has_response_format) {
+            auto constraint = p.optional(p.space() + p.optional(p.literal("<|constrain|>")) + constrain_type);
+            auto response_format = p.rule("response-format",
+                p.literal("<|channel|>final") + constraint + p.literal("<|message|>") +
+                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));
+
+            return p.zero_or_more(start + analysis) + start + response_format;
+        }
+
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto tool_choice = p.choice();
+
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string  name     = function.at("name");
+                const auto & params   = function.at("parameters");
+
+                auto func_name  = p.literal(" to=functions.") + p.tool_name(p.literal(name));
+                auto constraint = p.optional(p.space() + p.optional(p.literal("<|constrain|>")) + constrain_type);
+                auto args       = p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", params));
+
+                // recipient in role header
+                //   <|start|>assistant to=functions.NAME<|channel|>(commentary|analysis)[constraint]<|message|>ARGS
+                auto tool_in_role = p.tool(p.tool_open(func_name + channel + constraint + p.literal("<|message|>")) + args);
+
+                // recipient in channel header
+                //   <|channel|>(commentary|analysis) to=functions.NAME[constraint]<|message|>ARGS
+                auto tool_in_channel = p.tool(p.tool_open(channel + func_name + constraint + p.literal("<|message|>")) + args);
+
+                tool_choice |= p.rule("tool-" + name, tool_in_role | tool_in_channel);
+            });
+
+            auto tool_call  = p.trigger_rule("tool-call", tool_choice);
+
+            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
+                return p.zero_or_more(start + any) + start + tool_call;
+            }
+
+            return p.zero_or_more(start + any) + start + (tool_call | final_msg);
+        }
+
+        return p.zero_or_more(start + any) + start + (final_msg | unsolicited);
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^\\s+to$" },
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^<\\|channel\\|>(?:commentary|analysis)\\s+to=functions$" },
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(\\s+to)" },
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(<\\|channel\\|>(?:commentary|analysis)\\s+to)" }
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,133 @@
+#include "parsers.h"
+
+// Kimi K2 Thinking - uses unique tool call ID format: functions.<name>:<index>
+// The ID contains both the function name and an incrementing counter
+common_chat_params common_chat_params_init_kimi_k2(const common_chat_template &    tmpl,
+                                                          const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+    data.preserved_tokens  = {
+        "<|tool_calls_section_begin|>",
+        "<|tool_calls_section_end|>",
+        "<|tool_call_begin|>",
+        "<|tool_call_argument_begin|>",
+        "<|tool_call_end|>",
+        "<think>",
+        "</think>",
+    };
+
+    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    const std::string SECTION_BEGIN = "<|tool_calls_section_begin|>";
+    const std::string SECTION_END   = "<|tool_calls_section_end|>";
+    const std::string CALL_BEGIN    = "<|tool_call_begin|>";
+    const std::string ARGS_BEGIN    = "<|tool_call_argument_begin|>";
+    const std::string CALL_END      = "<|tool_call_end|>";
+
+    const std::string THINK_START = "<think>";
+    const std::string THINK_END   = "</think>";
+    const std::string GEN_PROMPT  = "<|im_assistant|>assistant<|im_middle|>";
+
+    data.thinking_start_tag = THINK_START;
+    data.thinking_end_tags  = {THINK_END};
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        // Kimi K2 Thinking format:
+        // - Reasoning: <think>{reasoning}</think>
+        // - Content: text after reasoning
+        // - Tool calls section:
+        //   <|tool_calls_section_begin|>
+        //   <|tool_call_begin|>functions.<name>:<index><|tool_call_argument_begin|>{json_args}<|tool_call_end|>
+        //   ...
+        //   <|tool_calls_section_end|>
+        // The ID format is: functions.<function_name>:<counter> where counter is 0, 1, 2, ...
+
+        // Tool call markers
+        auto end = p.end();
+
+        // Note: this model is CRAZY. It can diverge from its supposed tool calling pattern in so many ways it's not funny.
+        // For example, it can call tools at the end of reasoning without closing reasoning...
+        auto reasoning = extract_reasoning ? p.optional(THINK_START + p.reasoning(
+            p.until_one_of({ THINK_END, "<|tool_calls_section_begin|>", "<|tool_call_begin|>" })) +
+            p.optional(p.literal(THINK_END))) : p.eps();
+        auto generation_prompt = p.literal(GEN_PROMPT);
+
+
+        // Content only parser (no tools)
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return generation_prompt + reasoning + p.content(p.rest()) + end;
+        }
+
+        // Build tool call parsers for each available function
+        // The ID format is: functions.<name>:<index>
+        // We need to match: functions.<name>:<digits>
+        auto tool_choice = p.choice();
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool.at("function");
+            std::string  name     = function.at("name");
+            const auto & schema   = function.at("parameters");
+
+            // Match: functions.<name>:<digits>
+            // Capture the full call id (functions.<name>:<digits>) using tool_id tag
+            auto tool_id = p.tool_id(p.literal("functions.") + p.tool_name(p.literal(name)) + p.literal(":") + p.chars("[0-9]", 1, -1));
+            auto tool_parser = p.tool(
+                p.tool_open(tool_id + p.literal(ARGS_BEGIN)) +
+                p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)) +
+                p.tool_close(p.optional((p.literal(CALL_END))))
+            );
+
+            tool_choice |= p.rule("tool-" + name, tool_parser);
+        });
+
+        // Tool calls section: <|tool_calls_section_begin|> tool_calls <|tool_calls_section_end|>
+        auto min_calls  = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+        auto max_calls  = inputs.parallel_tool_calls ? -1 : 1;
+        // Use trigger_rule so grammar generator knows where to start generating rules
+        auto tool_calls = p.rule("tool-calls",
+            p.optional(p.literal(SECTION_BEGIN)) +
+            p.trigger_rule("tool-call", p.repeat(CALL_BEGIN + tool_choice, min_calls, max_calls) +
+                p.optional(p.literal(SECTION_END)))
+        );
+
+        auto content_before_tools = p.content(p.until_one_of({ SECTION_BEGIN, CALL_BEGIN }));
+
+        return generation_prompt + reasoning + content_before_tools + tool_calls + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|tool_call_begin|>" }
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,174 @@
+#include "parsers.h"
+
+// Kimi K3 - XTML tagged format, built by open_tag/close_tag macros:
+//   open_tag(t, attrs) = <|open|>t k="v"...<|sep|>   close_tag(t) = <|close|>t<|sep|>
+//   assistant := [think] [response] [tools] close_tag(message) <|end_of_msg|>
+// the generation prompt already opens the think (or response) section, so the
+// section opener is optional here - same as Kimi K2 Thinking
+common_chat_params common_chat_params_init_kimi_k3(const common_chat_template &          tmpl,
+                                                          const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+
+    const std::string SEP         = "<|sep|>";
+    const std::string MSG_START   = "<|open|>message role=\"assistant\"<|sep|>";
+    const std::string THINK_START = "<|open|>think<|sep|>";
+    const std::string THINK_END   = "<|close|>think<|sep|>";
+    const std::string RESP_START  = "<|open|>response<|sep|>";
+    const std::string RESP_END    = "<|close|>response<|sep|>";
+    const std::string TOOLS_START = "<|open|>tools<|sep|>";
+    const std::string TOOLS_END   = "<|close|>tools<|sep|>";
+    const std::string CALL_START  = "<|open|>call tool=\"";
+    const std::string CALL_END    = "<|close|>call<|sep|>";
+    const std::string ARG_START   = "<|open|>argument key=\"";
+    const std::string ARG_END     = "<|close|>argument<|sep|>";
+    const std::string MSG_END     = "<|close|>message<|sep|>";
+    const std::string EOM_TOKEN   = "<|end_of_msg|>";
+
+    // only the markers are special tokens. tag names ("think", "response", ...) are
+    // normal tokens and must not be preserved, or prose with those words is broken
+    data.preserved_tokens = {
+        "<|open|>",
+        "<|close|>",
+        "<|sep|>",
+        "<|end_of_msg|>",
+    };
+
+    data.thinking_start_tag = THINK_START;
+    data.thinking_end_tags  = { THINK_END };
+
+    // per-role message-start delimiters. user/assistant messages only have the role
+    // attribute, so the full opener is used. system and tool messages have more
+    // attributes, so those delimiters stop after the closing quote of the role
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|open|>message role=\"assistant\"<|sep|>" },
+        { COMMON_CHAT_ROLE_USER,      "<|open|>message role=\"user\"<|sep|>"      },
+        { COMMON_CHAT_ROLE_TOOL,      "<|open|>message role=\"tool\""             },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|open|>message role=\"system\""           },
+    };
+
+    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = MSG_START + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + RESP_START + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto end = p.end();
+
+        auto start = p.optional(p.literal(MSG_START));
+
+        // the think section is always consumed, even with reasoning extraction off:
+        // the generation prompt ends with open_tag('think'), so it is always present.
+        // reasoning stops at its own closer, or at the response opener if the model
+        // skips the closer
+        auto think_body = extract_reasoning ? p.reasoning(p.until_one_of({ THINK_END, RESP_START })) :
+                                              p.content(p.until_one_of({ THINK_END, RESP_START }));
+
+        auto reasoning = p.optional(p.optional(p.literal(THINK_START)) + think_body +
+                                    p.optional(p.literal(THINK_END)));
+
+        // content runs to the response closer, or to the next section if truncated
+        auto response = p.optional(p.literal(RESP_START)) +
+                        p.content(p.until_one_of({ RESP_END, TOOLS_START, MSG_END })) +
+                        p.optional(p.literal(RESP_END));
+
+        // the EOG token after the message closer reaches the parser as text,
+        // so it must be consumed or the parse stays incomplete
+        auto trailer = p.optional(p.literal(MSG_END)) + p.optional(p.literal(EOM_TOKEN));
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return start + reasoning + response + trailer + end;
+        }
+
+        auto tool_choices = p.choice();
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool.at("function");
+            std::string  name     = function.at("name");
+            const json   schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+
+            // arguments come one tag per key, with the JSON type in a type="..."
+            // attribute. the type is taken from the tool schema instead, as it tells
+            // us if the value is JSON or a literal string
+            auto args = p.eps();
+            if (schema.contains("properties") && !schema.at("properties").empty()) {
+                auto arg_choices = p.choice();
+                for (const auto & prop : schema.at("properties").items()) {
+                    const std::string & key = prop.key();
+
+                    std::string type = "string";
+                    if (prop.value().is_object() && prop.value().contains("type") &&
+                        prop.value().at("type").is_string()) {
+                        type = prop.value().at("type").get<std::string>();
+                    }
+
+                    auto value = type == "string" ? p.tool_arg_string_value(p.until(ARG_END)) :
+                                                    p.tool_arg_value(p.until(ARG_END));
+
+                    // skip the trailing type="..." attribute: anything up to <|sep|>
+                    arg_choices |= p.rule("kimi-k3-arg-" + name + "-" + key,
+                                          p.tool_arg(p.tool_arg_open(p.literal(ARG_START)) +
+                                                     p.tool_arg_name(p.literal(key)) + p.literal("\"") +
+                                                     p.until(SEP) + p.literal(SEP) + value +
+                                                     p.tool_arg_close(p.literal(ARG_END))));
+                }
+                args = p.zero_or_more(arg_choices);
+            }
+
+            // skip the trailing index="N" attribute the same way
+            auto call = p.tool(p.tool_open(p.literal(CALL_START) + p.tool_name(p.literal(name)) + p.literal("\"") +
+                                           p.until(SEP) + p.literal(SEP)) +
+                               p.tool_args(args) + p.tool_close(p.literal(CALL_END)));
+
+            tool_choices |= p.rule("kimi-k3-tool-" + name, call);
+        });
+
+        // all calls go inside one tools section, then the message is closed. the
+        // message closer is part of the trigger rule, or else the lazy grammar
+        // rejects it once tool calls have started
+        auto tools_section =
+            p.trigger_rule("kimi-k3-tool-call", p.literal(TOOLS_START) + p.one_or_more(tool_choices) +
+                                                    p.literal(TOOLS_END) + p.optional(p.literal(MSG_END)) +
+                                                    p.optional(p.literal(EOM_TOKEN)));
+
+        auto tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? tools_section :
+                                                                              p.optional(tools_section);
+
+        return start + reasoning + response + tools + trailer + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                if (function.contains("parameters")) {
+                    auto schema = function.at("parameters");
+                    builder.resolve_refs(schema);
+                }
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOLS_START },
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,119 @@
+#include "parsers.h"
+
+// LFM2 format detection: template uses <|tool_list_start|>[...]<|tool_list_end|> around the tool list
+// and <|tool_call_start|>[...]<|tool_call_end|> around each tool call
+bool is_lfm2_template(const std::string & src) {
+    return src.find("<|tool_list_start|>") != std::string::npos &&
+           src.find("<|tool_list_end|>")   != std::string::npos;
+}
+
+// LFM2/LFM2.5 parser. Tool calls are almost Python-style and parallel-capable
+// (except dotted names and JSON literals true/false/null).
+// Always wrapped in <|tool_call_start|>[name(args)]<|tool_call_end|> with optional <think> reasoning.
+// tool_list_tokens preserves LFM2 system tool-list markers.
+common_chat_params common_chat_params_init_lfm2(const common_chat_template &          tmpl,
+                                                       const autoparser::generation_params & inputs,
+                                                       bool tool_list_tokens) {
+    common_chat_params data;
+
+    const std::string TOOL_CALL_START = "<|tool_call_start|>";
+    const std::string TOOL_CALL_END   = "<|tool_call_end|>";
+    const std::string TOOL_LIST_START = "<|tool_list_start|>";
+    const std::string TOOL_LIST_END   = "<|tool_list_end|>";
+    const std::string THINK_START     = "<think>";
+    const std::string THINK_END       = "</think>";
+    const std::string GEN_PROMPT      = "<|im_start|>assistant\n";
+
+    // Copy reasoning to the "thinking" field the template expects
+    auto adjusted_messages = json::array();
+    for (auto msg : inputs.messages) {
+        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
+            msg["thinking"] = msg.at("reasoning_content");
+        }
+        adjusted_messages.push_back(msg);
+    }
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs, adjusted_messages);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, adjusted_messages);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+    data.preserved_tokens  = { TOOL_CALL_START, TOOL_CALL_END, THINK_START, THINK_END };
+    if (tool_list_tokens) {
+        data.preserved_tokens.push_back(TOOL_LIST_START);
+        data.preserved_tokens.push_back(TOOL_LIST_END);
+    }
+
+    data.thinking_start_tag = THINK_START;
+    data.thinking_end_tags  = {THINK_END};
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
+    // Gate by reasoning format and whether the template supports <think>
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE &&
+                             tmpl.source().find(THINK_START) != std::string::npos;
+    auto include_grammar   = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.literal(GEN_PROMPT);
+        auto end = p.end();
+
+        auto reasoning = p.eps();
+        if (extract_reasoning) {
+            reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
+        }
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            if (has_response_format) {
+                auto response_format = p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema));
+                return generation_prompt + reasoning + response_format + end;
+            }
+            return generation_prompt + reasoning + p.content(p.rest()) + end;
+        }
+        auto tool_calls = p.rule("tool-calls",
+            p.trigger_rule("tool-call",
+                p.literal(TOOL_CALL_START) +
+                p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls, /* allow_json_literals = */ true) +
+                p.literal(TOOL_CALL_END)
+            )
+        );
+
+        auto content = p.content(p.until(TOOL_CALL_START));
+
+        return generation_prompt + reasoning + content + tool_calls + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOL_CALL_START }
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,144 @@
+#include "parsers.h"
+
+// MiniCPM5 format:
+// - Reasoning: <think>{reasoning}</think> (optional)
+// - Tool calls: <function name="foo"><param name="bar">value</param></function>
+common_chat_params common_chat_params_init_minicpm5(const common_chat_template &          tmpl,
+                                                           const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+    data.preserved_tokens  = {
+        "<function",
+        "<param",
+        "</function>",
+        "</param>",
+        "<think>",
+        "</think>",
+    };
+
+    data.thinking_start_tag = "<think>";
+    data.thinking_end_tags  = {"</think>"};
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant"             },
+        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>user\n<tool_response>" },
+        { COMMON_CHAT_ROLE_USER,      "<|im_start|>user"                  },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|im_start|>system"                },
+    };
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "<|im_start|>assistant\n<think>\n" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "\n</think>\n\n" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.literal("<|im_start|>assistant\n");
+
+        auto reasoning = p.eps();
+        if (extract_reasoning) {
+            reasoning = ("<think>" << p.reasoning(p.until("</think>")) << "</think>") + p.space();
+        }
+
+        // Response format parser
+        if (has_response_format) {
+            return generation_prompt + reasoning + p.content(p.schema(p.json(), "response-format", inputs.json_schema));
+        }
+
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            // CDATA lets a value carry characters that would otherwise close the tag (e.g.
+            // </param>); capture the inner text only, excluding the CDATA markers.
+            auto string_value = p.choice({
+                p.literal("<![CDATA[") + p.ac(p.tool_arg_string_value(p.until("]]>")) + p.literal("]]>"), "]]>") + p.tool_arg_close(p.literal("</param>")),
+                p.negate(p.literal("<![CDATA[")) + p.ac(p.tool_arg_string_value(p.until("</param>")) + p.tool_arg_close(p.literal("</param>")), "</param>")
+            });
+
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto &      function = tool.at("function");
+                const std::string name     = function.at("name");
+                auto              params   = function.contains("parameters") ? function.at("parameters") : json::object();
+
+                auto args = p.eps();
+                if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
+                    auto schema_info = common_schema_info();
+                    schema_info.resolve_refs(params);
+
+                    auto arg_choice = p.choice();
+                    for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
+                        auto value_parser = p.eps();
+                        if (schema_info.resolves_to_string(prop_schema)) {
+                            value_parser = string_value;
+                        } else {
+                            value_parser = p.tool_arg_json_value(
+                                    p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false)
+                                ) + p.tool_arg_close(p.literal("</param>"));
+                        }
+
+                        auto arg_rule = p.tool_arg(
+                            p.tool_arg_open(p.literal("<param name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
+                            value_parser
+                        );
+
+                        arg_choice |= arg_rule;
+                    }
+                    args = p.zero_or_more(arg_choice + p.space());
+                }
+
+                auto tool_parser = p.tool(
+                    p.tool_open(p.literal("<function name=\"") + p.tool_name(p.literal(name)) + p.literal("\">"))
+                    << p.tool_args(args)
+                    << p.tool_close(p.literal("</function>")));
+
+                tool_choice |= p.rule("tool-" + name, tool_parser);
+            });
+
+            auto max_calls  = inputs.parallel_tool_calls ? -1 : 1;
+            auto tool_calls = p.trigger_rule("tool-call", p.repeat(tool_choice + p.space(), 1, max_calls));
+
+            auto content = p.content(p.until("<function"));
+
+            return generation_prompt + reasoning + content + tool_calls + p.end();
+        }
+
+        return generation_prompt + reasoning + p.content(p.rest()) + p.end();
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<function" },
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,259 @@
+#include "parsers.h"
+
+common_chat_params common_chat_params_init_minimax_m3(const common_chat_template &          tmpl,
+                                                             const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format             = COMMON_CHAT_FORMAT_PEG_MINIMAX_M3;
+    data.supports_thinking  = true;
+    data.thinking_start_tag = "<mm:think>";
+    data.thinking_end_tags  = {"</mm:think>"};
+
+    // M3 prefixes every tool tag with the namespace token "]<]minimax[>[";
+    // params use the parameter name as the tag (<file_path>...</file_path>).
+    const std::string NS          = "]<]minimax[>[";
+    const std::string THINK_START = "<mm:think>";
+    const std::string THINK_END   = "</mm:think>";
+    const std::string FC_START    = NS + "<tool_call>";
+    const std::string FC_END      = NS + "</tool_call>";
+    const std::string INVOKE_END  = NS + "</invoke>";
+
+    data.preserved_tokens = {
+        NS,
+        "<tool_call>",
+        "</tool_call>",
+        THINK_START,
+        THINK_END,
+    };
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "]~b]ai"        },
+        { COMMON_CHAT_ROLE_USER,      "]~b]user"      },
+        { COMMON_CHAT_ROLE_TOOL,      "]~b]tool"      },
+        { COMMON_CHAT_ROLE_SYSTEM,    "]~b]developer" },
+        { COMMON_CHAT_ROLE_SYSTEM,    "]~b]system"    },
+    };
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    const std::string GEN_PROMPT = data.generation_prompt;
+
+    using mm3 = common_chat_peg_minimax_m3_mapper;
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.prefix(GEN_PROMPT, THINK_START);
+        auto end = p.end();
+
+        auto reasoning = p.eps();
+        if (extract_reasoning) {
+            auto block = inputs.enable_thinking
+                             ? p.literal(THINK_START) + p.space() +
+                                   p.ac(p.reasoning(p.until(THINK_END)) + p.literal(THINK_END), THINK_END)
+                             : p.literal(THINK_START) + p.ac(p.until(THINK_END) + p.literal(THINK_END), THINK_END);
+
+            // A turn without reasoning is prefixed with a bare </mm:think>, written either by the
+            // generation prompt (thinking_mode = "disabled") or by the model itself.
+            reasoning = p.optional(p.choice({ block, p.literal(THINK_END) }));
+        }
+
+        if (has_response_format) {
+            auto response_format = p.rule("response-format",
+                p.literal("```json") + p.space() +
+                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
+                p.space() + p.literal("```"));
+            return generation_prompt + reasoning + response_format + end;
+        }
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return generation_prompt + reasoning + p.content(p.rest()) + end;
+        }
+
+        auto alternatives_of = [](const json & schema) -> std::optional<json> {
+            for (const auto * keyword : { "oneOf", "anyOf" }) {
+                if (schema.contains(keyword) && schema.at(keyword).is_array() && !schema.at(keyword).empty()) {
+                    return schema.at(keyword);
+                }
+            }
+            return std::nullopt;
+        };
+
+        auto tool_choice = p.choice();
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool.at("function");
+            std::string  name     = function.at("name");
+            auto         params   = function.contains("parameters") ? function.at("parameters") : json::object();
+
+            auto schema_info = common_schema_info();
+            schema_info.resolve_refs(params);
+
+            // The template expands argument values recursively in XML (see the to_xml() macro)
+            std::function<common_peg_parser(const json &, const std::string &, const std::string &)> value_of;
+            std::function<common_peg_parser(const json &, const std::string &)>                      members_of;
+
+            auto element_of = [&](const std::string & tag, const json & schema, const std::string & rule_name) {
+                const std::string close = NS + "</" + tag + ">";
+                return p.rule(rule_name,
+                    p.tool_arg(
+                        p.tool_arg_open(
+                            p.literal(NS + "<") +
+                            p.tool_arg_name(p.literal(tag)) +
+                            p.literal(">")) +
+                        value_of(schema, rule_name, close)));
+            };
+
+            value_of = [&](const json & schema,
+                           const std::string & rule_name,
+                           const std::string & close) -> common_peg_parser {
+                auto close_tag = p.tool_arg_close(p.literal(close));
+
+                // A string accepts anything, so a union with a string alternative is a string
+                if (schema_info.resolves_to_string(schema)) {
+                    return p.ac(p.tool_arg_string_value(p.until(close)) + close_tag, close);
+                }
+
+                if (auto alternatives = alternatives_of(schema)) {
+                    std::vector<common_peg_parser> choices;
+
+                    size_t index = 0;
+                    for (const auto & alternative : *alternatives) {
+                        const std::string alt_name = rule_name + "-" + std::to_string(index++);
+
+                        // There is a risk that this breaks streaming deltas, but that's a risk we
+                        // assume to provide tool arg streaming.
+                        choices.push_back(value_of(alternative, alt_name, close));
+                    }
+
+                    return p.choice(choices);
+                }
+
+                const std::string type = schema.contains("type") && schema.at("type").is_string()
+                                             ? schema.at("type").get<std::string>()
+                                             : "";
+
+                if (type == "object" && schema.contains("properties")) {
+                    return p.tag(mm3::TOOL_ARG_OBJECT, members_of(schema, rule_name)) + p.space() + close_tag;
+                }
+
+                if (type == "array" && schema.contains("items")) {
+                    const std::string item_close = NS + "</item>";
+                    auto item = p.rule(rule_name + "-item",
+                        p.tag(mm3::TOOL_ARG_ITEM,
+                              p.literal(NS + "<item>") +
+                                  value_of(schema.at("items"), rule_name + "-item", item_close)));
+                    return p.tag(mm3::TOOL_ARG_ARRAY, p.repeat(p.space() + item, 0, -1)) + p.space() + close_tag;
+                }
+
+                return p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", schema, false)) + close_tag;
+            };
+
+            // Required properties in schema order, then any number of optional ones in any order.
+            members_of = [&](const json & schema, const std::string & rule_prefix) -> common_peg_parser {
+                const auto & props = schema.at("properties");
+
+                std::set<std::string> required;
+                if (schema.contains("required")) {
+                    required = schema.at("required").get<std::set<std::string>>();
+                }
+
+                std::vector<common_peg_parser> required_elements;
+                std::vector<common_peg_parser> optional_elements;
+                for (const auto & [key, key_schema] : props.items()) {
+                    auto element = element_of(key, key_schema, rule_prefix + "-" + key);
+                    if (required.find(key) != required.end()) {
+                        required_elements.push_back(element);
+                    } else {
+                        optional_elements.push_back(element);
+                    }
+                }
+
+                common_peg_parser members = p.eps();
+                for (size_t i = 0; i < required_elements.size(); i++) {
+                    if (i > 0) {
+                        members = members + p.space();
+                    }
+                    members = members + required_elements[i];
+                }
+
+                if (!optional_elements.empty()) {
+                    common_peg_parser any_optional = p.choice();
+                    for (const auto & element : optional_elements) {
+                        any_optional |= element;
+                    }
+                    members = members + p.repeat(p.space() + any_optional, 0, -1);
+                }
+
+                return members;
+            };
+
+            common_peg_parser invoke_body =
+                params.contains("properties") ? members_of(params, "tool-" + name + "-arg") : p.eps();
+
+            auto func_parser = p.tool(
+                p.tool_open(p.literal(NS + "<invoke name=\"") +
+                            p.tool_name(p.literal(name)) + p.literal("\">")) +
+                p.space() + invoke_body + p.space() +
+                p.tool_close(p.literal(INVOKE_END)));
+
+            tool_choice |= p.rule("tool-" + name, func_parser);
+        });
+
+        auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+
+        common_peg_parser tool_calls = p.eps();
+        if (inputs.parallel_tool_calls) {
+            tool_calls = p.trigger_rule("tool-call",
+                p.literal(FC_START) + p.space() + tool_choice +
+                p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
+        } else {
+            tool_calls = p.trigger_rule("tool-call",
+                p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
+        }
+
+        if (!require_tools) {
+            tool_calls = p.optional(tool_calls);
+        }
+
+        auto content_before_tools = p.content(p.until(FC_START));
+        return generation_prompt + reasoning + content_before_tools + tool_calls + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, FC_START },
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,135 @@
+#include "parsers.h"
+
+common_chat_params common_chat_params_init_ministral_3(const common_chat_template &    tmpl,
+                                                              const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    // Build up messages to follow the format: https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512/blob/main/chat_template.jinja
+    auto adjusted_messages = json::array();
+    for (const auto & msg : inputs.messages) {
+        auto role = msg.value("role", "");
+        if (role != "system" && role != "assistant") {
+            // Only adjust system and assistant messages. Interestingly, the system message may contain thinking.
+            adjusted_messages.push_back(msg);
+            continue;
+        }
+
+        auto content = json::array();
+
+        // If message contains `reasoning_content`, add it as a block of type `thinking`
+        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
+            content.push_back({
+                { "type",     "thinking"                                     },
+                { "thinking", msg.at("reasoning_content").get<std::string>() },
+            });
+        }
+
+        // If message contains `content`, add it as a block of type `text`
+        if (msg.contains("content")) {
+            if (msg.at("content").is_string()) {
+                content.push_back({
+                    { "type", "text"                               },
+                    { "text", msg.at("content").get<std::string>() },
+                });
+            } else if (msg.at("content").is_array()) {
+                auto blocks = msg.at("content");
+                content.insert(blocks);
+            }
+        }
+
+        auto adjusted       = msg;
+        adjusted["content"] = content;
+        adjusted.erase("reasoning_content");
+        adjusted_messages.push_back(adjusted);
+    }
+
+    auto has_tools            = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format  = inputs.json_schema.is_object() && !inputs.json_schema.empty();
+    auto extract_reasoning    = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar      = true;
+
+    data.supports_thinking  = true;
+    data.thinking_start_tag = "[THINK]";
+    data.thinking_end_tags  = {"[/THINK]"};
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs, /* messages_override = */ adjusted_messages);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override = */ adjusted_messages);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.preserved_tokens  = {
+        "[THINK]",
+        "[/THINK]",
+        "[TOOL_CALLS]",
+        "[ARGS]",
+    };
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "[THINK]" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "[/THINK]" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.eps();
+        auto reasoning =
+            extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps();
+
+        // Response format parser
+        if (has_response_format) {
+            // Ministral wants to emit json surrounded by code fences
+            return generation_prompt + (reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```");
+        }
+
+        // Tool call parser
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string  name     = function.at("name");
+                const auto & schema   = function.at("parameters");
+
+                tool_choice |=
+                    p.rule("tool-" + name, p.tool_open(p.tool_name(p.literal(name)) + "[ARGS]") +
+                                               p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)));
+            });
+
+            auto min_calls  = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+            auto max_calls  = inputs.parallel_tool_calls ? -1 : 1;
+            auto tool_calls = p.trigger_rule("tool-call", p.repeat("[TOOL_CALLS]" + tool_choice, min_calls, max_calls));
+
+            return generation_prompt + (reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls);
+        }
+
+        // Content only parser
+        include_grammar = false;
+        return generation_prompt + (reasoning << p.content(p.rest()));
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]" }
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,148 @@
+#include "parsers.h"
+
+// An assistant turn is rendered as one or more messages, each
+// "<|start|>assistant to=<recipient><|message|>{content}{END}" where END is
+// <|eom|> (more messages follow) or <|eot|> (end of turn):
+//   - chain-of-thought: to=self, terminated by <|eom|>
+//   - final answer:     to=user, terminated by <|eot|>
+// The generation prompt is just "<|start|>assistant"; the model emits its own
+// " to=...<|message|>".
+common_chat_params common_chat_params_init_muse_glimmer(const common_chat_template &          tmpl,
+                                                               const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = "<|start|>assistant";
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+
+    data.preserved_tokens = {
+        "<|start|>", "<|message|>", "<|eom|>", "<|eot|>",
+        // ATEM tool-call markup emitted on " to=<tool>" turns.
+        "<atem:function_calls>", "<atem:invoke", "<atem:parameter", "</atem:parameter>",
+        "</atem:invoke>", "</atem:function_calls>",
+    };
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
+        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
+        { COMMON_CHAT_ROLE_TOOL,      "<|start|>tool"      },
+    };
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "<|start|>assistant to=self<|message|>" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "<|eom|><|start|>assistant to=user<|message|>" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+
+    auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
+    // Constrained grammar whenever tools are offered.
+    auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto start = p.rule("start", p.literal("<|start|>assistant"));
+
+        if (!extract_reasoning && !include_grammar) {
+            return start + p.content(p.rest());
+        }
+
+        if (extract_reasoning) {
+            p.rule("analysis", p.literal(" to=self<|message|>") + p.reasoning(p.until("<|eom|>")) + p.literal("<|eom|>"));
+        } else {
+            p.rule("analysis", p.literal(" to=self<|message|>") + p.content(p.until("<|eom|>")) + p.literal("<|eom|>"));
+        }
+        auto analysis = p.ref("analysis");
+
+        auto recipient  = p.optional(p.literal(" to=user"));
+        auto final_msg  = p.rule("final", recipient + p.literal("<|message|>") +
+                                              p.content(p.until_one_of({ "<|eot|>", "<|eom|>" })));
+
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto string_value = p.ac(
+                p.tool_arg_string_value(p.until("</atem:parameter>")) + p.tool_arg_close(p.literal("</atem:parameter>")),
+                "</atem:parameter>");
+
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto &      function = tool.at("function");
+                const std::string name     = function.at("name");
+                auto              params   = function.contains("parameters") ? function.at("parameters") : json::object();
+
+                auto args = p.eps();
+                if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
+                    auto schema_info = common_schema_info();
+                    schema_info.resolve_refs(params);
+
+                    auto arg_choice = p.choice();
+                    for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
+                        auto value_parser = p.eps();
+                        if (schema_info.resolves_to_string(prop_schema)) {
+                            value_parser = string_value;
+                        } else {
+                            value_parser = p.tool_arg_json_value(
+                                    p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false))
+                                + p.tool_arg_close(p.literal("</atem:parameter>"));
+                        }
+
+                        auto arg_rule = p.tool_arg(
+                            p.tool_arg_open(p.literal("<atem:parameter name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
+                            value_parser);
+
+                        arg_choice |= arg_rule;
+                    }
+                    args = p.zero_or_more(arg_choice + p.space());
+                }
+
+                auto tool_parser = p.tool(
+                    p.tool_open(p.literal(" to=") + p.until("<|message|>") +
+                                p.literal("<|message|><atem:function_calls>") + p.space() +
+                                p.literal("<atem:invoke name=\"") + p.tool_name(p.literal(name)) + p.literal("\">") + p.space())
+                    << p.tool_args(args)
+                    << p.tool_close(p.literal("</atem:invoke>") + p.space() + p.literal("</atem:function_calls>")));
+
+                tool_choice |= p.rule("tool-" + name, tool_parser);
+            });
+
+            auto tool_calls = inputs.parallel_tool_calls
+                ? p.trigger_rule("tool-call", tool_choice + p.zero_or_more(p.literal("<|eom|>") + start + tool_choice))
+                : p.trigger_rule("tool-call", tool_choice);
+
+
+            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
+                return p.zero_or_more(start + analysis) + start + tool_calls;
+            }
+            auto trailing_calls = p.optional(p.literal("<|eom|>") + start + tool_calls);
+            return p.zero_or_more(start + analysis) + start + (tool_calls | (final_msg + trailing_calls));
+        }
+
+        return p.zero_or_more(start + analysis) + start + final_msg;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
+              "<\\|start\\|>assistant( to=(?!self<\\|message\\|>)(?!user<\\|message\\|>)[^<]*?<\\|message\\|>)" },
+        };
+    }
+
+    return data;
+}
@@ -0,0 +1,34 @@
+#include "parsers.h"
+
+#include "log.h"
+
+#include <set>
+
+void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
+    for (const auto & tool : tools) {
+        if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) {
+            LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str());
+            continue;
+        }
+        fn(tool);
+    }
+}
+
+void foreach_parameter(const json & function, const std::function<void(const std::string &, const json &, bool)> & fn) {
+    if (!function.contains("parameters") || !function.at("parameters").is_object()) {
+        return;
+    }
+    const auto & params = function.at("parameters");
+    if (!params.contains("properties") || !params.at("properties").is_object()) {
+        return;
+    }
+    const auto & props = params.at("properties");
+    std::set<std::string> required;
+    if (params.contains("required") && params.at("required").is_array()) {
+        required = params.at("required").get<std::set<std::string>>();
+    }
+    for (const auto & [name, prop] : props.items()) {
+        bool is_required = (required.find(name) != required.end());
+        fn(name, prop, is_required);
+    }
+}
@@ -0,0 +1,77 @@
+#pragma once
+
+#include "chat.h"
+#include "chat-auto-parser.h"
+#include "chat-auto-parser-helpers.h"
+#include "chat-peg-parser.h"
+#include "common.h"
+#include "ggml.h"
+#include "json-schema-to-grammar.h"
+#include "json.h"
+
+#include <functional>
+#include <optional>
+#include <set>
+#include <string>
+#include <vector>
+
+using json = common_json;
+
+// iterate over the function tools of an OpenAI-style tools array
+void foreach_function(const json & tools, const std::function<void(const json &)> & fn);
+
+// iterate over the parameters of a function tool, flagging the ones listed as required
+void foreach_parameter(const json & function, const std::function<void(const std::string &, const json &, bool)> & fn);
+
+// render a template; the override arguments let a parser feed in messages, tools or context it has rewritten
+std::string common_chat_template_direct_apply_impl(
+    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);
+
+// the suffix a template appends when add_generation_prompt is set
+std::string common_chat_template_generation_prompt_impl(
+    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);
+
+bool is_lfm2_template(const std::string & src);
+
+namespace workaround {
+
+void convert_tool_responses_gemma4(json & messages);
+
+}
+
+common_chat_params common_chat_params_init_cohere2moe(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_gemma4(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_gigachat_v3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_kimi_k2(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_kimi_k3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+// tool_list_tokens preserves the LFM2 system tool-list markers; LFM2.5 renders without them
+common_chat_params common_chat_params_init_lfm2(const common_chat_template & tmpl, const autoparser::generation_params & inputs, bool tool_list_tokens);
+
+common_chat_params common_chat_params_init_minicpm5(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_minimax_m3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_ministral_3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_muse_glimmer(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
+
+common_chat_params common_chat_params_init_qwen3_coder(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
@@ -0,0 +1,181 @@
+#include "parsers.h"
+
+common_chat_params common_chat_params_init_qwen3_coder(const common_chat_template &          tmpl,
+                                                              const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    const std::string GEN_PREFIX = "<|im_start|>assistant\n";
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+
+    auto supports_reasoning = tmpl.source().find("<think>") != std::string::npos;
+
+    data.supports_thinking = supports_reasoning;
+    data.preserved_tokens  = {
+        "<tool_call>",
+        "</tool_call>",
+    };
+
+    auto is_qwen3_coder  = !supports_reasoning;
+
+    if (supports_reasoning) {
+        data.thinking_start_tag = "<think>";
+        // Support both </think> and <tool_call> as reasoning end sequences.
+        // <function= is omitted, as it is a workaround for Qwen3-Coder which is not a thinking model
+        data.thinking_end_tags = { "</think>", "<tool_call>" };
+        data.preserved_tokens.insert(data.preserved_tokens.end(), { "<think>", "</think>" });
+    }
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant"             },
+        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>user\n<tool_response>" }, // Qwen3-Coder, Qwen3.5, Nemotron Nano 3
+        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>tool_response"         }, // StepFun-3.5-Flash
+        { COMMON_CHAT_ROLE_USER,      "<|im_start|>user"                  },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|im_start|>system"                },
+    };
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PREFIX;
+        if (supports_reasoning) {
+            data.generation_prompt += "<think>\n" + msg.reasoning_content;
+            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+                data.generation_prompt += "\n</think>\n\n";
+            }
+        }
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    std::vector<std::string> tool_call_starts = { "<tool_call>" };
+
+    if (is_qwen3_coder) {
+        // Match complete <function=name> opener for Qwen3-Coder models that occasionally omit the
+        // starting <tool_call>. The model may hallucinate a tool name, but it is preferable over
+        // constraining on <function which may occur in valid content generation, e.g. #include <functional>
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const std::string name = tool.at("function").at("name");
+            tool_call_starts.push_back("<function=" + name + ">");
+        });
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.literal(GEN_PREFIX);
+
+        auto reasoning = p.eps();
+        if (supports_reasoning && extract_reasoning) {
+            reasoning = p.optional("<think>" + p.space() +
+                                   p.reasoning(p.until_one_of({ "</think>", "<tool_call>" })) +
+                                   (p.literal("</think>") | p.peek(p.literal("<tool_call>"))));
+        }
+
+        // Response format parser
+        if (has_response_format) {
+            return generation_prompt + (reasoning << p.content(p.schema(p.json(), "response-format", inputs.json_schema)));
+        }
+
+        // Tool call parser
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto arg_close  = p.tool_arg_close(p.literal("\n</parameter>\n"));
+            auto arg_string = p.rule("xml-arg-string",
+                p.ac(p.tool_arg_string_value(p.until("\n</parameter>\n")) + arg_close, "\n</parameter>\n"));
+
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function   = tool.at("function");
+                std::string  name       = function.at("name");
+                auto         parameters = function.contains("parameters") ? function.at("parameters") : json::object();
+
+                auto schema_info = common_schema_info();
+                schema_info.resolve_refs(parameters);
+
+                std::vector<common_peg_parser> required_args;
+                std::vector<common_peg_parser> optional_args;
+
+                foreach_parameter(function, [&](const std::string & param_name, const json & param_schema, bool is_required) {
+                    auto rule_name = "tool-" + name + "-arg-" + param_name;
+
+                    auto arg_open = p.tool_arg_open("<parameter=" + p.tool_arg_name(p.literal(param_name)) + ">\n");
+
+                    auto arg_value = schema_info.resolves_to_string(param_schema) ?
+                        arg_string :
+                        p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", param_schema)) + arg_close;
+
+                    auto arg_rule = p.rule(rule_name, p.tool_arg(arg_open + arg_value));
+
+                    (is_required ? required_args : optional_args).push_back(arg_rule);
+                });
+
+                // Accept required arguments in any order, as Qwen does not always adhere to the
+                // order provided.
+                auto args = p.permute("tool-" + name + "-args", required_args);
+                if (!optional_args.empty()) {
+                    args = args + p.zero_or_more(p.choice(optional_args));
+                }
+
+                auto func = p.tool(p.tool_open("<function=" + p.tool_name(p.literal(name)) + ">\n") +
+                                   p.tool_args(args) +
+                                   p.tool_close(p.literal("</function>\n")));
+
+                tool_choice |= p.rule("tool-" + name, func);
+            });
+
+            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+
+            auto tool_call_body = tool_choice + "</tool_call>" + p.space();
+            auto tool_call      = p.rule("tool-call", "<tool_call>\n" + tool_call_body);
+
+            // Qwen3-Coder models may occasionally omit the <tool_call> token.
+            auto tool_call_first = is_qwen3_coder ?
+                p.rule("tool-call-first", p.optional(p.literal("<tool_call>\n")) + tool_call_body) :
+                tool_call;
+
+            auto calls      = inputs.parallel_tool_calls ? tool_call_first + p.zero_or_more(tool_call) : tool_call_first;
+            auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(calls, min_calls, 1));
+
+            return generation_prompt +
+                   (reasoning << p.content(p.until_one_of(tool_call_starts)) << tool_calls);
+        }
+
+        // Content only parser
+        return generation_prompt + (reasoning << p.content(p.rest()));
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        if (data.grammar_lazy) {
+            for (const auto & start : tool_call_starts) {
+                data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, start });
+            }
+        }
+    }
+
+    return data;
+}
@@ -0,0 +1,20 @@
+# Specialized chat template parsers, listed explicitly so that adding or removing one re-runs CMake instead of leaving an incremental build stale.
+
+set(LLAMA_CHAT_PARSERS_SOURCES
+    ${CMAKE_CURRENT_LIST_DIR}/parsers.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/parsers.h
+    ${CMAKE_CURRENT_LIST_DIR}/cohere2moe.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/deepseek.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/functionary-v3-2.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/gemma4.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/gigachat-v3.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/gpt-oss.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/kimi-k2.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/kimi-k3.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/lfm2.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/minicpm5.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/minimax-m3.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/ministral3.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/muse-glimmer.cpp
+    ${CMAKE_CURRENT_LIST_DIR}/qwen3-coder.cpp
+)
@@ -10,7 +10,6 @@
 #include <initializer_list>
 #include <map>
 #include <memory>
-#include <nlohmann/json.hpp>
 #include <regex>
 #include <set>
 #include <stdexcept>
@@ -1120,8 +1119,8 @@ common_peg_parser common_peg_parser_builder::chars(const std::string & classes,
    return wrap(arena_.add_parser(common_peg_chars_parser{classes, ranges, negated, min, max}));
 }

-common_peg_parser common_peg_parser_builder::schema(const common_peg_parser & p, const std::string & name, const nlohmann::ordered_json & schema, bool raw) {
-    return wrap(arena_.add_parser(common_peg_schema_parser{p.id(), name, std::make_shared<nlohmann::ordered_json>(schema), raw}));
+common_peg_parser common_peg_parser_builder::schema(const common_peg_parser & p, const std::string & name, const common_json & schema, bool raw) {
+    return wrap(arena_.add_parser(common_peg_schema_parser{p.id(), name, std::make_shared<common_json>(schema), raw}));
 }

 common_peg_parser common_peg_parser_builder::rule(const std::string & name, const common_peg_parser & p, bool trigger) {
@@ -1805,8 +1804,8 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
    }
 }

-static nlohmann::json serialize_parser_variant(const common_peg_parser_variant & variant) {
-    using json = nlohmann::json;
+static common_json serialize_parser_variant(const common_peg_parser_variant & variant) {
+    using json = common_json;

    return std::visit([](const auto & p) -> json {
        using T = std::decay_t<decltype(p)>;
@@ -1860,7 +1859,7 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant &
                {"type", "schema"},
                {"child", p.child},
                {"name", p.name},
-                {"schema", p.schema ? *p.schema : nullptr},
+                {"schema", p.schema ? *p.schema : json(nullptr)},
                {"raw", p.raw}
            };
        } else if constexpr (std::is_same_v<T, common_peg_rule_parser>) {
@@ -1888,19 +1887,19 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant &
    }, variant);
 }

-nlohmann::json common_peg_arena::to_json() const {
-    auto parsers = nlohmann::json::array();
+common_json common_peg_arena::to_json() const {
+    auto parsers = common_json::array();
    for (const auto & parser : parsers_) {
        parsers.push_back(serialize_parser_variant(parser));
    }
-    return nlohmann::json{
+    return common_json{
        {"parsers", parsers},
        {"rules", rules_},
        {"root", root_}
    };
 }

-static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json & j) {
+static common_peg_parser_variant deserialize_parser_variant(const common_json & j) {
    if (!j.contains("type") || !j["type"].is_string()) {
        throw std::runtime_error("Parser variant JSON missing or invalid 'type' field");
    }
@@ -1969,9 +1968,9 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json
        }
        common_peg_chars_parser parser;
        parser.pattern = j["pattern"];
-        parser.negated = j["negated"];
-        parser.min_count = j["min_count"];
-        parser.max_count = j["max_count"];
+        parser.negated = j["negated"].get<bool>();
+        parser.min_count = j["min_count"].get<int>();
+        parser.max_count = j["max_count"].get<int>();
        for (const auto & range_json : j["ranges"]) {
            if (!range_json.contains("start") || !range_json.contains("end")) {
                throw std::runtime_error("char_range missing 'start' or 'end' field");
@@ -2007,7 +2006,7 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json
        parser.child = j["child"].get<common_peg_parser_id>();
        parser.name = j["name"];
        if (!j["schema"].is_null()) {
-            parser.schema = std::make_shared<nlohmann::ordered_json>(j["schema"]);
+            parser.schema = std::make_shared<common_json>(j["schema"]);
        }
        parser.raw = j["raw"].get<bool>();
        return parser;
@@ -2069,7 +2068,7 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json
    throw std::runtime_error("Unknown parser type: " + type);
 }

-common_peg_arena common_peg_arena::from_json(const nlohmann::json & j) {
+common_peg_arena common_peg_arena::from_json(const common_json & j) {
    if (!j.contains("parsers") || !j["parsers"].is_array()) {
        throw std::runtime_error("JSON missing or invalid 'parsers' array");
    }
@@ -2109,7 +2108,7 @@ std::string common_peg_arena::save() const {
 }

 void common_peg_arena::load(const std::string & data) {
-    *this = from_json(nlohmann::json::parse(data));
+    *this = from_json(common_json::parse(data));
 }

 common_peg_arena build_peg_parser(const std::function<common_peg_parser(common_peg_parser_builder & builder)> & fn) {
@@ -1,6 +1,6 @@
 #pragma once

-#include <nlohmann/json_fwd.hpp>
+#include "json.h"

 #include <memory>
 #include <set>
@@ -245,7 +245,7 @@ struct common_peg_until_parser {
 struct common_peg_schema_parser {
    common_peg_parser_id child;
    std::string name;
-    std::shared_ptr<nlohmann::ordered_json> schema;
+    std::shared_ptr<common_json> schema;

    // Indicates if the GBNF should accept a raw string that matches the schema.
    bool raw;
@@ -332,8 +332,8 @@ class common_peg_arena {

    std::string dump(common_peg_parser_id id) const;

-    nlohmann::json to_json() const;
-    static common_peg_arena from_json(const nlohmann::json & j);
+    common_json to_json() const;
+    static common_peg_arena from_json(const common_json & j);

    std::string save() const;
    void load(const std::string & data);
@@ -490,7 +490,7 @@ class common_peg_parser_builder {

    // Wraps a parser with JSON schema metadata for grammar generation.
    // Used internally to convert JSON schemas to GBNF grammar rules.
-    common_peg_parser schema(const common_peg_parser & p, const std::string & name, const nlohmann::ordered_json & schema, bool raw = false);
+    common_peg_parser schema(const common_peg_parser & p, const std::string & name, const common_json & schema, bool raw = false);

    // Creates a named rule, stores it in the grammar, and returns a ref.
    // If trigger=true, marks this rule as an entry point for lazy grammar generation.
@@ -14,6 +14,7 @@

 #include <algorithm>
 #include <cassert>
+#include <cmath>
 #include <cstring>
 #include <iomanip>
 #include <map>
@@ -138,6 +139,7 @@ struct common_speculative_impl {
    const common_speculative_type type;

    uint32_t n_seq;
+    int32_t n_max; // maximum draft length after implementation-specific limits

    size_t n_call_begin  = 0; // number of times this implementation was called for refresh.
    size_t n_call_draft  = 0; // number of times this implementation was called for generation.
@@ -157,7 +159,7 @@ struct common_speculative_impl {
    int64_t t_draft_us  = 0; // total time spent in generating drafts in this implementation in microseconds.
    int64_t t_accept_us = 0; // total time spent in accumulation of this implementation in microseconds.

-    common_speculative_impl(common_speculative_type type, uint32_t n_seq) : type(type), n_seq(n_seq) {}
+    common_speculative_impl(common_speculative_type type, uint32_t n_seq, int32_t n_max) : type(type), n_seq(n_seq), n_max(n_max) {}

    virtual ~common_speculative_impl() = default;

@@ -182,7 +184,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
    std::vector<common_sampler_ptr> smpls;

    common_speculative_impl_draft_simple(const common_params_speculative & params, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, n_seq, params.draft.n_max)
        , params(params.draft)
    {
        auto * ctx_dft = this->params.ctx_dft;
@@ -452,7 +454,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
    std::vector<float> g_embd_buf;

    common_speculative_impl_draft_eagle3(const common_params_speculative & params, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq, params.draft.n_max)
        , params(params.draft)
    {
        SPC_TRC("%s", "adding speculative implementation 'draft-eagle3'\n");
@@ -923,21 +925,25 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
    int32_t     block_size    = 0;
    llama_token mask_token_id = 0;

+    bool    is_dflash2     = false;
+    bool    is_mrope       = false;
+    int32_t selector_top_k = 0;
+
    // draft-dspark: the draft carries a Markov head and uses an anchor-first block layout
    const bool is_dspark;

    // dspark speculators
    bool sample_from_anchor = true;

+    // block-internal attention
+    bool causal_attn = false;
+
    const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
    uint32_t        target_layer_ids_n = 0;

-    // scratch buffer for concatenated target features [n_tokens, n_embd_enc]
-    std::vector<float> features_buf;
-
    common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq,
            common_speculative_type type = COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)
-        : common_speculative_impl(type, n_seq)
+        : common_speculative_impl(type, n_seq, params.draft.n_max)
        , params(params.draft)
        , is_dspark(type == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)
    {
@@ -966,9 +972,25 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            if (llama_model_meta_val_str(model_dft, "dflash.sample_from_anchor", buf, sizeof(buf)) >= 0) {
                sample_from_anchor = std::strcmp(buf, "true") == 0;
            }
+            if (llama_model_meta_val_str(model_dft, "dflash.attention.causal", buf, sizeof(buf)) >= 0) {
+                causal_attn = std::strcmp(buf, "true") == 0;
+            }
        }
+
+        selector_top_k = llama_model_dflash_selector_top_k(model_dft);
+        is_dflash2     = selector_top_k > 0;
        mask_token_id = llama_vocab_mask(llama_model_get_vocab(model_dft));

+        if (is_dspark && this->params.p_min > 0.0f) {
+            char buf[16] = {};
+            const bool has_conf =
+                llama_model_meta_val_str(model_dft, "dflash.has_confidence_head", buf, sizeof(buf)) < 0 ||
+                std::strcmp(buf, "true") == 0;
+            if (!has_conf) {
+                throw std::runtime_error("DSpark draft has no confidence head: please set --spec-draft-p-min 0");
+            }
+        }
+
        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(type).c_str());
        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
        LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u, sample_from_anchor=%s\n", __func__,
@@ -983,9 +1005,17 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            this->params.n_max = std::min(this->params.n_max, n_draft_max);
            this->params.n_min = std::min(this->params.n_min, n_draft_max);
        }
+        this->n_max = this->params.n_max;

        batch        = llama_batch_init(llama_n_batch(ctx_dft), 0,          n_seq);
-        batch_inject = llama_batch_init(llama_n_batch(ctx_dft), n_embd_dec, n_seq);
+        batch_inject = llama_batch_init(llama_n_ubatch(ctx_dft), n_embd_enc, n_seq);
+
+        // embd batches on an M-RoPE draft need 4 position rows per token
+        is_mrope = llama_model_rope_type(model_dft) == LLAMA_ROPE_TYPE_MROPE;
+        if (is_mrope) {
+            free(batch_inject.pos);
+            batch_inject.pos = (llama_pos *) malloc(sizeof(llama_pos) * 4 * llama_n_batch(ctx_dft));
+        }

        smpls.resize(n_seq);
        for (auto & s : smpls) {
@@ -998,7 +1028,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {

        // offload draft sampling to the backend
        backend_chains.assign(n_seq, nullptr);
-        if (this->params.backend_sampling) {
+        if (this->params.backend_sampling && !is_dflash2) {
            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
                llama_sampler * chain = llama_sampler_chain_init(llama_sampler_chain_default_params());
                llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
@@ -1017,8 +1047,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
        }

-        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
-        llama_set_causal_attn(ctx_dft, false); // DFlash needs non-causal attention
+        // DFlash2 reads its selector lattice from h_nextn and never consumes raw logits.
+        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ !is_dflash2);
+        llama_set_causal_attn(ctx_dft, causal_attn); // DFlash needs non-causal attention unless the model says otherwise
    }

    ~common_speculative_impl_draft_dflash() override {
@@ -1103,52 +1134,34 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            for (int32_t offset = 0; offset < n_rows; offset += n_ubatch) {
                const int32_t n_chunk = std::min(n_ubatch, n_rows - offset);

-                // gather this chunk's target features, interleaved by extract layer
-                features_buf.resize((size_t) n_chunk * n_embd_enc);
+                // gather target features per extract layer; the fused decode encodes and
+                // injects them into the K/V cache at the target positions
+                batch_inject.n_tokens = n_chunk;
                for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
                    const float * layer = llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]);
                    if (!layer) {
                        GGML_ABORT("DFlash: target layer %d input not extracted.", target_layer_ids[k]);
                    }
                    for (int32_t i = 0; i < n_chunk; ++i) {
-                        float       * dst = features_buf.data() + (size_t) i * n_embd_enc + k * (size_t) n_embd_tgt;
+                        float       * dst = batch_inject.embd + (size_t) i * n_embd_enc + k * (size_t) n_embd_tgt;
                        const float * src = layer + (size_t) (i_batch_beg[seq_id] + offset + i) * n_embd_tgt;
                        std::memcpy(dst, src, (size_t) n_embd_tgt * sizeof(float));
                    }
                }

-                // fuse extracted features through DFlash encoder
-                llama_batch enc_batch = {
-                    /*.n_tokens =*/ n_chunk,
-                    /*.token    =*/ nullptr,
-                    /*.embd     =*/ features_buf.data(),
-                    /*.pos      =*/ nullptr,
-                    /*.n_seq_id =*/ nullptr,
-                    /*.seq_id   =*/ nullptr,
-                    /*.logits   =*/ nullptr,
-                };
-
-                int32_t rc = llama_encode(ctx_dft, enc_batch);
-                if (rc != 0) {
-                    LOG_ERR("%s: llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
-                            __func__, rc, (int) n_chunk, (int) offset);
-                    return false;
-                }
-
-                const float * inp_g = llama_get_embeddings_nextn(ctx_dft);
-                GGML_ASSERT(inp_g && "DFlash encoder produced no output.");
-
-                // inject the DFlash decoder K/V cache at the tokens' target positions
-                batch_inject.n_tokens = n_chunk;
-                std::memcpy(batch_inject.embd, inp_g, (size_t) n_chunk * n_embd_dec * sizeof(float));
-
                for (int32_t i = 0; i < n_chunk; ++i) {
-                    batch_inject.pos[i]       = batch_in.pos[i_batch_beg[seq_id] + offset + i];
+                    const llama_pos p = batch_in.pos[i_batch_beg[seq_id] + offset + i];
+                    batch_inject.pos[i] = p;
+                    if (is_mrope) {
+                        batch_inject.pos[1 * n_chunk + i] = p;
+                        batch_inject.pos[2 * n_chunk + i] = p;
+                        batch_inject.pos[3 * n_chunk + i] = 0;
+                    }
                    batch_inject.n_seq_id[i]  = 1;
                    batch_inject.seq_id[i][0] = seq_id;
                    batch_inject.logits[i]    = false;
                }
-                rc = llama_decode(ctx_dft, batch_inject);
+                const int32_t rc = llama_decode(ctx_dft, batch_inject);
                if (rc != 0) {
                    LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
                            __func__, rc, (int) n_chunk, (int) offset);
@@ -1186,7 +1199,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            i_block_beg[seq_id] = batch.n_tokens;
            n_block    [seq_id] = n_block_tokens;
            for (int32_t i = 0; i < n_block_tokens; ++i) {
-                common_batch_add(batch, i == 0 ? dp.id_last : mask_token_id, n + i, { seq_id }, true);
+                common_batch_add(batch, i == 0 ? dp.id_last : mask_token_id, n + i, { seq_id }, !is_dflash2);
            }
        }

@@ -1214,6 +1227,36 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {

            auto & result = *dp.result;

+            if (is_dflash2) {
+                const float * lattice = llama_get_embeddings_nextn(ctx_dft);
+                GGML_ASSERT(lattice && "DFlash2 selector produced no lattice");
+
+                int32_t predecessor = 0;
+                for (int32_t i = 1; i < n_block_tokens; ++i) {
+                    const float * row = lattice + (size_t) (beg + i) * n_embd_dec;
+                    const float * scores = row + selector_top_k + (size_t) predecessor * selector_top_k;
+
+                    predecessor = (int32_t) std::distance(scores,
+                            std::max_element(scores, scores + selector_top_k));
+                    if (params.p_min > 0.0f) {
+                        // softmax(scores) at the argmax, i.e. 1 / sum(exp(s_k - s_max))
+                        float sum = 0.0f;
+                        for (int32_t k = 0; k < selector_top_k; ++k) {
+                            sum += std::exp(scores[k] - scores[predecessor]);
+                        }
+                        if (1.0f / sum < params.p_min) {
+                            break;
+                        }
+                    }
+                    result.push_back((llama_token) row[predecessor]);
+                }
+
+                if (result.size() < (size_t) params.n_min) {
+                    result.clear();
+                }
+                continue;
+            }
+
            if (is_dspark) {
                // DSpark: read from the first draft slot, truncate below the confidence threshold
                const float * conf = params.p_min > 0.0f ? llama_get_embeddings_nextn(ctx_dft) : nullptr;
@@ -1315,7 +1358,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
    std::vector<std::vector<float>> chain_h;

    common_speculative_impl_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq, params.draft.n_max)
        , params(params.draft)
    {
        auto * ctx_tgt = this->params.ctx_tgt;
@@ -1382,6 +1425,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                c.reserve((size_t) (this->params.n_max + 1) * n_embd);
            }
        }
+        this->n_max = this->params.n_max;

        pending_h.assign(n_seq, std::vector<float>(n_embd, 0.0f));

@@ -1726,7 +1770,7 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
    common_speculative_impl_ngram_simple(
            const common_params_speculative & params, uint32_t n_seq,
            common_ngram_simple_config config)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, n_seq, params.ngram_simple.size_m)
        , params(params.ngram_simple)
        , config(config)
    {
@@ -1770,7 +1814,7 @@ struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
            const common_ngram_map & config,
            uint32_t n_seq)
        : common_speculative_impl(config.key_only ? COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K
-            : COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, n_seq)
+            : COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, n_seq, config.size_value)
    {
        for (uint32_t i = 0; i < n_seq; i++) {
            this->config.push_back(config);
@@ -1841,7 +1885,7 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
    common_speculative_impl_ngram_mod(
            const common_params_speculative & params,
            uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, n_seq, params.ngram_mod.n_max)
        , params(params.ngram_mod)
        , mod(params.ngram_mod.n_match, 4*1024*1024)
        , verbose(std::getenv("LLAMA_TRACE") != nullptr) {
@@ -2017,7 +2061,7 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
            const std::string & path_dynamic,
            bool save_dynamic,
            bool save_static)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, n_seq, n_draft)
        , params(params.ngram_cache)
        , n_draft(n_draft)
        , save_dynamic(save_dynamic)
@@ -2138,6 +2182,8 @@ struct common_speculative {

    // which implementaion was used for a given seq_id
    std::vector<common_speculative_impl *> impl_last;
+
+    std::vector<double> synth_probs;
 };

 static common_ngram_map get_common_ngram_map(
@@ -2316,6 +2362,101 @@ int32_t common_speculative_n_max(const common_params_speculative * spec) {
    return n_max;
 }

+int32_t common_speculative_n_max(const common_speculative * spec) {
+    int32_t n_max = 0;
+
+    if (spec == nullptr) {
+        return n_max;
+    }
+
+    for (const auto & impl : spec->impls) {
+        n_max = std::max(n_max, std::max(0, impl->n_max));
+    }
+
+    return n_max;
+}
+
+std::vector<double> common_speculative_synth_rates_resolve(const common_params_speculative * spec, int32_t n_max) {
+    const bool has_length = spec->synth_len != -1.0;
+    const bool has_rates  = !spec->synth_rates.empty();
+
+    if (!has_length && !has_rates) {
+        return {};
+    }
+    if (has_length && has_rates) {
+        throw std::invalid_argument("synthetic acceptance length and rates are mutually exclusive");
+    }
+
+    if (n_max <= 0) {
+        throw std::invalid_argument("synthetic acceptance requires at least one speculative token");
+    }
+
+    if (has_rates) {
+        const auto & rates = spec->synth_rates;
+        if (rates.size() != (size_t) n_max) {
+            throw std::invalid_argument(string_format(
+                    "synthetic acceptance rates must contain %d values, got %zu", n_max, rates.size()));
+        }
+
+        for (size_t i = 0; i < rates.size(); ++i) {
+            if (!std::isfinite(rates[i]) || rates[i] < 0.0 || rates[i] > 1.0) {
+                throw std::invalid_argument("synthetic acceptance rates must be finite and within [0, 1]");
+            }
+            if (i > 0 && rates[i] > rates[i - 1]) {
+                throw std::invalid_argument("synthetic acceptance rates must be monotonically non-increasing");
+            }
+        }
+
+        return rates;
+    }
+
+    const double length = spec->synth_len;
+    const double length_max = (double) n_max + 1.0;
+    if (!std::isfinite(length) || length < 1.0 || length > length_max) {
+        throw std::invalid_argument(string_format(
+                "synthetic acceptance length must be finite and within [1, %.0f]", length_max));
+    }
+
+    double p = 0.0;
+    if (length == length_max) {
+        p = 1.0;
+    } else if (length > 1.0) {
+        double p_min = 0.0;
+        double p_max = 1.0;
+        for (int i = 0; i < 32; ++i) {
+            const double p_mid = 0.5 * (p_min + p_max);
+            double sum = 0.0;
+            double term = p_mid;
+            for (int32_t j = 0; j < n_max; ++j) {
+                sum += term;
+                term *= p_mid;
+            }
+
+            if (sum < length - 1.0) {
+                p_min = p_mid;
+            } else {
+                p_max = p_mid;
+            }
+        }
+        p = 0.5 * (p_min + p_max);
+    }
+
+    std::vector<double> rates;
+    rates.reserve(n_max);
+    double rate = p;
+    for (int32_t i = 0; i < n_max; ++i) {
+        rates.push_back(rate);
+        rate *= p;
+    }
+
+    return rates;
+}
+
+const std::vector<double> & common_speculative_get_synth_probs(const common_speculative * spec) {
+    GGML_ASSERT(spec);
+    return spec->synth_probs;
+}
+
 common_params common_base_params_to_speculative(const common_params & params) {
    const bool has_draft = params.speculative.has_dft();

@@ -2326,11 +2467,22 @@ common_params common_base_params_to_speculative(const common_params & params) {
    result.pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED;

    if (has_draft) {
-        result.devices               = params_spec.devices;
+        // default to global devices value
+        if (!params_spec.devices.empty()) {
+            result.devices           = params_spec.devices;
+        }
        result.model                 = params_spec.mparams;
        result.n_gpu_layers          = params_spec.n_gpu_layers;
        result.tensor_buft_overrides = params_spec.tensor_buft_overrides;

+        // a draft pinned to a single device doesn't need the meta wrapper an inherited -sm tensor would give it
+        // (the device list is null-terminated, so a single device means size 2)
+        const size_t n_devs = std::count_if(params_spec.devices.begin(), params_spec.devices.end(),
+                [](ggml_backend_dev_t d) { return d != nullptr; });
+        if (n_devs == 1) {
+            result.split_mode = LLAMA_SPLIT_MODE_LAYER;
+        }
+
        if (params_spec.cpuparams.n_threads > 0) {
            result.cpuparams.n_threads       = params_spec.cpuparams.n_threads;
            result.cpuparams_batch.n_threads = params_spec.cpuparams_batch.n_threads;
@@ -2388,6 +2540,9 @@ common_speculative_init_result::common_speculative_init_result(
        cparams.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
    }

+    // the draft context holds as many tokens per sequence as the target context
+    cparams.n_ctx = llama_n_ctx(ctx_tgt);
+
    // note: for small models maybe we can set this to the maximum possible draft from all speculative types
    //       the extra memory for small models is likely negligible?
    cparams.n_rs_seq  = 0;
@@ -2565,13 +2720,39 @@ common_speculative * common_speculative_init(common_params_speculative & params,
        return nullptr;
    }

-    auto * result = new common_speculative {
-        /* .dparams   = */ common_speculative_draft_params_vec(n_seq),
-        /* .impls     = */ std::move(impls),
-        /* .impl_last = */ std::vector<common_speculative_impl *>(n_seq, nullptr)
-    };
+    common_speculative_ptr result(new common_speculative {
+        /* .dparams     = */ common_speculative_draft_params_vec(n_seq),
+        /* .impls       = */ std::move(impls),
+        /* .impl_last   = */ std::vector<common_speculative_impl *>(n_seq, nullptr),
+        /* .synth_probs = */ {},
+    });

-    return result;
+    const int32_t n_max_configured = common_speculative_n_max(&params);
+    const int32_t n_max_effective  = common_speculative_n_max(result.get());
+    const auto rates = common_speculative_synth_rates_resolve(&params, n_max_effective);
+
+    std::vector<std::string> rates_str;
+    rates_str.reserve(rates.size());
+    result->synth_probs.reserve(rates.size());
+    double rate_prev = 1.0;
+    double acceptance_length = 1.0;
+    for (const double rate : rates) {
+        result->synth_probs.push_back(rate_prev > 0.0 ? rate / rate_prev : 0.0);
+        rates_str.push_back(string_format("%.6g", rate));
+        rate_prev = rate;
+        acceptance_length += rate;
+    }
+    if (!result->synth_probs.empty()) {
+        SPC_WRN("%s", "synthetic speculative acceptance is enabled for benchmarking; generated output is not valid\n");
+        if (n_max_effective != n_max_configured) {
+            SPC_WRN("synthetic acceptance draft limit was reduced from %d to %d by the initialized speculative implementations\n",
+                    n_max_configured, n_max_effective);
+        }
+        SPC_INF("synthetic acceptance: n_max = %zu, mean length = %.6f, rates = [%s]\n",
+                rates.size(), acceptance_length, string_join(rates_str, ", ").c_str());
+    }
+
+    return result.release();
 }

 void common_speculative_free(common_speculative * spec) {
@@ -26,6 +26,15 @@ std::string common_speculative_type_to_str(enum common_speculative_type type);
 // return the max number of draft tokens based on the speculative parameters
 int32_t common_speculative_n_max(const common_params_speculative * spec);

+// return the max number of draft tokens from the initialized implementations
+int32_t common_speculative_n_max(const common_speculative * spec);
+
+// validate and resolve the unconditional synthetic acceptance rates
+std::vector<double> common_speculative_synth_rates_resolve(const common_params_speculative * spec, int32_t n_max);
+
+// return the conditional synthetic acceptance probabilities
+const std::vector<double> & common_speculative_get_synth_probs(const common_speculative * spec);
+
 common_params common_base_params_to_speculative(const common_params & params);

 struct common_speculative_output_limits {
@@ -54,10 +54,12 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "DeepseekV3ForCausalLM": "deepseek",
    "DeepseekV32ForCausalLM": "deepseek",
    "DFlashDraftModel": "qwen",
+    "DFlash2DraftModel": "qwen",
    "Qwen3DSparkModel": "qwen",
    "DSparkDraftModel": "qwen",
    "DSparkSpeculator": "qwen",
    "Lfm2DSparkDraftModel": "qwen",
+    "LingDSparkModel": "qwen",
    "DeepseekV4ForCausalLM": "deepseek",
    "DeepseekV4DSparkModel": "deepseek",
    "DistilBertForMaskedLM": "bert",
@@ -122,6 +124,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "HunYuanMoEV1ForCausalLM": "hunyuan",
    "HunYuanVLForConditionalGeneration": "hunyuan",
    "HYV3ForCausalLM": "hunyuan",
+    "HYV4ForCausalLM": "hy_v4",
    "IQuestCoderForCausalLM": "llama",
    "InternLM2ForCausalLM": "internlm",
    "InternLM3ForCausalLM": "internlm",
@@ -186,6 +189,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "NanbeigeForCausalLM": "nanbeige",
    "NemotronForCausalLM": "nemotron",
    "NemotronHForCausalLM": "nemotron",
+    "NemotronHPuzzleForCausalLM": "nemotron",
    "NeoBERT": "bert",
    "NeoBERTForSequenceClassification": "bert",
    "NeoBERTLMHead": "bert",
@@ -234,6 +238,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "Qwen3_5ForConditionalGeneration": "qwen",
    "Qwen3_5MoeForCausalLM": "qwen",
    "Qwen3_5MoeForConditionalGeneration": "qwen",
+    "Qwen4ExpForCausalLM": "qwen4exp",
+    "Qwen4ExpForConditionalGeneration": "qwen4exp",
    "RND1": "qwen",
    "RWForCausalLM": "falcon",
    "RWKV6Qwen2ForCausalLM": "rwkv",
@@ -249,6 +255,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "SeedOssForCausalLM": "olmo",
    "SmallThinkerForCausalLM": "smallthinker",
    "SmolLM3ForCausalLM": "llama",
+    "Spark2_5ForCausalLM": "spark2_5",
    "SolarOpenForCausalLM": "glm",
    "StableLMEpochForCausalLM": "stablelm",
    "StableLmForCausalLM": "stablelm",
@@ -282,6 +289,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
    "CogVLMForCausalLM": "cogvlm",
    "DeepseekOCR2ForCausalLM": "deepseek",
    "DeepseekOCRForCausalLM": "deepseek",
+    "DeepseekV4ForCausalLM": "deepseek",
    "Dots3NoteForCausalLM": "dots3",
    "Dots3NoteForConditionalGeneration": "dots3",
    "DotsOCRForCausalLM": "dotsocr",
@@ -331,6 +339,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
    "Qwen3VLMoeForConditionalGeneration": "qwen3vl",
    "Qwen3_5ForConditionalGeneration": "qwen3vl",
    "Qwen3_5MoeForConditionalGeneration": "qwen3vl",
+    "Qwen4ExpForConditionalGeneration": "qwen4exp",
    "RADIOModel": "nemotron",
    "Sarashina2VisionForCausalLM": "sarashina2",
    "SmolVLMForConditionalGeneration": "smolvlm",
@@ -130,7 +130,8 @@ class ModelBase:
                 sentence_transformers_dense_modules: bool = False,
                 target_model_dir: Path | None = None,
                 fuse_gate_up_exps: bool = False,
-                 fp8_as_q8: bool = False):
+                 fp8_as_q8: bool = False,
+                 fuse_qkv: bool = False):
        if type(self) is ModelBase or \
                type(self) is TextModel or \
                type(self) is MmprojModel:
@@ -153,6 +154,15 @@ class ModelBase:
        self.fuse_gate_up_exps = fuse_gate_up_exps
        self._gate_exp_buffer: dict[int, Tensor] = {}
        self._up_exp_buffer: dict[int, Tensor] = {}
+        self.fuse_qkv = fuse_qkv
+        self._q_buffer: dict[int, Tensor] = {}
+        self._k_buffer: dict[int, Tensor] = {}
+        self._v_buffer: dict[int, Tensor] = {}
+        self._q_bias_buffer: dict[int, Tensor] = {}
+        self._k_bias_buffer: dict[int, Tensor] = {}
+        self._v_bias_buffer: dict[int, Tensor] = {}
+        self._fusable_qkv_weight_layers: set[int] = set()
+        self._fusable_qkv_bias_layers: set[int] = set()
        self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
        self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
        self.metadata_override = metadata_override
@@ -617,6 +627,43 @@ class ModelBase:
            raise ValueError(f"Can not map tensor {name!r}")
        return new_name

+    def prepare_qkv_fusion(self) -> None:
+        self._fusable_qkv_weight_layers.clear()
+        self._fusable_qkv_bias_layers.clear()
+        if not self.fuse_qkv or gguf.MODEL_TENSOR.ATTN_QKV not in gguf.MODEL_TENSORS[self.model_arch]:
+            return
+
+        qkv_types = {
+            gguf.MODEL_TENSOR.ATTN_Q,
+            gguf.MODEL_TENSOR.ATTN_K,
+            gguf.MODEL_TENSOR.ATTN_V,
+        }
+        weights: dict[int, set[gguf.MODEL_TENSOR]] = {}
+        biases: dict[int, set[gguf.MODEL_TENSOR]] = {}
+
+        for name in self.model_tensors:
+            mapped = self.tensor_map.get_type_and_name(name, try_suffixes=(".weight", ".bias"))
+            if mapped is None:
+                continue
+            tensor_type, new_name = mapped
+            if tensor_type not in qkv_types:
+                continue
+
+            bid = next((int(part) for part in new_name.split(".") if part.isdecimal()), None)
+            if bid is None:
+                continue
+            if new_name.endswith(".weight"):
+                weights.setdefault(bid, set()).add(tensor_type)
+            elif new_name.endswith(".bias"):
+                biases.setdefault(bid, set()).add(tensor_type)
+
+        for bid, weight_types in weights.items():
+            bias_types = biases.get(bid, set())
+            if weight_types == qkv_types and (not bias_types or bias_types == qkv_types):
+                self._fusable_qkv_weight_layers.add(bid)
+                if bias_types:
+                    self._fusable_qkv_bias_layers.add(bid)
+
    def set_gguf_parameters(self):
        raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")

@@ -645,6 +692,40 @@ class ModelBase:
               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
                return []

+        # Handle Q/K/V tensor fusion if enabled
+        qkv_bid = next((int(part) for part in new_name.split(".") if part.isdecimal()), None) if self.fuse_qkv else None
+        if qkv_bid is not None:
+            is_bias = new_name.endswith('.bias')
+            suffix = '.bias' if is_bias else '.weight'
+            fusable_layers = self._fusable_qkv_bias_layers if is_bias else self._fusable_qkv_weight_layers
+            if qkv_bid not in fusable_layers:
+                return [(new_name, data_torch)]
+
+            buf_q = self._q_bias_buffer if is_bias else self._q_buffer
+            buf_k = self._k_bias_buffer if is_bias else self._k_buffer
+            buf_v = self._v_bias_buffer if is_bias else self._v_buffer
+
+            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_Q, qkv_bid, suffix):
+                buf_q[qkv_bid] = data_torch
+            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_K, qkv_bid, suffix):
+                buf_k[qkv_bid] = data_torch
+            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_V, qkv_bid, suffix):
+                buf_v[qkv_bid] = data_torch
+
+            if qkv_bid in buf_q and qkv_bid in buf_k and qkv_bid in buf_v:
+                q_data = buf_q.pop(qkv_bid)
+                k_data = buf_k.pop(qkv_bid)
+                v_data = buf_v.pop(qkv_bid)
+                fused_data = torch.cat([q_data, k_data, v_data], dim=0)
+                fused_name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, qkv_bid, suffix=suffix)
+                logger.info(f"Fused Q, K, V {suffix[1:]} into QKV for layer {qkv_bid}")
+                return [(fused_name, fused_data)]
+
+            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_Q, qkv_bid, suffix) or \
+               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_K, qkv_bid, suffix) or \
+               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_V, qkv_bid, suffix):
+                return []
+
        return [(new_name, data_torch)]

    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
@@ -899,6 +980,8 @@ class ModelBase:

        self.dequant_model()

+        self.prepare_qkv_fusion()
+
        # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
        if self.tensor_map.mapping:
            max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
@@ -1006,12 +1089,16 @@ class ModelBase:
                    else:
                        raise ValueError(f"Unknown file type: {self.ftype.name}")

+                # a chunked tensor quantizes as one chunk at a time, while it is written
+                quantize = data.quantize if isinstance(data, gguf.LazyChunkedTensor) else (
+                    lambda qtype, d=data: gguf.quants.quantize(d, qtype))
+
                try:
-                    data = gguf.quants.quantize(data, data_qtype)
+                    data = quantize(data_qtype)
                except gguf.QuantError as e:
                    logger.warning("%s, %s", e, "falling back to F16")
                    data_qtype = gguf.GGMLQuantizationType.F16
-                    data = gguf.quants.quantize(data, data_qtype)
+                    data = quantize(data_qtype)

                shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape

@@ -1023,6 +1110,13 @@ class ModelBase:

                self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)

+        qkv_buffers = (
+            self._q_buffer, self._k_buffer, self._v_buffer,
+            self._q_bias_buffer, self._k_bias_buffer, self._v_bias_buffer,
+        )
+        if any(qkv_buffers):
+            raise ValueError("QKV fusion did not consume all buffered tensors")
+
    def set_type(self):
        self.gguf_writer.add_type(gguf.GGUFType.MODEL)

@@ -1503,6 +1597,9 @@ class TextModel(ModelBase):
        if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
            # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
            res = "hunyuan-dense"
+        if chkhsh == "e6ddf9c6686791c12d698d34c31ab9be1fea9af5a3d9a6909783ab382198ae1c":
+            # ref: https://huggingface.co/tencent/Hy4-preview
+            res = "hy_v4"
        if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
            # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
            res = "falcon-h1"
@@ -1536,6 +1633,9 @@ class TextModel(ModelBase):
        if chkhsh == "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7":
            # ref: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B
            res = "lfm2"
+        if chkhsh == "0a766d034107bc736a3f2dc4968fd62e54a3570f1454443e0c5a4cc6bd7941ed":
+            # ref: https://huggingface.co/XHToken/Spark-X2.5-1.7B
+            res = "spark2_5"
        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
            res = "llama-bpe"
@@ -578,6 +578,8 @@ class DeepseekV4Model(TextModel):
    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
+        if name.startswith(("aligner.", "image_")):
+            return None
        if name.startswith("mtp."):
            if not cls.mtp_only:
                cls._skipped_mtp_tensors += 1
@@ -853,6 +855,7 @@ class DeepseekV4Model(TextModel):
            "ffn_norm.weight": (gguf.MODEL_TENSOR.FFN_NORM, ".weight"),
            "ffn.gate.weight": (gguf.MODEL_TENSOR.FFN_GATE_INP, ".weight"),
            "ffn.gate.bias": (gguf.MODEL_TENSOR.FFN_EXP_PROBS_B, ".bias"),
+            "ffn.gate.bias_vl": (gguf.MODEL_TENSOR.FFN_EXP_PROBS_B_VL, ".bias"),
            "ffn.gate.tid2eid": (gguf.MODEL_TENSOR.FFN_GATE_TID2EID, ".weight"),
            "ffn.shared_experts.w1.weight": (gguf.MODEL_TENSOR.FFN_GATE_SHEXP, ".weight"),
            "ffn.shared_experts.w2.weight": (gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, ".weight"),
@@ -878,6 +881,10 @@ class DeepseekV4Model(TextModel):
        if re.match(r"layers\.\d+\.ffn\.experts\.\d+\.w[123]\.(weight|scale)$", name):
            return []

+        # hash layers route text tokens via tid2eid and image tokens via bias_vl; gate.bias is unused
+        if name.endswith(".ffn.gate.bias") and bid is not None and bid < self.hparams["num_hash_layers"]:
+            return []
+
        tensor_key, suffix = self._map_dsv4_tensor_name(name, bid)
        if tensor_key == gguf.MODEL_TENSOR.FFN_GATE_TID2EID:
            return []
@@ -1000,6 +1007,13 @@ class DeepseekV4DSparkModel(DeepseekV4Model):
            return self._DSPARK_ROOT_MAP[name]
        return super()._map_dsv4_tensor_name(name, bid)

+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # the DFlash draft uses the plain exp-probs bias (ffn.gate.bias -> FFN_EXP_PROBS_B);
+        # the mtmd-only hash routing tensors (bias_vl, tid2eid) are not part of the DFLASH arch
+        if name.endswith(".ffn.gate.bias_vl"):
+            return
+        yield from super().modify_tensors(data_torch, name, bid)
+
    def set_vocab(self):
        if self.target_model_dir is None:
            raise ValueError("DeepSeek-V4 DSpark requires --target-model-dir with the target tokenizer")
@@ -1018,3 +1032,73 @@ class DeepseekV4DSparkModel(DeepseekV4Model):

        self.gguf_writer.add_block_size(self.hparams["dspark_block_size"])
        self.gguf_writer.add_target_layers([layer + 1 for layer in self.hparams["dspark_target_layer_ids"]])
+
+
+@ModelBase.register("DeepseekV4ForCausalLM")
+@ModelBase.example("deepseek-ai/DeepSeek-V4-Flash-Vision-Exp")
+class DeepseekV4FlashVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        # no preprocessor_config.json in the repo; normalization is (x/255 - 0.5) / 0.5
+        # ref: inference/image_processor.py (load_image)
+        self.preprocessor_config = {
+            "image_mean": [0.5, 0.5, 0.5],
+            "image_std":  [0.5, 0.5, 0.5],
+            **self.preprocessor_config,
+        }
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        cfg = self.global_config
+        if cfg.get("vision_n_layers", 0) == 0:
+            raise ValueError("DeepseekV4FlashVisionModel requires vision_n_layers > 0 in the model config")
+        return {
+            "num_hidden_layers":   cfg["vision_n_layers"],
+            "hidden_size":         cfg["vision_dim"],
+            "num_attention_heads": cfg["vision_n_heads"],
+            "intermediate_size":   cfg["vision_inter_dim"],
+            "patch_size":          cfg["vision_patch_size"],
+            # dynamic resolution; only used for compat / warmup
+            "image_size":          cfg["vision_patch_size"] * cfg["vision_downsample_ratio"] * 16,
+            "rope_theta":          cfg.get("vision_rope_theta", 10000.0),
+            "downsample_ratio":    cfg["vision_downsample_ratio"],
+            "min_pixels":          cfg["vision_min_pixels"],
+        }
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DEEPSEEK4V)
+        # vision RMSNorm eps is the pytorch default, NOT the LLM's rms_norm_eps (1e-20)
+        # ref: inference/vision.py (RMSNorm)
+        self.gguf_writer.add_vision_attention_layernorm_eps(1e-6)
+        self.gguf_writer.add_vision_use_silu(True) # SwiGLU MLP
+        self.gguf_writer.add_vision_projector_scale_factor(self.hparams_vision["downsample_ratio"])
+        self.gguf_writer.add_vision_min_pixels(self.hparams_vision["min_pixels"])
+        # hardcoded on the C++ side (see PROJECTOR_TYPE_DEEPSEEK4V in clip.cpp)
+        # if future models use different values, add GGUF keys for those
+        assert self.global_config["vision_max_n_token"] == 384
+        assert self.global_config["vision_max_wh_ratio"] == 8
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, _ = item
+        if not (name.startswith(("vision.", "aligner.", "image_"))):
+            return None
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        assert self.hparams_vision is not None
+        if name == "vision.patch_embed.proj.weight":
+            # nn.Linear over flattened (3, p, p) patches == conv2d weight
+            p = self.hparams_vision["patch_size"]
+            data_torch = data_torch.reshape(data_torch.shape[0], 3, p, p)
+
+        if ".mlp.w1." in name:
+            # fused SwiGLU gate+up
+            gate, up = data_torch.chunk(2, dim=0)
+            yield from super().modify_tensors(gate, name.replace("w1", "w1_gate"), bid)
+            yield from super().modify_tensors(up, name.replace("w1", "w1_up"), bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
@@ -112,12 +112,38 @@ class GlmOCRModel(Glm4Model):
@ModelBase.example("zai-org/GLM-4.5-Air")
 class Glm4MoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GLM4_MOE
+    supports_mtp_export = True
+    _n_main_layers: int | None = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
-        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
-        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+        if not self.no_mtp:
+            self.block_count += self.hparams.get("num_nextn_predict_layers", 0)
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        hparams = {**self.hparams, **self.hparams.get("text_config", {})}
+        key = next((k for k in ["n_layers", "num_hidden_layers", "n_layer", "num_layers"] if k in hparams), None)
+        type(self)._n_main_layers = hparams.get(key)
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        if (titem := super().filter_tensors(item)) is None:
+            return None
+        name, gen = titem
+
+        assert cls._n_main_layers is not None
+        is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
+
+        if is_mtp and cls.no_mtp:
+            return None
+        if cls.mtp_only and not is_mtp and name not in (
+            "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+        ):
+            return None
+
+        return name, gen

    def set_vocab(self):
        return self._set_vocab_glm()
@@ -153,10 +179,22 @@ class Glm4MoeModel(TextModel):
        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)

-        # NextN/MTP prediction layers
-        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+        if not self.no_mtp and (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)

+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
    _experts: list[dict[str, Tensor]] | None = None

    # note: unlike GLM4V non-MoE, we don't need to permute Q/K here since GLM4V_MOE uses Neox ordering already
@@ -348,6 +386,7 @@ class GlmMoeDsaModel(DeepseekV2Model):
@ModelBase.example("upstage/Solar-Open-100B")
 class SolarOpenModel(Glm4MoeModel):
    model_arch = gguf.MODEL_ARCH.GLM4_MOE
+    supports_mtp_export = False

    def set_vocab(self):
        from transformers import AutoTokenizer
@@ -0,0 +1,244 @@
+from __future__ import annotations
+
+import re
+from typing import Iterable
+
+import torch
+
+from .base import ModelBase, gguf, logger
+from .deepseek import DeepseekV2Model
+
+
+def split_gate_up(weight: torch.Tensor, moe_intermediate_size: int):
+    """Split a fused stacked gate_up expert tensor into (gate, up).
+
+    weight: [n_expert, 2*moe_intermediate_size, hidden] (gate first, up second).
+    Returns (gate, up) each [n_expert, moe_intermediate_size, hidden].
+    """
+    assert weight.shape[1] == 2 * moe_intermediate_size, f"{weight.shape[1]} != 2*{moe_intermediate_size}"
+    gate = weight[:, :moe_intermediate_size, :].contiguous()
+    up = weight[:, moe_intermediate_size:, :].contiguous()
+    return gate, up
+
+
+@ModelBase.register("HYV4ForCausalLM")
+@ModelBase.example("tencent/Hy4-preview")
+class HYV4Model(DeepseekV2Model):
+    """HY_V4: DeepSeek-V3 style MLA + MoE with iHC, a gated MLA output and a learnable sink.
+
+    Reuses DeepseekV2Model for the vocab and the MLA metadata, but overrides the tensor mapping
+    because HY_V4 ships pre-stacked / fused experts plus extra iHC, gate and sink tensors. The
+    rope rows are mapped straight through (no permute) - the graph rotates consecutive pairs.
+
+    DSA is supported: indexer weights are exported for the layers marked "full" in indexer_types.
+    "shared" layers reuse the top-k of the last preceding full layer at inference time, so they
+    carry no indexer weights.
+
+    MTP (num_nextn_predict_layers) is dropped, so the GGUF cannot be used for speculative
+    decoding. The reference only runs the MTP layers while training or while speculating, so they
+    cannot change single-token logits.
+    """
+
+    model_arch = gguf.MODEL_ARCH.HY_V4
+
+    merge_expert = False
+
+    # tensors a "full" indexer layer must carry
+    INDEXER_SUFFIXES = frozenset({
+        "self_attn.indexer.wq_b.weight",
+        "self_attn.indexer.wk.weight",
+        "self_attn.indexer.k_norm.weight",
+        "self_attn.indexer.k_norm.bias",
+        "self_attn.indexer.weights_proj.weight",
+    })
+
+    @classmethod
+    def filter_tensors(cls, item):
+        # drop MTP here, not in modify_tensors, so the weights are never read
+        if item[0].startswith("model.mtp_layers."):
+            return None
+        return super().filter_tensors(item)
+
+    def _check_indexer_hparams(self):
+        for key in ("index_n_heads", "index_head_dim", "index_topk"):
+            if key not in self.hparams:
+                raise ValueError(f"HY_V4 has DSA layers but no {key}")
+
+    def indexer_is_full(self) -> list[bool] | None:
+        """Per-layer indexer ownership, or None when the checkpoint has no DSA.
+
+        indexer_types entries are "full" (owns an indexer) or "shared" (reuses the preceding
+        full layer's top-k). Missing indexer_types with sparse layers means every sparse layer
+        owns one.
+        """
+        hparams = self.hparams
+        n_layer = hparams["num_hidden_layers"]
+        indexer_types = hparams.get("indexer_types")
+
+        # the reference drives DSA off indexer_types alone; layer_types is only a fallback for
+        # checkpoints predating it (it was renamed to deepseek_sparse_attention upstream)
+        if indexer_types is None:
+            layer_types = hparams.get("layer_types") or []
+            sparse = {"sparse_attention", "deepseek_sparse_attention"}
+            if not any(t in sparse for t in layer_types):
+                return None
+            if len(layer_types) < n_layer:
+                raise ValueError(f"HY_V4 layer_types has {len(layer_types)} entries, need {n_layer}")
+            self._check_indexer_hparams()
+            return [t in sparse for t in layer_types[:n_layer]]
+
+        self._check_indexer_hparams()
+
+        if len(indexer_types) < n_layer:
+            raise ValueError(f"HY_V4 indexer_types has {len(indexer_types)} entries, need {n_layer}")
+        unknown = {t for t in indexer_types[:n_layer]} - {"full", "shared"}
+        if unknown:
+            raise ValueError(f"HY_V4 unknown indexer_types values: {sorted(unknown)}")
+        is_full = [t == "full" for t in indexer_types[:n_layer]]
+        if is_full and not is_full[0]:
+            raise ValueError("HY_V4 layer 0 must be indexer_types 'full' (nothing precedes it to share)")
+        return is_full
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+
+        # HY4 has n_group == topk_group == 1 (no group routing). Drop the keys so the base does
+        # not emit expert_group_count/used; llama.cpp then takes the ungrouped MoE path.
+        if hparams.get("n_group") == 1 and hparams.get("topk_group") == 1:
+            hparams.pop("n_group", None)
+            hparams.pop("topk_group", None)
+
+        # HY_V4 config expresses dense/sparse layers via mlp_layer_types, but DeepseekV2Model
+        # needs first_k_dense_replace. Derive it as the contiguous leading "dense" block
+        # (the real config.json also carries first_k_dense_replace; prefer it when present,
+        # but assert the two agree so a mismatch fails loudly).
+        mlp_types = hparams.get("mlp_layer_types")
+        explicit = hparams.get("first_k_dense_replace")
+        derived = None
+        if mlp_types is not None:
+            lead = 0
+            for t in mlp_types:
+                if t == "dense":
+                    lead += 1
+                else:
+                    break
+            if any(t == "dense" for t in mlp_types[lead:]):
+                raise NotImplementedError("HY_V4 converter expects a contiguous leading dense block")
+            derived = lead
+        if explicit is not None and derived is not None and explicit != derived:
+            raise ValueError(
+                f"HY_V4 first_k_dense_replace ({explicit}) disagrees with mlp_layer_types "
+                f"leading-dense count ({derived})"
+            )
+        if explicit is None:
+            if derived is None:
+                raise ValueError("HY_V4 needs first_k_dense_replace or mlp_layer_types to place dense layers")
+            hparams["first_k_dense_replace"] = derived
+
+        # reuse DeepseekV2 MLA + MoE metadata (forces num_key_value_heads=1, writes q/kv lora,
+        # key/value lengths, expert counts, weights scale/norm, rope dims, etc.)
+        super().set_gguf_parameters()
+
+        # HY4 uses DeepSeek-V3 sigmoid routing with e_score_correction_bias. The config has no
+        # scoring_func key, so the base does not write a gating func; set it explicitly.
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        # routed-expert SwiGLU logits clamp (only routed experts; shared/dense are not clamped,
+        # so swiglu_clamp_shexp is intentionally not written). 0.0 disables the clamp.
+        swiglu_limit = float(hparams.get("swiglu_limit", 0.0) or 0.0)
+        if swiglu_limit > 0.0:
+            self.gguf_writer.add_swiglu_clamp_exp([swiglu_limit] * self.block_count)
+
+        # iHC (independent Hyper-Connections)
+        self.gguf_writer.add_hyper_connection_count(hparams["hc_mult"])
+        self.gguf_writer.add_hyper_connection_epsilon(hparams["hc_eps"])
+        self.gguf_writer.add_hyper_connection_magnitude(hparams["hc_magnitude"])
+
+        # is_full is written explicitly; the graph must not infer it from tensor presence
+        is_full = self.indexer_is_full()
+        if is_full is not None:
+            self.gguf_writer.add_indexer_head_count(hparams["index_n_heads"])
+            self.gguf_writer.add_indexer_key_length(hparams["index_head_dim"])
+            self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
+            self.gguf_writer.add_indexer_types(is_full)
+            logger.info(
+                "HY_V4 DSA: %d/%d layers own an indexer (top_k=%d, n_heads=%d, head_dim=%d)",
+                sum(is_full), len(is_full), hparams["index_topk"],
+                hparams["index_n_heads"], hparams["index_head_dim"],
+            )
+
+        if hparams.get("num_nextn_predict_layers", 0):
+            logger.warning(
+                "HY_V4: dropping %d MTP (nextn) layer(s) - the reference runs them only under "
+                "training / speculative decoding. This GGUF cannot be used for speculative decoding.",
+                hparams["num_nextn_predict_layers"],
+            )
+
+    def prepare_tensors(self):
+        # Hy4-preview for some reason has num_key_value_heads equal to 8, so override it here
+        # without this conversion/deepseek.py fails on assert
+        self.hparams["num_key_value_heads"] = self.hparams["num_attention_heads"]
+
+        # validate before the base materializes tensors, so a mismatch fails early
+        is_full = self.indexer_is_full()
+        if is_full is not None:
+            present: dict[int, set[str]] = {}
+            for name in self.model_tensors:
+                m = re.match(r"model\.layers\.(\d+)\.(self_attn\.indexer\..+)$", name)
+                if m:
+                    present.setdefault(int(m.group(1)), set()).add(m.group(2))
+            for il, expect_full in enumerate(is_full):
+                seen = present.get(il, set())
+                if expect_full and seen != self.INDEXER_SUFFIXES:
+                    raise ValueError(
+                        f"HY_V4 layer {il} is indexer_types 'full' but is missing indexer tensors: "
+                        f"{sorted(self.INDEXER_SUFFIXES - seen)}"
+                    )
+                if not expect_full and seen:
+                    raise ValueError(
+                        f"HY_V4 layer {il} is indexer_types 'shared' but carries indexer tensors: "
+                        f"{sorted(seen)}"
+                    )
+
+        super().prepare_tensors()
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # iHC mixing matrices are 2D .weight tensors that the reference keeps in fp32
+        # (_keep_in_fp32_modules_strict). 1D tensors (hc_base/scale, attn_sinks,
+        # e_score_correction_bias) and the router (FFN_GATE_INP) are already forced F32 by the
+        # base rules. Force the HC *_fn matrices here.
+        if new_name.endswith(("hc_attn_fn.weight", "hc_ffn_fn.weight", "output_hc_fn.weight")):
+            return gguf.GGMLQuantizationType.F32
+        # indexer k_norm is fp32 in the reference; the base rules already cover
+        # *_norm.weight and INDEXER_PROJ, but not this bias
+        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.INDEXER_K_NORM, bid, suffix=".bias"):
+            return gguf.GGMLQuantizationType.F32
+        # enable_lm_head_fp32: mirror the reference fp32 LM-head matmul by keeping output F32.
+        if new_name == "output.weight" and self.hparams.get("enable_lm_head_fp32", False):
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
+        hparams = self.hparams
+        moe_inter = hparams["moe_intermediate_size"]
+
+        tn = self.format_tensor_name
+
+        # fused stacked experts: split gate_up into gate/up
+        if name.endswith("mlp.experts.gate_up_proj"):
+            gate, up = split_gate_up(data_torch, moe_inter)
+            yield from super().modify_tensors(gate, tn(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid)
+            yield from super().modify_tensors(up,   tn(gguf.MODEL_TENSOR.FFN_UP_EXP,   bid), bid)
+            return
+
+        # add .weight suffixes
+        if name.endswith("mlp.experts.down_proj") or name.endswith(".self_attn.learnable_sink_param"):
+            name += ".weight"
+
+        if re.search(r"\.hc_head\.hc_head_(?:fn|base|scale)$", name):
+            name += ".weight"
+
+        if re.search(r"\.hc_(?:attn|mlp)_layer\.hc_pre\.hc_(?:fn|base|scale)$", name):
+            name += ".weight"
+
+        yield from super().modify_tensors(data_torch, name, bid)
--- a/Show More
+++ b/Show More