model : support MTP in GLM-4.5-Air (#26534 )

readme : update links (#27617 )
* readme : update links * readme : update maintainer PRs list Add the new members of the `ggml-org` `maintainers` team to the author filter of the maintainer PRs link (nikwen, marty1885, Titaniumtown), keeping the canonical team ordering. The list now matches the team exactly (35 members). Assisted-by: pi:llama.cpp/Qwen3.8-27B
2026-08-23 21:22:34 +02:00 · 2026-08-23 21:20:44 +03:00 · 2026-08-23 20:55:56 +03:00 · 2026-08-23 19:37:19 +02:00 · 2026-08-23 19:59:42 +03:00 · 2026-08-23 18:38:51 +02:00
155 changed files with 4843 additions and 1778 deletions
@@ -1,23 +1,88 @@
 # note: place this as the last step of the job, so the new cache is saved by "Post ccache" right after the old one is cleared
 name: "ccache-clear"
-description: "Delete all GitHub Actions caches matching a key prefix"
+description: "Delete GitHub Actions caches matching a key prefix, oldest first"
 inputs:
  key:
    description: "Cache key prefix to match and delete"
    required: true
+  older:
+    description: "Only delete caches created more than this long ago (e.g. 90m, 1h, 1d). By default all matching caches are deleted"
+    required: false
+    default: ""
+  min:
+    description: "Stop deleting if fewer than this many caches would remain (e.g. 1). By default there is no minimum"
+    required: false
+    default: "0"
+  dry-run:
+    description: "Only print the caches that would be deleted, without deleting them"
+    required: false
+    default: "false"

 runs:
  using: "composite"
  steps:
    - name: Clear caches
      shell: bash
+      env:
+        CLEAR_KEY: ${{ inputs.key }}
+        CLEAR_OLDER: ${{ inputs.older }}
+        CLEAR_MIN: ${{ inputs.min }}
+        CLEAR_DRY_RUN: ${{ inputs.dry-run }}
      run: |
-        CACHES=$(gh cache list --key "ccache-${{ inputs.key }}" --json id,key --jq '.[] | "\(.id) \(.key)"' 2>/dev/null)
+        # 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: ${{ inputs.key }}"
+          echo "No caches found with key prefix: $CLEAR_KEY"
          exit 0
        fi
-        while read -r id key; do
-          echo "Deleting cache: $id ($key)"
-          gh cache delete "$id"
+
+        TOTAL=$(( $(wc -l <<< "$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"
@@ -117,6 +117,18 @@ 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 / ${{ matrix.build }}
    runs-on: windows-2025
@@ -774,6 +774,7 @@ jobs:
        with:
          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
          evict-old-files: 1d
+          max-size: "1G"

      # - name: Cache ROCm Installation
      #   id: cache-rocm
@@ -1286,123 +1287,134 @@ 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-22-rocm:
+    needs: [check-release, get-version]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

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

-#     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: "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'

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

-#       - 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-${{ matrix.ROCM_VERSION }}
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-ubuntu-rocm-${{ 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://repo.amd.com/rocm/whl-multi-arch/ "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="${{ matrix.gpu_targets }}" \
+            -DGGML_HIP=ON \
+            -DHIP_PLATFORM=amd \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
+            ${{ 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-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}

  ios-xcode:
    needs: [check-release, get-version]
@@ -1583,7 +1595,7 @@ jobs:
      - windows-sycl
      - windows-rocm
      - windows-openvino
-      #- ubuntu-22-rocm
+      - ubuntu-22-rocm
      - ubuntu-cpu
      - ubuntu-vulkan
      - ubuntu-24-openvino
@@ -1714,7 +1726,7 @@ 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 7.14)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.14-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)
@@ -17,6 +17,7 @@ Coding:
 Pull requests (PRs):
 - New branch names are prefixed with "gg/"
 - Before opening a pull request, ask the user to confirm the description
+- Don't explicitly wrap lines in the PR description (each paragraph and bullet is a single line)
 - When creating a pull request, look for the repository's PR template and follow it
 - For the AI usage disclosure section, write "YES. pi:llama.cpp/[MODEL]"
 - Ask the user to tell you what model was used and write it in place of [MODEL]
@@ -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

@@ -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%3Abartowski1182%20OR%20author%3Anikwen%20OR%20author%3Ahipudding%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%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>

@@ -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 {
@@ -751,6 +785,8 @@ 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 $ret -eq 0 && gg_run test_backend_ops_cpu
 fi
@@ -81,6 +81,8 @@ add_library(${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 = {
@@ -1898,7 +1897,7 @@ 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"
@@ -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,
@@ -6,6 +6,7 @@
 #include "common.h"
 #include "ggml.h"
 #include "json-schema-to-grammar.h"
+#include "json.h"
 #include "log.h"

 #include "jinja/value.h"
@@ -13,14 +14,13 @@
 #include "jinja/caps.h"
 #include "peg-parser.h"

-#include "nlohmann/json.hpp"
-
 #include <algorithm>
 #include <cstdio>
 #include <cstdlib>
 #include <ctime>
 #include <exception>
 #include <functional>
+#include <iomanip>
 #include <map>

 #include <optional>
@@ -30,7 +30,7 @@
 #include <utility>
 #include <vector>

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

 static std::string format_time(const std::chrono::system_clock::time_point & now, const std::string & format) {
    auto               time       = std::chrono::system_clock::to_time_t(now);
@@ -48,7 +48,7 @@ static json safe_args_parse(const std::string & to_parse) {
    }
    try {
        return json::parse(stripped);
-    } catch (json::exception & e) {
+    } catch (const common_json_error & e) {
        return stripped;
    }
 }
@@ -488,17 +488,17 @@ struct messages_inp_normalizer {
        json normalized = json::array();
        for (const auto & msg : messages) {
            json copy = msg;
-            auto it = copy.find("content");
-            if (it != copy.end()) {
-                if (only_typed && it->is_string()) {
-                    *it = json::array({
+            if (copy.contains("content")) {
+                json & it = copy.at("content");
+                if (only_typed && it.is_string()) {
+                    it = json::array({
                        json{
                            {"type", "text"},
-                            {"text", it->get<std::string>()},
+                            {"text", it.get<std::string>()},
                        }
                    });
-                } else if (only_string && it->is_array()) {
-                    *it = concat_content_parts(*it);
+                } else if (only_string && it.is_array()) {
+                    it = concat_content_parts(it);
                }
            }
            normalized.push_back(std::move(copy));
@@ -608,7 +608,7 @@ std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & too
    return result;
 }

-common_chat_continuation common_chat_continuation_parse(const nlohmann::ordered_json & value) {
+common_chat_continuation common_chat_continuation_parse(const common_json & value) {
    if (value.is_boolean() && value.get<bool>()) {
        return COMMON_CHAT_CONTINUATION_AUTO;
    }
@@ -920,7 +920,7 @@ static void foreach_parameter(const json &
    const auto &          props = params.at("properties");
    std::set<std::string> required;
    if (params.contains("required") && params.at("required").is_array()) {
-        params.at("required").get_to(required);
+        required = params.at("required").get<std::set<std::string>>();
    }
    for (const auto & [name, prop] : props.items()) {
        bool is_required = (required.find(name) != required.end());
@@ -937,7 +937,7 @@ static std::string common_chat_template_direct_apply_impl(
    jinja::context ctx(tmpl.source());

    // messages_override is already built for this template, do not touch its content parts
-    nlohmann::ordered_json inp = nlohmann::ordered_json{
+    json inp = json{
        {"messages", messages_override.has_value()
            ? *messages_override
            : messages_inp_normalizer(tmpl.original_caps()).normalize(inputs.messages)},
@@ -1058,7 +1058,7 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
                });
            } else if (msg.at("content").is_array()) {
                auto blocks = msg.at("content");
-                content.insert(content.end(), blocks.begin(), blocks.end());
+                content.insert(blocks);
            }
        }

@@ -2238,7 +2238,7 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha

                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();
@@ -2860,7 +2860,7 @@ static common_chat_params common_chat_params_init_minimax_m3(const common_chat_t

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

                std::vector<common_peg_parser> required_elements;
@@ -2972,10 +2972,10 @@ static void system_message_not_supported(json & messages) {
            auto & second_msg = messages[1];
            second_msg["content"] = first_msg.at("content").get<std::string>()
                + "\n" + second_msg.at("content").get<std::string>();
-            messages.erase(messages.begin());
+            messages.erase(0);
        } else {
            LOG_WRN("Removing system prompt due to template not supporting system role\n");
-            messages.erase(messages.begin());
+            messages.erase(0);
        }
    }
 }
@@ -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);
    }

@@ -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 {

@@ -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();
@@ -917,7 +916,11 @@ 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 +1114,7 @@ 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 +1122,7 @@ 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 +1230,7 @@ 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 +1251,10 @@ 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;
@@ -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.
@@ -2388,6 +2388,9 @@ 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;
@@ -58,6 +58,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "DSparkDraftModel": "qwen",
    "DSparkSpeculator": "qwen",
    "Lfm2DSparkDraftModel": "qwen",
+    "LingDSparkModel": "qwen",
    "DeepseekV4ForCausalLM": "deepseek",
    "DeepseekV4DSparkModel": "deepseek",
    "DistilBertForMaskedLM": "bert",
@@ -282,6 +283,8 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
    "CogVLMForCausalLM": "cogvlm",
    "DeepseekOCR2ForCausalLM": "deepseek",
    "DeepseekOCRForCausalLM": "deepseek",
+    "Dots3NoteForCausalLM": "dots3",
+    "Dots3NoteForConditionalGeneration": "dots3",
    "DotsOCRForCausalLM": "dotsocr",
    "Exaone4_5_ForConditionalGeneration": "exaone",
    "Gemma3ForConditionalGeneration": "gemma",
@@ -3,12 +3,14 @@ from __future__ import annotations
 import math
 import re

-from typing import TYPE_CHECKING, Callable, Iterable
+import torch
+
+from typing import TYPE_CHECKING, Any, Callable, Iterable

 if TYPE_CHECKING:
    from torch import Tensor

-from .base import ModelBase, gguf
+from .base import MmprojModel, ModelBase, gguf

 from .deepseek import DeepseekV2Model

@@ -193,3 +195,129 @@ class Dots3NoteModel(DeepseekV2Model):
            return

        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Dots3NoteForCausalLM", "Dots3NoteForConditionalGeneration")
+class Dots3NoteMmprojModel(MmprojModel):
+    has_vision_encoder = True
+    has_audio_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        assert self.hparams_audio is not None
+
+        # preprocessor_config.json nests the image params under vision_config
+        self.preprocessor_config = {**self.preprocessor_config, **self.preprocessor_config.get("vision_config", {})}
+
+        vis = self.hparams_vision
+        # in this config, hidden_size is the adapter output width; embed_dim is the tower width
+        vis["hidden_size"] = vis["embed_dim"]
+        vis["image_size"] = 0  # dynamic resolution
+        self.pyramid = [max(0, n) for n in vis["pyramid_num_routed"]]
+
+        if vis.get("adapter_type") != "patch_merger" or not vis.get("pre_pixel_shuffle"):
+            raise ValueError("dots3-note vision conversion requires adapter_type=patch_merger and pre_pixel_shuffle")
+        if vis.get("router_scoring_func", "sigmoid") != "sigmoid" or vis.get("router_scale", 1.0) != 1.0:
+            raise ValueError("dots3-note vision conversion only supports sigmoid routing with router_scale=1.0")
+        if vis.get("temporal_patch_size", 1) != 1 or vis.get("use_bias") or not vis.get("use_qk_norm"):
+            raise ValueError("unsupported dots3-note vision config variant")
+
+        aud = self.hparams_audio
+        if not aud.get("use_conv2d_stem") or not aud.get("use_rope") or not aud.get("use_rms_norm") or aud.get("use_causal"):
+            raise ValueError("unsupported dots3-note audio config variant")
+        if aud["whisper_config"].get("activation_function") != "swiglu":
+            raise ValueError("dots3-note audio conversion requires the swiglu activation")
+        if aud.get("merge_factor", 1) != 1 or aud.get("chunk_seconds") != 60:
+            raise ValueError("unsupported dots3-note audio chunking config")
+        # the graph hard-codes these rope parameters
+        rope = aud.get("rope_parameters", {})
+        if rope.get("partial_rotary_factor") != 0.5 or rope.get("rope_theta") != 10000.0:
+            raise ValueError("unsupported dots3-note audio rope config")
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        cfg = self.global_config.get("audio_config")
+        if cfg is not None:
+            # aliases so MmprojModel.find_aparam() / n_block_keys can resolve them
+            whisper = cfg["whisper_config"]
+            cfg["hidden_size"] = whisper["d_model"]
+            cfg["intermediate_size"] = whisper["encoder_ffn_dim"]
+            cfg["num_attention_heads"] = whisper["encoder_attention_heads"]
+            cfg["num_hidden_layers"] = whisper["encoder_layers"]
+        return cfg
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+        assert self.hparams_audio is not None
+
+        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.DOTS3NOTE_V)
+        self.gguf_writer.add_vision_use_silu(True)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision["rms_norm_eps"])
+        self.gguf_writer.add_vision_spatial_merge_size(self.hparams_vision["spatial_merge_size"])
+        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
+        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
+        # pyramid MoE: per-block routed expert count, 0 = dense block
+        self.gguf_writer.add_vision_expert_count_per_layer(self.pyramid)
+        self.gguf_writer.add_vision_expert_used_count(int(self.hparams_vision["capacity_factor"]))
+
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.DOTS3NOTE_A)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["whisper_config"]["num_mel_bins"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-6)  # Dots3NoteAudioRMSNorm default
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, _ = item
+        if not name.startswith(("vision_encoder.", "audio_encoder.")):
+            return None
+        return super().filter_tensors(item)
+
+    _vis_experts: dict[int, dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # router params have no .weight suffix in the checkpoint, but gguf tools expect one
+        if name.endswith((".gate_weight", ".router_bias")):
+            name += ".weight"
+
+        # audio fc1 fuses gate and up for swiglu; split it
+        if ".speech_encoder.layers." in name and ".fc1." in name:
+            gate, up = data_torch.chunk(2, dim=0)
+            yield from super().modify_tensors(gate, name.replace(".fc1.", ".fc1_gate."), bid)
+            yield from super().modify_tensors(up, name.replace(".fc1.", ".fc1_up."), bid)
+            return
+
+        # vision MoE: stack per-expert weights into a single 3D tensor per block
+        if ".mlp.experts." in name:
+            assert bid is not None
+            n_expert = self.pyramid[bid]
+            if self._vis_experts is None:
+                self._vis_experts = {}
+            buf = self._vis_experts.setdefault(bid, {})
+            buf[name] = data_torch
+
+            if len(buf) >= n_expert * 3:
+                for w_name in ("fc1", "fc2", "fc3"):
+                    datas: list[Tensor] = []
+                    for xid in range(n_expert):
+                        ename = f"vision_encoder.blocks.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(buf.pop(ename))
+                    merged = torch.stack(datas, dim=0)
+                    yield from super().modify_tensors(merged, f"vision_encoder.blocks.{bid}.mlp.experts.{w_name}.weight", bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._vis_experts is not None:
+            leftover = [k for d in self._vis_experts.values() for k in d.keys()]
+            if leftover:
+                raise ValueError(f"unprocessed vision experts: {leftover}")
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # FP32 routing is load-bearing for the vision MoE (near-tied expert scores)
+        if ".ffn_gate_inp." in new_name or ".exp_probs_b." in new_name:
+            return gguf.GGMLQuantizationType.F32
+        if ".conv2d" in new_name or "a.conv_out" in new_name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
@@ -112,12 +112,36 @@ 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):
+        type(self)._n_main_layers = self.hparams["num_hidden_layers"]
+        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 +177,22 @@ 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 +384,7 @@ 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
@@ -709,7 +709,13 @@ class DFlashModel(Qwen3Model):
        yield from super().modify_tensors(data_torch, name, bid)


-@ModelBase.register("Qwen3DSparkModel", "DSparkDraftModel", "DSparkSpeculator", "Lfm2DSparkDraftModel")
+@ModelBase.register(
+    "Qwen3DSparkModel",
+    "DSparkDraftModel",
+    "DSparkSpeculator",
+    "Lfm2DSparkDraftModel",
+    "LingDSparkModel",
+)
@ModelBase.example("satgeze/Qwen3.6-27B-DSpark")
 class DSparkModel(DFlashModel):
    # DSpark = DFlash + a semi-autoregressive Markov head.
@@ -443,21 +443,21 @@ Each returned parser is wrapped by `wrap_for_generation_prompt()`, which prepend
 |                                           | `wrap_for_generation_prompt()`, string helpers                                  |
 | `common/chat-peg-parser.h/cpp`            | `common_chat_peg_builder`, `common_chat_peg_mapper`, and helpers                |
 | `common/chat.cpp`                         | Entry point: `common_chat_templates_apply_jinja()`                              |
-| `tools/parser/debug-template-parser.cpp`  | Debug tool for template analysis                                                |
-| `tools/parser/template-analysis.cpp`      | Template analysis tool                                                          |
+| `tests/test-chat-auto-parser.cpp`         | Auto-parser unit tests; also a debug tool when given a template path            |
+| `tests/test-chat-analysis.cpp`            | Template differential analysis debug tool                                       |

 ## Testing & Debugging

 ### Debug Tools

-**Template Debugger**: `tools/parser/debug-template-parser.cpp`
+**Template Debugger**: `tests/test-chat-auto-parser.cpp`

- Usage: `./bin/llama-debug-template-parser path/to/template.jinja`
+- Usage: `./bin/test-chat-auto-parser path/to/template.jinja` (without a path, it runs the automated tests)
 - Shows detected format, markers, generated parser, and GBNF grammar

-**Template Analysis**: `tools/parser/template-analysis.cpp`
+**Template Analysis**: `tests/test-chat-analysis.cpp`

- Usage: `./bin/llama-template-analysis path/to/template.jinja`
+- Usage: `./bin/test-chat-analysis --template-file path/to/template.jinja` (without arguments, it runs on all templates from the test suite)

 **Debug Logging**: Enable with `LLAMA_ARG_LOG_VERBOSITY=2`

@@ -519,7 +519,7 @@ The following templates have active tests in `tests/test-chat.cpp`:

 To support a new template format:

-1. **If it follows standard patterns** — The auto-parser should detect it automatically. Run `llama-debug-template-parser` to verify markers are correctly extracted.
+1. **If it follows standard patterns** — The auto-parser should detect it automatically. Run `test-chat-auto-parser <template_path>` to verify markers are correctly extracted.
 2. **If differential analysis extracts incorrect markers** — Add a workaround lambda to the `workarounds` vector in `common/chat-diff-analyzer.cpp`. Inspect the template source for a unique identifying substring.
 3. **If it needs fundamentally different handling** — Add a dedicated handler function in `chat.cpp` before the auto-parser block (as done for GPT-OSS, Functionary v3.2, and Ministral).

@@ -602,27 +602,40 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            case GGML_BACKEND_SPLIT_AXIS_1:
            case GGML_BACKEND_SPLIT_AXIS_2:
            case GGML_BACKEND_SPLIT_AXIS_3: {
-                GGML_ASSERT(src_ss[0].n_segments == 1);
-                if (src_ss[0].axis == ggml_n_dims(tensor->src[0]) - 1 && src_ss[0].nr[0] == 1) {
-                    return {ggml_backend_meta_split_axis(ggml_n_dims(tensor) - 1), {0}, {1}, 1};
-                }
-                int64_t base_ne_in = tensor->src[0]->ne[0];
-                for (int dim = 1; dim <= src_ss[0].axis; dim++) {
+                int64_t base_ne_in = 1;
+                for (int dim = 0; dim <= src_ss[0].axis; dim++) {
                    base_ne_in *= tensor->src[0]->ne[dim];
                }
-                base_ne_in /= src_ss[0].nr[0];
+                if (src_ss[0].n_segments == 1) {
+                    base_ne_in /= src_ss[0].nr[0];
+                    if (src_ss[0].axis == ggml_n_dims(tensor->src[0]) - 1 && src_ss[0].nr[0] == 1) {
+                        return {ggml_backend_meta_split_axis(ggml_n_dims(tensor) - 1), {0}, {1}, 1};
+                    }
+                    if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0 && tensor->ne[0] == tensor->src[0]->ne[0] &&
+                            tensor->ne[1] == 1 && src_ss[0].nr[0] == 1) {
+                        bool complete_rows = true;
+                        for (size_t j = 0; j < n_bufs; j++) {
+                            const int64_t ne = src_ss[0].ne[j];
+                            complete_rows = complete_rows && (ne == 0 || ne == tensor->src[0]->ne[0]);
+                        }
+                        if (complete_rows) {
+                            // Move a complete dim-0 split to the following singleton dimension.
+                            return {GGML_BACKEND_SPLIT_AXIS_1, {0}, {1}, 1};
+                        }
+                    }
+                }
+                // Reshape outputs use one segment; split-state propagation merges source segments.
                int64_t base_ne_out = 1;
                for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
-                    const int64_t base_ne_out_next = base_ne_out *= tensor->ne[dim];
-                    if (base_ne_out_next % base_ne_in == 0) {
-                        return {ggml_backend_meta_split_axis(dim), {0}, {uint32_t(base_ne_out_next/base_ne_in)}, 1};
+                    base_ne_out *= tensor->ne[dim];
+                    if (base_ne_out % base_ne_in == 0) {
+                        return {ggml_backend_meta_split_axis(dim), {0}, {uint32_t(base_ne_out/base_ne_in)}, 1};
                    }
-                    if (base_ne_out_next > base_ne_in) {
+                    if (base_ne_out > base_ne_in) {
                        GGML_ASSERT(src_ss[0].n_segments == 1);
                        GGML_ASSERT(src_ss[0].nr[0]      == 1);
                        return {ggml_backend_meta_split_axis(dim), {0}, {1}, 1};
                    }
-                    base_ne_out = base_ne_out_next;
                }
                GGML_ABORT("shape mismatch for %s", ggml_op_name(tensor->op));
            }
@@ -792,7 +805,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            ggml_backend_dev_t dev = ggml_backend_buft_get_device(ggml_backend_buffer_get_type(tensor->buffer));
            const ggml_backend_meta_device_context * dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
            ggml_backend_meta_split_state ret = dev_ctx->get_split_state(tensor, dev_ctx->get_split_state_ud);
-            if (ret.axis >= 0 && ret.axis <= GGML_MAX_DIMS) {
+            if (ret.axis >= 0 && ret.axis < GGML_MAX_DIMS) {
                const int64_t granularity = ret.axis == GGML_BACKEND_SPLIT_AXIS_0 ? ggml_blck_size(tensor->type) : 1;
                int64_t ne_sum = 0;
                for (size_t s = 0; s < ret.n_segments; s++) {
@@ -802,6 +815,9 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
                    }
                }
                GGML_ASSERT(ne_sum == tensor->ne[ret.axis]);
+            } else if (ret.axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL) {
+                GGML_ASSERT(ret.n_segments == 1);
+                GGML_ASSERT(ret.nr[0] == 1);
            }
            return ret;
        }
@@ -1352,15 +1368,29 @@ static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, gg
        } break;
        case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
            GGML_ASSERT(tensor->type == GGML_TYPE_F32);
-            const int64_t ne = ggml_nelements(tensor);
-            std::vector<float> tmp;
-            tmp.reserve(ne);
-            for (int64_t i = 0; i < ne; i++) {
-                tmp.push_back(((const float *) data)[i] / n_bufs);
+            GGML_ASSERT(offset % sizeof(float) == 0);
+            GGML_ASSERT(size   % sizeof(float) == 0);
+            const size_t n_values = size / sizeof(float);
+            size_t n_contributors = 0;
+            for (size_t j = 0; j < n_bufs; j++) {
+                n_contributors += split_state.ne[j] != 0;
+            }
+            const bool has_contributor_mask = n_contributors != 0;
+            if (!has_contributor_mask) {
+                n_contributors = n_bufs;
+            }
+            std::vector<float> tmp(n_values);
+            for (size_t i = 0; i < n_values; i++) {
+                tmp[i] = ((const float *) data)[i] / n_contributors;
+            }
+            std::vector<float> zero;
+            if (has_contributor_mask) {
+                zero.resize(n_values, 0.0f);
            }
            for (size_t j = 0; j < n_bufs; j++) {
                ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
-                ggml_backend_tensor_set(simple_tensor, tmp.data(), offset, size);
+                const float * partial = has_contributor_mask && split_state.ne[j] == 0 ? zero.data() : tmp.data();
+                ggml_backend_tensor_set(simple_tensor, partial, offset, size);
            }
        } break;
        default: {
@@ -1896,7 +1896,6 @@ void ggml_compute_forward_repeat_back(
 }

 // ggml_compute_forward_concat
-
 static void ggml_compute_forward_concat_any(
    const ggml_compute_params * params,
    ggml_tensor * dst) {
@@ -1904,8 +1903,6 @@ static void ggml_compute_forward_concat_any(
    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];

-    const size_t len = ggml_type_size(src0->type);
-
    const int ith = params->ith;
    const int nth = params->nth;

@@ -1914,31 +1911,38 @@ static void ggml_compute_forward_concat_any(
    const int32_t dim = ggml_get_op_params_i32(dst, 0);

    GGML_ASSERT(dim >= 0 && dim < 4);
+    GGML_ASSERT(ggml_is_contiguous_rows(src0));
+    GGML_ASSERT(ggml_is_contiguous_rows(src1));

    int64_t o[4] = {0, 0, 0, 0};
+
    if (dim == 0) {
+        GGML_ASSERT(src0->ne[0] % ggml_blck_size(src0->type) == 0);
+        GGML_ASSERT(src1->ne[0] % ggml_blck_size(src1->type) == 0);
+
        o[dim] = src0->ne[dim]/ggml_blck_size(src0->type);
    } else {
        o[dim] = src0->ne[dim];
    }

-    const char * x;
+    // Region 1: copy rows from src0
+    for (int i3 = 0; i3 < ne03; i3++) {
+        for (int i2 = ith; i2 < ne02; i2 += nth) {
+            for (int i1 = 0; i1 < ne01; i1++) {
+                const char * x = (const char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03;
+                      char * y = (      char *) dst->data  + i1*nb1  + i2*nb2  + i3*nb3;
+                memcpy(y, x, ggml_row_size(src0->type, ne00));
+            }
+        }
+    }

-    // TODO: smarter multi-theading
-    for (int i3 = 0; i3 < ne3; i3++) {
-        for (int i2 = ith; i2 < ne2; i2 += nth) {
-            for (int i1 = 0; i1 < ne1; i1++) {
-                for (int i0 = 0; i0 < ne0/ggml_blck_size(dst->type); i0++) {
-                    if (i0 < ne00/ggml_blck_size(src0->type) && i1 < ne01 && i2 < ne02 && i3 < ne03) {
-                        x = (const char *)src0->data + (i0       )*nb00 + (i1       )*nb01 + (i2       )*nb02 + (i3       )*nb03;
-                    } else {
-                        x = (const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13;
-                    }
-
-                    char * y = (char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3;
-
-                    memcpy(y, x, len);
-                }
+    // Region 2: copy rows from src1, offset into dst by o[]
+    for (int i3 = 0; i3 < ne13; i3++) {
+        for (int i2 = ith; i2 < ne12; i2 += nth) {
+            for (int i1 = 0; i1 < ne11; i1++) {
+                const char * x = (const char *) src1->data + i1*nb11         + i2*nb12         + i3*nb13;
+                      char * y = (      char *)  dst->data + (i1 + o[1])*nb1 + (i2 + o[2])*nb2 + (i3 + o[3])*nb3 + o[0]*nb0;
+                memcpy(y, x, ggml_row_size(src1->type, ne10));
            }
        }
    }
@@ -2078,14 +2082,6 @@ void ggml_compute_forward_concat(
    ggml_tensor * dst) {

    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-
-    if (ggml_is_quantized(src0->type)) {
-        GGML_ASSERT(ggml_is_contiguous_rows(src0));
-        GGML_ASSERT(ggml_is_contiguous_rows(src1));
-        GGML_ASSERT(src0->ne[0] % ggml_blck_size(src0->type) == 0);
-        GGML_ASSERT(src1->ne[0] % ggml_blck_size(src1->type) == 0);
-    }

    switch (src0->type) {
        case GGML_TYPE_F16:
@@ -38,6 +38,7 @@
 #include "ggml-cuda/out-prod.cuh"
 #include "ggml-cuda/pad.cuh"
 #include "ggml-cuda/pool2d.cuh"
+#include "ggml-cuda/pool1d.cuh"
 #include "ggml-cuda/quantize.cuh"
 #include "ggml-cuda/rope.cuh"
 #include "ggml-cuda/roll.cuh"
@@ -2326,6 +2327,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
        case GGML_OP_POOL_2D:
            ggml_cuda_op_pool2d(ctx, dst);
            break;
+        case GGML_OP_POOL_1D:
+            ggml_cuda_op_pool1d(ctx, dst);
+            break;
        case GGML_OP_SUM:
            ggml_cuda_op_sum(ctx, dst);
            break;
@@ -5245,6 +5249,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
        case GGML_OP_CONV_2D_DW:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_CONV_TRANSPOSE_2D:
+        case GGML_OP_POOL_1D:
        case GGML_OP_POOL_2D:
            return true;
        case GGML_OP_ACC:
@@ -0,0 +1,85 @@
+#include "pool1d.cuh"
+
+static __global__ void pool1d_nchw_kernel(
+        const int iw, const int ow,
+        const int kw, const int sw, const int pw,
+        const int parallel_elements,
+        const float * src, float * dst, const enum ggml_op_pool op) {
+    const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (idx >= parallel_elements) {
+        return;
+    }
+
+    const int nc = idx / ow;
+    const int cur_ow = idx % ow;
+
+    const float * i_ptr = src + nc * iw;
+    float * o_ptr = dst + nc * ow;
+
+    const int start = cur_ow * sw - pw;
+    const int b = max(0, start);
+    const int e = min(iw, start + kw);
+
+    float res;
+    switch (op) {
+        case GGML_OP_POOL_AVG: res = 0.0f;     break;
+        case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
+        default: return;
+    }
+
+    int count = 0;
+    for (int i = b; i < e; i++) {
+#if __CUDA_ARCH__ >= 350
+        float cur = __ldg(i_ptr + i);
+#else
+        float cur = i_ptr[i];
+#endif
+        switch (op) {
+            case GGML_OP_POOL_AVG: res += cur;                break;
+            case GGML_OP_POOL_MAX: res = max(res, cur);       break;
+            default: break;
+        }
+        count++;
+    }
+
+    if (op == GGML_OP_POOL_AVG) {
+        res = (count > 0) ? (res / count) : 0.0f;
+    }
+
+    o_ptr[cur_ow] = res;
+}
+
+static void pool1d_nchw_kernel_f32_f32_cuda(
+        const int iw, const int ow,
+        const int kw, const int sw, const int pw,
+        const int parallel_elements,
+        const float * src, float * dst, const enum ggml_op_pool op,
+        cudaStream_t stream) {
+    const int num_blocks = (parallel_elements + CUDA_POOL1D_BLOCK_SIZE - 1) / CUDA_POOL1D_BLOCK_SIZE;
+    dim3 block_nums(num_blocks);
+    pool1d_nchw_kernel<<<block_nums, CUDA_POOL1D_BLOCK_SIZE, 0, stream>>>(iw, ow, kw, sw, pw, parallel_elements, src, dst, op);
+}
+
+void ggml_cuda_op_pool1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
+    const int k0 = opts[1];
+    const int s0 = opts[2];
+    const int p0 = opts[3];
+
+    const int64_t IW = src0->ne[0];
+    const int64_t OW = dst->ne[0];
+    const int64_t nr = ggml_nrows(src0);
+
+    const int parallel_elements = (int)(nr * OW);
+
+    pool1d_nchw_kernel_f32_f32_cuda(IW, OW, k0, s0, p0, parallel_elements, src0_d, dst_d, op, stream);
+}
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_POOL1D_BLOCK_SIZE 256
+
+void ggml_cuda_op_pool1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
@@ -955,6 +955,7 @@ struct vk_device_struct {
    vk_pipeline pipeline_diag[2];
    vk_pipeline pipeline_clamp[2];
    vk_pipeline pipeline_pad_f32;
+    vk_pipeline pipeline_pad_reflect_1d_f32;
    vk_pipeline pipeline_roll_f32;
    vk_pipeline pipeline_repeat_i32, pipeline_repeat_back_f32;
    vk_pipeline pipeline_repeat_i16;
@@ -5630,6 +5631,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
    ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_pad_reflect_1d_f32, "pad_reflect_1d_f32", pad_reflect_1d_f32_len, pad_reflect_1d_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_roll_f32, "roll_f32", roll_f32_len, roll_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

@@ -11336,6 +11338,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
            return ctx->device->pipeline_pad_f32;
        }
        return nullptr;
+    case GGML_OP_PAD_REFLECT_1D:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_pad_reflect_1d_f32;
+        }
+        return nullptr;
    case GGML_OP_ROLL:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_roll_f32;
@@ -12239,6 +12246,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
    case GGML_OP_CLAMP:
    case GGML_OP_LEAKY_RELU:
    case GGML_OP_PAD:
+    case GGML_OP_PAD_REFLECT_1D:
    case GGML_OP_ROLL:
    case GGML_OP_REPEAT:
    case GGML_OP_REPEAT_BACK:
@@ -13111,6 +13119,17 @@ static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_PAD, std::move(p));
 }

+static void ggml_vk_pad_reflect_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    const uint32_t p0 = (uint32_t)dst->op_params[0];
+    const uint32_t p1 = (uint32_t)dst->op_params[1];
+
+    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst));
+    memcpy(&p.param1, &p0, sizeof(float));
+    memcpy(&p.param2, &p1, sizeof(float));
+
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_PAD_REFLECT_1D, std::move(p));
+}
+
 static void ggml_vk_roll(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    const int32_t s0 = ggml_get_op_params_i32(dst, 0);
    const int32_t s1 = ggml_get_op_params_i32(dst, 1);
@@ -15520,6 +15539,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
    case GGML_OP_PAD:
        ggml_vk_pad(ctx, compute_ctx, src0, node);

+        break;
+    case GGML_OP_PAD_REFLECT_1D:
+        ggml_vk_pad_reflect_1d(ctx, compute_ctx, src0, node);
+
        break;
    case GGML_OP_ROLL:
        ggml_vk_roll(ctx, compute_ctx, src0, node);
@@ -18446,6 +18469,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
        case GGML_OP_SCALE:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_PAD:
+        case GGML_OP_PAD_REFLECT_1D:
        case GGML_OP_ROLL:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_DIAG_MASK_INF:
@@ -19228,6 +19252,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
        } else if (tensor->op == GGML_OP_PAD) {
            tensor_clone = ggml_pad_ext(ggml_ctx, src_clone[0], tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3],
                                                                tensor->op_params[4], tensor->op_params[5], tensor->op_params[6], tensor->op_params[7]);
+        } else if (tensor->op == GGML_OP_PAD_REFLECT_1D) {
+            tensor_clone = ggml_pad_reflect_1d(ggml_ctx, src_clone[0], tensor->op_params[0], tensor->op_params[1]);
        } else if (tensor->op == GGML_OP_REPEAT) {
            tensor_clone = ggml_repeat(ggml_ctx, src_clone[0], tensor);
        } else if (tensor->op == GGML_OP_REPEAT_BACK) {
@@ -0,0 +1,43 @@
+#version 450
+
+#include "types.glsl"
+#include "generic_unary_head.glsl" // included to use functions like fastdiv etc.
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+void main() {
+
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint p0 = floatBitsToUint(p.param1);
+    const uint p1 = floatBitsToUint(p.param2);
+
+    const uint i3 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0));
+    const uint i3_offset = i3 * p.ne12 * p.ne11 * p.ne10;
+
+    const uint i2 = fastdiv(idx - i3_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1));
+    const uint i2_offset = i2 * p.ne11 * p.ne10;
+
+    const uint i1 = fastdiv(idx - i3_offset - i2_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2));
+    const uint i0 = idx - i3_offset - i2_offset - i1 * p.ne10;
+
+    uint src_col;
+
+    if (i0 < p0) {
+        src_col = p0 - i0;    // left pad area
+    } else if (i0 < p0 + p.ne00) {
+        src_col = i0 - p0;    // center area
+    } else {
+        src_col = 2u * p.ne00 - 2u - (i0 - p0);  // right pad area
+    }
+
+    const uint src_idx = i3 * p.nb03 + i2 * p.nb02 + i1 * p.nb01 + src_col * p.nb00;
+    const uint d_idx = i3 * p.nb13 + i2 * p.nb12 + i1 * p.nb11 + i0 * p.nb10;
+
+    // copy the computed value to the destination tensor
+    data_d[get_doffset() + d_idx] = D_TYPE(data_a[get_aoffset() + src_idx]);
+}
@@ -896,6 +896,7 @@ void process_shaders() {
    string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});

    string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    string_to_spv("pad_reflect_1d_f32", "pad_reflect_1d.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});

    string_to_spv("concat_i8", "concat.comp", {{"A_TYPE", "uint8_t"}, {"B_TYPE", "uint8_t"}, {"D_TYPE", "uint8_t"}});
    string_to_spv("concat_i16", "concat.comp", {{"A_TYPE", "uint16_t"}, {"B_TYPE", "uint16_t"}, {"D_TYPE", "uint16_t"}});
@@ -364,6 +364,8 @@ class Keys:
        IMAGE_MEAN            = "clip.vision.image_mean"
        IMAGE_STD             = "clip.vision.image_std"
        SPATIAL_MERGE_SIZE    = "clip.vision.spatial_merge_size"
+        EXPERT_COUNT_PER_LAYER = "clip.vision.expert_count_per_layer" # dots3note pyramid MoE, 0 = dense layer
+        EXPERT_USED_COUNT     = "clip.vision.expert_used_count"
        USE_GELU              = "clip.use_gelu"
        USE_SILU              = "clip.use_silu"
        N_WA_PATTERN          = "clip.vision.n_wa_pattern"     # used by qwen2.5vl
@@ -874,6 +876,11 @@ class MODEL_TENSOR(IntEnum):
    V_ENC_FFN_UP         = auto()
    V_ENC_FFN_GATE       = auto()
    V_ENC_FFN_DOWN       = auto()
+    V_ENC_FFN_GATE_INP   = auto() # dots3note vision MoE router
+    V_ENC_FFN_GATE_EXPS  = auto()
+    V_ENC_FFN_UP_EXPS    = auto()
+    V_ENC_FFN_DOWN_EXPS  = auto()
+    V_ENC_FFN_EXP_PROBS_B = auto()
    V_ENC_ATTN_POST_NORM = auto() # gemma4
    V_ENC_FFN_POST_NORM  = auto()
    V_LAYER_SCALE_1      = auto()
@@ -1591,6 +1598,11 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.V_ENC_FFN_UP:              "v.blk.{bid}.ffn_up",
    MODEL_TENSOR.V_ENC_FFN_GATE:            "v.blk.{bid}.ffn_gate",
    MODEL_TENSOR.V_ENC_FFN_DOWN:            "v.blk.{bid}.ffn_down",
+    MODEL_TENSOR.V_ENC_FFN_GATE_INP:        "v.blk.{bid}.ffn_gate_inp",
+    MODEL_TENSOR.V_ENC_FFN_GATE_EXPS:       "v.blk.{bid}.ffn_gate_exps",
+    MODEL_TENSOR.V_ENC_FFN_UP_EXPS:         "v.blk.{bid}.ffn_up_exps",
+    MODEL_TENSOR.V_ENC_FFN_DOWN_EXPS:       "v.blk.{bid}.ffn_down_exps",
+    MODEL_TENSOR.V_ENC_FFN_EXP_PROBS_B:     "v.blk.{bid}.exp_probs_b",
    MODEL_TENSOR.V_ENC_ATTN_POST_NORM:      "v.blk.{bid}.attn_post_norm",
    MODEL_TENSOR.V_ENC_FFN_POST_NORM:       "v.blk.{bid}.ffn_post_norm",
    MODEL_TENSOR.V_LAYER_SCALE_1:           "v.blk.{bid}.ls1",
@@ -1913,6 +1925,11 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.V_ENC_FFN_UP,
        MODEL_TENSOR.V_ENC_FFN_GATE,
        MODEL_TENSOR.V_ENC_FFN_DOWN,
+        MODEL_TENSOR.V_ENC_FFN_GATE_INP,
+        MODEL_TENSOR.V_ENC_FFN_GATE_EXPS,
+        MODEL_TENSOR.V_ENC_FFN_UP_EXPS,
+        MODEL_TENSOR.V_ENC_FFN_DOWN_EXPS,
+        MODEL_TENSOR.V_ENC_FFN_EXP_PROBS_B,
        MODEL_TENSOR.V_ENC_ATTN_POST_NORM,
        MODEL_TENSOR.V_ENC_FFN_POST_NORM,
        MODEL_TENSOR.V_LAYER_SCALE_1,
@@ -3805,7 +3822,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_DOWN_SHEXP,
        MODEL_TENSOR.FFN_UP_SHEXP,
        MODEL_TENSOR.FFN_EXP_PROBS_B,
-        # NextN/MTP tensors - preserved but unused
+        # NextN/MTP tensors
        MODEL_TENSOR.NEXTN_EH_PROJ,
        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
        MODEL_TENSOR.NEXTN_ENORM,
@@ -5497,6 +5514,8 @@ class VisionProjectorType:
    COGVLM = "cogvlm"
    JANUS_PRO = "janus_pro"
    DOTSOCR = "dots_ocr"
+    DOTS3NOTE_V = "dots3note_v"
+    DOTS3NOTE_A = "dots3note_a" # audio
    DEEPSEEKOCR = "deepseekocr"
    DEEPSEEKOCR2 = "deepseekocr2"
    LFM2A = "lfm2a" # audio
@@ -1327,6 +1327,12 @@ class GGUFWriter:
    def add_vision_spatial_merge_size(self, value: int) -> None:
        self.add_uint32(Keys.ClipVision.SPATIAL_MERGE_SIZE, value)

+    def add_vision_expert_count_per_layer(self, value: Sequence[int]) -> None:
+        self.add_array(Keys.ClipVision.EXPERT_COUNT_PER_LAYER, value)
+
+    def add_vision_expert_used_count(self, value: int) -> None:
+        self.add_uint32(Keys.ClipVision.EXPERT_USED_COUNT, value)
+
    def add_vision_use_gelu(self, value: bool) -> None:
        self.add_bool(Keys.ClipVision.USE_GELU, value)

@@ -1454,6 +1454,7 @@ class TensorNameMap:
            "mlp_AR.linear_{bid}", # PaddleOCR-VL
            "merger.mlp.{bid}",
            "vision_tower.merger.mlp.{bid}", # dots.ocr
+            "vision_encoder.adapter.mlp.{bid}", # dots3note
            "vit.perceive.proj.{bid}", # HunyuanVL (proj.0 = conv1, proj.2 = conv2)
        ),

@@ -1504,6 +1505,7 @@ class TensorNameMap:
            "vision_model.radio_model.model.patch_generator.embedder", # Nemotron Nano v2 VL
            "model.vision_tower.patch_embedder.input_proj", # gemma4
            "vision_tower.patch_embed.patchifier.proj", # dots.ocr
+            "vision_encoder.patch_embed.proj", # dots3note
            "vision_model.conv1", # Step3-VL
            "model.vision_embedder.patch_dense", # gemma4 unified
            "model.vision_tower.patch_embedder.patch_embedding", # muse-glimmer
@@ -1512,6 +1514,7 @@ class TensorNameMap:
        MODEL_TENSOR.V_ENC_EMBD_NORM: (
            "visual.post_conv_layernorm", # glm4v
            "vision_tower.patch_embed.patchifier.norm", # dots.ocr
+            "vision_encoder.patch_embed.norm", # dots3note
        ),

        MODEL_TENSOR.V_ENC_EMBD_PATCH_NORM: (
@@ -1551,6 +1554,7 @@ class TensorNameMap:
        MODEL_TENSOR.V_ENC_ATTN_QKV: (
            "visual.blocks.{bid}.attn.qkv", # qwen3vl
            "vision_tower.blocks.{bid}.attn.qkv", # dots.ocr
+            "vision_encoder.blocks.{bid}.attn.qkv", # dots3note
            "model.vision.transformer.layers.{bid}.attention.query_key_value", # cogvlm
            "model.vision_model.transformer.layers.{bid}.self_attn.qkv_proj", # Deepseek-OCR CLIP
            "vision_tower.encoder.blocks.{bid}.wqkv", # Kimi-K2.5
@@ -1579,6 +1583,7 @@ class TensorNameMap:
        ),

        MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
+            "vision_encoder.blocks.{bid}.attn.q_norm", # dots3note
            "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL
            "model.vision_tower.encoder.layer.{bid}.attention.q_norm", # Intern-S1
            "visual.blocks.{bid}.attn.q_norm", # GLM-OCR
@@ -1606,6 +1611,7 @@ class TensorNameMap:
        ),

        MODEL_TENSOR.V_ENC_ATTN_K_NORM: (
+            "vision_encoder.blocks.{bid}.attn.k_norm", # dots3note
            "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL
            "model.vision_tower.encoder.layer.{bid}.attention.k_norm", # Intern-S1
            "visual.blocks.{bid}.attn.k_norm", # GLM-OCR
@@ -1651,6 +1657,7 @@ class TensorNameMap:
            "siglip2.vision_model.encoder.layers.{bid}.layer_norm1",
            "vision_model.radio_model.model.blocks.{bid}.norm1", # Nemotron Nano v2 VL
            "vision_tower.blocks.{bid}.norm1", # dots.ocr
+            "vision_encoder.blocks.{bid}.norm_1", # dots3note
            "vision_model.transformer.resblocks.{bid}.ln_1", # Step3-VL
            "model.qwen2_model.model.model.layers.{bid}.input_layernorm", # Deepseek-OCR-2 qwen2
            "model.vision_tower.layers.{bid}.norm1", # muse-glimmer
@@ -1678,6 +1685,7 @@ class TensorNameMap:
            "model.qwen2_model.model.model.layers.{bid}.self_attn.o_proj", # Deepseek-OCR-2 qwen2
            "vision_model.model.layers.{bid}.self_attn.o_proj.linear", # gemma4
            "vision_tower.blocks.{bid}.attn.proj", # dots.ocr
+            "vision_encoder.blocks.{bid}.attn.proj", # dots3note
            "vision_model.transformer.resblocks.{bid}.attn.out_proj", # Step3-VL
            "model.vision_tower.layers.{bid}.attn.proj", # muse-glimmer
        ),
@@ -1706,12 +1714,14 @@ class TensorNameMap:
            "vision_model.radio_model.model.blocks.{bid}.norm2", # Nemotron Nano v2 VL
            "vision_model.model.layers.{bid}.pre_feedforward_layernorm", # gemma4
            "vision_tower.blocks.{bid}.norm2", # dots.ocr
+            "vision_encoder.blocks.{bid}.norm_2", # dots3note
            "vision_model.transformer.resblocks.{bid}.ln_2", # Step3-VL
            "model.qwen2_model.model.model.layers.{bid}.post_attention_layernorm", # Deepseek-OCR-2 qwen2
            "model.vision_tower.layers.{bid}.norm2", # muse-glimmer
        ),

        MODEL_TENSOR.V_ENC_FFN_UP: (
+            "vision_encoder.blocks.{bid}.mlp.fc3", # dots3note
            "model.vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", # Granite4Vision
            "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1",
            "model.vision_tower.encoder.layers.{bid}.mlp.fc1", # minicpmv4_6
@@ -1737,6 +1747,7 @@ class TensorNameMap:
        ),

        MODEL_TENSOR.V_ENC_FFN_GATE: (
+            "vision_encoder.blocks.{bid}.mlp.fc1", # dots3note
            "vision_tower.transformer.layers.{bid}.feed_forward.gate_proj", # pixtral-hf
            "vision_encoder.transformer.layers.{bid}.feed_forward.w1", # pixtral
            "visual.blocks.{bid}.mlp.gate_proj", # qwen2.5vl
@@ -1745,6 +1756,7 @@ class TensorNameMap:
        ),

        MODEL_TENSOR.V_ENC_FFN_DOWN: (
+            "vision_encoder.blocks.{bid}.mlp.fc2", # dots3note
            "model.vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", # Granite4Vision
            "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2",
            "model.vision_tower.encoder.layers.{bid}.mlp.fc2", # minicpmv4_6
@@ -1769,6 +1781,29 @@ class TensorNameMap:
            "model.vision_tower.layers.{bid}.mlp.fc2", # muse-glimmer
        ),

+
+        MODEL_TENSOR.V_ENC_FFN_GATE_INP: (
+            "vision_encoder.blocks.{bid}.mlp.gate_weight", # dots3note
+        ),
+
+        MODEL_TENSOR.V_ENC_FFN_EXP_PROBS_B: (
+            "vision_encoder.blocks.{bid}.mlp.router_bias", # dots3note
+        ),
+
+        # note: expert weights are stacked into a single 3D tensor in conversion code,
+        # which emits the pseudo-names below
+        MODEL_TENSOR.V_ENC_FFN_GATE_EXPS: (
+            "vision_encoder.blocks.{bid}.mlp.experts.fc1", # dots3note
+        ),
+
+        MODEL_TENSOR.V_ENC_FFN_UP_EXPS: (
+            "vision_encoder.blocks.{bid}.mlp.experts.fc3", # dots3note
+        ),
+
+        MODEL_TENSOR.V_ENC_FFN_DOWN_EXPS: (
+            "vision_encoder.blocks.{bid}.mlp.experts.fc2", # dots3note
+        ),
+
        MODEL_TENSOR.V_ENC_ATTN_POST_NORM: (
            "vision_model.model.layers.{bid}.post_attention_layernorm", # gemma4
        ),
@@ -1800,6 +1835,7 @@ class TensorNameMap:
            "vision_model.layernorm_pre", # llama4
            "model.vision_model.pre_layrnorm", # Deepseek-OCR CLIP
            "vision_tower.patch_embed.patchifier.norm", # dots.ocr
+            "vision_encoder.patch_embed.norm", # dots3note
            "vision_model.ln_pre", # Step3-VL
            "model.vision_tower.ln_pre", # muse-glimmer
        ),
@@ -1821,6 +1857,7 @@ class TensorNameMap:
        MODEL_TENSOR.V_MM_POST_NORM: (
            "visual.merger.post_projection_norm", # glm4v
            "vision_tower.post_trunk_norm", # dots.ocr
+            "vision_encoder.post_trunk_norm", # dots3note
            "vit.perceive.after_rms", # HunyuanVL
        ),

@@ -1838,6 +1875,7 @@ class TensorNameMap:
            "mlp_AR.pre_norm", # PaddleOCR-VL
            "merger.ln_q",
            "vision_tower.merger.ln_q", # dots.ocr
+            "vision_encoder.adapter.ln_q", # dots3note
            "model.merger.mlp.0.pre_norm", # minicpmv4_6
        ),

@@ -2173,10 +2211,12 @@ class TensorNameMap:

        MODEL_TENSOR.A_ENC_CONV2D: (
            "audio_tower.conv2d{bid}", # qwen3omni
+            "audio_encoder.dots_encoder.speech_encoder.conv2d{bid}", # dots3note
        ),

        MODEL_TENSOR.A_ENC_CONV_OUT: (
            "audio_tower.conv_out", # qwen3omni
+            "audio_encoder.dots_encoder.speech_encoder.conv_out", # dots3note
            "speaker_encoder.mfa.conv", # qwen3tts speaker encoder: multi-layer feature aggregation
        ),

@@ -2184,12 +2224,14 @@ class TensorNameMap:

        MODEL_TENSOR.A_POST_NORM: (
            "audio_tower.layer_norm", # ultravox
+            "audio_encoder.dots_encoder.speech_encoder.layer_norm", # dots3note
            "audio_tower.ln_post", # qwen2omni
            "encoder.layer_norm", # mimo-audio-tokenizer
        ),

        MODEL_TENSOR.A_ENC_ATTN_Q: (
            "audio_tower.layers.{bid}.self_attn.q_proj", # ultravox
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn.q_proj", # dots3note
            "conformer.layers.{bid}.self_attn.linear_q", # lfm2
            "conformer.layers.{bid}.attention.attn.q_proj", # gemma3n
            "conformer.layers.{bid}.self_attn.q_proj", # gemma4
@@ -2200,6 +2242,7 @@ class TensorNameMap:

        MODEL_TENSOR.A_ENC_ATTN_K: (
            "audio_tower.layers.{bid}.self_attn.k_proj", # ultravox
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn.k_proj", # dots3note
            "conformer.layers.{bid}.self_attn.linear_k", # lfm2
            "conformer.layers.{bid}.attention.attn.k_proj", # gemma3n
            "conformer.layers.{bid}.self_attn.k_proj", # gemma4
@@ -2210,6 +2253,7 @@ class TensorNameMap:

        MODEL_TENSOR.A_ENC_ATTN_V: (
            "audio_tower.layers.{bid}.self_attn.v_proj", # ultravox
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn.v_proj", # dots3note
            "conformer.layers.{bid}.self_attn.linear_v", # lfm2
            "conformer.layers.{bid}.attention.attn.v_proj", # gemma3n
            "conformer.layers.{bid}.self_attn.v_proj", # gemma4
@@ -2241,6 +2285,7 @@ class TensorNameMap:

        MODEL_TENSOR.A_ENC_INPUT_NORM: (
            "audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn_layer_norm", # dots3note
            "conformer.layers.{bid}.norm_self_att", # lfm2
            "conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n
            "sound_encoder.encoder.layers.{bid}.norm_self_att", # parakeet
@@ -2250,6 +2295,7 @@ class TensorNameMap:

        MODEL_TENSOR.A_ENC_OUTPUT: (
            "audio_tower.layers.{bid}.self_attn.out_proj", # ultravox
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.self_attn.out_proj", # dots3note
            "conformer.layers.{bid}.self_attn.linear_out", # lfm2
            "conformer.layers.{bid}.attention.post", # gemma3n
            "conformer.layers.{bid}.self_attn.post", # gemma4
@@ -2260,6 +2306,7 @@ class TensorNameMap:

        MODEL_TENSOR.A_ENC_OUTPUT_NORM: (
            "audio_tower.layers.{bid}.final_layer_norm", # ultravox
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.final_layer_norm", # dots3note
            "conformer.layers.{bid}.norm_out", # lfm2
            "conformer.layers.{bid}.attention.post_norm", # gemma3n
            "sound_encoder.encoder.layers.{bid}.norm_out", # parakeet
@@ -2285,6 +2332,7 @@ class TensorNameMap:
        ),

        MODEL_TENSOR.A_ENC_FFN_UP: (
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.fc1_up", # dots3note (split from fc1 in conversion code)
            "audio_tower.layers.{bid}.fc1", # ultravox
            "conformer.layers.{bid}.feed_forward1.linear1", # lfm2
            "conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n
@@ -2294,9 +2342,12 @@ class TensorNameMap:
            "encoder.layers.{bid}.fc1", # mimo-audio-tokenizer
        ),

-        MODEL_TENSOR.A_ENC_FFN_GATE: (),
+        MODEL_TENSOR.A_ENC_FFN_GATE: (
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.fc1_gate", # dots3note (split from fc1 in conversion code)
+        ),

        MODEL_TENSOR.A_ENC_FFN_DOWN: (
+            "audio_encoder.dots_encoder.speech_encoder.layers.{bid}.fc2", # dots3note
            "audio_tower.layers.{bid}.fc2", # ultravox
            "conformer.layers.{bid}.feed_forward1.linear2", # lfm2
            "conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n
@@ -2380,6 +2431,7 @@ class TensorNameMap:

        MODEL_TENSOR.A_MMPROJ: (
            "audio.multi_modal_projector.linear_{bid}", # ultravox, meralion
+            "audio_encoder.audio_adapter.proj.{bid}", # dots3note (proj.1, proj.3)
            "audio_adapter.model.{bid}", # lfm2
            "audio_tower.proj{bid}", # qwen3omni
            "sound_projection.linear{bid}", # parakeet (linear1, linear2)
@@ -2394,6 +2446,7 @@ class TensorNameMap:

        MODEL_TENSOR.A_MM_NORM_PRE: (
            "audio.multi_modal_projector.ln_pre", # ultravox
+            "audio_encoder.audio_adapter.proj.0", # dots3note
            "sound_projection.norm", # parakeet
        ),

@@ -733,7 +733,7 @@ extern "C" {

    // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
    // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
-    // seq_id < 0 : match any sequence
+    // seq_id < 0 : match any sequence [TAG_LLAMA_SEQ_ID_NEG]
    // p0 < 0     : [0,  p1]
    // p1 < 0     : [p0, inf)
    LLAMA_API bool llama_memory_seq_rm(
@@ -27,7 +27,7 @@ vendor = {
    f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/split.py":  "split.py",
    f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/LICENSE":   "vendor/cpp-httplib/LICENSE",

-    "https://raw.githubusercontent.com/sheredom/subprocess.h/9ce0d701b6fb10f8f8c4445edd31e7c60a1237e3/subprocess.h": "vendor/sheredom/subprocess.h",
+    "https://raw.githubusercontent.com/sheredom/subprocess.h/0dccaa9aa176dd6d7ef8afeca3c18d6e80a32795/subprocess.h": "vendor/sheredom/subprocess.h",

    f"https://raw.githubusercontent.com/Cyan4973/xxHash/{XXHASH_COMMIT}/xxhash.c":      "vendor/hash/xxhash/xxhash.c",
    f"https://raw.githubusercontent.com/Cyan4973/xxHash/{XXHASH_COMMIT}/xxhash.h":      "vendor/hash/xxhash/xxhash.h",
@@ -66,7 +66,7 @@ These recur often enough in review comments on past add-model PRs that they're w
 - Optional hparams that are genuinely absent from some configs (e.g. a shared-expert count) should be read with an explicit optional/fallback accessor, not assumed present.
 - Hparams that are actually load-bearing (the model produces wrong output or crashes without them, e.g. `sliding_window_pattern`, norm-eps) must hard-error if missing, not silently fall back to a default.
 - Don't bake a default chat template into the C++ binary - inject it into the GGUF at conversion time instead, since one `llm_arch` can be reused by multiple fine-tunes with different templates, and a baked-in C++ default fails silently for those.
- Before writing a dedicated tool-call/output parser, check whether the existing autoparser already handles the template (`llama-debug-template-parser <jinja>` shows what it detects).
+- Before writing a dedicated tool-call/output parser, check whether the existing autoparser already handles the template (`test-chat-auto-parser <jinja>` shows what it detects).
 - Marking a custom EOS/closing-tag token as `eot` at conversion time isn't always sufficient - in long/agentic generations a model can emit the closing sequence as literal text instead of the token, so generation never stops on EOG and raw text leaks past the parser. Verify this case, not just the token path.
 - If reusing or aliasing an existing pre-tokenizer for convenience, justify and test that choice explicitly - silent reuse is an easy source of subtle tokenizer bugs.
 - Watch for excessive graph splits caused by building per-layer view/index tensors inside the layer loop - hoist tensors that don't vary per layer out of the loop (relevant if you hit `GGML_SCHED_MAX_SPLIT_INPUTS`).
@@ -1038,6 +1038,7 @@ bool llm_arch_supports_rs_rollback(const llm_arch & arch) {
        case LLM_ARCH_NEMOTRON_H_MOE:
        case LLM_ARCH_LFM2:
        case LLM_ARCH_LFM2MOE:
+        case LLM_ARCH_BAILINGMOE3:
            return true;
        default:
            return false;
@@ -3218,8 +3218,6 @@ size_t llama_context::state_read_data(llama_io_read_i & io) {
 }

 size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
-    GGML_UNUSED(seq_id);
-
    if (memory) {
        memory->state_write(io, seq_id, flags);
    }
@@ -3228,8 +3226,6 @@ size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id s
 }

 size_t llama_context::state_seq_read_data(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
-    GGML_UNUSED(seq_id);
-
    if (memory) {
        memory->state_read(io, seq_id, flags);
    }
@@ -599,6 +599,33 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
        }
    }

+    if (ratio == DSV4_HCA_RATIO && !plan.state_pos.empty() && plan.state_write_idxs.empty()) {
+        assert(kv_size > 0);
+        // the last slot must not be live, or the dummy write would corrupt it;
+        // a full stream implies a completed block, which implies real writes
+        assert(plan.n_kv < (int64_t) kv_size);
+
+        // Keep the compress/write ops in the graph when no HCA block completes
+        // in this ubatch. The dummy block writes to the last cache slot and is
+        // masked out.
+        uint32_t i = 0;
+        while (i < ubatch.n_tokens && ubatch.pos[i] < 0) {
+            ++i;
+        }
+        assert(i < ubatch.n_tokens);
+
+        const llama_seq_id seq_id = ubatch.seq_id[i][0];
+        const int64_t cache_off = dsv4_stream_offset(n_stream, seq_id, kv_size);
+        const int32_t source_idx = state_source_idx(seq_id, ubatch.pos[i]);
+
+        plan.state_write_idxs.push_back(cache_off + kv_size - 1);
+        plan.state_write_pos .push_back(0);
+
+        for (uint32_t j = 0; j < ratio; ++j) {
+            plan.state_read_idxs.push_back(source_idx);
+        }
+    }
+
    if (overlap) {
        // [ all blocks' prev-window indices | all blocks' cur-window indices ]
        plan.state_read_idxs.reserve(overlap_prev_reads.size() + overlap_cur_reads.size());
@@ -608,7 +635,10 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
                overlap_cur_reads.begin(), overlap_cur_reads.end());
    }

-    plan.n_kv = GGML_PAD(plan.n_kv, 256u);
+    // Keep the mask (and with it the compressed-attention branch) present even
+    // before the first block is visible, so the graph topology never changes.
+    // Padded slots are masked out; comp cache buffers are zero-initialized.
+    plan.n_kv = std::max<int64_t>(GGML_PAD(plan.n_kv, 256u), 256);

    std::sort(persist_rows.begin(), persist_rows.end(),
            [](const persist_row & a, const persist_row & b) {
@@ -620,16 +650,26 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
        plan.state_persist_dst_idxs.push_back(row.dst);
    }

-
    if (n_rs_seq > 0) {
-        for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
-            const llama_seq_id seq_id = ubatch.seq_id_unq[s];
-            if (seq_id < 0 || (uint32_t) seq_id >= n_stream) {
-                continue;
+        // Emit restore/snapshot entries for all layout streams so that the
+        // graph tensor sizes do not depend on the ubatch's sequence count.
+        // Streams not present in the ubatch get no-op entries.
+        for (uint32_t stream = 0; stream < n_stream; ++stream) {
+            llama_seq_id seq_id = -1;
+            if (n_stream == 1) {
+                // a unified stream serves any single sequence
+                seq_id = ubatch.n_seqs_unq > 0 ? ubatch.seq_id_unq[0] : -1;
+            } else {
+                for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+                    if (ubatch.seq_id_unq[s] == (llama_seq_id) stream) {
+                        seq_id = ubatch.seq_id_unq[s];
+                        break;
+                    }
+                }
            }

-            const int64_t stream_off = dsv4_stream_offset(n_stream, seq_id, state_size);
-            const uint32_t rollback = (uint32_t) seq_id < rs_idx.size() ? rs_idx[seq_id] : 0;
+            const int64_t stream_off = (int64_t) stream*state_size;
+            const uint32_t rollback = seq_id >= 0 && (uint32_t) seq_id < rs_idx.size() ? rs_idx[seq_id] : 0;
            // Keep the restore graph fixed-width when no rollback is pending.
            const int64_t src_plane = rollback > 0 && rollback <= n_rs_seq ? (int64_t) rollback*state_rows : 0;
            for (uint32_t r = 0; r < state_size; ++r) {
@@ -639,35 +679,33 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(

            std::vector<uint32_t> token_idxs;
            token_idxs.reserve(ubatch.n_tokens);
-            for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
-                if (dsv4_token_has_seq(ubatch, i, seq_id)) {
-                    token_idxs.push_back(i);
+            if (seq_id >= 0) {
+                for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+                    if (dsv4_token_has_seq(ubatch, i, seq_id)) {
+                        token_idxs.push_back(i);
+                    }
                }
            }
-            if (token_idxs.empty()) {
-                continue;
-            }

            const uint32_t n_seq_tokens = (uint32_t) token_idxs.size();
            const int64_t scratch_off = (int64_t) state_rows*(1 + n_rs_seq);
            for (uint32_t d = 1; d <= n_rs_seq; ++d) {
                const int64_t dst_plane = (int64_t) d*state_rows;
+                const uint32_t prefix = d <= n_seq_tokens ? n_seq_tokens - d : 0;

                for (uint32_t r = 0; r < state_size; ++r) {
-                    int32_t src;
-                    if (d <= n_seq_tokens) {
-                        const uint32_t prefix = n_seq_tokens - d;
-                        src = (int32_t) (stream_off + r);
+                    int32_t src = (int32_t) (stream_off + r);

-                        for (uint32_t j = 0; j < prefix; ++j) {
-                            const uint32_t i_tok = token_idxs[j];
-                            if (ubatch.pos[i_tok] >= 0 && (uint32_t) (ubatch.pos[i_tok]%state_size) == r) {
-                                src = (int32_t) (scratch_off + i_tok);
-                            }
+                    for (uint32_t j = 0; j < prefix; ++j) {
+                        const uint32_t i_tok = token_idxs[j];
+                        if (ubatch.pos[i_tok] >= 0 && (uint32_t) (ubatch.pos[i_tok]%state_size) == r) {
+                            src = (int32_t) (scratch_off + i_tok);
                        }
-                    } else {
-                        const int64_t src_plane = (int64_t) (d - n_seq_tokens)*state_rows;
-                        src = (int32_t) (src_plane + stream_off + r);
+                    }
+
+                    if (n_seq_tokens == 0) {
+                        // no-op: copy the snapshot plane onto itself
+                        src = (int32_t) (dst_plane + stream_off + r);
                    }

                    plan.state_snapshot_src_idxs.push_back(src);
@@ -683,10 +721,16 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
    }();

    if (debug) {
-        LLAMA_LOG_INFO("%s: ratio=%u, n_tokens=%u, state_persist_dst=%s, state_write_pos=%s\n",
-                __func__, ratio, ubatch.n_tokens,
+        LLAMA_LOG_DEBUG("%s: ratio=%u, n_tokens=%u, n_seqs_unq=%u, state_persist_dst=%s, state_write_pos=%s\n",
+                __func__, ratio, ubatch.n_tokens, ubatch.n_seqs_unq,
                dsv4_plan_positions(plan.state_persist_dst_idxs).c_str(),
                dsv4_plan_positions(plan.state_write_pos).c_str());
+        for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+            const llama_seq_id seq_id = ubatch.seq_id_unq[s];
+            const uint32_t rollback = seq_id >= 0 && (uint32_t) seq_id < rs_idx.size() ? rs_idx[seq_id] : 0;
+            LLAMA_LOG_DEBUG("%s:   seq %d pos [%d, %d] rollback=%u\n", __func__, seq_id,
+                    ubatch.pos[0], ubatch.pos[ubatch.n_tokens - 1], rollback);
+        }
    }

    return plan;
@@ -704,8 +748,17 @@ static std::vector<llama_kv_cache_dsv4_context::comp_plan> dsv4_build_comp_plans
    std::vector<llama_kv_cache_dsv4_context::comp_plan> plans;
    plans.reserve(ubatches.size());

+    // the first ubatch touching a seq consumes its rollback restore
+    std::vector<uint32_t> rs(rs_idx);
    for (const llama_ubatch & ubatch : ubatches) {
-        plans.push_back(dsv4_build_comp_plan(ubatch, ratio, overlap, state_size, kv_size, n_stream, n_rs_seq, rs_idx));
+        plans.push_back(dsv4_build_comp_plan(ubatch, ratio, overlap, state_size, kv_size, n_stream, n_rs_seq, rs));
+
+        for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+            const llama_seq_id seq_id = ubatch.seq_id_unq[s];
+            if (seq_id >= 0 && (size_t) seq_id < rs.size()) {
+                rs[seq_id] = 0;
+            }
+        }
    }

    return plans;
@@ -803,16 +856,15 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_reserve_comp_plan(
        return plan;
    }

-    const uint32_t n_seqs       = std::max<uint32_t>(1, ubatch.n_seqs);
-    const uint32_t n_seq_tokens = std::max<uint32_t>(1, ubatch.n_seq_tokens);
-    const uint64_t n_blocks_u64 = (uint64_t) n_seqs*((n_seq_tokens + ratio - 1)/ratio);
-    const size_t n_blocks = (size_t) std::max<uint64_t>(1, n_blocks_u64);
-    GGML_ASSERT((uint64_t) n_blocks == std::max<uint64_t>(1, n_blocks_u64));
+    // worst case over every seq split: sum of per-seq ceil(tokens/ratio) is at
+    // most floor(n_tokens/ratio) + n_seqs
+    const uint32_t n_seqs = std::max<uint32_t>(1, ubatch.n_seqs);
+    const size_t n_blocks = (size_t) ubatch.n_tokens/ratio + n_seqs;

    const uint64_t state_rows = (uint64_t) state_size*n_stream;
    const size_t n_persist = (size_t) std::min<uint64_t>(ubatch.n_tokens, state_rows);
-    const size_t n_restore = n_rs_seq > 0 ? (size_t) state_size*std::max<uint32_t>(1, ubatch.n_seqs_unq) : 0;
-    const size_t n_snapshot = (size_t) n_rs_seq*state_size*std::max<uint32_t>(1, ubatch.n_seqs_unq);
+    const size_t n_restore = n_rs_seq > 0 ? (size_t) state_size*n_stream : 0;
+    const size_t n_snapshot = (size_t) n_rs_seq*state_size*n_stream;

    plan.state_pos .resize(ubatch.n_tokens);
    plan.state_persist_src_idxs.resize(n_persist);
@@ -1356,7 +1408,9 @@ llama_memory_context_ptr llama_kv_cache_dsv4::init_batch(
            if (has_coupled) {
                ubatch = balloc.split_seq(n_ubatch);
            } else {
-                ubatch = balloc.split_equal(n_ubatch, raw_per_seq || comp_per_seq, 0);
+                // [TAG_RECURRENT_ROLLBACK_SPLITS]
+                // the trailing (1 + n_rs_seq) tokens of each seq must stay in the same ubatch
+                ubatch = balloc.split_equal(n_ubatch, raw_per_seq || comp_per_seq, n_rs_seq > 0 ? n_rs_seq + 1 : 0);
            }

            if (ubatch.n_tokens == 0) {
@@ -1433,6 +1487,11 @@ bool llama_kv_cache_dsv4::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1
            return false;
        }

+        // pending rollback is single-use: stacked partial removals don't compose
+        if (rs_idx[seq_id] != 0) {
+            return false;
+        }
+
        const bool res = kv_raw->seq_rm(seq_id, p0, p1);
        if (res) {
            rs_idx[seq_id] = (uint32_t) rollback;
@@ -1594,9 +1653,7 @@ void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id,
    kv_raw->state_read(io, seq_id, flags);

    if (!partial_only) {
-        kv_csa->clear(true);
-        kv_hca->clear(true);
-        kv_lid->clear(true);
+        clear_compressed(seq_id, true);

        dsv4_state_read_k_cache(io, kv_csa.get(), seq_id, flags);
        dsv4_state_read_k_cache(io, kv_hca.get(), seq_id, flags);
@@ -383,6 +383,7 @@ bool llama_kv_cache::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
        return true;
    }

+    // TODO: fix incosistent handling of `seq_id < 0` and `seq_id == -1` in the codebase [TAG_LLAMA_SEQ_ID_NEG]
    GGML_ASSERT(seq_id == -1 || (seq_id >= 0 && (size_t) seq_id < seq_to_stream.size()));

    if (p0 < 0) {
@@ -2043,6 +2044,7 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama

    GGML_UNUSED(flags);

+    // TODO: fix incosistent handling of `seq_id < 0` and `seq_id == -1` in the codebase [TAG_LLAMA_SEQ_ID_NEG]
    GGML_ASSERT(seq_id == -1 || (seq_id >= 0 && (size_t) seq_id < seq_to_stream.size()));

    uint32_t n_stream_cur;
@@ -158,13 +158,14 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
        p1 = std::numeric_limits<llama_pos>::max();
    }

+    if ((uint32_t) seq_id >= this->n_seq_max) {
+        LLAMA_LOG_ERROR("%s: invalid seq_id (%d) - larger than n_seq_max (%d)\n", __func__, seq_id, this->n_seq_max);
+        return false;
+    }
+
    const bool rm_all = p0 == 0 && p1 == std::numeric_limits<llama_pos>::max();
    if (rm_all) {
-        if (seq_id >= 0) {
-            set_rs_idx(seq_id, 0);
-        } else {
-            std::fill(rs_idx.begin(), rs_idx.end(), 0);
-        }
+        set_rs_idx(seq_id, 0);
    }

    // models like Mamba or RWKV can't have a state partially erased at the end
@@ -181,7 +182,9 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
            // partial rollback via per-token snapshot index (bounded by n_rs_seq)
            if (0 < p0 && p0 <= cell.pos && p1 > cell.pos) {
                const llama_pos rollback = cell.pos - (p0 - 1);
-                if (rollback >= 1 && rollback <= (llama_pos) n_rs_seq) {
+                // pending rollback is single-use
+                const bool pending = rs_idx[seq_id] != 0;
+                if (!pending && rollback >= 1 && rollback <= (llama_pos) n_rs_seq) {
                    set_rs_idx(seq_id, (uint32_t) rollback);
                    cell.pos = p0 - 1;
                    return true;
@@ -390,10 +393,17 @@ llama_pos llama_memory_recurrent::seq_pos_max(llama_seq_id seq_id) const {
 }

 void llama_memory_recurrent::set_rs_idx(llama_seq_id seq_id, uint32_t idx) {
-    if (seq_id < 0 || (size_t) seq_id >= rs_idx.size()) {
+    if (seq_id < 0) {
+        std::fill(rs_idx.begin(), rs_idx.end(), 0);
        return;
    }
-    rs_idx[seq_id] = (idx > n_rs_seq) ? n_rs_seq : idx;
+
+    assert(n_seq_max == rs_idx.size());
+
+    GGML_ASSERT((uint32_t) seq_id < n_seq_max);
+    GGML_ASSERT(idx <= n_rs_seq);
+
+    rs_idx[seq_id] = idx;
 }

 std::map<ggml_backend_buffer_type_t, size_t> llama_memory_recurrent::memory_breakdown() const {
@@ -742,6 +752,7 @@ void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq
    uint32_t cell_range_begin = size;
    for (uint32_t i = 0; i < size; ++i) {
        const auto & cell = cells[i];
+        // TODO: fix incosistent handling of `seq_id < 0` and `seq_id == -1` in the codebase [TAG_LLAMA_SEQ_ID_NEG]
        if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
            ++cell_count;
            uint32_t rs_idx_cur = 0;
@@ -827,6 +838,7 @@ void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_i
    }

    if (!res) {
+        // TODO: fix incosistent handling of `seq_id < 0` and `seq_id == -1` in the codebase [TAG_LLAMA_SEQ_ID_NEG]
        if (seq_id == -1) {
            clear(true);
        } else {
@@ -836,11 +848,7 @@ void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_i
    }

    if (n_rs_seq != 0) {
-        if (seq_id == -1) {
-            std::fill(rs_idx.begin(), rs_idx.end(), 0);
-        } else {
-            set_rs_idx(seq_id, 0);
-        }
+        set_rs_idx(seq_id, 0);
    }
 }

@@ -293,6 +293,14 @@ void llama_model_saver::add_kv_from_model() {
    add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS,    hparams.indexer_local_blocks);
    add_kv(LLM_KV_ATTENTION_INDEXER_TYPES,           hparams.is_indexer_full_impl, true);
    add_kv(LLM_KV_ATTENTION_RECURRENT_LAYERS,        hparams.is_recr_impl, true);
+    add_kv(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,      hparams.dsv4_o_group_count);
+    add_kv(LLM_KV_ATTENTION_OUTPUT_LORA_RANK,        hparams.dsv4_o_lora_rank);
+    add_kv(LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, hparams.dsv4_compress_rope_base);
+    add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS,         hparams.dsv4_compress_ratios, true);
+    add_kv(LLM_KV_HYPER_CONNECTION_COUNT,            hparams.dsv4_hc_mult);
+    add_kv(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters);
+    add_kv(LLM_KV_HYPER_CONNECTION_EPSILON,          hparams.dsv4_hc_eps);
+    add_kv(LLM_KV_HASH_LAYER_COUNT,                  hparams.dsv4_hash_layer_count);

    const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;

@@ -422,6 +430,9 @@ void llama_model_saver::add_tensors_from_model() {
    add_tensor(model->cls_out);
    add_tensor(model->cls_out_b);
    add_tensor(model->cls_norm);
+    add_tensor(model->hc_head_fn);
+    add_tensor(model->hc_head_base);
+    add_tensor(model->hc_head_scale);

    for (const struct llama_layer & layer : model->layers) {
        for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) {
@@ -520,7 +520,7 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
            return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_MIRRORED);
        }
        if (std::regex_match(tensor_name, pattern_ffn_down_exps_bias)) {
-            return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_PARTIAL);
+            return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_PARTIAL, "ffn_down_exps.weight");
        }

        // output
@@ -554,6 +554,9 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
                    GGML_ASSERT(tensor->ne[axis] == 2*key_dim + value_dim);
                    return {{key_dim, 2}, {value_dim, 1}};
                }
+                if (std::regex_match(tensor_name, pattern_r_cache)) {
+                    return {{key_dim * (hparams.ssm_d_conv - 1), 2}, {value_dim * (hparams.ssm_d_conv - 1), 1}};
+                }
            } else {
                const int64_t head_ratio = n_v_heads / n_k_heads;
                if (std::regex_match(tensor_name, pattern_qkv_weight) || std::regex_match(tensor_name, pattern_ssm_conv1d)) {
@@ -642,12 +645,12 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
                blck_size_perf *= 2;
            }

+            const int64_t granularity_q    = std::lcm(n_embd_q, blck_size_perf);
+            const int64_t granularity_head = granularity_q / hparams.n_embd_head_k(il); // for tensors with one value per head
            if (std::regex_match(tensor_name, pattern_attn_sinks)) {
                GGML_ASSERT(segments.size() == 1);
-                return {std::lcm(n_embd_q, blck_size_perf)/n_embd_q * n_gqa};
+                return {granularity_head};
            }
-
-            const int64_t granularity_q = std::lcm(n_embd_q, blck_size_perf);
            if (std::regex_match(tensor_name, pattern_q_weight) || std::regex_match(tensor_name, pattern_q_bias)) {
                GGML_ASSERT(segments.size() == 1);
                // some models have Q gate tensors, for those cases the granularity needs to be doubled:
@@ -660,6 +663,13 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
                GGML_ASSERT(segments.size() == 1);
                return {granularity_q};
            }
+            if (std::regex_match(tensor_name, pattern_attn_gate_weight)) {
+                GGML_ASSERT(segments.size() == 1);
+                if (tensor->ne[1] == hparams.n_head(il)) {
+                    return {granularity_head};
+                }
+                return {granularity_q};
+            }

            const int64_t granularity_kv = granularity_q / n_gqa;
            if (std::regex_match(tensor_name, pattern_kv_weight) ||
@@ -728,6 +738,16 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
        memset(split_state.ne, 0, sizeof(split_state.ne));
        split_state.nr[0] = 1;
        split_state.n_segments = 1;
+        if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL) {
+            GGML_ASSERT(tc.tensor_axis_0 != tensor);
+            const ggml_backend_meta_split_state source_split_state = llama_meta_device_get_split_state(tc.tensor_axis_0, userdata);
+            GGML_ASSERT(source_split_state.axis >= 0 && source_split_state.axis < GGML_MAX_DIMS);
+            for (size_t j = 0; j < ud->n_devices; j++) {
+                for (size_t is = 0; is < source_split_state.n_segments; is++) {
+                    split_state.ne[j] += source_split_state.ne[is*ud->n_devices + j] * source_split_state.nr[is];
+                }
+            }
+        }
    }
    return split_state;
    GGML_UNUSED(userdata);
@@ -1,6 +1,8 @@
 #include "models.h"
 #include "llama-memory-recurrent.h"

+#include <algorithm>
+
 void llama_model_bailingmoe3::load_arch_hparams(llama_model_loader & ml) {
    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      hparams.f_norm_rms_eps);
    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,         hparams.n_embd_head_k_mla_impl);
@@ -179,7 +181,9 @@ static ggml_tensor * bailingmoe3_causal_conv1d(
        int64_t n_seq_tokens,
        int64_t n_seqs,
        int64_t n_tokens,
-        int64_t cache_head) {
+        int64_t cache_head,
+        uint32_t mem_size,
+        uint32_t n_rs_seq) {
    const int64_t d_inner = head_dim * n_head;
    const int64_t conv_state_size = (d_conv - 1) * d_inner;
    const int64_t total_state_size = 3 * conv_state_size;
@@ -193,13 +197,18 @@ static ggml_tensor * bailingmoe3_causal_conv1d(
    x_proj = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs);
    ggml_tensor * conv_x = ggml_concat(ctx0, conv_state, ggml_transpose(ctx0, x_proj), 0);

-    ggml_tensor * last_conv_x = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs,
-            conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]);
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_x,
-            ggml_view_3d(ctx0, conv_states_all, d_conv - 1, d_inner, n_seqs,
-                (d_conv - 1) * ggml_element_size(conv_states_all),
-                total_state_size * ggml_element_size(conv_states_all),
-                (cache_head * total_state_size + qkv * conv_state_size) * ggml_element_size(conv_states_all))));
+    const int64_t K = (int64_t) n_rs_seq + 1;
+    const int64_t n_written = std::min<int64_t>(n_seq_tokens, K);
+
+    for (int64_t slot = 0; slot < n_written; ++slot) {
+        ggml_tensor * conv_snap = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs,
+                conv_x->nb[1], conv_x->nb[2], (conv_x->ne[0] - (d_conv - 1) - slot) * conv_x->nb[0]);
+        ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_snap,
+                ggml_view_3d(ctx0, conv_states_all, d_conv - 1, d_inner, n_seqs,
+                    (d_conv - 1) * ggml_element_size(conv_states_all),
+                    total_state_size * ggml_element_size(conv_states_all),
+                    ((slot * mem_size + cache_head) * total_state_size + qkv * conv_state_size) * ggml_element_size(conv_states_all))));
+    }

    ggml_tensor * conv_weight = ggml_reshape_2d(ctx0, conv_w, d_conv, d_inner);
    ggml_tensor * out = ggml_ssm_conv(ctx0, conv_x, conv_weight);
@@ -237,6 +246,8 @@ llama_model_bailingmoe3::graph::graph(const llama_model & model, const llm_graph
    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);

    for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
        const auto & layer = model.layers[il];
        ggml_tensor * inpSA = inpL;
        ggml_tensor * cur = build_norm(inpL, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
@@ -245,18 +256,19 @@ llama_model_bailingmoe3::graph::graph(const llama_model & model, const llm_graph
        if (hparams.is_recr(il)) {
            const auto * mctx_cur = inp_rs->mctx;
            const auto cache_head = mctx_cur->get_head();
+            const auto mem_size = mctx_cur->get_size();
            ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
            ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);

            ggml_tensor * q = bailingmoe3_causal_conv1d(
                    gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv,
-                    d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, cache_head);
+                    d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, cache_head, mem_size, cparams.n_rs_seq);
            ggml_tensor * k = bailingmoe3_causal_conv1d(
                    gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv,
-                    d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, cache_head);
+                    d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, cache_head, mem_size, cparams.n_rs_seq);
            ggml_tensor * v = bailingmoe3_causal_conv1d(
                    gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv,
-                    d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, cache_head);
+                    d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, cache_head, mem_size, cparams.n_rs_seq);

            ggml_tensor * gate = ggml_mul_mat(ctx0, layer.ssm_f_a, cur);
            gate = ggml_add(ctx0, gate, layer.ssm_dt_b);
@@ -276,11 +288,8 @@ llama_model_bailingmoe3::graph::graph(const llama_model & model, const llm_graph
            ggml_tensor * state = build_rs(inp_rs, states_all, hparams.n_embd_s(), n_seqs);
            state = ggml_reshape_4d(ctx0, state, head_dim, head_dim, n_head, n_seqs);

-            auto result = build_delta_net(q, k, v, gate, beta, state, il);
-            ggml_tensor * out = ggml_cont(ctx0, result.first);
-            ggml_build_forward_expand(gf, ggml_cpy(ctx0, result.second,
-                    ggml_view_1d(ctx0, states_all, hparams.n_embd_s() * n_seqs,
-                        cache_head * hparams.n_embd_s() * ggml_element_size(states_all))));
+            ggml_tensor * out = ggml_cont(ctx0, build_recurrent_attn(
+                    inp_rs, states_all, q, k, v, gate, beta, state, il));

            ggml_tensor * out_gate = ggml_mul_mat(ctx0, layer.ssm_g_a, cur);
            out_gate = ggml_reshape_3d(ctx0, out_gate, head_dim, n_head, n_tokens);
@@ -1,3 +1,4 @@
+#include "llama-hparams.h"
 #include "models.h"

 #include "llama-kv-cache-dsv4.h"
@@ -58,6 +59,7 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
    if (n_compress_ratios < hparams.n_layer_all) {
        throw std::runtime_error("DeepSeek-V4 compress_ratios is shorter than block_count");
    }
+    GGML_ASSERT(n_compress_ratios <= LLAMA_MAX_LAYERS);
    ml.get_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios);

    ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
@@ -29,10 +29,19 @@ void llama_model_glm4_moe::load_arch_hparams(llama_model_loader & ml) {
    }
 }

-void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
+void llama_model_glm4_moe::load_arch_tensors(llama_model_loader & ml) {
    LLAMA_LOAD_LOCALS;
    const int64_t n_expert_shared = hparams.n_expert_shared;

+    const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
+    const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
+    const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
+    const int trunk_flags = mtp_only  ? TENSOR_NOT_REQUIRED : 0;
+    int       mtp_flags   = trunk_only ? TENSOR_NOT_REQUIRED : 0;
+
+    if (!ml.load_mtp) {
+        mtp_flags |= TENSOR_SKIP;
+    }

    GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
    GGML_ASSERT(hparams.n_expert_used > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
@@ -47,16 +56,9 @@ void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
    }

-    // Load ALL tensors including NextN layer to satisfy total tensor count
-    // but only PROCESS up to last layer (skipping final NextN layer) in forward pass
    for (int i = 0; i < n_layer_all; ++i) {
-        int flags = 0;
-        if (i >= n_layer) {
-            // skip all tensors in the NextN layers
-            flags |= TENSOR_SKIP;
-        }
-
        auto & layer = layers[i];
+        const int flags = i < n_layer ? trunk_flags : mtp_flags;

        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);

@@ -110,24 +112,186 @@ void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, flags);
        }

-        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
+        // NextN/MTP tensors
        if (i >= n_layer) {
            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);

            // Optional tensors
-            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
-            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
-            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
+            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
        }
    }
 }

 std::unique_ptr<llm_graph_context> llama_model_glm4_moe::build_arch_graph(const llm_graph_params & params) const {
+    if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
+        return std::make_unique<graph_mtp>(*this, params);
+    }
    return std::make_unique<graph>(*this, params);
 }

+llama_model_glm4_moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
+    : llm_graph_context(params) {
+    GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM4_MOE MTP requires n_layer_nextn > 0");
+    GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM4_MOE MTP currently only supports a single MTP block");
+
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+    const int il = hparams.n_layer() + cparams.nextn_layer_offset;
+    GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
+                cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
+                "nextn_layer_offset out of range [0, n_layer_nextn)");
+
+    const auto & layer = model.layers[il];
+
+    GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
+    GGML_ASSERT(layer.nextn.enorm   && "MTP block missing nextn.enorm");
+    GGML_ASSERT(layer.nextn.hnorm   && "MTP block missing nextn.hnorm");
+    GGML_ASSERT(layer.ffn_gate_inp  && "MTP block missing ffn_gate_inp");
+
+    auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
+
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    ggml_set_input(inp->tokens);
+
+    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
+    ggml_set_input(inp->embd);
+
+    ggml_tensor * tok_embd;
+    if (ubatch.token) {
+        ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
+        tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
+    } else {
+        tok_embd = inp->embd;
+    }
+    cb(tok_embd, "mtp_tok_embd", il);
+
+    inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
+    ggml_set_input(inp->h);
+    ggml_set_name(inp->h, "mtp_h_input");
+
+    ggml_tensor * h_embd = inp->h;
+
+    res->add_input(std::move(inp));
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
+    cb(h_norm, "mtp_hnorm", il);
+
+    ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
+    cb(e_norm, "mtp_enorm", il);
+
+    ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, 0);
+    cb(concat, "mtp_concat", il);
+
+    ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
+    cb(cur, "mtp_eh_proj", il);
+
+    ggml_tensor * inpSA = cur;
+
+    cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_attn_norm", il);
+
+    auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
+            n_embd_head, n_head, n_head_kv, il);
+
+    if (layer.attn_q_norm) {
+        Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
+        cb(Qcur, "mtp_Qcur_normed", il);
+    }
+    if (layer.attn_k_norm) {
+        Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
+        cb(Kcur, "mtp_Kcur_normed", il);
+    }
+
+    Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
+            rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+
+    Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
+            rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+
+    cb(Qcur, "mtp_Qcur", il);
+    cb(Kcur, "mtp_Kcur", il);
+    cb(Vcur, "mtp_Vcur", il);
+
+    cur = build_attn(inp_attn,
+            layer.wo, nullptr, layer.wo_s,
+            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
+            1.0f / sqrtf(float(n_embd_head)), il);
+    cb(cur, "mtp_attn_out", il);
+
+    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+    cb(ffn_inp, "mtp_ffn_inp", il);
+
+    cur = build_norm(ffn_inp, layer.attn_post_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_post_attn_norm", il);
+
+    ggml_tensor * routed_out = build_moe_ffn(cur,
+            layer.ffn_gate_inp,
+            layer.ffn_up_exps,
+            layer.ffn_gate_exps,
+            layer.ffn_down_exps,
+            layer.ffn_exp_probs_b,
+            n_expert, n_expert_used,
+            LLM_FFN_SILU, hparams.expert_weights_norm,
+            hparams.expert_weights_scale,
+            (llama_expert_gating_func_type) hparams.expert_gating_func,
+            il);
+    cb(routed_out, "mtp_ffn_moe_out", il);
+
+    ggml_tensor * shared_out = build_ffn(cur,
+            layer.ffn_up_shexp,   nullptr, nullptr,
+            layer.ffn_gate_shexp, nullptr, nullptr,
+            layer.ffn_down_shexp, nullptr, nullptr,
+            nullptr,
+            LLM_FFN_SILU, LLM_FFN_PAR, il);
+    cb(shared_out, "mtp_ffn_shexp_out", il);
+
+    cur = ggml_add(ctx0, routed_out, shared_out);
+    cb(cur, "mtp_ffn_out", il);
+
+    cur = ggml_add(ctx0, cur, ffn_inp);
+    cb(cur, "mtp_post_ffn", il);
+
+    ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
+            ? layer.nextn.shared_head_norm
+            : model.output_norm;
+    GGML_ASSERT(head_norm_w && "GLM4_MOE MTP: missing both nextn.shared_head_norm and output_norm");
+
+    cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
+
+    if (inp_out_ids) {
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+    }
+    cb(cur, "mtp_shared_head_norm", -1);
+
+    ggml_tensor * head_w = layer.nextn.shared_head_head
+            ? layer.nextn.shared_head_head
+            : model.output;
+    ggml_tensor * head_s = layer.nextn.shared_head_head
+            ? layer.nextn.shared_head_head_s
+            : model.output_s;
+    GGML_ASSERT(head_w && "GLM4_MOE MTP: missing LM head (nextn.shared_head_head or model.output)");
+
+    cur = build_lora_mm(head_w, cur, head_s);
+    cb(cur, "result_output", -1);
+
+    res->t_logits = cur;
+    ggml_build_forward_expand(gf, cur);
+}
+
 llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
    const int64_t n_embd_head = hparams.n_embd_head_v();

@@ -154,8 +318,7 @@ llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_pa

    ggml_tensor * inp_out_ids = build_inp_out_ids();

-    // Only process up to last layer (skip final NextN layer)
-    // Final layer tensors are loaded but not processed in forward pass
+    // NextN layers are processed by graph_mtp.
    for (int il = 0; il < n_layer; ++il) {
        ggml_tensor * inpSA = inpL;

@@ -205,7 +368,7 @@ llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_pa
                    model.layers[il].wo, NULL, model.layers[il].wo_s,
                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
        }
-        if (il == n_layer - 1 && inp_out_ids) {
+        if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
        }
@@ -265,6 +428,13 @@ llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_pa
    cur = inpL;
    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);

+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
+
+    if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+    }
+
    cb(cur, "result_norm", -1);
    res->t_embd = cur;

@@ -1412,6 +1412,10 @@ struct llama_model_glm4_moe : public llama_model_base {
        graph(const llama_model & model, const llm_graph_params & params);
    };

+    struct graph_mtp : public llm_graph_context {
+        graph_mtp(const llama_model & model, const llm_graph_params & params);
+    };
+
    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };

@@ -228,6 +228,15 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
    set_tests_properties(test-recurrent-state-rollback-nemotron-h PROPERTIES
        FIXTURES_REQUIRED generate-models
    )
+    llama_test(
+        test-recurrent-state-rollback
+        NAME test-recurrent-state-rollback-dsv4
+        LABEL main
+        ARGS -m "${MODEL_DIR}/deepseek4-moe.gguf"
+    )
+    set_tests_properties(test-recurrent-state-rollback-dsv4 PROPERTIES
+        FIXTURES_REQUIRED generate-models
+    )
 endif()

 llama_build_and_test(test-chat-peg-parser.cpp peg-parser/simple-tokenize.cpp)
@@ -235,6 +244,8 @@ llama_build_and_test(test-jinja.cpp)
 llama_test(test-jinja NAME test-jinja-py ARGS -py LABEL python)
 llama_build_and_test(test-chat-auto-parser.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
 llama_build_and_test(test-chat-template.cpp)
+# debug tool for chat template differential analysis (not registered as a test, run it manually)
+llama_build(test-chat-analysis.cpp)
 llama_build_and_test(test-log.cpp)
 llama_build_and_test(
    test-peg-parser.cpp
@@ -8,7 +8,7 @@ void test_json_serialization(testing &t) {
    auto json_serialized = original.to_json().dump();

    t.test("compare before/after", [&](testing &t) {
-        auto deserialized = common_peg_arena::from_json(nlohmann::json::parse(json_serialized));
+        auto deserialized = common_peg_arena::from_json(common_json::parse(json_serialized));

        // Test complex JSON
        std::string input = R"({"name": "test", "values": [1, 2, 3], "nested": {"a": true}})";
@@ -23,6 +23,6 @@ void test_json_serialization(testing &t) {
    });

    t.bench("deserialize", [&]() {
-        auto deserialized = common_peg_arena::from_json(nlohmann::json::parse(json_serialized));
+        auto deserialized = common_peg_arena::from_json(common_json::parse(json_serialized));
    }, 100);
 }
@@ -1,7 +1,7 @@
 #pragma once

 // Common includes for all test files
-#include <nlohmann/json.hpp>
+#include "json.h"
 #include <string>
 #include <vector>

@@ -11,9 +11,9 @@
 #include "simple-tokenize.h"

 struct bench_tool_call {
-    std::string            id;
-    std::string            name;
-    nlohmann::ordered_json args;
+    std::string id;
+    std::string name;
+    common_json args;
 };

 // Test function declarations
@@ -11,9 +11,9 @@
 #include <vector>
 #include <algorithm>

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

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

 // ANSI color codes - using 256-color palette for brighter colors (all bold)
 #define ANSI_RESET       "\033[0m"
@@ -84,11 +84,12 @@ static std::string read_file(const std::string & path) {
 }

 static void print_usage(const char * program_name) {
-    LOG_ERR("Usage: %s [options]\n", program_name);
+    LOG_ERR("Debug the auto-parser's differential analysis: render a template with/without tools, reasoning, etc. and show the diffs.\n");
+    LOG_ERR("\nUsage: %s [options]\n", program_name);
    LOG_ERR("\nOptions:\n");
    LOG_ERR("  --template <name>       Analyze specific template from test suite (e.g., 'deepseek' or 'DeepSeek-V3.1')\n");
    LOG_ERR("  --template-file <path>  Analyze custom template file\n");
-    LOG_ERR("  --all                   Analyze all templates from test suite\n");
+    LOG_ERR("  --all                   Analyze all templates from test suite (default when no arguments are given)\n");
    LOG_ERR("\nExamples:\n");
    LOG_ERR("  %s --all\n", program_name);
    LOG_ERR("  %s --template deepseek\n", program_name);
@@ -97,14 +98,17 @@ static void print_usage(const char * program_name) {

 static bool parse_options(int argc, char ** argv, analysis_options & opts) {
    if (argc < 2) {
-        print_usage(argv[0]);
-        return false;
+        // default mode: analyze all templates from the test suite
+        opts.analyze_all = true;
    }

    for (int i = 1; i < argc; ++i) {
        std::string arg = argv[i];

-        if (arg == "--all") {
+        if (arg == "-h" || arg == "--help") {
+            print_usage(argv[0]);
+            return false;
+        } else if (arg == "--all") {
            opts.analyze_all = true;
        } else if (arg == "--template") {
            if (i + 1 >= argc) {
@@ -2,11 +2,18 @@
 #include "chat-auto-parser.h"
 #include "chat-peg-parser.h"
 #include "chat.h"
+#include "gguf.h"
+#include "jinja/runtime.h"
+#include "log.h"
 #include "peg-parser.h"
 #include "testing.h"

+#include <cstdlib>
+#include <filesystem>
 #include <fstream>
 #include <iostream>
+#include <iterator>
+#include <optional>
 #include <sstream>
 #include <string>

@@ -94,11 +101,447 @@ static void test_bailing_v3_tool_format(testing & t);

 static void test_role_markers_all_templates(testing & t);

+static json build_tools_definition();
+
+//
+// debug mode: analyze a single template and dump the generated parser and grammar
+//
+
+enum class output_mode {
+    ANALYSIS,  // Only output analysis results (default)
+    TEMPLATE,  // Only output rendered template
+    BOTH       // Output both
+};
+
+enum class input_message_type {
+    NONE,                    // Don't render any message scenarios (only analysis)
+    CONTENT_ONLY,            // Simple assistant message with content
+    REASONING_CONTENT,       // Message with reasoning_content + content
+    TOOL_CALL_ONLY,          // Message with tool_calls only
+    CONTENT_TOOL_CALL,       // Message with content + tool_calls
+    REASONING_TOOL_CALL,     // Message with reasoning_content + tool_calls
+    CONTENT_FAKE_TOOL_CALL,  // Message with content but no actual tool_calls (for testing)
+    ALL                      // Render all scenarios
+};
+
+struct debug_options {
+    std::string        template_path;
+    bool               with_tools        = true;
+    bool               generation_prompt = true;
+    bool               enable_reasoning  = true;
+    bool               debug_jinja       = false;
+    bool               force_tool_call   = false;
+    bool               parallel_tool_calls = true;
+    output_mode        mode              = output_mode::BOTH;
+    input_message_type input_message     = input_message_type::NONE;
+};
+
+static std::string read_file(const std::string & path) {
+    std::ifstream fin(path, std::ios::binary);
+    if (!fin.is_open()) {
+        throw std::runtime_error("Could not open file: " + path);
+    }
+    std::ostringstream buf;
+    buf << fin.rdbuf();
+    return buf.str();
+}
+
+static std::string read_gguf_chat_template(const std::string & path) {
+    struct gguf_init_params params = { /*no_alloc =*/true,  // We only need metadata, not tensor data
+                                       /*ctx=*/nullptr };
+
+    struct gguf_context * ctx = gguf_init_from_file(path.c_str(), params);
+    if (ctx == nullptr) {
+        throw std::runtime_error("Could not open GGUF file: " + path);
+    }
+
+    const char * key    = "tokenizer.chat_template";
+    int64_t      key_id = gguf_find_key(ctx, key);
+
+    if (key_id == -1) {
+        gguf_free(ctx);
+        throw std::runtime_error("GGUF file does not contain chat template key: " + std::string(key));
+    }
+
+    const char * template_str = gguf_get_val_str(ctx, key_id);
+    if (template_str == nullptr) {
+        gguf_free(ctx);
+        throw std::runtime_error("GGUF file contains chat template key but value is null");
+    }
+
+    std::string result = template_str;
+    gguf_free(ctx);
+    return result;
+}
+
+static void print_usage(const char * program_name) {
+    LOG_ERR("Test the chat template auto-parser; also usable as a debug tool that shows the generated PEG parser, GBNF grammar and triggers for a given template.\n");
+    LOG_ERR("\nUsage: %s [filter_regex]                       run the automated tests (default)\n", program_name);
+    LOG_ERR("       %s <template_or_gguf_path> [options]     debug a single template\n", program_name);
+    LOG_ERR("\nDebug mode options:\n");
+    LOG_ERR("  --no-tools              Disable tool definitions\n");
+    LOG_ERR("  --force-tool-call       Set tool calls to forced\n");
+    LOG_ERR("  --parallel-tool-calls=0|1 Set parallel_tool_calls (default: 1)\n");
+    LOG_ERR("  --generation-prompt=0|1 Set add_generation_prompt (default: 1)\n");
+    LOG_ERR("  --enable-reasoning=0|1  Enable reasoning parsing (default: 1)\n");
+    LOG_ERR("  --output=MODE           Output mode: analysis, template, both (default: both)\n");
+    LOG_ERR("  --debug-jinja           Enable Jinja fine-grained debug\n");
+    LOG_ERR("  --input-message=TYPE    Message type to render:\n");
+    LOG_ERR("                          content_only, reasoning_content, tool_call_only,\n");
+    LOG_ERR("                          content_tool_call, reasoning_tool_call,\n");
+    LOG_ERR("                          content_fake_tool_call, all\n");
+    LOG_ERR("\nExamples:\n");
+    LOG_ERR("  %s template.jinja --input-message=all --generation-prompt=1\n", program_name);
+    LOG_ERR("  %s template.jinja --output=template --input-message=tool_call_only\n", program_name);
+}
+
+static bool parse_bool_option(const std::string & value) {
+    return value == "1" || value == "true" || value == "yes";
+}
+
+static bool parse_debug_options(int argc, char ** argv, debug_options & opts) {
+    opts.template_path = argv[1];
+
+    for (int i = 2; i < argc; ++i) {
+        std::string arg = argv[i];
+
+        if (arg == "--force-tool-call") {
+            opts.force_tool_call = true;
+        } else if (arg == "--debug-jinja") {
+            opts.debug_jinja = true;
+        } else if (arg == "--no-tools") {
+            opts.with_tools = false;
+        } else if (arg.rfind("--parallel-tool-calls=", 0) == 0) {
+            opts.parallel_tool_calls = parse_bool_option(arg.substr(22));
+        } else if (arg.rfind("--generation-prompt=", 0) == 0) {
+            opts.generation_prompt = parse_bool_option(arg.substr(20));
+        } else if (arg.rfind("--enable-reasoning=", 0) == 0) {
+            opts.enable_reasoning = parse_bool_option(arg.substr(19));
+        } else if (arg.rfind("--output=", 0) == 0) {
+            std::string mode = arg.substr(9);
+            if (mode == "analysis") {
+                opts.mode = output_mode::ANALYSIS;
+            } else if (mode == "template") {
+                opts.mode = output_mode::TEMPLATE;
+            } else if (mode == "both") {
+                opts.mode = output_mode::BOTH;
+            } else {
+                LOG_ERR("Unknown output mode: %s\n", mode.c_str());
+                return false;
+            }
+        } else if (arg.rfind("--input-message=", 0) == 0) {
+            std::string type = arg.substr(16);
+            if (type == "content_only") {
+                opts.input_message = input_message_type::CONTENT_ONLY;
+            } else if (type == "reasoning_content") {
+                opts.input_message = input_message_type::REASONING_CONTENT;
+            } else if (type == "tool_call_only") {
+                opts.input_message = input_message_type::TOOL_CALL_ONLY;
+            } else if (type == "content_tool_call") {
+                opts.input_message = input_message_type::CONTENT_TOOL_CALL;
+            } else if (type == "reasoning_tool_call") {
+                opts.input_message = input_message_type::REASONING_TOOL_CALL;
+            } else if (type == "content_fake_tool_call") {
+                opts.input_message = input_message_type::CONTENT_FAKE_TOOL_CALL;
+            } else if (type == "all") {
+                opts.input_message = input_message_type::ALL;
+            } else {
+                LOG_ERR("Unknown input message type: %s\n", type.c_str());
+                return false;
+            }
+        } else {
+            LOG_ERR("Unknown option: %s\n", arg.c_str());
+            print_usage(argv[0]);
+            return false;
+        }
+    }
+
+    return true;
+}
+
+static json build_debug_user_message() {
+    return json{
+        { "role",    "user"                               },
+        { "content", "Hello, please help me with a task." }
+    };
+}
+
+static json build_content_only_message() {
+    return json{
+        { "role",    "assistant"                                   },
+        { "content", "Hello! I'm here to help you with your task." }
+    };
+}
+
+static json build_reasoning_content_message() {
+    return json{
+        { "role",              "assistant"                                                               },
+        { "content",           "Hello! I'm here to help you with your task."                             },
+        { "reasoning_content", "The user is greeting me and asking for help. I should respond politely." }
+    };
+}
+
+static json build_tool_call_only_message() {
+    return json{
+        { "role",       "assistant"      },
+        { "content",    nullptr          },
+        { "tool_calls",
+         json::array({ json{
+              { "type", "function" },
+              { "function", json{ { "name", "test_function_name" },
+                                  { "arguments", json::object({ { "param1", "value1" }, { "param2", "value2" } }) } } },
+              { "id", "123456789" } } }) }
+    };
+}
+
+static json build_content_tool_call_message() {
+    return json{
+        { "role",       "assistant"                                                                              },
+        { "content",    "I'll help you by calling a function."                                                   },
+        { "tool_calls",
+         json::array({ json{
+              { "type", "function" },
+              { "function",
+                json{ { "name", "test_function_name" },
+                      { "arguments", json::object({ { "param1", "value1" }, { "param2", "value2" } }) } } } } }) }
+    };
+}
+
+static json build_reasoning_tool_call_message() {
+    return json{
+        { "role",              "assistant"                                                                       },
+        { "content",           nullptr                                                                           },
+        { "reasoning_content", "I need to call a function to help with this task."                               },
+        { "tool_calls",
+         json::array({ json{
+              { "type", "function" },
+              { "function",
+                json{ { "name", "test_function_name" },
+                      { "arguments", json::object({ { "param1", "value1" }, { "param2", "value2" } }) } } } } }) }
+    };
+}
+
+static json build_content_fake_tool_call_message() {
+    // This message has content but NO tool_calls field
+    // It's used to test if a template renders tool definitions but not tool calls
+    return json{
+        { "role",    "assistant"                            },
+        { "content", "I'll help you by calling a function." }
+    };
+}
+
+static void render_scenario(const common_chat_template & tmpl,
+                            const std::string &          scenario_name,
+                            const json &                 messages,
+                            const json &                 tools,
+                            bool                         add_generation_prompt,
+                            bool                         enable_thinking) {
+    LOG_ERR("\n=== Scenario: %s ===\n", scenario_name.c_str());
+    LOG_ERR("add_generation_prompt: %s, enable_thinking: %s\n", add_generation_prompt ? "true" : "false",
+            enable_thinking ? "true" : "false");
+
+    // When add_generation_prompt is true, add a trailing user message to trigger the prompt
+    json final_messages = messages;
+    if (add_generation_prompt && !messages.empty() && messages.back().value("role", "") == "assistant") {
+        final_messages.push_back(json{
+            { "role",    "user"                                       },
+            { "content", "Now please continue with another response." }
+        });
+    }
+
+    LOG_ERR("Messages:\n%s\n", final_messages.dump(2).c_str());
+
+    try {
+        generation_params inputs;
+        inputs.messages                         = final_messages;
+        inputs.add_generation_prompt            = add_generation_prompt;
+        inputs.extra_context["enable_thinking"] = enable_thinking;
+
+        if (!tools.is_null() && tools.is_array() && !tools.empty()) {
+            inputs.tools = tools;
+        }
+
+        std::string output = common_chat_template_direct_apply(tmpl, inputs);
+
+        LOG_ERR("\n--- Rendered Output ---\n");
+        LOG_ERR("%s\n", output.c_str());
+        LOG_ERR("--- End Output (length: %zu) ---\n", output.length());
+    } catch (const std::exception & e) {
+        LOG_ERR("Rendering failed: %s\n", e.what());
+    }
+}
+
+static void render_all_scenarios(const common_chat_template & tmpl,
+                                 const json &                 tools,
+                                 bool                         add_generation_prompt,
+                                 bool                         enable_thinking,
+                                 input_message_type           message_type) {
+    json user_msg = build_debug_user_message();
+
+    auto render_if = [&](input_message_type type, const std::string & name, const json & assistant_msg) {
+        if (message_type == input_message_type::ALL || message_type == type) {
+            json messages = json::array({ user_msg, assistant_msg });
+            render_scenario(tmpl, name, messages, tools, add_generation_prompt, enable_thinking);
+        }
+    };
+
+    render_if(input_message_type::CONTENT_ONLY, "content_only", build_content_only_message());
+    render_if(input_message_type::REASONING_CONTENT, "reasoning_content", build_reasoning_content_message());
+    render_if(input_message_type::TOOL_CALL_ONLY, "tool_call_only", build_tool_call_only_message());
+    render_if(input_message_type::CONTENT_TOOL_CALL, "content_tool_call", build_content_tool_call_message());
+    render_if(input_message_type::REASONING_TOOL_CALL, "reasoning_tool_call", build_reasoning_tool_call_message());
+    render_if(input_message_type::CONTENT_FAKE_TOOL_CALL, "content_fake_tool_call",
+              build_content_fake_tool_call_message());
+
+    // Also render with add_generation_prompt=true to show the prompt ending
+    if (message_type == input_message_type::ALL) {
+        LOG_ERR("\n\n=== Generation Prompt Scenarios (add_generation_prompt=true) ===\n");
+
+        json prompt_messages = json::array({ user_msg });
+        render_scenario(tmpl, "generation_prompt_only", prompt_messages, tools, true, enable_thinking);
+
+        // With enable_thinking toggled
+        render_scenario(tmpl, "generation_prompt_thinking_disabled", prompt_messages, tools, true, false);
+    }
+}
+
+static generation_params prepare_debug_params(const debug_options & opts, const json & tools) {
+    generation_params params;
+    params.messages         = json::array({ build_debug_user_message() });
+    params.reasoning_format = opts.enable_reasoning ? COMMON_REASONING_FORMAT_DEEPSEEK : COMMON_REASONING_FORMAT_NONE;
+    params.enable_thinking  = opts.enable_reasoning;
+    params.add_generation_prompt = opts.generation_prompt;
+
+    if (opts.with_tools) {
+        params.tools       = tools;
+        params.tool_choice = opts.force_tool_call ? COMMON_CHAT_TOOL_CHOICE_REQUIRED : COMMON_CHAT_TOOL_CHOICE_AUTO;
+    } else {
+        params.tools       = json();
+        params.tool_choice = COMMON_CHAT_TOOL_CHOICE_NONE;
+    }
+    params.parallel_tool_calls = opts.parallel_tool_calls;
+    return params;
+}
+
+static int debug_single_template(const debug_options & opts) {
+    std::string template_source;
+    try {
+        // Check if the file is a GGUF file
+        if (opts.template_path.size() >= 5 &&
+            opts.template_path.compare(opts.template_path.size() - 5, 5, ".gguf") == 0) {
+            template_source = read_gguf_chat_template(opts.template_path);
+        } else {
+            template_source = read_file(opts.template_path);
+        }
+    } catch (const std::exception & e) {
+        LOG_ERR("Error reading template: %s\n", e.what());
+        return 1;
+    }
+
+    LOG_ERR("Analyzing template: %s\n", opts.template_path.c_str());
+    LOG_ERR("Options: with_tools=%s, generation_prompt=%s, enable_reasoning=%s\n", opts.with_tools ? "true" : "false",
+            opts.generation_prompt ? "true" : "false", opts.enable_reasoning ? "true" : "false");
+
+    try {
+        common_chat_template chat_template(template_source, "", "");
+
+        json tools = opts.with_tools ? build_tools_definition() : json();
+
+        generation_params params = prepare_debug_params(opts, tools);
+        common_chat_params            parser_data;
+        if (std::optional<common_chat_params> spec_tmpl =
+                common_chat_try_specialized_template(chat_template, template_source, params)) {
+            LOG_ERR("\n");
+            LOG_ERR("This template uses a specialized parser, analysis results will not be available.\n");
+            parser_data = *spec_tmpl;
+        } else {
+            // Render template scenarios if requested
+            if (opts.input_message != input_message_type::NONE &&
+                (opts.mode == output_mode::TEMPLATE || opts.mode == output_mode::BOTH)) {
+                LOG_ERR("\n");
+                LOG_ERR("================================================================================\n");
+                LOG_ERR("                         TEMPLATE RENDERING OUTPUT\n");
+                LOG_ERR("================================================================================\n");
+
+                render_all_scenarios(chat_template, tools, opts.generation_prompt, opts.enable_reasoning,
+                                     opts.input_message);
+            }
+
+            // Output analysis if requested
+            if (opts.mode == output_mode::ANALYSIS || opts.mode == output_mode::BOTH) {
+                LOG_ERR("\n");
+                LOG_ERR("================================================================================\n");
+                LOG_ERR("                           TEMPLATE ANALYSIS\n");
+                LOG_ERR("================================================================================\n");
+
+                struct autoparser analysis;
+                analysis.analyze_template(chat_template);
+
+                // Generate Parser
+                parser_data = peg_generator::generate_parser(chat_template, params, analysis);
+            }
+        }
+
+        if (!std::empty(parser_data.parser)) {
+            LOG_ERR("\n=== Generated Parser ===\n");
+            common_peg_arena arena;
+            arena.load(parser_data.parser);
+            LOG_ERR("%s\n", arena.dump(arena.root()).c_str());
+
+            LOG_ERR("\n=== Generated Grammar ===\n");
+            LOG_ERR("%s\n", parser_data.grammar.c_str());
+
+            LOG_ERR("\n=== Generated Lazy Grammar ===\n");
+            LOG_ERR("%d\n", parser_data.grammar_lazy);
+
+            LOG_ERR("\n=== Generated Grammar Triggers ===\n");
+            for (const common_grammar_trigger & cgt : parser_data.grammar_triggers) {
+                LOG_ERR("Token: %d | Type: %d | Value: %s\n", cgt.token, cgt.type, cgt.value.c_str());
+            }
+
+            LOG_ERR("\n=== Preserved Tokens ===\n");
+            for (const std::string & token : parser_data.preserved_tokens) {
+                LOG_ERR("  '%s'\n", token.c_str());
+            }
+        }
+    } catch (const std::exception & e) {
+        LOG_ERR("Analysis failed: %s\n", e.what());
+        return 1;
+    }
+
+    return 0;
+}
+
 int main(int argc, char * argv[]) {
+    if (argc > 1) {
+        std::string arg = argv[1];
+        if (arg == "-h" || arg == "--help") {
+            common_log_set_verbosity_thold(99);
+            print_usage(argv[0]);
+            return 0;
+        }
+
+        // debug mode: if the first argument is an existing file, analyze that template instead of running the automated tests
+        if (std::filesystem::is_regular_file(arg)) {
+            common_log_set_verbosity_thold(99);
+
+            debug_options opts;
+            if (!parse_debug_options(argc, argv, opts)) {
+                return 1;
+            }
+
+            if (opts.debug_jinja || std::getenv("LLAMA_DEBUG_JINJA") != nullptr) {
+                jinja::enable_debug(true);
+            }
+
+            return debug_single_template(opts);
+        }
+    }
+
    testing t(std::cout);
    t.verbose = true;

-    // usage: test-chat-auto-parser-helpers [filter_regex]
+    // usage: test-chat-auto-parser [filter_regex]

    if (argc > 1) {
        t.set_filter(argv[1]);
@@ -11,9 +11,9 @@
 #include <regex>
 #include <string>

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

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

 static json create_tools();
 static void test_example_native(testing & t);
@@ -63,10 +63,10 @@ static json create_tools() {
                          { { "type", "string" }, { "description", "The city and state, e.g. San Francisco, CA" } } },
                        { "unit",
                          { { "type", "string" },
-                            { "enum", { "celsius", "fahrenheit" } },
+                            { "enum", json::array({ "celsius", "fahrenheit" }) },
                            { "description",
                              "The temperature unit to use. Infer this from the users location." } } } } },
-                    { "required", { "location", "unit" } },
+                    { "required", json::array({ "location", "unit" }) },
                } },
          }                      }
    };
@@ -86,14 +86,14 @@ static json create_tools() {
                          { { "type", "string" }, { "description", "The city and state, e.g. San Francisco, CA" } } },
                        { "unit",
                          { { "type", "string" },
-                            { "enum", { "celsius", "fahrenheit" } },
+                            { "enum", json::array({ "celsius", "fahrenheit" }) },
                            { "description", "The temperature unit to use. Infer this from the users location." } } },
                        { "days",
                          { { "type", "integer" },
                            { "description", "Number of days to forecast (1-10)" },
                            { "minimum", 1 },
                            { "maximum", 10 } } } } },
-                    { "required", { "location", "unit" } },
+                    { "required", json::array({ "location", "unit" }) },
                } },
          }                      }
    };
@@ -114,9 +114,9 @@ static json create_tools() {
                        { "default", 5 } } },
                    { "category",
                      { { "type", "string" },
-                        { "enum", { "api", "troubleshooting", "billing", "general" } },
+                        { "enum", json::array({ "api", "troubleshooting", "billing", "general" }) },
                        { "description", "Filter search by specific category." } } } } },
-                { "required", { "query", "category" } },
+                { "required", json::array({ "query", "category" }) },
                { "additionalProperties", false } } },
            { "strict", true } } }
    };
@@ -341,7 +341,7 @@ static void test_example_native(testing & t) {
                { { "invoice_number", { { "type", "string" } } },
                  { "amount", { { "type", "number" } } },
                  { "due_date", { { "type", "string" } } } } },
-              { "required", { "invoice_number", "amount", "due_date" } } },
+              { "required", json::array({ "invoice_number", "amount", "due_date" }) } },
         /* .parallel_tool_calls =  */ false,
         /* .generation_prompt =    */ "<think>",
         /* .input =                */
@@ -406,7 +406,7 @@ static void test_example_qwen3_coder(testing & t) {

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

            std::vector<common_peg_parser> arg_parsers;
@@ -661,8 +661,8 @@ void test_command7_parser_compare(testing & t) {
        "5. Provide a detailed cost breakdown that includes accommodation, transportation, meals, and entry fees "
        "to attractions.";

-    std::vector<std::tuple<std::string, std::string, nlohmann::json>> tool_calls = {
-        { "call_0", "plan_trip", nlohmann::json::parse(R"({
+    std::vector<std::tuple<std::string, std::string, common_json>> tool_calls = {
+        { "call_0", "plan_trip", common_json::parse(R"({
            "destination": "Japan",
            "duration": 14,
            "budget": 4000,
@@ -686,16 +686,16 @@ void test_command7_parser_compare(testing & t) {
    if (!tool_calls.empty()) {
        tokens.emplace_back("<|START_ACTION|>");

-        auto json = nlohmann::json::array();
+        auto json = common_json::array();
        for (const auto & tc : tool_calls) {
-            auto tc_json            = nlohmann::json::object();
+            auto tc_json            = common_json::object();
            tc_json["tool_call_id"] = std::get<0>(tc);
            tc_json["tool_name"]    = std::get<1>(tc);
            tc_json["parameters"]   = std::get<2>(tc);
            json.push_back(tc_json);
        }

-        auto tokenized = simple_tokenize(json.dump(-1, ' ', true));
+        auto tokenized = simple_tokenize(json.dump(-1));
        tokens.insert(tokens.end(), tokenized.begin(), tokenized.end());

        tokens.emplace_back("<|END_ACTION|>");
@@ -737,7 +737,7 @@ static void test_prefix_tool_names(testing & t) {
                      {
                          { "arg1", { { "type", "integer" } } },
                      } },
-                    { "required", { "arg1" } },
+                    { "required", json::array({ "arg1" }) },
                } },
          } }
    };
@@ -757,7 +757,7 @@ static void test_prefix_tool_names(testing & t) {
                          { "arg1", { { "type", "integer" } } },
                          { "arg2", { { "type", "integer" } } },
                      } },
-                    { "required", { "arg1" } },
+                    { "required", json::array({ "arg1" }) },
                } },
          } }
    };
@@ -7,7 +7,7 @@
 #include <fstream>
 #include <filesystem>

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

 #undef NDEBUG
 #include <cassert>
@@ -20,7 +20,7 @@
 #include "jinja/lexer.h"
 #include "jinja/caps.h"

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

 static int main_automated_tests(void);

@@ -28,6 +28,8 @@ static void run_multiple(const std::string& dir_path, bool stop_on_first_failure
 static void run_single(const std::string& contents, json input, bool use_common = false, bool dump_prog = false, const std::string & output_path = "");

 static std::string HELP = R"(
+Test the Jinja engine by rendering chat templates and comparing the output against expected results.
+
 Usage: test-chat-template [OPTIONS] PATH_TO_TEMPLATE
 Options:
  -h, --help               Show this help message and exit.
@@ -304,8 +306,8 @@ void run_single(const std::string& contents, json input, bool use_common, bool d
        if (input.contains("eos_token")) {
            eos_token = input["eos_token"].get<std::string>();
        }
-        nlohmann::ordered_json msgs_json = input["messages"];
-        nlohmann::ordered_json tools_json = input["tools"];
+        common_json msgs_json = input["messages"];
+        common_json tools_json = input["tools"];
        auto messages = common_chat_msgs_parse_oaicompat(msgs_json);
        auto tools = common_chat_tools_parse_oaicompat(tools_json);
        auto output = format_using_common(contents, bos_token, eos_token, messages, tools);
@@ -19,12 +19,12 @@
 #include <fstream>
 #include <functional>
 #include <iostream>
-#include <nlohmann/json.hpp>
+#include "json.h"
 #include <set>
 #include <stdexcept>
 #include <string>

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

 static std::ostream & operator<<(std::ostream & os, const common_chat_msg_diff & diff) {
    os << "{ content_delta: " << diff.content_delta << "; ";
@@ -7,13 +7,13 @@
 #include "../src/unicode.h"
 #include "../src/llama-grammar.h"

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

 #include <cassert>
 #include <string>
 #include <vector>

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

 static llama_grammar * build_grammar_with_root(const std::string & grammar_str, const char * grammar_root) {
    return llama_grammar_init_impl(nullptr, grammar_str.c_str(), grammar_root, false, nullptr, 0, nullptr, 0);
@@ -3,7 +3,7 @@
 #include <random>
 #include <cstdlib>

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

 #include "jinja/runtime.h"
@@ -14,7 +14,7 @@

 #include "testing.h"

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

 static void test_template(testing & t, const std::string & name, const std::string & tmpl, const json & vars, const std::string & expect);

@@ -240,7 +240,7 @@ static void test_conditionals(testing & t) {

    test_template(t, "is undefined key falsy",
        "{{ 'yes' if not y['x'] else 'no' }}",
-        {{"y", {{}}}},
+        {{"y", json::array({nullptr})}},
        "yes"
    );

@@ -282,7 +282,7 @@ static void test_conditionals(testing & t) {

    test_template(t, "is non-empty object truthy",
        "{{ 'yes' if y else 'no' }}",
-        {{"y", {"x", false}}},
+        {{"y", json::array({"x", false})}},
        "yes"
    );

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

 #include "../src/llama-grammar.h"

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

 #include <cassert>
 #include <fstream>
@@ -1442,7 +1442,7 @@ static void test_resolves_to_string() {
    auto test = [](const std::string & name, const std::string & schema_str, bool expected) {
        fprintf(stderr, "- %s\n", name.c_str());
        common_schema_info info;
-        auto schema = nlohmann::ordered_json::parse(schema_str);
+        auto schema = common_json::parse(schema_str);
        info.resolve_refs(schema);
        bool result = info.resolves_to_string(schema);
        if (result != expected) {
@@ -1517,7 +1517,7 @@ int main() {

    test_all("C++", [](const TestCase & tc) {
        try {
-            tc.verify(json_schema_to_grammar(nlohmann::ordered_json::parse(tc.schema), true));
+            tc.verify(json_schema_to_grammar(common_json::parse(tc.schema), true));
            tc.verify_status(SUCCESS);
        } catch (const std::invalid_argument & ex) {
            fprintf(stderr, "Error: %s\n", ex.what());
@@ -1531,7 +1531,7 @@ int main() {
        auto run = [](const TestCase & tc) {
            fprintf(stderr, "- %s\n", tc.name.c_str());
            try {
-                tc.verify(json_schema_to_grammar(nlohmann::ordered_json::parse(tc.schema), true));
+                tc.verify(json_schema_to_grammar(common_json::parse(tc.schema), true));
                tc.verify_status(SUCCESS);
            } catch (const std::invalid_argument & ex) {
                fprintf(stderr, "Error: %s\n", ex.what());
@@ -101,6 +101,15 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
        n_head = 1;
        n_ff   = 96;
        n_layer = 22; // hparams.n_layer_kv_from_start = 20 is hardcoded
+    } else if (arch == LLM_ARCH_DEEPSEEK4) {
+        n_embd  = 128;
+        n_head  = 1;
+        n_ff    = 192;
+        n_layer = 3; // uncompressed + csa + hca, one layer of each ratio kind
+    } else if (arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_LAGUNA) {
+        n_embd = 160; // exercise per-head tensor split granularity with head size 80
+    } else if (arch == LLM_ARCH_QWEN3 || arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_AFMOE) {
+        n_head = 4;
    } else if (arch == LLM_ARCH_DEEPSEEK2
            || arch == LLM_ARCH_DEEPSEEK32
            || arch == LLM_ARCH_GLM_DSA
@@ -120,6 +129,12 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
        n_vocab = 4096; // must be >= the hard-coded codec head size (3072)
    }

+    uint32_t n_head_kv = n_head;
+    if (arch == LLM_ARCH_QWEN3) {
+        n_head_kv = 1; // MQA coverage
+    } else if (arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_AFMOE) {
+        n_head_kv = 2; // GQA coverage
+    }
    const uint32_t n_embd_head = n_embd / n_head;

    ms.add_kv(LLM_KV_GENERAL_ARCHITECTURE,      llm_arch_name(arch));
@@ -160,7 +175,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
        ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_per_layer);
    } else {
        ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head);
-        ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head);
+        ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_kv);
    }

    ms.add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, 8.0f);
@@ -193,6 +208,10 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
            }
            ms.add_kv(LLM_KV_ATTENTION_INDEXER_TYPES, indexer_types);
        }
+    } else if (arch == LLM_ARCH_DEEPSEEK4) {
+        ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH,       uint32_t(128));
+        ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH,     uint32_t(128));
+        ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT,       uint32_t(64));
    } else if (arch == LLM_ARCH_MINIMAX_M3) {
        // partial rotary: n_rot must not exceed the indexer key length (64)
        ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT,       uint32_t(64));
@@ -229,6 +248,20 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {

    // MSA requires one indexer head per GQA (KV) head, unlike the DSA archs where the
    // indexer head count is independent of the main attention head count.
+    if (arch == LLM_ARCH_DEEPSEEK4) {
+        ms.add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE,                 2.5f);
+        ms.add_kv(LLM_KV_EXPERT_WEIGHTS_NORM,                  true);
+        ms.add_kv(LLM_KV_SWIGLU_CLAMP_EXP,                     7.0f);
+        ms.add_kv(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,         uint32_t(1));
+        ms.add_kv(LLM_KV_ATTENTION_OUTPUT_LORA_RANK,           uint32_t(64));
+        ms.add_kv(LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE,    10000.0f);
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_COUNT,               uint32_t(4));
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, uint32_t(4));
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_EPSILON,             1e-6f);
+        ms.add_kv(LLM_KV_HASH_LAYER_COUNT,                     uint32_t(0));
+        ms.add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS,            std::vector<uint32_t>({0, 4, 128}));
+    }
+
    ms.add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,   arch == LLM_ARCH_MINIMAX_M3 ? n_head : uint32_t(1));
    ms.add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,   uint32_t(64));
    ms.add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K,        uint32_t(8));
@@ -247,7 +280,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
        ms.add_kv(LLM_KV_EXPERT_COUNT,               uint32_t(2));
        ms.add_kv(LLM_KV_EXPERT_USED_COUNT,          uint32_t(1));
        ms.add_kv(LLM_KV_EXPERT_SHARED_COUNT,        uint32_t(1));
-        ms.add_kv(LLM_KV_EXPERT_GATING_FUNC,         uint32_t(2)); // sigmoid
+        ms.add_kv(LLM_KV_EXPERT_GATING_FUNC,         arch == LLM_ARCH_DEEPSEEK4 ? uint32_t(4) : uint32_t(2)); // sqrtsoftplus : sigmoid
        ms.add_kv(LLM_KV_EXPERT_GROUP_SCALE,         1.0f);
        ms.add_kv(LLM_KV_EXPERTS_PER_GROUP,          uint32_t(1));
    }
@@ -385,6 +418,7 @@ static bool moe_mandatory(const llm_arch arch) {
        case LLM_ARCH_DEEPSEEK2:
        case LLM_ARCH_DEEPSEEK32:
        case LLM_ARCH_DOTS3NOTE:
+        case LLM_ARCH_DEEPSEEK4:
        case LLM_ARCH_GLM4_MOE:
        case LLM_ARCH_GLM_DSA:
        case LLM_ARCH_EXAONE_MOE:
@@ -470,9 +504,6 @@ static bool arch_supported(const llm_arch arch) {
    if (arch == LLM_ARCH_DEEPSEEK2OCR) {
        return false;
    }
-    if (arch == LLM_ARCH_DEEPSEEK4) {
-        return false;
-    }

    // FIXME: these hit scheduler/view-backed-output issues with WebGPU on CI.
 #ifdef GGML_USE_WEBGPU
@@ -9,7 +9,7 @@
 #include "http.h"
 #include "log.h"

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

 #include <algorithm>
 #include <cstdio>
@@ -55,7 +55,7 @@ static const char * COMMIT = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa";
 static void serve_repos(httplib::Server & server) {
    server.Get(R"(/api/models/(.+)/refs)", [](const httplib::Request & req, httplib::Response & res) {
        if (g_repos.count(req.matches[1])) {
-            res.set_content(nlohmann::json{{"branches", {{{"name", "main"}, {"targetCommit", COMMIT}}}}}.dump(),
+            res.set_content(common_json{{"branches", common_json::array({ common_json{{"name", "main"}, {"targetCommit", COMMIT}} })}}.dump(),
                            "application/json");
        } else {
            res.status = 404;
@@ -66,7 +66,7 @@ static void serve_repos(httplib::Server & server) {
            res.status = 404;
            return;
        }
-        auto files = nlohmann::json::array();
+        auto files = common_json::array();
        size_t i = 0;
        for (const auto & p : g_repos[req.matches[1]]) {
            char oid[41];
@@ -35,6 +35,178 @@ static bool decode_one(llama_context * ctx, llama_token tok, llama_pos pos) {
    return ok;
 }

+// Roll back multiple sequences, then replay them in a single batch whose
+// per-seq token count exceeds n_ubatch: each seq's replay spans several
+// ubatches while its rollback restore is still pending. Compared against a
+// reference context that never advanced past the rollback point and decodes
+// the identical replay batch.
+static bool test_multi_seq_split_replay(const common_params & params, llama_model * model, const int n_vocab) {
+    constexpr uint32_t  n_seqs     = 2;
+    constexpr uint32_t  n_ubatch   = 16;
+    constexpr uint32_t  n_prompt   = 19;
+    constexpr uint32_t  n_rollback = 3;
+    constexpr uint32_t  n_replay   = 40; // > n_ubatch so each seq spans multiple ubatches
+    constexpr llama_pos p0         = n_prompt - n_rollback;
+
+    const auto make_ctx_multi = [&]() {
+        auto cparams = common_context_params_to_llama(params);
+        cparams.n_seq_max  = n_seqs;
+        cparams.n_rs_seq   = 8;
+        cparams.n_ctx      = 256;
+        cparams.n_batch    = 256;
+        cparams.n_ubatch   = n_ubatch;
+        cparams.kv_unified = false;
+        return llama_init_from_model(model, cparams);
+    };
+
+    llama_context * ctx_roll = make_ctx_multi();
+    llama_context * ctx_ref  = make_ctx_multi();
+    if (ctx_roll == nullptr || ctx_ref == nullptr) {
+        fprintf(stderr, "%s : failed to init multi-seq contexts\n", __func__);
+        return false;
+    }
+
+    const auto cleanup = [&]() {
+        llama_free(ctx_roll);
+        llama_free(ctx_ref);
+    };
+
+    if (llama_n_rs_seq(ctx_roll) < n_rollback) {
+        fprintf(stderr, "%s : skipping because n_rs_seq is too small\n", __func__);
+        cleanup();
+        return true;
+    }
+
+    const auto tok = [&](uint32_t seq, llama_pos pos) {
+        return (llama_token) ((7*(uint32_t) pos + 31*seq + 1) % (uint32_t) n_vocab);
+    };
+
+    bool ok = true;
+
+    // both contexts decode the identical [0, p0) prefill; only ctx_roll decodes
+    // the tail, which is then rolled back so its restore is pending at replay
+    for (uint32_t s = 0; s < n_seqs && ok; ++s) {
+        llama_batch batch = llama_batch_init(n_prompt, 0, 1);
+        for (llama_pos pos = 0; pos < (llama_pos) p0; ++pos) {
+            common_batch_add(batch, tok(s, pos), pos, { (llama_seq_id) s }, false);
+        }
+        ok = ok && llama_decode(ctx_roll, batch) == 0;
+        ok = ok && llama_decode(ctx_ref,  batch) == 0;
+
+        common_batch_clear(batch);
+        for (llama_pos pos = p0; pos < (llama_pos) n_prompt; ++pos) {
+            common_batch_add(batch, tok(s, pos), pos, { (llama_seq_id) s }, false);
+        }
+        ok = ok && llama_decode(ctx_roll, batch) == 0;
+        llama_batch_free(batch);
+
+        ok = ok && llama_memory_seq_rm(llama_get_memory(ctx_roll), (llama_seq_id) s, p0, -1);
+
+        // a second partial removal while one is pending must be refused
+        ok = ok && !llama_memory_seq_rm(llama_get_memory(ctx_roll), (llama_seq_id) s, p0 - 1, -1);
+    }
+    if (!ok) {
+        fprintf(stderr, "%s : multi-seq prefill/rollback failed\n", __func__);
+        cleanup();
+        return false;
+    }
+
+    llama_batch batch = llama_batch_init(n_seqs*n_replay, 0, 1);
+    for (uint32_t s = 0; s < n_seqs; ++s) {
+        for (uint32_t i = 0; i < n_replay; ++i) {
+            const llama_pos pos = p0 + (llama_pos) i;
+            common_batch_add(batch, tok(s, pos), pos, { (llama_seq_id) s }, true);
+        }
+    }
+    ok = llama_decode(ctx_roll, batch) == 0;
+    ok = ok && llama_decode(ctx_ref, batch) == 0;
+    llama_batch_free(batch);
+    if (!ok) {
+        fprintf(stderr, "%s : multi-seq replay decode failed\n", __func__);
+        cleanup();
+        return false;
+    }
+
+    // identical ubatch shapes from bit-exact states: a correct implementation
+    // matches bitwise, so eps only allows backend scheduling noise
+    constexpr float eps = 1e-7f;
+
+    float    diff_max  = 0.0f;
+    uint32_t seq_first = 0;
+    int32_t  pos_first = -1;
+    for (uint32_t i = 0; i < n_seqs*n_replay; ++i) {
+        const float * l_roll = llama_get_logits_ith(ctx_roll, i);
+        const float * l_ref  = llama_get_logits_ith(ctx_ref,  i);
+        if (l_roll == nullptr || l_ref == nullptr) {
+            fprintf(stderr, "%s : missing multi-seq logits at index %u\n", __func__, i);
+            cleanup();
+            return false;
+        }
+        for (int t = 0; t < n_vocab; ++t) {
+            const float diff = std::fabs(l_roll[t] - l_ref[t]);
+            if (diff > eps && pos_first < 0) {
+                seq_first = i/n_replay;
+                pos_first = p0 + (int32_t) (i%n_replay);
+            }
+            diff_max = std::max(diff_max, diff);
+        }
+    }
+
+    if (diff_max > eps) {
+        fprintf(stderr, "%s : multi-seq split replay logits mismatch (max diff %g, first at seq %u pos %d)\n",
+                __func__, (double) diff_max, seq_first, pos_first);
+        cleanup();
+        return false;
+    }
+
+    fprintf(stderr, "%s : multi-seq split replay matched (max diff %g)\n", __func__, (double) diff_max);
+
+    // seq-1-only decodes must be independent of seq 0's content: diverge seq 0
+    // in ctx_ref only, then compare identical seq-1-only continuations bitwise
+    constexpr uint32_t n_tail = 4;
+
+    {
+        llama_batch batch_tail = llama_batch_init(n_tail, 0, 1);
+        for (uint32_t i = 0; i < n_tail; ++i) {
+            const llama_pos pos = p0 + (llama_pos) (n_replay + i);
+            common_batch_add(batch_tail, tok(0, pos + 7), pos, { 0 }, false);
+        }
+        ok = llama_decode(ctx_ref, batch_tail) == 0;
+        llama_batch_free(batch_tail);
+    }
+
+    float diff_tail = 0.0f;
+    for (uint32_t i = 0; i < n_tail && ok; ++i) {
+        const llama_pos pos = p0 + (llama_pos) (n_replay + i);
+        llama_batch batch_one = llama_batch_init(1, 0, 1);
+        common_batch_add(batch_one, tok(1, pos), pos, { 1 }, true);
+        ok = llama_decode(ctx_roll, batch_one) == 0;
+        ok = ok && llama_decode(ctx_ref, batch_one) == 0;
+        llama_batch_free(batch_one);
+        if (!ok) {
+            break;
+        }
+
+        const float * l_roll = llama_get_logits_ith(ctx_roll, 0);
+        const float * l_ref  = llama_get_logits_ith(ctx_ref,  0);
+        ok = l_roll != nullptr && l_ref != nullptr;
+        for (int t = 0; ok && t < n_vocab; ++t) {
+            diff_tail = std::max(diff_tail, std::fabs(l_roll[t] - l_ref[t]));
+        }
+    }
+
+    if (!ok || diff_tail > eps) {
+        fprintf(stderr, "%s : seq-1-only decode leaked seq 0 state (ok=%d, max diff %g)\n",
+                __func__, ok ? 1 : 0, (double) diff_tail);
+        cleanup();
+        return false;
+    }
+
+    fprintf(stderr, "%s : seq-1-only decode independent of seq 0 (max diff %g)\n", __func__, (double) diff_tail);
+    cleanup();
+    return true;
+}
+
 int main(int argc, char ** argv) {
    std::setlocale(LC_NUMERIC, "C");

@@ -220,5 +392,10 @@ int main(int argc, char ** argv) {
    llama_free(ctx_src);
    llama_free(ctx_dst);
    llama_free(ctx_dirty);
+
+    if (!test_multi_seq_split_replay(params, model, n_vocab)) {
+        return 1;
+    }
+
    return 0;
 }
@@ -27,7 +27,6 @@ else()
        add_subdirectory(server)
    endif()
    add_subdirectory(tokenize)
-    add_subdirectory(parser)
    add_subdirectory(tts)
    add_subdirectory(mtmd)
    if (GGML_RPC)
@@ -6,8 +6,7 @@
 #include "log.h"
 #include "console.h"

-#define JSON_ASSERT GGML_ASSERT
-#include <nlohmann/json.hpp>
+#include "json.h"

 #include <algorithm>
 #include <cctype>
@@ -16,7 +15,7 @@
 #include <map>
 #include <set>

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

 struct cli_context_impl {
    json messages      = json::array();
@@ -73,7 +72,7 @@ static std::string format_error_message(const json & err) {

 // err is the raw response body of a failed request; it may or may not be JSON
 static std::string format_error_message(const std::string & err) {
-    json parsed = json::parse(err, nullptr, false);
+    json parsed = json::parse_no_throw(err);
    if (!parsed.is_discarded()) {
        return format_error_message(parsed);
    }
@@ -157,7 +156,7 @@ bool cli_context::init() {
            if (!list_and_ask_models()) {
                return false;
            }
-        } catch (const json::parse_error & e) {
+        } catch (const common_json_error & e) {
            ui::show_error(e.what());
            ui::show_message("This might be caused by an incorrect server-base endpoint URL");
            return false;
@@ -364,7 +363,7 @@ bool cli_context::generate_completion(generated_content & content_out, cli_timin
    ui::assistant_turn a;

    std::string err = client.post_sse("/v1/chat/completions", body.dump(), should_stop, [&](const std::string & payload) {
-        json chunk = json::parse(payload, nullptr, false);
+        json chunk = json::parse_no_throw(payload);
        if (chunk.is_discarded()) {
            return;
        }
@@ -33,6 +33,7 @@ int llama_fit_params(int argc, char ** argv) {
    if (!params.fit_params_print) {
        const common_params_fit_status status = 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,
+                nullptr,
                params.verbosity >= LOG_LEVEL_DEBUG ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
        if (status != COMMON_PARAMS_FIT_STATUS_SUCCESS) {
            LOG_ERR("%s: failed to fit CLI arguments to free memory, exiting...\n", __func__);
@@ -2294,6 +2294,7 @@ int llama_bench(int argc, char ** argv) {
                fit_overrides.data(),
                margins.data(),
                inst.fit_min_ctx,
+                nullptr,
                params.verbose ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
       }

@@ -30,6 +30,7 @@ add_library(mtmd
            models/models.h
            models/cogvlm.cpp
            models/conformer.cpp
+            models/dots3note.cpp
            models/dotsocr.cpp
            models/exaone4_5.cpp
            models/gemma4a.cpp
@@ -120,6 +120,12 @@ struct clip_graph {
            ffn_op_type type_op,
            int il) const;

+    ggml_tensor * build_moe_ffn(
+            ggml_tensor * cur,
+            const clip_layer & layer,
+            ffn_op_type type_op,
+            int il) const;
+
    ggml_tensor * build_attn(
            ggml_tensor * wo,
            ggml_tensor * wo_b,
@@ -131,9 +137,15 @@ struct clip_graph {
            int il,
            ggml_tensor * sinks = nullptr) const;

-    // implementation of the 2D RoPE without adding a new op in ggml
-    // this is not efficient (use double the memory), but works on all backends
-    // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065
+    // implementation of the 2D RoPE using two ggml_rope_ext calls
+    //
+    // unlike GGML_ROPE_TYPE_VISION which forces NEOX ordering, this rotates adjacent pairs (normal ordering)
+    //
+    // example:
+    //  given a single head with size = 8 --> [00000000]
+    //  dims [0, 4) rotate with pos_a, dims [4, 8) rotate with pos_b --> [aaaabbbb]
+    //  interleave_freq = false --> both halves use the same inv_freq set (like GGML_ROPE_TYPE_VISION)
+    //  interleave_freq = true  --> first half uses even inv_freq, second half uses odd inv_freq (used by pixtral)
    ggml_tensor * build_rope_2d(
        ggml_context * ctx0,
        ggml_tensor * cur,
@@ -75,6 +75,7 @@
 #define KEY_SAM_N_HEAD             "clip.vision.sam.head_count"
 #define KEY_SAM_N_BLOCK            "clip.vision.sam.block_count"
 #define KEY_SAM_N_EMBD             "clip.vision.sam.embedding_length"
+#define KEY_VISION_N_EXPERT_USED   "clip.vision.expert_used_count"
 // audio-specific
 #define KEY_AUDIO_PROJ_TYPE        "clip.audio.projector_type" // for models with mixed modalities
 #define KEY_A_NUM_MEL_BINS         "clip.audio.num_mel_bins"
@@ -119,7 +120,11 @@
 #define TN_FFN_DOWN        "%s.blk.%d.ffn_down.%s"
 #define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
 #define TN_FFN_UP          "%s.blk.%d.ffn_up.%s"
-#define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
+#define TN_FFN_GATE_INP    "%s.blk.%d.ffn_gate_inp.%s"    // MoE router (dots3note)
+#define TN_FFN_GATE_EXPS   "%s.blk.%d.ffn_gate_exps.%s"
+#define TN_FFN_UP_EXPS     "%s.blk.%d.ffn_up_exps.%s"
+#define TN_FFN_DOWN_EXPS   "%s.blk.%d.ffn_down_exps.%s"
+#define TN_FFN_EXP_PROBS_B "%s.blk.%d.exp_probs_b.%s"
 #define TN_LN_1            "%s.blk.%d.ln1.%s" // layer norm
 #define TN_LN_2            "%s.blk.%d.ln2.%s" // layer norm
 #define TN_LS_1            "%s.blk.%d.ls1.%s"         // layer scale
@@ -471,6 +476,8 @@ enum projector_type {
    PROJECTOR_TYPE_COGVLM,
    PROJECTOR_TYPE_JANUS_PRO,
    PROJECTOR_TYPE_DOTS_OCR,
+    PROJECTOR_TYPE_DOTS3NOTE_V,
+    PROJECTOR_TYPE_DOTS3NOTE_A,
    PROJECTOR_TYPE_DEEPSEEKOCR,
    PROJECTOR_TYPE_DEEPSEEKOCR2,
    PROJECTOR_TYPE_LFM2A,
@@ -533,6 +540,8 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
    { PROJECTOR_TYPE_COGVLM,            "cogvlm"},
    { PROJECTOR_TYPE_JANUS_PRO,         "janus_pro"},
    { PROJECTOR_TYPE_DOTS_OCR,          "dots_ocr"},
+    { PROJECTOR_TYPE_DOTS3NOTE_V,       "dots3note_v"},
+    { PROJECTOR_TYPE_DOTS3NOTE_A,       "dots3note_a"},
    { PROJECTOR_TYPE_DEEPSEEKOCR,       "deepseekocr"},
    { PROJECTOR_TYPE_DEEPSEEKOCR2,      "deepseekocr2"},
    { PROJECTOR_TYPE_LFM2A,             "lfm2a"},
@@ -29,10 +29,10 @@ enum patch_merge_type {
    PATCH_MERGE_SPATIAL_UNPAD,
 };

+// all algos are Pillow-compatible (matching PIL.Image.resize output)
 enum resize_algo {
-    RESIZE_ALGO_BILINEAR, // stretch to target resolution
-    RESIZE_ALGO_BICUBIC, // center-crop when aspect ratio doesn't match
-    RESIZE_ALGO_BICUBIC_PILLOW,
+    RESIZE_ALGO_BILINEAR,
+    RESIZE_ALGO_BICUBIC,
    RESIZE_ALGO_LANCZOS,
 };

@@ -73,7 +73,7 @@ struct clip_hparams {
    int32_t preproc_max_tiles = 0;
    int32_t preproc_tile_size = 0; // local tile size (deepseek-ocr)
    resize_algo image_resize_algo_rf = RESIZE_ALGO_BICUBIC;
-    resize_algo image_resize_algo_ov = RESIZE_ALGO_BILINEAR;
+    resize_algo image_resize_algo_ov = RESIZE_ALGO_BICUBIC;
    pad_style image_pad_rf = PAD_CEIL;  // padding style for the refined image (e.g. llava-1.6)
    pad_style image_pad_ov = PAD_NONE;  // padding style for the overview image (e.g. llava-1.6)
    std::array<uint8_t, 3> image_pad_color_rf = {0, 0, 0}; // padding color for refined image
@@ -93,6 +93,7 @@ struct clip_hparams {

    float eps = 1e-6;
    float rope_theta = 0.0;
+    int32_t n_expert_used = 0;
    std::vector<int32_t> feature_layers;
    int32_t attn_window_size = 0;
    int32_t n_wa_pattern = 0;
@@ -259,6 +260,13 @@ struct clip_layer {
    ggml_tensor * ff_down_w = nullptr;
    ggml_tensor * ff_down_b = nullptr;

+    // MoE FFN (dots3note vision pyramid blocks)
+    ggml_tensor * ff_gate_inp_w  = nullptr;
+    ggml_tensor * ff_gate_exps_w = nullptr;
+    ggml_tensor * ff_up_exps_w   = nullptr;
+    ggml_tensor * ff_down_exps_w = nullptr;
+    ggml_tensor * ff_exp_probs_b = nullptr;
+
    // layernorm 2 (or pre-FFN norm)
    ggml_tensor * ln_2_w = nullptr;
    ggml_tensor * ln_2_b = nullptr;
@@ -514,11 +514,13 @@ ggml_tensor * clip_graph::build_vit(
        cb(cur, "ffn_inp_normed", il);

        // ffn
-        cur = build_ffn(cur,
-            layer.ff_up_w, layer.ff_up_b,
-            layer.ff_gate_w, layer.ff_gate_b,
-            layer.ff_down_w, layer.ff_down_b,
-            ffn_t, il);
+        cur = layer.ff_gate_exps_w
+            ? build_moe_ffn(cur, layer, ffn_t, il)
+            : build_ffn(cur,
+                layer.ff_up_w, layer.ff_up_b,
+                layer.ff_gate_w, layer.ff_gate_b,
+                layer.ff_down_w, layer.ff_down_b,
+                ffn_t, il);

        cb(cur, "ffn_out", il);

@@ -699,6 +701,50 @@ ggml_tensor * clip_graph::build_ffn(
    return cur;
 }

+// MoE FFN with sigmoid router and normalized top-k weights (dots3note vision)
+// the router runs in fp32; exp_probs_b only affects expert selection, not the weights
+ggml_tensor * clip_graph::build_moe_ffn(ggml_tensor * cur, const clip_layer & layer, ffn_op_type type_op, int il) const {
+    const int64_t n_tokens      = cur->ne[1];
+    const int64_t n_expert      = layer.ff_gate_exps_w->ne[2];
+    const int64_t n_expert_used = std::min((int64_t) hparams.n_expert_used, n_expert);
+    GGML_ASSERT(n_expert_used > 0);
+    GGML_ASSERT(type_op == FFN_SILU);
+
+    ggml_tensor * probs = ggml_sigmoid(ctx0, build_mm(layer.ff_gate_inp_w, cur)); // [n_expert, n_tokens]
+    cb(probs, "ffn_moe_probs", il);
+
+    ggml_tensor * sel = layer.ff_exp_probs_b
+        ? ggml_add(ctx0, probs, layer.ff_exp_probs_b)
+        : probs;
+    ggml_tensor * selected = ggml_top_k(ctx0, sel, n_expert_used); // [n_expert_used, n_tokens]
+
+    ggml_tensor * weights = ggml_get_rows(ctx0,
+        ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected);
+    weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens);
+    weights = ggml_div(ctx0, weights, ggml_sum_rows(ctx0, weights));
+    weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens);
+    cb(weights, "ffn_moe_weights", il);
+
+    cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], 1, n_tokens);
+    ggml_tensor * gate = ggml_mul_mat_id(ctx0, layer.ff_gate_exps_w, cur, selected); // [n_ff, n_expert_used, n_tokens]
+    ggml_tensor * up   = ggml_mul_mat_id(ctx0, layer.ff_up_exps_w,   cur, selected);
+    cur = ggml_mul(ctx0, ggml_silu(ctx0, gate), up);
+    cur = ggml_mul_mat_id(ctx0, layer.ff_down_exps_w, cur, selected); // [n_embd, n_expert_used, n_tokens]
+    cur = ggml_mul(ctx0, cur, weights);
+
+    // sum over the selected experts
+    ggml_tensor * out = nullptr;
+    for (int64_t i = 0; i < n_expert_used; i++) {
+        ggml_tensor * v = ggml_view_2d(ctx0, cur, cur->ne[0], n_tokens, cur->nb[2], i * cur->nb[1]);
+        out = out ? ggml_add(ctx0, out, v) : v;
+    }
+    if (n_expert_used == 1) {
+        out = ggml_cont(ctx0, out);
+    }
+    cb(out, "ffn_moe_out", il);
+    return out;
+}
+
 ggml_tensor * clip_graph::build_attn(
        ggml_tensor * wo,
        ggml_tensor * wo_b,
@@ -773,8 +819,6 @@ ggml_tensor * clip_graph::build_attn(
 }

 // implementation of the 2D RoPE without adding a new op in ggml
-// this is not efficient (use double the memory), but works on all backends
-// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065
 ggml_tensor * clip_graph::build_rope_2d(
    ggml_context * ctx0,
    ggml_tensor * cur,
@@ -783,9 +827,7 @@ ggml_tensor * clip_graph::build_rope_2d(
    const float freq_base,
    const bool interleave_freq
 ) {
-    const int64_t n_dim  = cur->ne[0];
-    const int64_t n_head = cur->ne[1];
-    const int64_t n_pos  = cur->ne[2];
+    const int64_t n_dim = cur->ne[0];

    // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos)
    // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3
@@ -799,46 +841,30 @@ ggml_tensor * clip_graph::build_rope_2d(
                                ? std::pow(freq_base, (float)-2/n_dim)
                                : 1.0;

-    // first half
-    ggml_tensor * first;
-    {
-        first = ggml_view_3d(ctx0, cur,
-            n_dim/2, n_head, n_pos,
-            cur->nb[1],
-            cur->nb[2],
-            0);
-        first = ggml_rope_ext(
-            ctx0,
-            first,
-            pos_a,      // positions
-            nullptr,    // freq factors
-            n_dim/2,    // n_dims
-            0, 0, freq_base,
-            1.0f, 0.0f, 1.0f, 0.0f, 0.0f
-        );
-    }
+    // first half, dims [0, n_dim/2)
+    cur = ggml_rope_ext(
+        ctx0,
+        cur,
+        pos_a,      // positions
+        nullptr,    // freq factors
+        n_dim/2,    // n_dims
+        0, 0, freq_base,
+        1.0f, 0.0f, 1.0f, 0.0f, 0.0f
+    );

-    // second half
-    ggml_tensor * second;
-    {
-        second = ggml_view_3d(ctx0, cur,
-            n_dim/2, n_head, n_pos,
-            cur->nb[1],
-            cur->nb[2],
-            n_dim/2 * ggml_element_size(cur));
-        second = ggml_rope_ext(
-            ctx0,
-            second,
-            pos_b,      // positions
-            nullptr,    // freq factors
-            n_dim/2,    // n_dims
-            0, 0, freq_base,
-            freq_scale_odd,
-            0.0f, 1.0f, 0.0f, 0.0f
-        );
-    }
+    // second half, dims [n_dim/2, n_dim)
+    cur = ggml_rope_ext(
+        ctx0,
+        cur,
+        pos_b,      // positions
+        nullptr,    // freq factors
+        n_dim/2,    // n_dims
+        0, 0, freq_base,
+        freq_scale_odd,
+        0.0f, 1.0f, 0.0f, 0.0f
+    );
+    cur = ggml_rope_set_offset(cur, n_dim/2);

-    cur = ggml_concat(ctx0, first, second, 0);
    return cur;
 }

@@ -933,9 +959,14 @@ static std::unique_ptr<clip_graph> clip_get_graph_builder(clip_ctx * ctx, const
                builder = std::make_unique<clip_graph_pixtral>(ctx, img);
            } break;
        case PROJECTOR_TYPE_DOTS_OCR:
+        case PROJECTOR_TYPE_DOTS3NOTE_V: // same ViT + merger; pyramid MoE is handled by build_vit
            {
                builder = std::make_unique<clip_graph_dotsocr>(ctx, img);
            } break;
+        case PROJECTOR_TYPE_DOTS3NOTE_A:
+            {
+                builder = std::make_unique<clip_graph_dots3note_a>(ctx, img);
+            } break;
        case PROJECTOR_TYPE_QWEN2VL:
        case PROJECTOR_TYPE_QWEN25VL:
            {
@@ -1389,20 +1420,18 @@ struct clip_model_loader {
                        hparams.image_pad_color     = {122, 116, 104};
                        if (!hparams.image_res_candidates.empty()) {
                            hparams.image_resize_pad  = PAD_CEIL;
-                            hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                            hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        } else {
                            // llava-1.6 default params
                            hparams.image_pad_ov         = PAD_NONE;
                            hparams.image_pad_rf         = PAD_CEIL;
                            hparams.image_pad_color_rf   = {122, 116, 104};
-                            hparams.image_resize_algo_rf = RESIZE_ALGO_BICUBIC;
-                            hparams.image_resize_algo_ov = RESIZE_ALGO_BILINEAR;
                        }
                    } break;
                case PROJECTOR_TYPE_GLM_EDGE:
                    {
                        hparams.image_resize_pad  = PAD_CEIL;
-                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                    } break;
                case PROJECTOR_TYPE_MINICPMV:
                    {
@@ -1459,6 +1488,7 @@ struct clip_model_loader {
                case PROJECTOR_TYPE_IDEFICS3:
                    {
                        // use default llava-uhd preprocessing params
+                        hparams.image_resize_algo = RESIZE_ALGO_LANCZOS;
                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                        get_u32(KEY_PREPROC_IMAGE_SIZE, hparams.image_longest_edge, false);
                        hparams.set_limit_image_tokens();
@@ -1485,7 +1515,7 @@ struct clip_model_loader {
                        // ref: https://huggingface.co/mistral-community/pixtral-12b/blob/main/preprocessor_config.json
                        // TODO: verify the image_min_tokens
                        hparams.n_merge = 1; // the original pixtral does not use patch merging
-                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        hparams.rope_theta = 10000.0f;
                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
                        hparams.set_limit_image_tokens(8, 1024);
@@ -1510,9 +1540,28 @@ struct clip_model_loader {
                        get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels);
                        hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup
                    } break;
+                case PROJECTOR_TYPE_DOTS3NOTE_V:
+                    {
+                        hparams.rope_theta = 10000.0f;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
+                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge);
+                        get_u32(KEY_IMAGE_MIN_PIXELS, hparams.image_min_pixels);
+                        get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels);
+                        get_u32(KEY_VISION_N_EXPERT_USED, hparams.n_expert_used);
+                        hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup
+                    } break;
+                case PROJECTOR_TYPE_DOTS3NOTE_A:
+                    {
+                        hparams.rope_theta = 10000.0f;
+                        hparams.audio_chunk_len   = 60; // in seconds
+                        hparams.audio_sample_rate = 16000;
+                        hparams.audio_n_fft       = 400;
+                        hparams.audio_window_len  = 400;
+                        hparams.audio_hop_len     = 160;
+                    } break;
                case PROJECTOR_TYPE_KIMIVL:
                    {
-                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        hparams.rope_theta = 10000.0f;
                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                        // TODO: check kimivl preprocessor for exact values
@@ -1551,7 +1600,7 @@ struct clip_model_loader {
                    {
                        hparams.rope_theta = 100.0f;
                        hparams.n_merge = 3; // pooling_kernel_size
-                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                        if (model.proj_type == PROJECTOR_TYPE_GEMMA4UV) {
                            // for "unified" variant, we directly use a bigger patch size, because the "token merging" is done directly on conv layer
@@ -1568,6 +1617,7 @@ struct clip_model_loader {
                        // Gemma3n uses MobileNetV5 which produces 256 tokens (16x16)
                        // Similar configuration to Gemma3
                        hparams.n_merge = 1;  // MobileNetV5 handles resizing internally
+                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                    } break;
                case PROJECTOR_TYPE_QWEN2VL:
@@ -1575,7 +1625,7 @@ struct clip_model_loader {
                case PROJECTOR_TYPE_QWEN3VL:
                    {
                        hparams.n_merge = 2; // default value for Qwen 2 and 2.5
-                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
                        get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern, model.proj_type == PROJECTOR_TYPE_QWEN25VL); // only 2.5 requires it
                        // ref: https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/preprocessor_config.json
@@ -1591,7 +1641,7 @@ struct clip_model_loader {
                case PROJECTOR_TYPE_MINIMAX_M3:
                    {
                        hparams.n_merge = 2; // spatial_merge_size
-                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        hparams.image_resize_pad  = PAD_NONE;
                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
                        // n_merge is used as a divisor in clip_image_batch_encode
@@ -1616,7 +1666,7 @@ struct clip_model_loader {
                case PROJECTOR_TYPE_MIMOVL:
                    {
                        hparams.n_merge = 2; // spatial_merge_size
-                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
                        get_u32(string_format(KEY_N_HEAD_KV, "vision"), hparams.n_head_kv);
                        // 1D banded sliding-window radius (visual_token_window_size); required
@@ -1663,15 +1713,15 @@ struct clip_model_loader {
                        log_ffn_op = "gelu_erf";
                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;

-                        // reka model performs better when using resize_bicubic, which stretches
-                        // the image to fit fixed square size
+                        // reka model performs better when the image is stretched to fit
+                        // fixed square size (no padding)
                        hparams.image_resize_pad = PAD_NONE;
                    } break;
                case PROJECTOR_TYPE_GLM4V:
                    {
                        hparams.rope_theta = 10000.0f;
                        hparams.n_merge = 2; // default value for GLM4-V
-                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
                        hparams.set_limit_image_tokens(8, 4096);
                        hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup
@@ -1679,6 +1729,7 @@ struct clip_model_loader {
                case PROJECTOR_TYPE_LLAMA4:
                    {
                        hparams.rope_theta = 10000.0f;
+                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                        set_llava_uhd_res_candidates(model, 3);
                    } break;
@@ -1790,7 +1841,7 @@ struct clip_model_loader {
                case PROJECTOR_TYPE_PADDLEOCR:
                    {
                        hparams.n_merge = 2;
-                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        get_u32(KEY_IMAGE_MIN_PIXELS, hparams.image_min_pixels);
                        get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels);

@@ -1802,7 +1853,7 @@ struct clip_model_loader {
                        hparams.patch_size = 16;
                        hparams.image_size = 1024;
                        hparams.warmup_image_size = 1024;
-                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        hparams.image_pad_color = {127, 127, 127};

                        get_u32(KEY_SAM_N_BLOCK, hparams.sam_n_layer, true);
@@ -1832,7 +1883,7 @@ struct clip_model_loader {
                case PROJECTOR_TYPE_HUNYUANVL:
                    {
                        hparams.n_merge = 2;
-                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
+                        hparams.image_resize_algo = RESIZE_ALGO_LANCZOS;
                        hparams.image_resize_pad = PAD_NONE;
                        hparams.ffn_op = FFN_GELU;
                        hparams.set_limit_image_tokens(256, 16384);
@@ -1905,12 +1956,12 @@ struct clip_model_loader {
                case PROJECTOR_TYPE_JANUS_PRO:
                    {
                        hparams.image_pad_color   = {127, 127, 127};
-                        hparams.image_resize_algo = RESIZE_ALGO_BILINEAR;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                    } break;
                case PROJECTOR_TYPE_GRANITE4_VISION:
                    {
                        // SigLIP tower.
-                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
                        hparams.image_resize_pad = PAD_CEIL;

                        // NOTE: feature_layers loaded in common path as optional
@@ -2190,12 +2241,20 @@ struct clip_model_loader {
            layer.ln_1_b = get_tensor(string_format(TN_LN_1,        prefix, il, "bias"), false);
            layer.ln_2_b = get_tensor(string_format(TN_LN_2,        prefix, il, "bias"), false);

+            // MoE ffn (dots3note vision pyramid blocks); replaces the dense ffn when present
+            layer.ff_gate_inp_w  = get_tensor(string_format(TN_FFN_GATE_INP,  prefix, il, "weight"), false);
+            layer.ff_gate_exps_w = get_tensor(string_format(TN_FFN_GATE_EXPS, prefix, il, "weight"), false);
+            layer.ff_up_exps_w   = get_tensor(string_format(TN_FFN_UP_EXPS,   prefix, il, "weight"), false);
+            layer.ff_down_exps_w = get_tensor(string_format(TN_FFN_DOWN_EXPS, prefix, il, "weight"), false);
+            layer.ff_exp_probs_b = get_tensor(string_format(TN_FFN_EXP_PROBS_B, prefix, il, "weight"), false);
+            const bool is_moe = layer.ff_gate_exps_w != nullptr;
+
            // ffn
-            layer.ff_up_w   = get_tensor(string_format(TN_FFN_UP,   prefix, il, "weight"));
+            layer.ff_up_w   = get_tensor(string_format(TN_FFN_UP,   prefix, il, "weight"), !is_moe);
            layer.ff_up_b   = get_tensor(string_format(TN_FFN_UP,   prefix, il, "bias"),   false);
            layer.ff_gate_w = get_tensor(string_format(TN_FFN_GATE, prefix, il, "weight"), false);
            layer.ff_gate_b = get_tensor(string_format(TN_FFN_GATE, prefix, il, "bias"),   false);
-            layer.ff_down_w = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "weight"));
+            layer.ff_down_w = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "weight"), !is_moe);
            layer.ff_down_b = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "bias"),   false);

            // mimovl per-head attention sink bias
@@ -2677,6 +2736,7 @@ struct clip_model_loader {
                    model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"), false);
                } break;
            case PROJECTOR_TYPE_DOTS_OCR:
+            case PROJECTOR_TYPE_DOTS3NOTE_V:
                {
                    model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
                    model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
@@ -2687,6 +2747,23 @@ struct clip_model_loader {
                    // post_trunk_norm: applied after all ViT blocks, before the merger
                    model.post_ln_w = get_tensor(string_format(TN_MM_POST_NORM, "weight"));
                } break;
+            case PROJECTOR_TYPE_DOTS3NOTE_A:
+                {
+                    model.conv2d_1_w = get_tensor(string_format(TN_CONV2D, 1, "weight"));
+                    model.conv2d_1_b = get_tensor(string_format(TN_CONV2D, 1, "bias"));
+                    model.conv2d_2_w = get_tensor(string_format(TN_CONV2D, 2, "weight"));
+                    model.conv2d_2_b = get_tensor(string_format(TN_CONV2D, 2, "bias"));
+                    model.conv2d_3_w = get_tensor(string_format(TN_CONV2D, 3, "weight"));
+                    model.conv2d_3_b = get_tensor(string_format(TN_CONV2D, 3, "bias"));
+                    model.conv_out_w = get_tensor(string_format(TN_CONV_OUT, "weight")); // no bias
+                    // adapter: LayerNorm -> Linear -> GELU -> Linear
+                    model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight"));
+                    model.mm_norm_pre_b = get_tensor(string_format(TN_MM_NORM_PRE, "bias"));
+                    model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
+                    model.mm_1_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "bias"));
+                    model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 3, "weight"));
+                    model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 3, "bias"));
+                } break;
            case PROJECTOR_TYPE_ULTRAVOX:
                {
                    model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
@@ -4075,12 +4152,18 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
            } break;
        case PROJECTOR_TYPE_PADDLEOCR:
        case PROJECTOR_TYPE_DOTS_OCR:
+        case PROJECTOR_TYPE_DOTS3NOTE_V:
            {
                // dynamic size
                int n_merge = ctx->model.hparams.n_merge;
                int stride = n_merge * n_merge;
                n_patches = CLIP_ALIGN(n_patches, stride) / stride;
            } break;
+        case PROJECTOR_TYPE_DOTS3NOTE_A:
+            {
+                // 3x stride-2 conv2d over mel frames
+                n_patches = (img->nx() + 7) / 8;
+            } break;
        case PROJECTOR_TYPE_PIXTRAL:
        case PROJECTOR_TYPE_LIGHTONOCR:
            {
@@ -4727,6 +4810,7 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
                set_input_i32("minimax_pos_w", pos_w);
            } break;
        case PROJECTOR_TYPE_DOTS_OCR:
+        case PROJECTOR_TYPE_DOTS3NOTE_V:
            {
                const int pw = image_size_width / patch_size;
                const int ph = image_size_height / patch_size;
@@ -5217,6 +5301,16 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
                }
                set_input_i32("pos_w", pos_data);
            } break;
+        case PROJECTOR_TYPE_DOTS3NOTE_A:
+            {
+                GGML_ASSERT(imgs.entries.size() == 1);
+                const int n_pos = (imgs.entries.front().nx() + 7) / 8; // 3x stride-2 conv2d
+                std::vector<int32_t> positions(n_pos);
+                for (int i = 0; i < n_pos; i++) {
+                    positions[i] = i;
+                }
+                set_input_i32("positions", positions);
+            } break;
        case PROJECTOR_TYPE_GEMMA4A:
            {
                GGML_ASSERT(imgs.entries.size() == 1);
@@ -5713,6 +5807,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
        case PROJECTOR_TYPE_PIXTRAL:
        case PROJECTOR_TYPE_LIGHTONOCR:
        case PROJECTOR_TYPE_DOTS_OCR:
+        case PROJECTOR_TYPE_DOTS3NOTE_V:
+        case PROJECTOR_TYPE_DOTS3NOTE_A:
            return ctx->model.mm_2_w->ne[1];
        case PROJECTOR_TYPE_MLP_NORM:
            return ctx->model.mm_3_b->ne[0];
@@ -0,0 +1,61 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_dots3note_a::build() {
+    // inp_raw: [n_frames, n_mel, 1], one 60s chunk, mel frames not padded
+    // the reference impl zero-masks conv inputs beyond the valid length at each stage;
+    // running on exactly the valid frames with the convs' zero padding is equivalent
+    ggml_tensor * inp = build_inp_raw(1);
+    GGML_ASSERT(inp->type == GGML_TYPE_F32);
+
+    // 3x conv2d (k=3, s=2, p=1) + gelu
+    {
+        auto conv_block = [&](ggml_tensor * x, ggml_tensor * w, ggml_tensor * b) {
+            x = ggml_conv_2d(ctx0, w, x, 2, 2, 1, 1, 1, 1);
+            x = ggml_add(ctx0, x, ggml_reshape_4d(ctx0, b, 1, 1, x->ne[2], 1));
+            return ggml_gelu_erf(ctx0, x);
+        };
+
+        inp = conv_block(inp, model.conv2d_1_w, model.conv2d_1_b);
+        inp = conv_block(inp, model.conv2d_2_w, model.conv2d_2_b);
+        inp = conv_block(inp, model.conv2d_3_w, model.conv2d_3_b);
+        // inp: [OW=n_frames/8, OH=n_mel/8, OC=480, 1]
+        cb(inp, "after_conv_stem", -1);
+    }
+
+    // [OW, OH, OC, 1] -> [OH*OC, OW], feature index f + OH*c (matches the reference permute+reshape)
+    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 2, 0, 1, 3));
+    inp = ggml_reshape_2d(ctx0, inp, inp->ne[0] * inp->ne[1], inp->ne[2]);
+
+    // project to d_model (no bias)
+    inp = ggml_mul_mat(ctx0, model.conv_out_w, inp);
+    cb(inp, "after_conv_out", -1);
+
+    const int64_t n_pos = inp->ne[1];
+
+    ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
+    ggml_set_name(positions, "positions");
+    ggml_set_input(positions);
+
+    // partial rotary: first half of each head, NEOX style
+    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
+        return ggml_rope_ext(ctx0, cur, positions, nullptr, d_head/2,
+                             GGML_ROPE_TYPE_NEOX, 0, hparams.rope_theta, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
+    };
+
+    ggml_tensor * cur = build_vit(inp, n_pos,
+        NORM_TYPE_RMS, hparams.ffn_op,
+        nullptr, add_pos);
+    cb(cur, "after_transformer", -1);
+
+    // adapter: LayerNorm -> Linear -> GELU -> Linear
+    cur = build_norm(cur, model.mm_norm_pre_w, model.mm_norm_pre_b, NORM_TYPE_NORMAL, 1e-5, -1);
+    cur = build_ffn(cur,
+        model.mm_1_w, model.mm_1_b,
+        nullptr, nullptr,
+        model.mm_2_w, model.mm_2_b,
+        FFN_GELU_ERF, -1);
+    cb(cur, "projected", -1);
+
+    ggml_build_forward_expand(gf, cur);
+    return gf;
+}
@@ -44,51 +44,31 @@ ggml_cgraph * clip_graph_gemma4v::build() {

    // similar to build_rope_2d, but use neox ordering
    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
-        const int64_t n_dim  = cur->ne[0];
-        const int64_t n_head = cur->ne[1];
-        const int64_t n_pos  = cur->ne[2];
+        const int64_t n_dim = cur->ne[0];

-        // first half
-        ggml_tensor * first;
-        {
-            first = ggml_view_4d(ctx0, cur,
-                n_dim/2, n_head, n_pos, n_batch,
-                cur->nb[1],
-                cur->nb[2],
-                cur->nb[3],
-                0);
-            first = ggml_rope_ext(
-                ctx0,
-                first,
-                pos_x,      // positions
-                nullptr,    // freq factors
-                n_dim/2,    // n_dims
-                GGML_ROPE_TYPE_NEOX, 0, hparams.rope_theta,
-                1.0f, 0.0f, 1.0f, 0.0f, 0.0f
-            );
-        }
+        // first half, dims [0, n_dim/2)
+        cur = ggml_rope_ext(
+            ctx0,
+            cur,
+            pos_x,      // positions
+            nullptr,    // freq factors
+            n_dim/2,    // n_dims
+            GGML_ROPE_TYPE_NEOX, 0, hparams.rope_theta,
+            1.0f, 0.0f, 1.0f, 0.0f, 0.0f
+        );

-        // second half
-        ggml_tensor * second;
-        {
-            second = ggml_view_4d(ctx0, cur,
-                n_dim/2, n_head, n_pos, n_batch,
-                cur->nb[1],
-                cur->nb[2],
-                cur->nb[3],
-                n_dim/2 * ggml_element_size(cur));
-            second = ggml_rope_ext(
-                ctx0,
-                second,
-                pos_y,      // positions
-                nullptr,    // freq factors
-                n_dim/2,    // n_dims
-                GGML_ROPE_TYPE_NEOX, 0, hparams.rope_theta,
-                1.0f, 0.0f, 1.0f, 0.0f, 0.0f
-            );
-        }
+        // second half, dims [n_dim/2, n_dim)
+        cur = ggml_rope_ext(
+            ctx0,
+            cur,
+            pos_y,      // positions
+            nullptr,    // freq factors
+            n_dim/2,    // n_dims
+            GGML_ROPE_TYPE_NEOX, 0, hparams.rope_theta,
+            1.0f, 0.0f, 1.0f, 0.0f, 0.0f
+        );
+        cur = ggml_rope_set_offset(cur, n_dim/2);

-        cur = ggml_concat(ctx0, first, second, 0);
        return cur;
    };

@@ -2,30 +2,22 @@

 ggml_tensor * clip_graph_minimax_m3::apply_rope(
        ggml_tensor * x, ggml_tensor * pos_h, ggml_tensor * pos_w) {
-    const int64_t Hn  = x->ne[1];
-    const int64_t P   = x->ne[2];
-    const size_t  es  = ggml_element_size(x);
-    const int     dh  = (int) x->ne[0];
-    const int     axd = 2 * ((2 * (dh / 2) / 3) / 2);
+    const int dh  = (int) x->ne[0];
+    const int axd = 2 * ((2 * (dh / 2) / 3) / 2);

-    GGML_ASSERT(x->nb[0] == es);
    GGML_ASSERT(3 * axd <= dh);

    const float th  = hparams.rope_theta;

    // layout of x is [t, h, w, pad]
    // t is unrotated, h and w are rotated, pad is unrotated
-    // note: everything from n_dims onward untouched, so w and pad are rotated in one call.
-    auto sl = [&](int off, int n) {
-        return ggml_cont(ctx0, ggml_view_3d(ctx0, x, n, Hn, P, x->nb[1], x->nb[2], (size_t) off * es));
-    };
-    ggml_tensor * t = sl(0,       axd);
-    ggml_tensor * h = sl(axd,     axd);
-    ggml_tensor * w = sl(2 * axd, dh - 2 * axd); // w + pad
+    x = ggml_rope_ext(ctx0, x, pos_h, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
+    x = ggml_rope_set_offset(x, axd);

-    h = ggml_rope_ext(ctx0, h, pos_h, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
-    w = ggml_rope_ext(ctx0, w, pos_w, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
-    return ggml_concat(ctx0, ggml_concat(ctx0, t, h, 0), w, 0);
+    x = ggml_rope_ext(ctx0, x, pos_w, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
+    x = ggml_rope_set_offset(x, 2 * axd);
+
+    return x;
 }

 ggml_cgraph * clip_graph_minimax_m3::build() {
@@ -119,6 +119,11 @@ struct clip_graph_dotsocr : clip_graph {
    ggml_cgraph * build() override;
 };

+struct clip_graph_dots3note_a : clip_graph {
+    clip_graph_dots3note_a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
 struct clip_graph_cogvlm : clip_graph {
    clip_graph_cogvlm(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
    ggml_cgraph * build() override;
@@ -723,6 +723,100 @@ bool mtmd_audio_preprocessor_qwen3a::preprocess(const float *                 sa
    return true;
 }

+//
+// mtmd_audio_preprocessor_dots3note
+//
+// Matches Dots3NoteFeatureExtractor: the waveform is split into 60s chunks and each chunk gets
+// its own whisper-style log-mel (center=True, log10 + (max-8)/4). Only sample_length//hop frames
+// per chunk are valid; the reference masks everything beyond them, so we emit exactly that many.
+//
+
+void mtmd_audio_preprocessor_dots3note::initialize() {
+    cache.fill_sin_cos_table(hparams.audio_n_fft);
+    cache.fill_hann_window(hparams.audio_window_len, true);
+    cache.fill_mel_filterbank_matrix(hparams.n_mel_bins, hparams.audio_n_fft, hparams.audio_sample_rate);
+}
+
+bool mtmd_audio_preprocessor_dots3note::preprocess(const float *                 samples,
+                                                   size_t                        n_samples,
+                                                   std::vector<mtmd_audio_mel> & output) {
+    if (n_samples == 0) {
+        return false;
+    }
+
+    GGML_ASSERT(!cache.sin_vals.empty());
+    GGML_ASSERT(!cache.cos_vals.empty());
+    GGML_ASSERT(!cache.filters.data.empty());
+
+    const int    pad           = hparams.audio_n_fft / 2; // center=True padding
+    const int    hop           = hparams.audio_hop_len;
+    const size_t chunk_samples = (size_t) hparams.audio_chunk_len * hparams.audio_sample_rate;
+
+    for (size_t start = 0; start < n_samples; start += chunk_samples) {
+        const size_t n_chunk = std::min(chunk_samples, n_samples - start);
+        const float * chunk  = samples + start;
+
+        const int64_t n_valid = n_chunk / hop;
+        if (n_valid == 0) {
+            continue; // sub-hop tail, contributes no frames
+        }
+
+        // reflect-pad the start; the reference zero-pads partial chunks to 60s before the STFT,
+        // so a partial chunk sees zeros past its end while a full chunk reflects its own tail
+        std::vector<float> padded(n_chunk + 2 * pad, 0.0f);
+        for (int i = 0; i < pad; i++) {
+            int src = pad - i;
+            padded[i] = (src < (int) n_chunk) ? chunk[src] : 0.0f;
+        }
+        std::copy(chunk, chunk + n_chunk, padded.begin() + pad);
+        if (n_chunk == chunk_samples) {
+            for (int i = 0; i < pad; i++) {
+                int src = (int) n_chunk - 2 - i;
+                padded[n_chunk + pad + i] = (src >= 0) ? chunk[src] : 0.0f;
+            }
+        }
+
+        filter_params params;
+        params.n_mel            = hparams.n_mel_bins;
+        params.n_fft_bins       = 1 + (hparams.audio_n_fft / 2);
+        params.hann_window_size = hparams.audio_window_len;
+        params.hop_length       = hop;
+        params.sample_rate      = hparams.audio_sample_rate;
+        params.no_padding       = true; // padding already applied above
+        params.use_natural_log  = false;
+
+        mtmd_audio_mel mel_full;
+        if (!log_mel_spectrogram(padded.data(), (int) padded.size(), 4, params, cache, mel_full)) {
+            return false;
+        }
+        GGML_ASSERT(mel_full.n_len >= n_valid);
+
+        // per-chunk whisper-style normalization, then keep only the valid frames
+        mtmd_audio_mel out;
+        out.n_mel     = mel_full.n_mel;
+        out.n_len     = n_valid;
+        out.n_len_org = n_valid;
+        out.data.resize((size_t) out.n_mel * (size_t) out.n_len);
+
+        double mmax = -1e20;
+        for (int64_t m = 0; m < out.n_mel; m++) {
+            for (int64_t t = 0; t < n_valid; t++) {
+                mmax = std::max(mmax, (double) mel_full.data[(size_t) m * mel_full.n_len + t]);
+            }
+        }
+        mmax -= 8.0;
+        for (int64_t m = 0; m < out.n_mel; m++) {
+            for (int64_t t = 0; t < n_valid; t++) {
+                const double v = std::max((double) mel_full.data[(size_t) m * mel_full.n_len + t], mmax);
+                out.data[(size_t) m * n_valid + t] = (float) ((v + 4.0) / 4.0);
+            }
+        }
+
+        output.push_back(std::move(out));
+    }
+    return !output.empty();
+}
+
 //
 // mtmd_audio_preprocessor_mimo_audio
 //
@@ -111,6 +111,15 @@ struct mtmd_audio_preprocessor_qwen3a : mtmd_audio_preprocessor {
    mtmd_audio_cache cache;
 };

+struct mtmd_audio_preprocessor_dots3note : mtmd_audio_preprocessor {
+    mtmd_audio_preprocessor_dots3note(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
+    void initialize() override;
+    bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
+
+  private:
+    mtmd_audio_cache cache;
+};
+
 struct mtmd_audio_preprocessor_mimo_audio : mtmd_audio_preprocessor {
    mtmd_audio_preprocessor_mimo_audio(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
    void initialize() override;
@@ -58,22 +58,7 @@ struct img_tool {

        if (padding == PAD_NONE) {
            // direct resize
-            switch (algo) {
-                case RESIZE_ALGO_BILINEAR:
-                    resize_bilinear(src, dst, target_resolution.width, target_resolution.height);
-                    break;
-                case RESIZE_ALGO_BICUBIC:
-                    resize_bicubic(src, dst, target_resolution.width, target_resolution.height);
-                    break;
-                case RESIZE_ALGO_BICUBIC_PILLOW:
-                    resize_bicubic_pillow(src, dst, target_resolution.width, target_resolution.height);
-                    break;
-                case RESIZE_ALGO_LANCZOS:
-                    resize_lanczos_pillow(src, dst, target_resolution.width, target_resolution.height);
-                    break;
-                default:
-                    throw std::runtime_error("Unsupported resize algorithm");
-            }
+            resize_pillow(src, dst, target_resolution.width, target_resolution.height, algo);
        } else {
            // resize with padding
            clip_image_u8 resized_image;
@@ -90,22 +75,7 @@ struct img_tool {
                new_height = std::min(static_cast<int>(std::ceil(src.get_size().height * scale)), target_resolution.height);
            }

-            switch (algo) {
-                case RESIZE_ALGO_BILINEAR:
-                    resize_bilinear(src, resized_image, new_width, new_height);
-                    break;
-                case RESIZE_ALGO_BICUBIC:
-                    resize_bicubic(src, resized_image, new_width, new_height);
-                    break;
-                case RESIZE_ALGO_BICUBIC_PILLOW:
-                    resize_bicubic_pillow(src, resized_image, new_width, new_height);
-                    break;
-                case RESIZE_ALGO_LANCZOS:
-                    resize_lanczos_pillow(src, resized_image, new_width, new_height);
-                    break;
-                default:
-                    throw std::runtime_error("Unsupported resize algorithm");
-            }
+            resize_pillow(src, resized_image, new_width, new_height, algo);

            // fill dst with pad_color
            fill(dst, pad_color);
@@ -224,152 +194,37 @@ struct img_tool {
    }

 private:
-    // Bilinear resize function
-    static void resize_bilinear(const clip_image_u8 & src, clip_image_u8 & dst, int target_width, int target_height) {
-        const auto src_size = src.get_size();
-        if (src_size.width == 0 || src_size.height == 0) { dst.set_size({0, 0}, false); return; }
-        if (target_width  <= 0) target_width  = 1;
-        if (target_height <= 0) target_height = 1;
-
-        dst.set_size({target_width, target_height}, false);
-
-        if (src.is_placeholder()) {
-            // no-op for placeholder image, just set the size and return
-            return;
-        }
-
-        float x_ratio = target_width  > 1 ? static_cast<float>(src_size.width  - 1) / (target_width  - 1) : 0.0f;
-        float y_ratio = target_height > 1 ? static_cast<float>(src_size.height - 1) / (target_height - 1) : 0.0f;
-
-        for (int y = 0; y < target_height; ++y) {
-            for (int x = 0; x < target_width; ++x) {
-                float px = x * x_ratio;
-                float py = y * y_ratio;
-
-                int x0 = std::min(static_cast<int>(px), src_size.width  - 1);
-                int y0 = std::min(static_cast<int>(py), src_size.height - 1);
-                int x1 = std::min(x0 + 1, src_size.width  - 1);
-                int y1 = std::min(y0 + 1, src_size.height - 1);
-
-                float xf = px - x0;
-                float yf = py - y0;
-
-                const auto p00 = src.get_pixel(x0, y0);
-                const auto p10 = src.get_pixel(x1, y0);
-                const auto p01 = src.get_pixel(x0, y1);
-                const auto p11 = src.get_pixel(x1, y1);
-
-                std::array<uint8_t, 3> pixel;
-                for (int c = 0; c < 3; ++c) {
-                    float top    = lerp(static_cast<float>(p00[c]), static_cast<float>(p10[c]), xf);
-                    float bottom = lerp(static_cast<float>(p01[c]), static_cast<float>(p11[c]), xf);
-                    pixel[c] = static_cast<uint8_t>(lerp(top, bottom, yf));
-                }
-                dst.set_pixel(x, y, pixel);
-            }
-        }
-    }
-
-    // Bicubic resize function
-    // part of image will be cropped if the aspect ratio is different
-    static void resize_bicubic(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
-        const auto img_size = img.get_size();
-        const int nx = img_size.width;
-        const int ny = img_size.height;
-
-        dst.set_size({target_width, target_height}, false);
-
-        if (img.is_placeholder()) {
-            // no-op for placeholder image, just set the size and return
-            return;
-        }
-
-        float Cc;
-        float C[5] = {};
-        float d0, d2, d3, a0, a1, a2, a3;
-        int i, j, k, jj;
-        int x, y;
-        float dx, dy;
-        float tx, ty;
-
-        tx = (float)nx / (float)target_width;
-        ty = (float)ny / (float)target_height;
-
-        // Bicubic interpolation; adapted from ViT.cpp, inspired from :
-        //    -> https://github.com/yglukhov/bicubic-interpolation-image-processing/blob/master/libimage.c#L36
-        //    -> https://en.wikipedia.org/wiki/Bicubic_interpolation
-
-        for (i = 0; i < target_height; i++) {
-            for (j = 0; j < target_width; j++) {
-                x = (int)(tx * j);
-                y = (int)(ty * i);
-
-                dx = tx * j - x;
-                dy = ty * i - y;
-
-                std::array<uint8_t, 3> pixel;
-                for (k = 0; k < 3; k++) {
-                    for (jj = 0; jj <= 3; jj++) {
-                        d0 = img.get_pixel(clip(x - 1, 0, nx - 1), clip(y - 1 + jj, 0, ny - 1))[k] - img.get_pixel(clip(x, 0, nx - 1), clip(y - 1 + jj, 0, ny - 1))[k];
-                        d2 = img.get_pixel(clip(x + 1, 0, nx - 1), clip(y - 1 + jj, 0, ny - 1))[k] - img.get_pixel(clip(x, 0, nx - 1), clip(y - 1 + jj, 0, ny - 1))[k];
-                        d3 = img.get_pixel(clip(x + 2, 0, nx - 1), clip(y - 1 + jj, 0, ny - 1))[k] - img.get_pixel(clip(x, 0, nx - 1), clip(y - 1 + jj, 0, ny - 1))[k];
-                        a0 = img.get_pixel(clip(x, 0, nx - 1), clip(y - 1 + jj, 0, ny - 1))[k];
-
-                        a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
-                        a2 =  1.0 / 2 * d0 +      1.0 / 2 * d2;
-                        a3 = -1.0 / 6 * d0 -      1.0 / 2 * d2 + 1.0 / 6 * d3;
-
-                        C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx;
-
-                        d0 = C[0] - C[1];
-                        d2 = C[2] - C[1];
-                        d3 = C[3] - C[1];
-                        a0 = C[1];
-                        a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
-                        a2 =  1.0 / 2 * d0 +      1.0 / 2 * d2;
-                        a3 = -1.0 / 6 * d0 -      1.0 / 2 * d2 + 1.0 / 6 * d3;
-                        Cc = a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy;
-
-                        const uint8_t Cc2 = std::min(std::max(std::round(Cc), 0.0f), 255.0f);
-                        pixel[k] = Cc2;
-                    }
-                }
-                dst.set_pixel(j, i, pixel);
-            }
-        }
-    }
-
-    // Pillow-compatible separable resampling (Bicubic and Lanczos)
+    // Pillow-compatible separable resampling (Bilinear, Bicubic and Lanczos)
    // Adapted from https://github.com/python-pillow/Pillow/blob/main/src/libImaging/Resample.c
    //
    // Key properties:
    // 1. Separable filtering: horizontal pass followed by vertical pass
    // 2. Pre-computes normalized filter coefficients for each output pixel
    // 3. Fixed-point integer arithmetic (22 fractional bits) for speed and determinism
-    static bool resize_bicubic_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
-        return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/false);
-    }
-
-    // Lanczos-3 (support radius 3), matches Pillow's Image.LANCZOS
-    static bool resize_lanczos_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
-        return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/true);
-    }
-
    static bool resize_pillow(
            const clip_image_u8 & img,
            clip_image_u8 & dst,
            int target_width,
            int target_height,
-            bool use_lanczos) {
+            resize_algo algo) {
        // Fixed-point precision: 22 bits = 32 (int32_t) - 8 (uint8_t pixels) - 2 (headroom for accumulation)
        // This allows encoding fractional weights as integers: weight * 2^22
        const int PRECISION_BITS = 32 - 8 - 2;

-        // Resample filter: Lanczos-3 (support [-3, 3]) or bicubic with a = -0.5 (support [-2, 2])
-        // Note: GGML/PyTorch bicubic uses a = -0.75, Pillow uses a = -0.5
+        // Filter support radius
+        double filter_support;
+        switch (algo) {
+            case RESIZE_ALGO_BILINEAR: filter_support = 1.0; break;
+            case RESIZE_ALGO_BICUBIC:  filter_support = 2.0; break;
+            case RESIZE_ALGO_LANCZOS:  filter_support = 3.0; break;
+            default:
+                throw std::runtime_error("Unsupported resize algorithm");
+        }
+
        // Returns filter weight for distance x from pixel center
-        auto resample_filter = [use_lanczos](double x) -> double {
-            if (use_lanczos) {
+        // Note: for bicubic, Pillow uses a = -0.5 while GGML/PyTorch use a = -0.75
+        auto resample_filter = [algo](double x) -> double {
+            if (algo == RESIZE_ALGO_LANCZOS) {
                if (-3.0 <= x && x < 3.0) {
                    auto sinc = [](double v) {
                        if (v == 0.0) {
@@ -383,10 +238,15 @@ private:
                return 0.0;
            }

-            constexpr double a = -0.5;
            if (x < 0.0) {
                x = -x;
            }
+
+            if (algo == RESIZE_ALGO_BILINEAR) {
+                return x < 1.0 ? 1.0 - x : 0.0;
+            }
+
+            constexpr double a = -0.5;
            if (x < 1.0) {
                return ((a + 2.0) * x - (a + 3.0)) * x * x + 1;
            }
@@ -396,9 +256,6 @@ private:
            return 0.0;  // Zero outside [-2, 2]
        };

-        // Filter support radius: 2 for bicubic, 3 for lanczos
-        const double filter_support = use_lanczos ? 3.0 : 2.0;
-
        // Clipping function for 8-bit values
        auto clip8 = [](int val) -> uint8_t {
            if (val < 0) return 0;
@@ -493,100 +350,92 @@ private:
            const double fxp_scale = std::ldexp(1.0, PRECISION_BITS); // 1.0 * 2^PRECISION_BITS

            for (int i = 0; i < outSize * ksize; i++) {
-                if (use_lanczos) {
-                    // Pillow adds +/- 0.5 then truncates toward zero; std::round would round twice
-                    const double rounded = pre_weights[i] * fxp_scale + (pre_weights[i] < 0 ? -0.5 : 0.5);
-                    weights[i] = static_cast<int32_t>(rounded);
-                    continue;
-                }
-                double tmp_val = pre_weights[i] * fxp_scale;
-                if (pre_weights[i] < 0) {
-                    tmp_val -= 0.5;
-                } else {
-                    tmp_val += 0.5;
-                }
-                tmp_val = std::round(tmp_val);
-                tmp_val = std::clamp(tmp_val,
-                                     static_cast<double>(std::numeric_limits<int32_t>::min()),
-                                     static_cast<double>(std::numeric_limits<int32_t>::max()));
-                weights[i] = static_cast<int32_t>(tmp_val);
+                // Pillow adds +/- 0.5 then truncates toward zero; std::round would round twice
+                const double rounded = pre_weights[i] * fxp_scale + (pre_weights[i] < 0 ? -0.5 : 0.5);
+                weights[i] = static_cast<int32_t>(rounded);
            }

            return ksize;
        };

        // Horizontal resampling pass
-        // Resizes width from imIn to out_nx, preserving height
-        auto resample_horizontal = [&](const clip_image_u8 & imIn, clip_image_u8 & imOut,
+        // Resizes width from src to out_nx, preserving height
+        auto resample_horizontal = [&](const uint8_t * src, int in_nx, int in_ny,
                                       int out_nx,
                                       int ksize, const std::vector<int> & bounds, const std::vector<int32_t> & weights) {
-            const int in_ny = imIn.get_size().height;
-            imOut.set_size({out_nx, in_ny}, false);
+            std::vector<uint8_t> out((size_t) out_nx * in_ny * 3);

            // Process each row independently
            for (int yy = 0; yy < in_ny; yy++) {
+                const uint8_t * src_row = src + (size_t) yy * in_nx * 3;
+                uint8_t * dst_row = out.data() + (size_t) yy * out_nx * 3;
+
                // For each output pixel in this row
                for (int xx = 0; xx < out_nx; xx++) {
-                    // Get the range of input pixels and filter coefficients
-                    int xmin = bounds[xx * 2 + 0];  // First input pixel index
-                    int xcnt = bounds[xx * 2 + 1];  // Number of input pixels
+                    const int xmin = bounds[xx * 2 + 0];  // First input pixel index
+                    const int xcnt = bounds[xx * 2 + 1];  // Number of input pixels
+                    const int32_t * k = &weights[xx * ksize];
+                    const uint8_t * p = src_row + (size_t) xmin * 3;

-                    // Initialize accumulators for RGB channels with rounding bias (0.5 in fixed-point)
+                    // Accumulators for RGB channels, with rounding bias (0.5 in fixed-point)
                    int32_t ss0 = 1 << (PRECISION_BITS - 1);
                    int32_t ss1 = 1 << (PRECISION_BITS - 1);
                    int32_t ss2 = 1 << (PRECISION_BITS - 1);

                    // Convolve: sum weighted input pixels
                    for (int x = 0; x < xcnt; x++) {
-                        const auto src_px = imIn.get_pixel(x + xmin, yy);
-                        ss0 += src_px[0] * weights[xx * ksize + x];  // R channel
-                        ss1 += src_px[1] * weights[xx * ksize + x];  // G channel
-                        ss2 += src_px[2] * weights[xx * ksize + x];  // B channel
+                        ss0 += p[0] * k[x];
+                        ss1 += p[1] * k[x];
+                        ss2 += p[2] * k[x];
+                        p += 3;
                    }

                    // Convert back from fixed-point (divide by 2^PRECISION_BITS) and clamp to [0,255]
-                    imOut.set_pixel(xx, yy, {clip8(ss0 >> PRECISION_BITS),
-                                             clip8(ss1 >> PRECISION_BITS),
-                                             clip8(ss2 >> PRECISION_BITS)});
+                    dst_row[xx * 3 + 0] = clip8(ss0 >> PRECISION_BITS);
+                    dst_row[xx * 3 + 1] = clip8(ss1 >> PRECISION_BITS);
+                    dst_row[xx * 3 + 2] = clip8(ss2 >> PRECISION_BITS);
                }
            }
+
+            return out;
        };

        // Vertical resampling pass
-        // Resizes height from imIn to out_ny, preserving width
-        auto resample_vertical = [&](const clip_image_u8 & imIn, clip_image_u8 & imOut,
+        // Resizes height from src to out_ny, preserving width
+        // Accumulates whole rows at once (contiguous access, auto-vectorizes well)
+        auto resample_vertical = [&](const uint8_t * src, int in_nx,
                                     int out_ny,
                                     int ksize, const std::vector<int> & bounds, const std::vector<int32_t> & weight) {
-            const int in_nx = imIn.get_size().width;
-            imOut.set_size({in_nx, out_ny}, false);
+            const size_t row_elems = (size_t) in_nx * 3;
+            std::vector<uint8_t> out(row_elems * out_ny);
+            std::vector<int32_t> acc(row_elems);

            // For each output row
            for (int yy = 0; yy < out_ny; yy++) {
-                // Get the range of input rows and filter coefficients
-                int ymin = bounds[yy * 2 + 0];  // First input row index
-                int ycnt = bounds[yy * 2 + 1];  // Number of input rows
+                const int ymin = bounds[yy * 2 + 0];  // First input row index
+                const int ycnt = bounds[yy * 2 + 1];  // Number of input rows
+                const int32_t * k = &weight[yy * ksize];

-                // Process each column in this output row
-                for (int xx = 0; xx < in_nx; xx++) {
-                    // Initialize accumulators for RGB channels with rounding bias
-                    int32_t ss0 = 1 << (PRECISION_BITS - 1);
-                    int32_t ss1 = 1 << (PRECISION_BITS - 1);
-                    int32_t ss2 = 1 << (PRECISION_BITS - 1);
+                // Rounding bias (0.5 in fixed-point)
+                std::fill(acc.begin(), acc.end(), 1 << (PRECISION_BITS - 1));

-                    // Convolve: sum weighted input pixels vertically
-                    for (int y = 0; y < ycnt; y++) {
-                        const auto src_px = imIn.get_pixel(xx, y + ymin);
-                        ss0 += src_px[0] * weight[yy * ksize + y];  // R channel
-                        ss1 += src_px[1] * weight[yy * ksize + y];  // G channel
-                        ss2 += src_px[2] * weight[yy * ksize + y];  // B channel
+                // Convolve: accumulate each weighted input row
+                for (int y = 0; y < ycnt; y++) {
+                    const uint8_t * src_row = src + (size_t) (ymin + y) * row_elems;
+                    const int32_t w = k[y];
+                    for (size_t i = 0; i < row_elems; i++) {
+                        acc[i] += src_row[i] * w;
                    }
+                }

-                    // Convert back from fixed-point and clamp to [0,255]
-                    imOut.set_pixel(xx, yy, {clip8(ss0 >> PRECISION_BITS),
-                                             clip8(ss1 >> PRECISION_BITS),
-                                             clip8(ss2 >> PRECISION_BITS)});
+                // Convert back from fixed-point and clamp to [0,255]
+                uint8_t * dst_row = out.data() + (size_t) yy * row_elems;
+                for (size_t i = 0; i < row_elems; i++) {
+                    dst_row[i] = clip8(acc[i] >> PRECISION_BITS);
                }
            }
+
+            return out;
        };

        // Main resampling logic using separable two-pass approach
@@ -610,36 +459,25 @@ private:
        }

        // Perform two-pass resampling
+        const uint8_t * src = img.get_ro_buf().data();
        if (need_horizontal && need_vertical) {
-            // Both horizontal and vertical
-            clip_image_u8 temp;
-            resample_horizontal(img, temp, target_width, ksize_horiz, bounds_horiz, weights_horiz);
-            resample_vertical(temp, dst, target_height, ksize_vert, bounds_vert, weights_vert);
+            auto temp = resample_horizontal(src, src_width, src_height, target_width, ksize_horiz, bounds_horiz, weights_horiz);
+            dst.set_size({target_width, target_height}, false);
+            dst.cpy_buf(resample_vertical(temp.data(), target_width, target_height, ksize_vert, bounds_vert, weights_vert));
        } else if (need_horizontal) {
-            // Only horizontal
-            resample_horizontal(img, dst, target_width, ksize_horiz, bounds_horiz, weights_horiz);
+            dst.set_size({target_width, src_height}, false);
+            dst.cpy_buf(resample_horizontal(src, src_width, src_height, target_width, ksize_horiz, bounds_horiz, weights_horiz));
        } else if (need_vertical) {
-            // Only vertical
-            resample_vertical(img, dst, target_height, ksize_vert, bounds_vert, weights_vert);
+            dst.set_size({src_width, target_height}, false);
+            dst.cpy_buf(resample_vertical(src, src_width, target_height, ksize_vert, bounds_vert, weights_vert));
        } else {
            // No resizing needed - direct copy
-            dst.set_size(img.get_size(), img.is_placeholder());
-            if (!img.is_placeholder()) {
-                dst.cpy_buf(img.get_ro_buf());
-            }
+            dst.set_size(img.get_size(), false);
+            dst.cpy_buf(img.get_ro_buf());
        }

        return true;
    }
-
-    static inline int clip(int x, int lower, int upper) {
-        return std::max(lower, std::min(x, upper));
-    }
-
-    // Linear interpolation between two points
-    static inline float lerp(float s, float e, float t) {
-        return s + (e - s) * t;
-    }
 };


@@ -1264,7 +1102,7 @@ mtmd_image_preproc_out mtmd_image_preprocessor_deepseekocr::preprocess(const cli
    clip_image_u8 padded;
    img_tool::resize(img, padded,
                    { base_size, base_size },
-                    RESIZE_ALGO_BICUBIC_PILLOW,
+                    RESIZE_ALGO_BICUBIC,
                    PAD_NEAREST,
                    hparams.image_pad_color);
    output.append_overview(hparams, padded, true);
@@ -1280,7 +1118,7 @@ mtmd_image_preproc_out mtmd_image_preprocessor_deepseekocr::preprocess(const cli
        grid_h = grid.height;

        clip_image_u8 refined;
-        img_tool::resize(img, refined, { tile_size * grid_w, tile_size * grid_h }, RESIZE_ALGO_BICUBIC_PILLOW,
+        img_tool::resize(img, refined, { tile_size * grid_w, tile_size * grid_h }, RESIZE_ALGO_BICUBIC,
                         PAD_NONE);

        for (int row = 0; row < grid_h; row++) {
@@ -825,6 +825,7 @@ struct mtmd_context {
                    image_preproc = std::make_unique<mtmd_image_preprocessor_longest_edge>(ctx_v);
                } break;
            case PROJECTOR_TYPE_DOTS_OCR:
+            case PROJECTOR_TYPE_DOTS3NOTE_V:
                {
                    // <|img|> ... (image embeddings) ... <|endofimg|>
                    img_beg = "<|img|>";
@@ -976,6 +977,13 @@ struct mtmd_context {
                    aud_end = "<audio|>";
                    audio_preproc = std::make_unique<mtmd_audio_preprocessor_gemma4ua>(ctx_a);
                } break;
+            case PROJECTOR_TYPE_DOTS3NOTE_A:
+                {
+                    // <|audio_comp_start|> ... (embeddings) ... <|audio_comp_end|>
+                    aud_beg = "<|audio_comp_start|>";
+                    aud_end = "<|audio_comp_end|>";
+                    audio_preproc = std::make_unique<mtmd_audio_preprocessor_dots3note>(ctx_a);
+                } break;
            case PROJECTOR_TYPE_MIMO_AUDIO:
                {
                    aud_beg = "<|mimo_audio_start|>";
@@ -1,20 +0,0 @@
-if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
-    # this tool is disabled on Windows when building with shared libraries because it uses internal functions not exported with LLAMA_API
-    set(TARGET llama-debug-template-parser)
-    add_executable(${TARGET} debug-template-parser.cpp)
-    target_link_libraries(${TARGET} PRIVATE llama-common llama ${CMAKE_THREAD_LIBS_INIT})
-    target_compile_features(${TARGET} PRIVATE cxx_std_17)
-
-    if(LLAMA_TOOLS_INSTALL)
-        install(TARGETS ${TARGET} RUNTIME)
-    endif()
-endif()
-
-set(TARGET llama-template-analysis)
-add_executable(${TARGET} template-analysis.cpp)
-target_link_libraries(${TARGET} PRIVATE llama-common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
-
-if(LLAMA_TOOLS_INSTALL)
-    install(TARGETS ${TARGET} RUNTIME)
-endif()
@@ -1,469 +0,0 @@
-#include "../src/llama-grammar.h"
-#include "chat-auto-parser.h"
-#include "chat.h"
-#include "common.h"
-#include "gguf.h"
-#include "jinja/runtime.h"
-#include "log.h"
-#include "nlohmann/json.hpp"
-#include "peg-parser.h"
-
-#include <fstream>
-#include <iterator>
-#include <numeric>
-#include <optional>
-#include <sstream>
-#include <string>
-
-using json = nlohmann::ordered_json;
-
-enum class output_mode {
-    ANALYSIS,  // Only output analysis results (default)
-    TEMPLATE,  // Only output rendered template
-    BOTH       // Output both
-};
-
-enum class input_message_type {
-    NONE,                    // Don't render any message scenarios (only analysis)
-    CONTENT_ONLY,            // Simple assistant message with content
-    REASONING_CONTENT,       // Message with reasoning_content + content
-    TOOL_CALL_ONLY,          // Message with tool_calls only
-    CONTENT_TOOL_CALL,       // Message with content + tool_calls
-    REASONING_TOOL_CALL,     // Message with reasoning_content + tool_calls
-    CONTENT_FAKE_TOOL_CALL,  // Message with content but no actual tool_calls (for testing)
-    ALL                      // Render all scenarios
-};
-
-struct debug_options {
-    std::string        template_path;
-    bool               with_tools        = true;
-    bool               generation_prompt = true;
-    bool               enable_reasoning  = true;
-    bool               debug_jinja       = false;
-    bool               force_tool_call   = false;
-    bool               parallel_tool_calls = true;
-    output_mode        mode              = output_mode::BOTH;
-    input_message_type input_message     = input_message_type::NONE;
-};
-
-static std::string read_file(const std::string & path) {
-    std::ifstream fin(path, std::ios::binary);
-    if (!fin.is_open()) {
-        throw std::runtime_error("Could not open file: " + path);
-    }
-    std::ostringstream buf;
-    buf << fin.rdbuf();
-    return buf.str();
-}
-
-static std::string read_gguf_chat_template(const std::string & path) {
-    struct gguf_init_params params = { /*no_alloc =*/true,  // We only need metadata, not tensor data
-                                       /*ctx=*/nullptr };
-
-    struct gguf_context * ctx = gguf_init_from_file(path.c_str(), params);
-    if (ctx == nullptr) {
-        throw std::runtime_error("Could not open GGUF file: " + path);
-    }
-
-    const char * key    = "tokenizer.chat_template";
-    int64_t      key_id = gguf_find_key(ctx, key);
-
-    if (key_id == -1) {
-        gguf_free(ctx);
-        throw std::runtime_error("GGUF file does not contain chat template key: " + std::string(key));
-    }
-
-    const char * template_str = gguf_get_val_str(ctx, key_id);
-    if (template_str == nullptr) {
-        gguf_free(ctx);
-        throw std::runtime_error("GGUF file contains chat template key but value is null");
-    }
-
-    std::string result = template_str;
-    gguf_free(ctx);
-    return result;
-}
-
-static void print_usage(const char * program_name) {
-    LOG_ERR("Usage: %s <template_or_gguf_path> [options]\n", program_name);
-    LOG_ERR("\nOptions:\n");
-    LOG_ERR("  --no-tools              Disable tool definitions\n");
-    LOG_ERR("  --force-tool-call       Set tool calls to forced\n");
-    LOG_ERR("  --parallel-tool-calls=0|1 Set parallel_tool_calls (default: 1)\n");
-    LOG_ERR("  --generation-prompt=0|1 Set add_generation_prompt (default: 1)\n");
-    LOG_ERR("  --enable-reasoning=0|1  Enable reasoning parsing (default: 1)\n");
-    LOG_ERR("  --output=MODE           Output mode: analysis, template, both (default: both)\n");
-    LOG_ERR("  --debug-jinja           Enable Jinja fine-grained debug\n");
-    LOG_ERR("  --input-message=TYPE    Message type to render:\n");
-    LOG_ERR("                          content_only, reasoning_content, tool_call_only,\n");
-    LOG_ERR("                          content_tool_call, reasoning_tool_call,\n");
-    LOG_ERR("                          content_fake_tool_call, all\n");
-    LOG_ERR("\nExamples:\n");
-    LOG_ERR("  %s template.jinja --input-message=all --generation-prompt=1\n", program_name);
-    LOG_ERR("  %s template.jinja --output=template --input-message=tool_call_only\n", program_name);
-}
-
-static bool parse_bool_option(const std::string & value) {
-    return value == "1" || value == "true" || value == "yes";
-}
-
-static bool parse_options(int argc, char ** argv, debug_options & opts) {
-    if (argc < 2) {
-        print_usage(argv[0]);
-        return false;
-    }
-
-    opts.template_path = argv[1];
-
-    for (int i = 2; i < argc; ++i) {
-        std::string arg = argv[i];
-
-        if (arg == "--force-tool-call") {
-            opts.force_tool_call = true;
-        } else if (arg == "--debug-jinja") {
-            opts.debug_jinja = true;
-        } else if (arg == "--no-tools") {
-            opts.with_tools = false;
-        } else if (arg.rfind("--parallel-tool-calls=", 0) == 0) {
-            opts.parallel_tool_calls = parse_bool_option(arg.substr(22));
-        } else if (arg.rfind("--generation-prompt=", 0) == 0) {
-            opts.generation_prompt = parse_bool_option(arg.substr(20));
-        } else if (arg.rfind("--enable-reasoning=", 0) == 0) {
-            opts.enable_reasoning = parse_bool_option(arg.substr(19));
-        } else if (arg.rfind("--output=", 0) == 0) {
-            std::string mode = arg.substr(9);
-            if (mode == "analysis") {
-                opts.mode = output_mode::ANALYSIS;
-            } else if (mode == "template") {
-                opts.mode = output_mode::TEMPLATE;
-            } else if (mode == "both") {
-                opts.mode = output_mode::BOTH;
-            } else {
-                LOG_ERR("Unknown output mode: %s\n", mode.c_str());
-                return false;
-            }
-        } else if (arg.rfind("--input-message=", 0) == 0) {
-            std::string type = arg.substr(16);
-            if (type == "content_only") {
-                opts.input_message = input_message_type::CONTENT_ONLY;
-            } else if (type == "reasoning_content") {
-                opts.input_message = input_message_type::REASONING_CONTENT;
-            } else if (type == "tool_call_only") {
-                opts.input_message = input_message_type::TOOL_CALL_ONLY;
-            } else if (type == "content_tool_call") {
-                opts.input_message = input_message_type::CONTENT_TOOL_CALL;
-            } else if (type == "reasoning_tool_call") {
-                opts.input_message = input_message_type::REASONING_TOOL_CALL;
-            } else if (type == "content_fake_tool_call") {
-                opts.input_message = input_message_type::CONTENT_FAKE_TOOL_CALL;
-            } else if (type == "all") {
-                opts.input_message = input_message_type::ALL;
-            } else {
-                LOG_ERR("Unknown input message type: %s\n", type.c_str());
-                return false;
-            }
-        } else {
-            LOG_ERR("Unknown option: %s\n", arg.c_str());
-            print_usage(argv[0]);
-            return false;
-        }
-    }
-
-    return true;
-}
-
-static json build_user_message() {
-    return json{
-        { "role",    "user"                               },
-        { "content", "Hello, please help me with a task." }
-    };
-}
-
-static json build_content_only_message() {
-    return json{
-        { "role",    "assistant"                                   },
-        { "content", "Hello! I'm here to help you with your task." }
-    };
-}
-
-static json build_reasoning_content_message() {
-    return json{
-        { "role",              "assistant"                                                               },
-        { "content",           "Hello! I'm here to help you with your task."                             },
-        { "reasoning_content", "The user is greeting me and asking for help. I should respond politely." }
-    };
-}
-
-static json build_tool_call_only_message() {
-    return json{
-        { "role",       "assistant"      },
-        { "content",    nullptr          },
-        { "tool_calls",
-         json::array({ json{
-              { "type", "function" },
-              { "function", json{ { "name", "test_function_name" },
-                                  { "arguments", json::object({ { "param1", "value1" }, { "param2", "value2" } }) } } },
-              { "id", "123456789" } } }) }
-    };
-}
-
-static json build_content_tool_call_message() {
-    return json{
-        { "role",       "assistant"                                                                              },
-        { "content",    "I'll help you by calling a function."                                                   },
-        { "tool_calls",
-         json::array({ json{
-              { "type", "function" },
-              { "function",
-                json{ { "name", "test_function_name" },
-                      { "arguments", json::object({ { "param1", "value1" }, { "param2", "value2" } }) } } } } }) }
-    };
-}
-
-static json build_reasoning_tool_call_message() {
-    return json{
-        { "role",              "assistant"                                                                       },
-        { "content",           nullptr                                                                           },
-        { "reasoning_content", "I need to call a function to help with this task."                               },
-        { "tool_calls",
-         json::array({ json{
-              { "type", "function" },
-              { "function",
-                json{ { "name", "test_function_name" },
-                      { "arguments", json::object({ { "param1", "value1" }, { "param2", "value2" } }) } } } } }) }
-    };
-}
-
-static json build_content_fake_tool_call_message() {
-    // This message has content but NO tool_calls field
-    // It's used to test if a template renders tool definitions but not tool calls
-    return json{
-        { "role",    "assistant"                            },
-        { "content", "I'll help you by calling a function." }
-    };
-}
-
-static json build_tools_definition() {
-    json parameters_schema                    = json::object();
-    parameters_schema["type"]                 = "object";
-    parameters_schema["properties"]           = json::object();
-    parameters_schema["properties"]["param1"] = json::object({
-        { "type",        "string"          },
-        { "description", "First parameter" }
-    });
-    parameters_schema["properties"]["param2"] = json::object({
-        { "type",        "string"           },
-        { "description", "Second parameter" }
-    });
-    parameters_schema["required"]             = json::array({ "param1" });
-
-    return json::array({
-        json{ { "type", "function" },
-             { "function", json{ { "name", "test_function_name" },
-                                  { "description", "A test function for debugging" },
-                                  { "parameters", parameters_schema } } } }
-    });
-}
-
-static void render_scenario(const common_chat_template & tmpl,
-                            const std::string &          scenario_name,
-                            const json &                 messages,
-                            const json &                 tools,
-                            bool                         add_generation_prompt,
-                            bool                         enable_thinking) {
-    LOG_ERR("\n=== Scenario: %s ===\n", scenario_name.c_str());
-    LOG_ERR("add_generation_prompt: %s, enable_thinking: %s\n", add_generation_prompt ? "true" : "false",
-            enable_thinking ? "true" : "false");
-
-    // When add_generation_prompt is true, add a trailing user message to trigger the prompt
-    json final_messages = messages;
-    if (add_generation_prompt && !messages.empty() && messages.back().value("role", "") == "assistant") {
-        final_messages.push_back(json{
-            { "role",    "user"                                       },
-            { "content", "Now please continue with another response." }
-        });
-    }
-
-    LOG_ERR("Messages:\n%s\n", final_messages.dump(2).c_str());
-
-    try {
-        autoparser::generation_params inputs;
-        inputs.messages                         = final_messages;
-        inputs.add_generation_prompt            = add_generation_prompt;
-        inputs.extra_context["enable_thinking"] = enable_thinking;
-
-        if (!tools.is_null() && tools.is_array() && !tools.empty()) {
-            inputs.tools = tools;
-        }
-
-        std::string output = common_chat_template_direct_apply(tmpl, inputs);
-
-        LOG_ERR("\n--- Rendered Output ---\n");
-        LOG_ERR("%s\n", output.c_str());
-        LOG_ERR("--- End Output (length: %zu) ---\n", output.length());
-    } catch (const std::exception & e) {
-        LOG_ERR("Rendering failed: %s\n", e.what());
-    }
-}
-
-static void render_all_scenarios(const common_chat_template & tmpl,
-                                 const json &                 tools,
-                                 bool                         add_generation_prompt,
-                                 bool                         enable_thinking,
-                                 input_message_type           message_type) {
-    json user_msg = build_user_message();
-
-    auto render_if = [&](input_message_type type, const std::string & name, const json & assistant_msg) {
-        if (message_type == input_message_type::ALL || message_type == type) {
-            json messages = json::array({ user_msg, assistant_msg });
-            render_scenario(tmpl, name, messages, tools, add_generation_prompt, enable_thinking);
-        }
-    };
-
-    render_if(input_message_type::CONTENT_ONLY, "content_only", build_content_only_message());
-    render_if(input_message_type::REASONING_CONTENT, "reasoning_content", build_reasoning_content_message());
-    render_if(input_message_type::TOOL_CALL_ONLY, "tool_call_only", build_tool_call_only_message());
-    render_if(input_message_type::CONTENT_TOOL_CALL, "content_tool_call", build_content_tool_call_message());
-    render_if(input_message_type::REASONING_TOOL_CALL, "reasoning_tool_call", build_reasoning_tool_call_message());
-    render_if(input_message_type::CONTENT_FAKE_TOOL_CALL, "content_fake_tool_call",
-              build_content_fake_tool_call_message());
-
-    // Also render with add_generation_prompt=true to show the prompt ending
-    if (message_type == input_message_type::ALL) {
-        LOG_ERR("\n\n=== Generation Prompt Scenarios (add_generation_prompt=true) ===\n");
-
-        json prompt_messages = json::array({ user_msg });
-        render_scenario(tmpl, "generation_prompt_only", prompt_messages, tools, true, enable_thinking);
-
-        // With enable_thinking toggled
-        render_scenario(tmpl, "generation_prompt_thinking_disabled", prompt_messages, tools, true, false);
-    }
-}
-
-static autoparser::generation_params prepare_params(const debug_options & opts, const json & tools) {
-    autoparser::generation_params params;
-    params.messages         = json::array({ build_user_message() });
-    params.reasoning_format = opts.enable_reasoning ? COMMON_REASONING_FORMAT_DEEPSEEK : COMMON_REASONING_FORMAT_NONE;
-    params.enable_thinking  = opts.enable_reasoning;
-    params.add_generation_prompt = opts.generation_prompt;
-
-    if (opts.with_tools) {
-        params.tools       = tools;
-        params.tool_choice = opts.force_tool_call ? COMMON_CHAT_TOOL_CHOICE_REQUIRED : COMMON_CHAT_TOOL_CHOICE_AUTO;
-    } else {
-        params.tools       = json();
-        params.tool_choice = COMMON_CHAT_TOOL_CHOICE_NONE;
-    }
-    params.parallel_tool_calls = opts.parallel_tool_calls;
-    return params;
-}
-
-int main(int argc, char ** argv) {
-    // Set log level to most verbose to capture all debug output
-    common_log_set_verbosity_thold(99);
-
-    debug_options opts;
-    if (!parse_options(argc, argv, opts)) {
-        return 1;
-    }
-
-    if (opts.debug_jinja || std::getenv("LLAMA_DEBUG_JINJA") != nullptr) {
-        jinja::enable_debug(true);
-    }
-
-    std::string template_source;
-    try {
-        // Check if the file is a GGUF file
-        if (opts.template_path.size() >= 5 &&
-            opts.template_path.compare(opts.template_path.size() - 5, 5, ".gguf") == 0) {
-            template_source = read_gguf_chat_template(opts.template_path);
-        } else {
-            template_source = read_file(opts.template_path);
-        }
-    } catch (const std::exception & e) {
-        LOG_ERR("Error reading template: %s\n", e.what());
-        return 1;
-    }
-
-    LOG_ERR("Analyzing template: %s\n", opts.template_path.c_str());
-    LOG_ERR("Options: with_tools=%s, generation_prompt=%s, enable_reasoning=%s\n", opts.with_tools ? "true" : "false",
-            opts.generation_prompt ? "true" : "false", opts.enable_reasoning ? "true" : "false");
-
-    try {
-        common_chat_template chat_template(template_source, "", "");
-
-        json tools = opts.with_tools ? build_tools_definition() : json();
-
-        autoparser::generation_params params = prepare_params(opts, tools);
-        common_chat_params            parser_data;
-        if (std::optional<common_chat_params> spec_tmpl =
-                common_chat_try_specialized_template(chat_template, template_source, params)) {
-            LOG_ERR("\n");
-            LOG_ERR("This template uses a specialized parser, analysis results will not be available.\n");
-            parser_data = *spec_tmpl;
-        } else {
-            // Render template scenarios if requested
-            if (opts.input_message != input_message_type::NONE &&
-                (opts.mode == output_mode::TEMPLATE || opts.mode == output_mode::BOTH)) {
-                LOG_ERR("\n");
-                LOG_ERR("================================================================================\n");
-                LOG_ERR("                         TEMPLATE RENDERING OUTPUT\n");
-                LOG_ERR("================================================================================\n");
-
-                render_all_scenarios(chat_template, tools, opts.generation_prompt, opts.enable_reasoning,
-                                     opts.input_message);
-            }
-
-            // Output analysis if requested
-            if (opts.mode == output_mode::ANALYSIS || opts.mode == output_mode::BOTH) {
-                LOG_ERR("\n");
-                LOG_ERR("================================================================================\n");
-                LOG_ERR("                           TEMPLATE ANALYSIS\n");
-                LOG_ERR("================================================================================\n");
-
-                autoparser::autoparser analysis;
-                analysis.analyze_template(chat_template);
-
-                // Generate Parser
-                parser_data = autoparser::peg_generator::generate_parser(chat_template, params, analysis);
-            }
-        }
-
-        if (!std::empty(parser_data.parser)) {
-            LOG_ERR("\n=== Generated Parser ===\n");
-            common_peg_arena arena;
-            arena.load(parser_data.parser);
-            LOG_ERR("%s\n", arena.dump(arena.root()).c_str());
-
-            LOG_ERR("\n=== Generated Grammar ===\n");
-            LOG_ERR("%s\n", parser_data.grammar.c_str());
-
-            LOG_ERR("\n=== Generated Lazy Grammar ===\n");
-            LOG_ERR("%d\n", parser_data.grammar_lazy);
-
-            LOG_ERR("\n=== Generated Grammar Triggers ===\n");
-            for (const common_grammar_trigger & cgt : parser_data.grammar_triggers) {
-                LOG_ERR("Token: %d | Type: %d | Value: %s\n", cgt.token, cgt.type, cgt.value.c_str());
-            }
-
-            LOG_ERR("\n=== Preserved Tokens ===\n");
-            for (const std::string & token : parser_data.preserved_tokens) {
-                LOG_ERR("  '%s'\n", token.c_str());
-            }
-
-            if (!parser_data.grammar.empty()) {
-                LOG_ERR("\n=== Verifying created grammar ===\n");
-                auto * grammar = llama_grammar_init_impl(nullptr, parser_data.grammar.c_str(), "root",
-                                                         parser_data.grammar_lazy, nullptr, 0, nullptr, 0);
-                if (grammar != nullptr) {
-                    LOG_ERR("\n=== Grammar successfully created ===\n");
-                }
-            }
-        }
-    } catch (const std::exception & e) {
-        LOG_ERR("Analysis failed: %s\n", e.what());
-        return 1;
-    }
-
-    return 0;
-}
@@ -153,7 +153,7 @@ json server_chat_convert_responses_to_chatcmpl(const json & response_body) {
                        prev_msg["content"] = json::array();
                    }
                    auto & prev_content = prev_msg["content"];
-                    prev_content.insert(prev_content.end(), chatcmpl_content.begin(), chatcmpl_content.end());
+                    prev_content.insert(chatcmpl_content);
                } else {
                    item.erase("status");
                    item.erase("type");
@@ -6,9 +6,7 @@
 #include "server-common.h"
 #include "server-http.h"

-#include <nlohmann/json_fwd.hpp>
-
-using json = nlohmann::ordered_json;
+#include "json.h"

 // Convert OpenAI Responses API format to OpenAI Chat Completions API format
 json server_chat_convert_responses_to_chatcmpl(const json & body);
@@ -1540,7 +1540,7 @@ std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int i
 }

 std::string safe_json_to_str(const json & data) {
-    return data.dump(-1, ' ', false, json::error_handler_t::replace);
+    return data.dump_safe();
 }

 // TODO: reuse llama_detokenize
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
jacekpoplawski	c060ca974c	model : support MTP in GLM-4.5-Air (#26534 )	2026-08-23 21:20:44 +03:00
Georgi Gerganov	ccc8fd2baa	readme : update links (#27617 ) * readme : update links * readme : update maintainer PRs list Add the new members of the `ggml-org` `maintainers` team to the author filter of the maintainer PRs link (nikwen, marty1885, Titaniumtown), keeping the canonical team ordering. The list now matches the team exactly (35 members). Assisted-by: pi:llama.cpp/Qwen3.8-27B	2026-08-23 20:55:56 +03:00
Aleksander Grygier	d05f89562d	fix: Change chat tabs nav shortcuts (#27609 )	2026-08-23 19:37:19 +02:00
Georgi Gerganov	8d9af25633	test : fix multi-GPU server tests (#27614 ) * tests : fix tests for multi-gpu environment * cont : not needed	2026-08-23 19:59:42 +03:00
Xuan-Son Nguyen	4a08fa2970	test: move tools/parser to tests (#27548 )	2026-08-23 18:38:51 +02:00
Xuan-Son Nguyen	56db501e73	mtmd: use pillow-accurate algo, correct resize_algo for all models (#27594 ) * mtmd: use pillow-accurate resize algo, correct resize_algo for all models * speed optimization	2026-08-23 18:35:41 +02:00
Georgi Gerganov	95b8e33e16	ci : add test-llama-archs tensor split for Metal (#27598 ) Run test-llama-archs with 1 to 4 GGML_METAL_DEVICES, mirroring the existing CUDA runs, and dispatch the job unconditionally since the per-backend guards now decide what to run. Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731	2026-08-23 15:57:07 +03:00
Niklas Wenzel	a278dcef04	contrib : recommend waiting for CI before merging (#27603 )	2026-08-23 15:56:47 +03:00
Georgi Gerganov	e8eed4525a	server : add LLAMA_SERVER_SLOTS_N_DIFF (#27600 )	2026-08-23 15:55:51 +03:00
Bartosz Taudul	ba8e0eddfb	common : skip device_info loop if it's not going to be printed (#26692 ) The device_info loop iterates over the discovered devices and gets the available and total memory counts. With the CUDA backend (and possibly others too) this requires creating a GPU context, which, in case of CUDA, results in a 550 MB VRAM allocation. For this information to be used in any way, the log verbosity must be set to LOG_LEVEL_TRACE. If it's not, including in the default configuration, the contexts get created, memory sizes get queried, then the log function quietly discards the data. In certain cases the user may not want to use any GPU resources. The device_loop iteration is the only place touching the GPU that cannot be skipped. Fix by checking the verbosity level and skipping the loop if there would be no output.	2026-08-23 14:39:16 +02:00
Aman Gupta	b0539c43ed	DeepseekV4: fix rollback with multi-seq (#26756 ) * DeepseekV4: fix rollback with multi-seq * fix model loading * make pending rollback single use * only clear cache for seq_id for full load * add assert for compress ratio * make graph topology static * pass true instead of flags in clear_compressed * cont : clean-up + TODOs --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2026-08-23 13:57:49 +03:00
Gaurav Garg	d3371929bb	[Tensor parallel] Fix meta tensor split state propagation (#27574 ) * ggml : fix meta tensor split state propagation * Add test-llama-archs to CI	2026-08-23 18:49:12 +08:00
Aleksander Grygier	8144f3192e	ui: Chat Conversation Tabbed navigation (#27263 ) * ui : add browser-style conversation tabs store Track open conversation tabs in order, persisted to localStorage and pruned against the loaded conversation list on init. The chat layout syncs the route's tab on every navigation, so any way of reaching a conversation opens a tab for it. * ui : add temporary new-chat tabs New-chat tabs are unsaved conversations carrying a temporary id used directly as the route (#/chat/<id>). They live in memory and are only persisted to the database - keeping the same id so the route and tab stay stable - when the first message is sent. Deleting one drops it without confirmation, and deleting conversations now closes their tabs. * ui : render conversation tab bar in chat layout Desktop-only tab bar above the chat screen, one tab per open conversation or new-chat tab. The active tab follows the route id; clicking navigates, middle-click or the close button closes (switching to the left neighbor), and a trailing + starts a new chat. Tabs appear only on chat-id routes; the bare #/ new-chat view has none. The bare route stays put unless a prompt/model deep-link routes it to a new-chat tab. * ui : route new-chat entry points through tabs The sidebar New chat item, Cmd+Shift+O, the search page and the arrow-key fallback now open a new-chat tab instead of navigating to the ?new_chat URL, which is removed. New chat is no longer a special route but a tab like any other conversation. * ui : track sidebar expanded state in a shared ui store Move the desktop sidebar expanded/collapsed state out of deviceStore into a dedicated uiStore so the chat tab bar can react to it. Assisted-by: pi * chat : add opt-in conversation tabs setting Add a Display setting that turns browser-style conversation tabs on or off, enabled by default. Assisted-by: pi * chat : add browser-style conversation tabs with a new-chat screen Track open conversations as tabs above the chat, one per open chat, plus a single New chat tab for the bare `#/` route. New chat is just the `#/` screen - no temporary conversations - and its tab is dropped when navigating away. Sending the first message creates a real conversation and opens a tab for it. Assisted-by: pi * chat : turn tab bar into a horizontally scrollable carousel Make the tab bar a horizontally scrollable carousel with edge scroll buttons and active-tab centering, and align its styling with the sidebar. Assisted-by: pi * chat : restyle the scroll-to-bottom button to match tab styling Assisted-by: pi * chat : add close-tab keyboard shortcut Assisted-by: pi * chat : soften tab bar fade and dim inactive tabs Assisted-by: pi * feat: Add stop button to tabs * refactor: Componentize * ui : fix carousel scrollability detection Observe the content wrapper as well as the container, since adding overflowing items does not change the container's own box size. Also expose an onScrollableChange callback. Assisted-by: pi * ui : add unified ScrollCarousel component Single carousel component with top/center variants, gap and scroll options, and hover-revealed chevrons. Rename the HorizontalScrollCarousel accessibility story accordingly. Assisted-by: pi * ui : migrate carousels to ScrollCarousel Switch the settings mobile header, attachments list, thumbnail strip, and MCP resources to the unified component, and drop HorizontalScrollCarousel. Assisted-by: pi * ui : improve chat tabs carousel UX Scroll newly added tabs into view, fade overflowing tabs at the edges, and hide the New chat button while a new-chat tab is open. Assisted-by: pi * refactor: Naming * chat : add keyboard shortcut to jump between conversation tabs Shift+Cmd/Ctrl+Left/Right cycles the open tabs, mirroring the existing Shift+Cmd/Ctrl+Up/Down conversation navigation. Assisted-by: pi * chat : make the whole tab item act as a link The full tab is now a link instead of only the inner label button, while the stop and close buttons stay interactive by swallowing their clicks. Assisted-by: pi * chat : adjust tab bar width and use a shared offset variable Widen the tab bar for the expanded sidebar and rename the tab bar height variable to --chat-tabs-offset with a smaller value so the chat screen min-height accounts for the overlay without overshooting. Assisted-by: pi * chat : account for the tab bar offset in the assistant min-height Subtract the tab bar offset when it is shown so the last assistant message does not overflow the available viewport space. Assisted-by: pi * refactor: Post-review fixes * ui : restore deep links on the chat start page - handle ?model selection, with ?load=true eager router loading - ?q now creates a conversation, sends the prompt, and clears the params - show the not-available-model dialog for unknown models - never block mount on the conversation list Assisted-by: pi * ui : fix tab item link nesting and centralize tab constants - the tab anchor covers the whole item while stop/close stay siblings, so interactive elements are never nested inside the anchor - cmd/ctrl/middle clicks are left to the browser (new window) - extract the tab labels, the active-tab data attribute, and the sidebar-offset max widths into constants Assisted-by: pi * ui : tidy scroll carousel hook and keep mobile header arrows on - drop the dead scrollLeft/scrollRight helpers and the unused onScrollableChange/scrollBy props - init the carousel once instead of inside a derived - restore items-start on the center variant - always show the settings header arrows on touch Assisted-by: pi * ui : keep the new-chat tab across reloads and fall back on close - the new-chat sentinel is no longer pruned on init, so reloading on the bare new-chat route keeps the tab the user is on - closing the active conversation falls back to the new-chat screen when Conversation tabs are off Assisted-by: pi * ui : don't block startup on the conversation list - prune persisted tabs after the list loads in the background instead of awaiting it during init - openNewChat now returns void; its return value was never read Assisted-by: pi * ui: fix routing nits * chore: Update doc comments * refactor: Mark fire-and-forget openNewChat calls as `void` * chat: fix the deep-linked prompt, the tab width and the tab shortcuts The chat start page creates the conversation and hands the prompt over to the chat route, which still sees it in the query string. Sending it on both sides queues the second copy as a pending message, which shows up as a stray user bubble once the answer lands and vanishes on reload since it never reaches the database. The tab bar takes the max width of the collapsed sidebar while it is expanded, and the other way round. The tab list is pruned against a snapshot of the loaded conversations, so a conversation created while that list is still loading loses its tab even though the route just opened it. The active tab then falls out of the list and the cycling shortcut jumps to an edge on every keypress instead of moving one tab over. Tabs synced from the route are kept as they are, only the persisted ones are pruned. The rich chat input claims ctrl or alt with shift and an arrow for its badge-aware word jump, which now belongs to the tab cycling shortcut. Holding shift hands the key combination over, the plain word jump is unchanged. The close-tab shortcut consumes the event before checking whether the setting is on, and the logo background loses its importance flag. --------- Co-authored-by: Pascal <admin@serveurperso.com>	2026-08-23 10:46:49 +02:00
Alessandro de Oliveira Faria (A.K.A.CABELO)	6657ded4fa	vendor : update subprocess.h (#27409 )	2026-08-23 10:38:29 +03:00
Aman Karki	29ea9412a6	cuda : add POOL_1D support (#27573 ) * cuda : add POOL_1D support * fix: add missing trailing newline for editorconfig compliance	2026-08-23 10:37:32 +03:00
Xuan-Son Nguyen	70adb1b4ce	common: json.h: fix clang lto (#27575 )	2026-08-23 01:11:10 +02:00
Safi Ullah	3f545becce	vulkan : added the PAD_REFLECT_1D operation (#26586 ) * vulkan : added PAD_REFLECT_1D operation Implemented the GGML_OP_PAD_REFLECT_1D operation for the Vulkan backend Changes: - pad_reflect_1d.comp: implemented the GLSL compute shader with reflection logic - vulkan-shaders-gen.cpp: register the shader for SPIR-V compilation - ggml-vulkan.cpp: pushed constants struct, pipeline creation, supports_op, dispatch function, compute switch and debug validation Tested the PAD_REFLECT_1D on Intel Iris Xe (Vulkan 1.4, Mesa 25.2.8): Correctness: PAD_REFLECT_1D(type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9) = Pass PAD_REFLECT_1D(type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9) = Pass 2/2 tests passed - All test are passed Performance: ne_a=[512,34,2,1] -> 5.38 us/run, 24.55 GB/s ne_a=[3000,80,1,1] -> 30.09 us/run, 59.62 GB/s ne_a=[3000,384,4,1] -> 158.31 us/run, 54.39 GB/s * Update ggml/src/ggml-vulkan/vulkan-shaders/pad_reflect_1d.comp Co-authored-by: Jeff Bolz <jbolz@nvidia.com> --------- Co-authored-by: Jeff Bolz <jbolz@nvidia.com>	2026-08-22 14:42:20 -05:00
Xuan-Son Nguyen	b21e4de745	mtmd: use ggml_rope_set_offset (#27521 ) * mtmd: use ggml_rope_set_offset * add comment	2026-08-22 16:33:47 +02:00
Xuan-Son Nguyen	d9f918d2d0	common: add json.h abstraction (#27511 ) * add common/json * migrate common * adapt jinja * migrate server * big wip * migrate tests * wip * revert some excessive changes * wip * wip 2 * revert redundant changes * fix server crash * various fixes * fix ci * harden a bit * clean up * rm json-shim * add some comments * rm redundant decl	2026-08-22 16:28:28 +02:00
Xuan-Son Nguyen	2fb989b9e7	fit: also take into account n_streams (#27496 ) * fit: also take into account n_streams * server: make the draft context follow the target context With a non-unified KV cache the target context now holds n_ctx_train tokens per sequence, while the draft context was still created with n_ctx = 0 and fell back to n_ctx_train / n_streams per sequence. A slot filled beyond that point makes the draft batch fail to decode, and the server answers 500 on the request. The draft context now takes its size from the target context, so both hold the same number of tokens per sequence. Contexts that share their cells with the target no longer need the kv_size override. The memory reserved for the draft model before fitting is measured at the largest context the target can take, since the draft context grows with the target and a fixed byte margin cannot express that. * fit: take an optional second model into account Illustrates the alternative discussed on the draft context fix. The memory of a draft or MTP context is currently handed to the fit as a fixed byte margin, which cannot express a memory that grows with the context the fit is still deciding on. common_fit_params now takes an optional second model that shares the devices of the main one. Its context follows the main context and its memory is measured again whenever that context changes, so the reduce path stays exact instead of conservative. A model that cannot be measured on its own, such as a shared cell MTP context, is skipped with a warning and the main model is fitted alone. This drops the reservation block in the server, which no longer has to probe the trained context size of the target to guess an upper bound. --------- Co-authored-by: Pascal <admin@serveurperso.com>	2026-08-22 16:16:06 +02:00
Xuan-Son Nguyen	9fee29e943	arg: remove -no-cnv from cli [no ci] (#27542 ) * arg: remove -no-cnv from cli * clarify about not adding exccesive test cases	2026-08-22 15:53:56 +02:00
Mario Limonciello	e85caa81ea	ci : Restore ROCm job for Ubuntu (#27399 ) * Revert "ci : disable ubuntu-rocm (#26969)" This reverts commit `9558fa44c9`. * ci: set ccache compiler_check=content for ROCm build The ROCm toolchain is pip-installed fresh on every run, so the clang binary's mtime changes each time. With ccache's default compiler_check=mtime that invalidates the whole cache and warm builds only reached ~70% hits. Hash the compiler contents instead so the cache survives toolchain reinstalls. * Update ccache size to 1GB We're waivering with so many architectures built, we need a bigger ccache limit. * merge fix --------- Co-authored-by: Jim Wu <ywu@xilinx.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>	2026-08-22 13:28:30 +03:00
Tiwei Bie	2115b73d8e	model : support DSpark for bailingmoe3 (#27508 )	2026-08-22 12:19:48 +03:00
Xuan-Son Nguyen	54ee5ee643	mtmd: support dots3-note vision+audio (#27524 ) * text: conversion * init impl * mtmd: conversion * impl mtmd cpp * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>	2026-08-22 10:35:50 +02:00
Georgi Gerganov	3a653fea93	ci : add older, min and dry-run options to ccache-clear (#27504 ) * ci : add older, min and dry-run options to ccache-clear Assisted-by: pi:llama.cpp/Qwen3.8-27B * pi : add note about not wrapping lines in PR descriptions [no ci] Assisted-by: pi:llama.cpp/Qwen3.8-27B	2026-08-22 11:31:30 +03:00
Kartik Sirohi	369e1cd614	ggml: optimize concat op by replacing per-element memcpy with row-level memcpy (#24575 ) * ggml: optimize concat op by replacing per-element memcpy with row-level memcpy * ggml: fix concat offsets for row-level copies * ggml: add concat row contiguity asserts * ggml: move concat block size asserts * ggml: remove redundant concat asserts * Update ggml/src/ggml-cpu/ops.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>	2026-08-22 11:30:31 +03:00