sched : reintroduce less synchronizations during split compute (#20793)

* CUDA:  Improve performance via less synchronizations between token (#17795)

* Adds CPU-to-CUDA copy capability to
ggml_backend_cuda_cpy_tensor_async()

* Adds function to relax sync requirements between input copies on
supported backends (CUDA for now)

* Exchanges synchronous copy with async copy function.

* Adds macro guards to allow compilation in non-CUDA builds

* Reworked backend detection in ggml-backend.cpp to avoid linking
conflicts

* Relax requirement of checks in async CUDA copies from backend and buffer type to just buffer type, to avoid linking issues

* Minor cleanup

* Makes opt-in to relax use of explicit syncs more general. Backends like
vulkan which require a synchronization between HtoD copies and graph
execution could also adopt this change now.

* Reintroduces stricter check for CPU->CUDA backend async copy via
GGML_DEVICE_TYPE_CPU.

* Corrects initialization of ggml_backend_sync_mode in
ggml_backend_sched_split initialization

* Simplifies synchronizations to adhere to `saaasg` pattern.

* Apply suggestion from @ggerganov (src->buffer to buf_src)

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Apply suggestion from @ggerganov (src->buffer to buf_src) v2

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Apply suggestions from @johannesgaessler code review

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Adds single-GPU synchronizations to multi-GPU settings to fix hip backend pipeline parallel bugs.

* Scheduler Hardening: Exclude hip/MUSA from copy_from_host CPU split ->
GPU split optimization

* Scheduler Hardening: Re-adding original additional synchronizations for
non-async backends

* Adds disclaimer to hip/musa exclusion of copy_from_host. Highlights that it is out of
precaution, but that no perf-impact is visible, and that it can be
revisited separately anytime.

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

.devops/cann.Dockerfile

@@ -13,6 +13,20 @@ ARG APP_REVISION=N/A
 # BUILD STAGE
 # Compile all binary files and libraries
 # ==============================================================================
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM ${CANN_BASE_IMAGE} AS build
 
 # -- Install build dependencies --
@@ -26,6 +40,8 @@ WORKDIR /app
 # -- Copy project files --
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 # -- Set CANN environment variables (required for compilation) --
 # Using ENV instead of `source` allows environment variables to persist across the entire image layer
 ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
@@ -129,7 +145,7 @@ ENTRYPOINT ["/app/tools.sh"]
 # ==============================================================================
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 ENTRYPOINT [ "/app/llama-cli" ]
 
@@ -140,7 +156,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 HEALTHCHECK --interval=5m CMD [ "curl", "-f", "http://localhost:8080/health" ]
 

.devops/cpu.Dockerfile

@@ -3,6 +3,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM docker.io/ubuntu:$UBUNTU_VERSION AS build
 
 ARG TARGETARCH
@@ -16,6 +30,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN if [ "$TARGETARCH" = "amd64" ] || [ "$TARGETARCH" = "arm64" ]; then \
         cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
     else \
@@ -88,7 +104,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -99,7 +115,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 

.devops/cuda.Dockerfile

@@ -11,6 +11,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 ARG GCC_VERSION
@@ -26,6 +40,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
     export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
     fi && \
@@ -97,7 +113,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -108,7 +124,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 

.devops/intel.Dockerfile

@@ -5,6 +5,20 @@ ARG APP_REVISION=N/A
 
 ## Build Image
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM docker.io/intel/deep-learning-essentials:$ONEAPI_VERSION AS build
 
 ARG GGML_SYCL_F16=ON
@@ -22,6 +36,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
         echo "GGML_SYCL_F16 is set" \
         && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON" \
@@ -125,7 +141,7 @@ ENTRYPOINT ["/app/tools.sh"]
 FROM base AS light
 
 COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -137,7 +153,7 @@ FROM base AS server
 ENV LLAMA_ARG_HOST=0.0.0.0
 
 COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 

.devops/musa.Dockerfile

@@ -10,6 +10,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM ${BASE_MUSA_DEV_CONTAINER} AS build
 
 # MUSA architecture to build for (defaults to all supported archs)
@@ -29,6 +43,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
         export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
     fi && \
@@ -99,7 +115,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -110,7 +126,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 

.devops/openvino.Dockerfile

@@ -1,12 +1,12 @@
-ARG OPENVINO_VERSION_MAJOR=2026.2
-ARG OPENVINO_VERSION_FULL=2026.2.0.21903.52ddc073857
+ARG OPENVINO_VERSION_MAJOR=2026.2.1
+ARG OPENVINO_VERSION_FULL=2026.2.1.21919.ede283a88e3
 ARG UBUNTU_VERSION=24.04
 
 # Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
-ARG IGC_VERSION=v2.34.4
-ARG IGC_VERSION_FULL=2_2.34.4+21428
-ARG COMPUTE_RUNTIME_VERSION=26.18.38308.1
-ARG COMPUTE_RUNTIME_VERSION_FULL=26.18.38308.1-0
+ARG IGC_VERSION=v2.36.3
+ARG IGC_VERSION_FULL=2_2.36.3+21719
+ARG COMPUTE_RUNTIME_VERSION=26.22.38646.4
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.22.38646.4-0
 ARG IGDGMM_VERSION=22.10.0
 
 # Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
@@ -22,6 +22,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 ## Build Image
 FROM docker.io/ubuntu:${UBUNTU_VERSION} AS build
 
@@ -69,6 +83,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 # Build Stage
 RUN bash -c "source ${OpenVINO_DIR}/setupvars.sh && \
     cmake -B build/ReleaseOV -G Ninja \
@@ -198,7 +214,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app/
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app/
 
 WORKDIR /app
 
@@ -209,7 +225,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app/
+COPY --from=build /app/full/llama /app/full/llama-server /app/
 
 WORKDIR /app
 

.devops/rocm.Dockerfile

@@ -11,6 +11,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 ### Build image
 FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
@@ -38,6 +52,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
     cmake -S . -B build \
         -DGGML_HIP=ON \
@@ -111,7 +127,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -122,7 +138,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 

.devops/s390x.Dockerfile

@@ -124,7 +124,7 @@ WORKDIR /llama.cpp/bin
 
 # Copy llama.cpp binaries and libraries
 COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin
-COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin/llama-completion /llama.cpp/bin
+COPY --from=collector /llama.cpp/bin/llama /llama.cpp/bin/llama-cli /llama.cpp/bin/llama-completion /llama.cpp/bin
 
 ENTRYPOINT [ "/llama.cpp/bin/llama-cli" ]
 
@@ -138,7 +138,7 @@ WORKDIR /llama.cpp/bin
 
 # Copy llama.cpp binaries and libraries
 COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin
-COPY --from=collector /llama.cpp/bin/llama-server /llama.cpp/bin
+COPY --from=collector /llama.cpp/bin/llama /llama.cpp/bin/llama-server /llama.cpp/bin
 
 EXPOSE 8080
 

.devops/vulkan.Dockerfile

@@ -3,6 +3,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM docker.io/ubuntu:$UBUNTU_VERSION AS build
 
 # Install build tools
@@ -17,6 +31,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=ON -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
     cmake --build build --config Release -j$(nproc)
 
@@ -91,7 +107,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -102,7 +118,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 

.devops/zendnn.Dockerfile

@@ -3,6 +3,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM docker.io/ubuntu:$UBUNTU_VERSION AS build
 
 RUN apt-get update && \
@@ -14,6 +28,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_ZENDNN=ON && \
     cmake --build build -j $(nproc)
 
@@ -81,7 +97,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -92,7 +108,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 

.dockerignore

@@ -10,6 +10,8 @@
 
 build*/
 
+tools/ui/node_modules/
+
 models/*
 
 /llama-cli

.github/labeler.yml

@@ -35,8 +35,20 @@ AMD ZenDNN:
 documentation:
     - changed-files:
         - any-glob-to-any-file:
+            - "**/*.md"
             - docs/**
             - media/**
+examples:
+    - all:
+        - changed-files:
+            - any-glob-to-any-file:
+                - app/**
+                - examples/**
+                - tools/**
+            - all-globs-to-all-files:
+                - '!tools/server/**'
+                - '!tools/mtmd/**'
+                - '!tools/ui/**'
 testing:
     - changed-files:
         - any-glob-to-any-file:
@@ -47,28 +59,12 @@ build:
             - cmake/**
             - CMakeLists.txt
             - CMakePresets.json
-examples:
-    - changed-files:
-        - any-glob-to-any-file:
-            - examples/**
-            - tools/**
 devops:
     - changed-files:
         - any-glob-to-any-file:
             - .devops/**
             - .github/**
             - ci/**
-python:
-    - changed-files:
-        - any-glob-to-any-file:
-            - "**/*.py"
-            - requirements/**
-            - gguf-py/**
-            - .flake8
-script:
-    - changed-files:
-        - any-glob-to-any-file:
-            - scripts/**
 android:
     - changed-files:
         - any-glob-to-any-file:
@@ -81,9 +77,20 @@ server:
     - changed-files:
         - any-glob-to-any-file:
             - tools/server/**
-
-
-
+mtmd:
+    - changed-files:
+        - any-glob-to-any-file:
+            - tools/mtmd/**
+conversion:
+    - changed-files:
+        - any-glob-to-any-file:
+            - conversion/**
+            - convert_*.py
+            - gguf-py/**
+vendor:
+    - changed-files:
+        - any-glob-to-any-file:
+            - vendor/**
 ggml:
     - changed-files:
         - any-glob-to-any-file:

.github/workflows/build-cache.yml

@@ -68,8 +68,8 @@ jobs:
 
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone
@@ -96,8 +96,8 @@ jobs:
 
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone

.github/workflows/build-openvino.yml

@@ -39,8 +39,8 @@ jobs:
 
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone
@@ -96,8 +96,8 @@ jobs:
 
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone

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

@@ -266,8 +266,8 @@ jobs:
 
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone

.github/workflows/docker.yml

@@ -58,6 +58,13 @@ jobs:
           git tag ${{ steps.srctag.outputs.name }} || exit 0
           git push origin ${{ steps.srctag.outputs.name }} || exit 0
 
+  build_ui:
+    name: Build UI
+    needs: create_tag
+    uses: ./.github/workflows/ui-build.yml
+    with:
+      hf_ui_version: ${{ needs.create_tag.outputs.source_tag }}
+
   prepare_matrices:
     name: Prepare Docker matrices
     runs-on: ubuntu-24.04
@@ -79,7 +86,7 @@ jobs:
           [
             { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
             { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-arm" },
-            { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x" },
+            { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x", "prebuilt_ui": true },
             { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.8.1", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
             { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.8.1", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
             { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.3.0", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
@@ -135,7 +142,7 @@ jobs:
 
   push_to_registry:
     name: Push Docker image to Docker Registry
-    needs: [prepare_matrices, create_tag]
+    needs: [prepare_matrices, create_tag, build_ui]
 
     runs-on: ${{ matrix.config.runs_on }}
     strategy:
@@ -150,6 +157,13 @@ jobs:
           fetch-depth: 0
           ref: ${{ needs.create_tag.outputs.source_tag }}
 
+      - name: Download prebuilt UI
+        if: ${{ matrix.config.prebuilt_ui == true }}
+        uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8
+        with:
+          name: ui-build
+          path: tools/ui/dist
+
       - name: Set up QEMU
         if: ${{ contains(matrix.config.platforms, 'linux/amd64') }}
         uses: docker/setup-qemu-action@ce360397dd3f832beb865e1373c09c0e9f86d70a # v4

.github/workflows/release.yml

@@ -46,11 +46,13 @@ jobs:
 
     steps:
       - id: check
+        env:
+          COMMIT_MESSAGE: ${{ github.event.head_commit.message }}
         run: |
           if [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
             echo "should_release=true" >> $GITHUB_OUTPUT
           elif [[ "${{ github.event_name }}" == "push" && "${{ github.ref }}" == "refs/heads/master" ]]; then
-            if echo "${{ github.event.head_commit.message }}" | grep -q '\[no release\]'; then
+            if echo "$COMMIT_MESSAGE" | grep -q '\[no release\]'; then
               echo "should_release=false" >> $GITHUB_OUTPUT
             else
               echo "should_release=true" >> $GITHUB_OUTPUT
@@ -444,8 +446,8 @@ jobs:
 
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Set OpenVINO version output
@@ -504,8 +506,11 @@ jobs:
           cmake -B build/ReleaseOV -G Ninja \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENVINO=ON \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }}
-          cmake --build build/ReleaseOV --config Release -j $(nproc)
+            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build/ReleaseOV --config Release --parallel
 
       - name: ccache-clear
         uses: ./.github/actions/ccache-clear
@@ -519,8 +524,26 @@ jobs:
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          cp LICENSE ./build/ReleaseOV/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/ReleaseOV/bin .
+          dest=./build/ReleaseOV/bin
+          OPENVINO_ROOT=./openvino_toolkit
+          ov_lib="$OPENVINO_ROOT/runtime/lib/intel64"
+
+          # Bundle OpenVINO runtime libs + TBB. Binaries built with RPATH=$ORIGIN
+          # load these siblings without setupvars.sh / LD_LIBRARY_PATH.
+          cp -P "$ov_lib"/libopenvino.so* \
+                "$ov_lib"/libopenvino_c.so* \
+                "$ov_lib"/libopenvino_*_plugin.so \
+                "$ov_lib"/libopenvino_intel_npu_compiler*.so \
+                "$OPENVINO_ROOT"/runtime/3rdparty/tbb/lib/*.so* \
+                "$dest"
+          cp -P /usr/lib/x86_64-linux-gnu/libOpenCL.so.1* "$dest" 2>/dev/null || true
+          cp "$ov_lib"/cache.json "$dest" 2>/dev/null || true
+
+          # OpenVINO licensing
+          cp -r "$OPENVINO_ROOT"/docs/licensing "$dest"/openvino-licensing
+
+          cp LICENSE "$dest"
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C "$dest" .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -536,12 +559,13 @@ jobs:
 
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Set OpenVINO version output
         id: openvino_version
+        shell: bash
         run: echo "value=${{ env.OPENVINO_VERSION_MAJOR }}" >> $GITHUB_OUTPUT
 
       - name: Clone
@@ -604,7 +628,9 @@ jobs:
             -A x64 ^
             -DCMAKE_BUILD_TYPE=Release ^
             -DGGML_OPENVINO=ON ^
-            -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
+            -DLLAMA_BUILD_BORINGSSL=ON ^
+            -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake ^
+            ${{ env.CMAKE_ARGS }}
 
           cmake --build build\ReleaseOV --config Release -- /m
 
@@ -621,8 +647,29 @@ jobs:
         id: pack_artifacts
         shell: powershell
         run: |
-          Copy-Item LICENSE .\build\ReleaseOV\bin\
-          7z a -snl llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip .\build\ReleaseOV\bin\*
+          # Locate the extracted OpenVINO toolkit root (same pattern as the Build step).
+          $OPENVINO_ROOT = (Get-ChildItem -Directory openvino_toolkit | Select-Object -First 1).FullName
+          if (-not $OPENVINO_ROOT) {
+            Write-Error "OpenVINO toolkit folder not found under .\openvino_toolkit"
+            exit 1
+          }
+
+          $dest = ".\build\ReleaseOV\bin\Release"
+
+          $ovBin = Join-Path $OPENVINO_ROOT 'runtime\bin\intel64\Release'
+          Copy-Item -Path (Join-Path $ovBin '*.dll')       -Destination $dest -Force
+          Copy-Item -Path (Join-Path $ovBin 'cache.json')  -Destination $dest -Force
+
+          $tbbBin = Join-Path $OPENVINO_ROOT 'runtime\3rdparty\tbb\bin'
+          Copy-Item -Path (Join-Path $tbbBin 'tbb*.dll') -Destination $dest -Force
+
+          # OpenVINO licensing
+          $licensingDest = Join-Path $dest 'openvino-licensing'
+          New-Item -ItemType Directory -Force -Path $licensingDest | Out-Null
+          Copy-Item -Path (Join-Path $OPENVINO_ROOT 'docs\licensing\*') -Destination $licensingDest -Recurse -Force
+
+          Copy-Item LICENSE $dest
+          7z a -snl llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip $dest\*
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -1624,6 +1671,7 @@ jobs:
             **Windows:**
             - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip)
             - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
+            - [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-opencl-adreno-arm64.zip)
             - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
             - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.3-x64.zip)
             - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)

.pi/gg/SYSTEM.md

@@ -25,13 +25,3 @@ Commits:
 - Do not explicitly set the git author in commits - rely on the default git config
 - Always use `--no-gpg-sign` when committing
 - Never `git push` without explicit confirmation from the user
-
-Resources (read on demand):
-- [CONTRIBUTING.md](CONTRIBUTING.md)
-- [Build documentation](docs/build.md)
-- [Server usage documentation](tools/server/README.md)
-- [Server development documentation](tools/server/README-dev.md)
-- [PEG parser](docs/development/parsing.md)
-- [Auto parser](docs/autoparser.md)
-- [Jinja engine](common/jinja/README.md)
-- [PR template](.github/pull_request_template.md)

CMakeLists.txt

@@ -222,6 +222,16 @@ if (LLAMA_BUILD_APP)
     add_subdirectory(app)
 endif()
 
+# Standalone libmtmd build without pulling in the rest of the tools/ tree.
+# Useful when packaging just the mtmd library for language bindings (e.g. an
+# Apple XCFramework, or a WASM build). When the full tools build is enabled,
+# mtmd is already built by the tools/ subdirectory above; this hook only fires
+# when LLAMA_BUILD_TOOLS is OFF to avoid double-adding the target.
+option(LLAMA_BUILD_MTMD "llama: build tools/mtmd library standalone" OFF)
+if (LLAMA_BUILD_MTMD AND NOT (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS))
+    add_subdirectory(tools/mtmd)
+endif()
+
 #
 # install
 #

CODEOWNERS

@@ -10,7 +10,7 @@
 # ggml-org/ggml-rpc         : rgerganov
 # ggml-org/ggml-sycl        : arthw
 # ggml-org/ggml-vulkan      : 0cc4m, jeffbolznv
-# ggml-org/ggml-webgpu      : reeselevine
+# ggml-org/ggml-webgpu      : reeselevine, yomaytk
 # ggml-org/ggml-zdnn        : taronaeo
 # ggml-org/llama-common     : ggerganov, aldehir, angt, danbev, ngxson, pwilkin
 # ggml-org/llama-mtmd       : ngxson

README.md

@@ -142,7 +142,9 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
 - [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview)
 - [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
-- [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38)
+- [x] [Liquid LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2)
+- [x] [Liquid LFM2.5 models](https://huggingface.co/collections/LiquidAI/lfm25)
+- [x] [Liquid Nanos](https://huggingface.co/collections/LiquidAI/liquid-nanos)
 - [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
 - [x] [BailingMoeV2 (Ring/Ling 2.0) models](https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
 - [x] [Mellum models](https://huggingface.co/JetBrains/models?search=mellum)

app/CMakeLists.txt

@@ -1,6 +1,6 @@
 set(TARGET llama-app)
 
-add_executable(${TARGET} llama.cpp)
+add_executable(${TARGET} llama.cpp download.cpp)
 set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama)
 
 target_link_libraries(${TARGET} PRIVATE

app/download.cpp

@@ -0,0 +1,71 @@
+#include "arg.h"
+#include "common.h"
+#include "download.h"
+#include "log.h"
+
+#include <cstdio>
+#include <filesystem>
+
+static void print_usage(int /*argc*/, char ** argv) {
+    printf(
+        "\nexamples:\n"
+        "  %s -hf ggml-org/gemma-3-4b-it-qat-GGUF\n"
+        "  %s -hf ggml-org/gemma-3-4b-it-qat-GGUF:Q4_K_M\n"
+        "  %s -hf ggml-org/models -hff model.gguf\n"
+        "  %s -mu https://example.com/model.gguf -m model.gguf\n"
+        "\n",
+        argv[0], argv[0], argv[0], argv[0]
+    );
+}
+
+int llama_download(int argc, char ** argv);
+
+int llama_download(int argc, char ** argv) {
+    common_init();
+
+    common_params params;
+    params.verbosity = LOG_LEVEL_ERROR;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DOWNLOAD, print_usage)) {
+        return 1;
+    }
+
+    const bool has_source = !params.model.hf_repo.empty() || !params.model.url.empty() ||
+                            !params.model.path.empty()    || !params.model.docker_repo.empty();
+    if (!has_source) {
+        fprintf(stderr, "error: no model source specified (use --hf-repo, --model-url, --model or --docker-repo)\n");
+        return 1;
+    }
+
+    try {
+        common_models_handler handler = common_models_handler_init(params, LLAMA_EXAMPLE_DOWNLOAD);
+        common_models_handler_apply(handler, params);
+    } catch (const std::exception & e) {
+        fprintf(stderr, "error: %s\n", e.what());
+        return 1;
+    }
+
+    if (!params.models_preset.empty()) {
+        // -hf pointed at a preset repo: print the preset path and stop
+        printf("%s\n", params.models_preset.c_str());
+        return 0;
+    }
+    if (params.model.path.empty()) {
+        fprintf(stderr, "error: model download failed\n");
+        return 1;
+    }
+    if (!std::filesystem::exists(params.model.path)) {
+        fprintf(stderr, "error: model file does not exist: %s\n", params.model.path.c_str());
+        return 1;
+    }
+
+    printf("%s\n", params.model.path.c_str());
+    if (!params.mmproj.path.empty()) {
+        printf("%s\n", params.mmproj.path.c_str());
+    }
+    if (!params.speculative.draft.mparams.path.empty()) {
+        printf("%s\n", params.speculative.draft.mparams.path.c_str());
+    }
+
+    return 0;
+}

app/llama.cpp

@@ -19,17 +19,23 @@ int llama_batched_bench(int argc, char ** argv);
 int llama_fit_params(int argc, char ** argv);
 int llama_quantize(int argc, char ** argv);
 int llama_perplexity(int argc, char ** argv);
+int llama_download(int argc, char ** argv);
 
-// hands the update over to the install script, which downloads and swaps the binary
+// Self-update is only supported for binaries built with llama-install.sh
 static int llama_update(int argc, char ** argv) {
     (void) argc;
     (void) argv;
 
+#ifdef LLAMA_INSTALL_BUILD
 #if defined(_WIN32)
     return system("powershell -NoProfile -ExecutionPolicy Bypass -Command \"irm https://llama.app/install.ps1 | iex\"");
 #else
     return system("curl -fsSL https://llama.app/install.sh | sh");
 #endif
+#else
+    printf("Updates are available only when installed from https://llama.app\n");
+    return 1;
+#endif
 }
 
 static const char * progname;
@@ -46,21 +52,30 @@ struct command {
     int (*func)(int, char **);
 };
 
+#ifdef LLAMA_INSTALL_BUILD
+#define UPDATE_HIDDEN false
+#else
+#define UPDATE_HIDDEN true
+#endif
+
 static const command cmds[] = {
-    {"serve",         "HTTP API server",                                    {"server"},   false, llama_server       },
-    {"cli",           "Command-line interactive interface",                 {"client"},   false, llama_cli          },
-    {"update",        "Update llama to the latest release",                 {},           false, llama_update       },
-    {"completion",    "Text completion",                                    {"complete"}, true,  llama_completion   },
-    {"bench",         "Benchmark prompt processing and text generation",    {},           true,  llama_bench        },
-    {"batched-bench", "Benchmark batched decoding performance",             {},           true,  llama_batched_bench},
-    {"fit-params",    "Compute parameters to fit a model in device memory", {},           true,  llama_fit_params   },
-    {"quantize",      "Quantize a model",                                   {},           true,  llama_quantize     },
-    {"perplexity",    "Compute model perplexity and KL divergence",         {},           true,  llama_perplexity   },
-    {"version",       "Show version",                                       {},           false, version            },
-    {"licenses",      "Show third-party licenses",                          {"credits"},  false, licenses           },
-    {"help",          "Show available commands",                            {},           false, help               },
+    {"serve",         "HTTP API server",                                    {"server"},   false,         llama_server       },
+    {"cli",           "Command-line interactive interface",                 {"client"},   false,         llama_cli          },
+    {"update",        "Update llama to the latest release",                 {},           UPDATE_HIDDEN, llama_update       },
+    {"download",      "Download a model",                                   {"get"},      false,         llama_download     },
+    {"completion",    "Text completion",                                    {"complete"}, true,          llama_completion   },
+    {"bench",         "Benchmark prompt processing and text generation",    {},           true,          llama_bench        },
+    {"batched-bench", "Benchmark batched decoding performance",             {},           true,          llama_batched_bench},
+    {"fit-params",    "Compute parameters to fit a model in device memory", {},           true,          llama_fit_params   },
+    {"quantize",      "Quantize a model",                                   {},           true,          llama_quantize     },
+    {"perplexity",    "Compute model perplexity and KL divergence",         {},           true,          llama_perplexity   },
+    {"version",       "Show version",                                       {},           false,         version            },
+    {"licenses",      "Show third-party licenses",                          {"credits"},  false,         licenses           },
+    {"help",          "Show available commands",                            {},           false,         help               },
 };
 
+#undef UPDATE_HIDDEN
+
 static int version(int argc, char ** argv) {
     printf("%s\n", llama_build_info());
     return 0;

build-xcframework.sh

@@ -13,6 +13,7 @@ LLAMA_BUILD_EXAMPLES=OFF
 LLAMA_BUILD_TOOLS=OFF
 LLAMA_BUILD_TESTS=OFF
 LLAMA_BUILD_SERVER=OFF
+LLAMA_BUILD_MTMD=ON
 GGML_METAL=ON
 GGML_METAL_EMBED_LIBRARY=ON
 GGML_BLAS_DEFAULT=ON
@@ -39,6 +40,7 @@ COMMON_CMAKE_ARGS=(
     -DLLAMA_BUILD_TOOLS=${LLAMA_BUILD_TOOLS}
     -DLLAMA_BUILD_TESTS=${LLAMA_BUILD_TESTS}
     -DLLAMA_BUILD_SERVER=${LLAMA_BUILD_SERVER}
+    -DLLAMA_BUILD_MTMD=${LLAMA_BUILD_MTMD}
     -DGGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY}
     -DGGML_BLAS_DEFAULT=${GGML_BLAS_DEFAULT}
     -DGGML_METAL=${GGML_METAL}
@@ -126,6 +128,8 @@ setup_framework_structure() {
     cp ggml/include/ggml-cpu.h     ${header_path}
     cp ggml/include/ggml-blas.h    ${header_path}
     cp ggml/include/gguf.h         ${header_path}
+    cp tools/mtmd/mtmd.h           ${header_path}
+    cp tools/mtmd/mtmd-helper.h    ${header_path}
 
     # Create module map (common for all platforms)
     cat > ${module_path}module.modulemap << EOF
@@ -247,6 +251,7 @@ combine_static_libraries() {
         "${base_dir}/${build_dir}/ggml/src/${release_dir}/libggml-cpu.a"
         "${base_dir}/${build_dir}/ggml/src/ggml-metal/${release_dir}/libggml-metal.a"
         "${base_dir}/${build_dir}/ggml/src/ggml-blas/${release_dir}/libggml-blas.a"
+        "${base_dir}/${build_dir}/tools/mtmd/${release_dir}/libmtmd.a"
     )
 
     # Create temporary directory for processing
@@ -410,6 +415,7 @@ cmake -B build-ios-sim -G Xcode \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-ios-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -424,6 +430,7 @@ cmake -B build-ios-device -G Xcode \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-ios-device --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -450,6 +457,7 @@ cmake -B build-visionos -G Xcode \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-visionos --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -465,6 +473,7 @@ cmake -B build-visionos-sim -G Xcode \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-visionos-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -481,6 +490,7 @@ cmake -B build-tvos-sim -G Xcode \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-tvos-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -496,6 +506,7 @@ cmake -B build-tvos-device -G Xcode \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-tvos-device --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 

common/CMakeLists.txt

@@ -80,8 +80,6 @@ add_library(${TARGET}
     http.h
     imatrix-loader.cpp
     imatrix-loader.h
-    json-partial.cpp
-    json-partial.h
     json-schema-to-grammar.cpp
     llguidance.cpp
     log.cpp

common/arg.cpp

@@ -17,6 +17,7 @@
 #   define NOMINMAX
 #endif
 #include <windows.h>
+#include <shellapi.h>
 #endif
 
 #define JSON_ASSERT GGML_ASSERT
@@ -285,108 +286,17 @@ static std::string clean_file_name(const std::string & fname) {
     return clean_fname;
 }
 
-static bool common_params_handle_remote_preset(common_params & params, llama_example ex) {
-    GGML_ASSERT(!params.model.hf_repo.empty());
-
-    // the returned hf_repo is without tag
-    auto [hf_repo, hf_tag] = common_download_split_repo_tag(params.model.hf_repo);
-
-    // "latest" tag (default if not specified) is translated to "default" preset
-    if (hf_tag == "latest") {
-        hf_tag = "default";
-    }
-
-    std::string model_endpoint = common_get_model_endpoint();
-    auto preset_url = model_endpoint + hf_repo + "/resolve/main/preset.ini";
-
-    // prepare local path for caching
-    auto preset_fname = clean_file_name(hf_repo + "_preset.ini");
-    auto preset_path = fs_get_cache_file(preset_fname);
-    common_download_opts opts;
-    opts.bearer_token = params.hf_token;
-    opts.offline = params.offline;
-
-    LOG_TRC("%s: looking for remote preset at %s\n", __func__, preset_url.c_str());
-    const int status = common_download_file_single(preset_url, preset_path, opts);
-    const bool has_preset = status >= 200 && status < 400;
-
-    // remote preset is optional, so we don't error out if not found
-    if (has_preset) {
-        LOG_TRC("%s: applying remote preset from %s\n", __func__, preset_url.c_str());
-        common_preset_context ctx(ex, /* only_remote_allowed */ true);
-        common_preset global;
-        auto remote_presets = ctx.load_from_ini(preset_path, global);
-        remote_presets = ctx.cascade(global, remote_presets);
-        if (remote_presets.find(hf_tag) != remote_presets.end()) {
-            common_preset preset = remote_presets.at(hf_tag);
-            LOG_INF("\n%s", preset.to_ini().c_str()); // to_ini already added trailing newline
-            preset.apply_to_params(params);
-        } else {
-            throw std::runtime_error("Remote preset.ini does not contain [" + std::string(hf_tag) + "] section");
-        }
-    } else {
-        LOG_TRC("%s: no remote preset found, skipping\n", __func__);
-    }
-
-    return has_preset;
-}
-
 struct handle_model_result {
     bool found_mmproj = false;
     common_params_model mmproj;
 
     bool found_mtp = false;
     common_params_model mtp;
+
+    bool found_preset = false;
+    std::string preset_path;
 };
 
-static handle_model_result common_params_handle_model(struct common_params_model & model,
-                                                      const common_download_opts & opts) {
-    handle_model_result result;
-
-    if (!model.docker_repo.empty()) {
-        model.path = common_docker_resolve_model(model.docker_repo);
-        model.name = model.docker_repo;
-    } else if (!model.hf_repo.empty()) {
-        // If -m was used with -hf, treat the model "path" as the hf_file to download
-        if (model.hf_file.empty() && !model.path.empty()) {
-            model.hf_file = model.path;
-            model.path = "";
-        }
-        common_download_opts hf_opts = opts;
-        auto download_result = common_download_model(model, hf_opts);
-
-        if (download_result.model_path.empty()) {
-            throw std::runtime_error("failed to download model from Hugging Face");
-        }
-
-        model.name = model.hf_repo;
-        model.path = download_result.model_path;
-
-        if (!download_result.mmproj_path.empty()) {
-            result.found_mmproj = true;
-            result.mmproj.path  = download_result.mmproj_path;
-        }
-
-        if (!download_result.mtp_path.empty()) {
-            result.found_mtp = true;
-            result.mtp.path  = download_result.mtp_path;
-        }
-    } else if (!model.url.empty()) {
-        if (model.path.empty()) {
-            auto f = string_split<std::string>(model.url, '#').front();
-            f = string_split<std::string>(f, '?').front();
-            model.path = fs_get_cache_file(string_split<std::string>(f, '/').back());
-        }
-
-        auto download_result = common_download_model(model, opts);
-        if (download_result.model_path.empty()) {
-            throw std::runtime_error("failed to download model from " + model.url);
-        }
-    }
-
-    return result;
-}
-
 const std::vector<ggml_type> kv_cache_types = {
     GGML_TYPE_F32,
     GGML_TYPE_F16,
@@ -431,61 +341,241 @@ static bool parse_bool_value(const std::string & value) {
 }
 
 //
-// CLI argument parsing functions
+// common_models_handler
 //
 
-bool common_params_handle_models(common_params & params, llama_example curr_ex) {
-    const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
-                                         params.speculative.types.end(),
-                                         COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
+static std::string get_default_local_path(const std::string & url) {
+    auto f = string_split<std::string>(url, '#').front();
+    f = string_split<std::string>(f, '?').front();
+    return fs_get_cache_file(string_split<std::string>(f, '/').back());
+}
 
+common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) {
+    common_download_hf_plan plan;
+    common_download_hf_plan plan_spec;
+    common_download_hf_plan plan_voc;
     common_download_opts opts;
+
+    const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
+                                        params.speculative.types.end(),
+                                        COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
+
+    // only download mmproj if the current example is using it
+    bool use_mmproj = false;
+    for (const auto & ex : mmproj_examples) {
+        if (curr_ex == ex) {
+            use_mmproj = true;
+            break;
+        }
+    }
+
     opts.bearer_token    = params.hf_token;
     opts.offline         = params.offline;
-    opts.skip_download   = params.skip_download;
     opts.download_mtp    = spec_type_draft_mtp;
-    opts.download_mmproj = !params.no_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty();
+    opts.download_mmproj = use_mmproj && !params.no_mmproj
+                        && params.mmproj.path.empty() && params.mmproj.url.empty();
 
-    // sub-models (draft, mmproj, vocoder) are explicitly specified by the user,
-    // so we should not auto-discover mtp/mmproj siblings for them
-    common_download_opts sub_opts = opts;
-    sub_opts.download_mtp    = false;
-    sub_opts.download_mmproj = false;
+    if (!params.model.hf_repo.empty()) {
+        plan = common_download_get_hf_plan(params.model, opts);
+    }
 
-    try {
-        auto res = common_params_handle_model(params.model, opts);
-        if (params.no_mmproj) {
-            params.mmproj = {};
-        } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
-            // optionally, handle mmproj model when -hf is specified
-            params.mmproj = res.mmproj;
+    if (!params.speculative.draft.mparams.hf_repo.empty()) {
+        plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts);
+    }
+
+    if (!params.vocoder.model.hf_repo.empty()) {
+        plan_voc = common_download_get_hf_plan(params.vocoder.model, opts);
+    }
+
+    return common_models_handler{plan, plan_spec, plan_voc, opts};
+}
+
+bool common_models_handler_is_preset_repo(const common_models_handler & handler) {
+    return !handler.plan.preset.url.empty();
+}
+
+static std::vector<common_download_task> build_url_tasks(const common_params_model & model, common_download_opts opts) {
+    auto parts = common_download_get_all_parts(model.url);
+    std::vector<common_download_task> tasks;
+
+    // single-part: download straight to model.path if the user gave one (-m), else the cache default
+    if (parts.size() == 1) {
+        common_download_task task;
+        task.url        = parts[0];
+        task.local_path = model.path.empty() ? get_default_local_path(parts[0]) : model.path;
+        task.opts       = opts;
+        tasks.push_back(std::move(task));
+        return tasks;
+    }
+
+    // multi-part: place each part under the user's -m directory (if given), else the cache default
+    std::string base_dir;
+    if (!model.path.empty()) {
+        auto pos = model.path.rfind('/');
+        base_dir = pos == std::string::npos ? std::string(".") : model.path.substr(0, pos);
+    }
+
+    for (const auto & part : parts) {
+        common_download_task task;
+        task.url  = part;
+        task.opts = opts;
+
+        std::string local = get_default_local_path(part);
+        if (!base_dir.empty()) {
+            auto pos = local.rfind('/');
+            std::string name = pos == std::string::npos ? local : local.substr(pos + 1);
+            local = base_dir + "/" + name;
         }
-        // only download mmproj if the current example is using it
-        for (const auto & ex : mmproj_examples) {
-            if (curr_ex == ex) {
-                common_params_handle_model(params.mmproj, sub_opts);
-                break;
+        task.local_path = local;
+        tasks.push_back(std::move(task));
+    }
+    return tasks;
+}
+
+void common_models_handler_apply(common_models_handler & handler, common_params & params, common_download_callback * callback) {
+    std::vector<common_download_task> tasks;
+
+    auto & plan      = handler.plan;
+    auto & plan_spec = handler.plan_spec;
+    auto & plan_voc  = handler.plan_voc;
+
+    auto opts = handler.opts; // copy
+    opts.callback = callback;
+
+    // handle plain "url" if needed
+    auto handle_url = [&](common_params_model & model) {
+        if (!model.url.empty()) {
+            if (model.path.empty()) {
+                model.path = get_default_local_path(model.url);
             }
         }
+    };
+    handle_url(params.model);
+    handle_url(params.mmproj);
+    handle_url(params.vocoder.model);
+    handle_url(params.speculative.draft.mparams);
 
-        // when --spec-type mtp is set and no draft model was provided explicitly,
-        // fall back to the MTP head discovered alongside the -hf model
-        if (spec_type_draft_mtp && res.found_mtp &&
-            params.speculative.draft.mparams.path.empty() &&
-            params.speculative.draft.mparams.hf_repo.empty() &&
-            params.speculative.draft.mparams.url.empty()) {
-            params.speculative.draft.mparams.path = res.mtp.path;
+    // optionally, if docker repo is set, resolve it
+    if (!params.model.docker_repo.empty()) {
+        params.model.url  = common_docker_resolve_model(params.model.docker_repo);
+        params.model.path = get_default_local_path(params.model.url);
+    }
+
+    // handle plain "url" tasks (non-hf)
+    if (!params.model.url.empty()) {
+        auto url_tasks = build_url_tasks(params.model, opts);
+        // the first part is what gets loaded, so point params.model.path at it
+        if (!url_tasks.empty()) {
+            std::string first_path = url_tasks.front().local_path;
+            url_tasks.front().on_done = [&]() { params.model.path = first_path; };
+        }
+        for (auto & task : url_tasks) {
+            tasks.push_back(std::move(task));
+        }
+    }
+    if (!params.mmproj.url.empty()) {
+        common_download_task task;
+        task.url        = params.mmproj.url;
+        task.local_path = params.mmproj.path;
+        task.opts       = opts;
+        tasks.push_back(task);
+    }
+    if (!params.vocoder.model.url.empty()) {
+        common_download_task task;
+        task.url        = params.vocoder.model.url;
+        task.local_path = params.vocoder.model.path;
+        task.opts       = opts;
+        tasks.push_back(task);
+    }
+    if (!params.speculative.draft.mparams.url.empty()) {
+        common_download_task task;
+        task.url        = params.speculative.draft.mparams.url;
+        task.local_path = params.speculative.draft.mparams.path;
+        task.opts       = opts;
+        tasks.push_back(task);
+    }
+
+    // handle hf_plan tasks
+    auto add_tasks = [&opts, &tasks](const hf_cache::hf_files & model_files, common_params_model & model) {
+        for (size_t i = 0; i < model_files.size(); ++i) {
+            auto & model_file = model_files[i];
+            bool is_first = (i == 0);
+            tasks.emplace_back(model_file, opts, [&, is_first]() {
+                if (is_first) {
+                    // only use first part as model path
+                    model.path = hf_cache::finalize_file(model_file);
+                } else {
+                    hf_cache::finalize_file(model_file);
+                }
+            });
+        }
+    };
+    if (!plan.model_files.empty()) {
+        add_tasks(plan.model_files, params.model);
+    }
+    if (!plan.mmproj.local_path.empty()) {
+        tasks.emplace_back(plan.mmproj, opts, [&]() {
+            params.mmproj.path = hf_cache::finalize_file(plan.mmproj);
+        });
+    }
+    if (!plan.mtp.local_path.empty()) {
+        tasks.emplace_back(plan.mtp, opts, [&]() {
+            // only fall back to the discovered MTP head when no draft was explicitly provided
+            if (params.speculative.draft.mparams.empty()) {
+                params.speculative.draft.mparams.path = hf_cache::finalize_file(plan.mtp);
+            } else {
+                hf_cache::finalize_file(plan.mtp);
+            }
+        });
+    }
+    if (!plan.preset.local_path.empty()) {
+        tasks.emplace_back(plan.preset, opts, [&]() {
+            // if HF repo is a preset repo, we simply run server in router mode with the preset.ini file
+            params.models_preset_hf = params.model.hf_repo; // only for showing a warning
+            params.models_preset    = hf_cache::finalize_file(plan.preset);
+            params.model = common_params_model{}; // make sure to clear model, so server starts in router mode
+        });
+    }
+
+    // handle plan_spec (e.g. --spec-draft-hf)
+    if (!plan_spec.model_files.empty()) {
+        add_tasks(plan_spec.model_files, params.speculative.draft.mparams);
+    }
+
+    // handle vocoder plan (e.g. --hf-repo-v)
+    if (!plan_voc.model_files.empty()) {
+        add_tasks(plan_voc.model_files, params.vocoder.model);
+    }
+
+    // run all tasks in parallel
+    if (!params.offline) {
+        // if duplicated files are found, only download once (but still call on_done for each task)
+        std::unordered_map<std::string, common_download_task *> unique_tasks;
+        for (auto & task : tasks) {
+            auto it = unique_tasks.find(task.local_path);
+            if (it == unique_tasks.end()) {
+                unique_tasks[task.local_path] = &task;
+            }
+        }
+        std::vector<common_download_task> unique_tasks_vec;
+        for (auto & pair : unique_tasks) {
+            unique_tasks_vec.push_back(*pair.second);
+        }
+        common_download_run_tasks(unique_tasks_vec);
+    }
+
+    // download successful, update params with the downloaded paths
+    for (const auto & task : tasks) {
+        if (task.on_done) {
+            task.on_done();
         }
-        common_params_handle_model(params.speculative.draft.mparams, sub_opts);
-        common_params_handle_model(params.vocoder.model,             sub_opts);
-        return true;
-    } catch (const common_skip_download_exception &) {
-        return false;
-    } catch (const std::exception &) {
-        throw;
     }
 }
 
+//
+// CLI argument parsing functions
+//
+
 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
     common_params & params = ctx_arg.params;
 
@@ -601,30 +691,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     // parse the first time to get -hf option (used for remote preset)
     parse_cli_args();
 
-    // export_graph_ops loads only metadata
-    const bool skip_model_download = ctx_arg.ex == LLAMA_EXAMPLE_EXPORT_GRAPH_OPS;
-
-    // maybe handle remote preset
-    if (!params.model.hf_repo.empty() && !skip_model_download) {
-        std::string cli_hf_repo = params.model.hf_repo;
-        bool has_preset = common_params_handle_remote_preset(params, ctx_arg.ex);
-
-        // special case: if hf_repo explicitly set by preset, we need to preserve it (ignore CLI value)
-        // this is useful when we have one HF repo pointing to other HF repos (one model - multiple GGUFs)
-        std::string preset_hf_repo = params.model.hf_repo;
-        bool preset_has_hf_repo = preset_hf_repo != cli_hf_repo;
-
-        if (has_preset) {
-            // re-parse CLI args to override preset values
-            parse_cli_args();
-        }
-
-        // preserve hf_repo from preset if needed
-        if (preset_has_hf_repo) {
-            params.model.hf_repo = preset_hf_repo;
-        }
-    }
-
     postprocess_cpu_params(params.cpuparams,       nullptr);
     postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
 
@@ -635,15 +701,26 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
-    // handle model and download
-    if (!skip_model_download) {
-        common_params_handle_models(params, ctx_arg.ex);
-    }
+    const bool skip_model_download =
+        // server will call common_params_handle_models() later, so we skip it here
+        ctx_arg.ex == LLAMA_EXAMPLE_SERVER ||
+        // download calls common_params_handle_models() itself and prints the paths
+        ctx_arg.ex == LLAMA_EXAMPLE_DOWNLOAD ||
+        // export_graph_ops loads only metadata
+        ctx_arg.ex == LLAMA_EXAMPLE_EXPORT_GRAPH_OPS;
 
-    // model is required (except for server)
-    // TODO @ngxson : maybe show a list of available models in CLI in this case
-    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !skip_model_download && !params.usage && !params.completion) {
-        throw std::invalid_argument("error: --model is required\n");
+    if (!skip_model_download) {
+        // handle model and download
+        common_models_handler handler = common_models_handler_init(params, ctx_arg.ex);
+        common_models_handler_apply(handler, params);
+
+        // model is required (except for server)
+        // TODO @ngxson : maybe show a list of available models in CLI in this case
+        if (params.model.path.empty()
+                && !params.usage
+                && !params.completion) {
+            throw std::invalid_argument("error: --model is required\n");
+        }
     }
 
     if (params.escape) {
@@ -707,15 +784,19 @@ static void common_params_print_usage(common_params_context & ctx_arg) {
             common_options.push_back(&opt);
         }
     }
-    printf("----- common params -----\n\n");
-    print_options(common_options);
-    printf("\n\n----- sampling params -----\n\n");
-    print_options(sampling_options);
-    printf("\n\n----- speculative params -----\n\n");
-    print_options(spec_options);
-    // TODO: maybe convert enum llama_example to string
-    printf("\n\n----- example-specific params -----\n\n");
-    print_options(specific_options);
+    bool first = true;
+    auto print_section = [&](const char * header, std::vector<common_arg *> & options) {
+        if (options.empty()) {
+            return;
+        }
+        printf("%s----- %s -----\n\n", first ? "" : "\n\n", header);
+        first = false;
+        print_options(options);
+    };
+    print_section("common params",           common_options);
+    print_section("sampling params",         sampling_options);
+    print_section("speculative params",      spec_options);
+    print_section("example-specific params", specific_options);
 }
 
 static void common_params_print_completion(common_params_context & ctx_arg) {
@@ -937,7 +1018,44 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
     return true;
 }
 
+#ifdef _WIN32
+struct utf8_argv {
+    std::vector<std::string> buf;
+    std::vector<char*> ptrs;
+};
+
+static utf8_argv make_utf8_argv() {
+    utf8_argv out;
+    int wargc = 0;
+    LPWSTR* wargv = CommandLineToArgvW(GetCommandLineW(), &wargc);
+    if (!wargv) return out;
+
+    out.buf.reserve(wargc);
+    for (int i = 0; i < wargc; ++i) {
+        int n = WideCharToMultiByte(CP_UTF8, WC_ERR_INVALID_CHARS, wargv[i], -1, nullptr, 0, nullptr, nullptr);
+        if (n <= 0) { out.buf.emplace_back(); continue; }
+        auto& s = out.buf.emplace_back();
+        s.resize(static_cast<size_t>(n - 1));
+        (void)WideCharToMultiByte(CP_UTF8, 0, wargv[i], -1, s.data(), n, nullptr, nullptr);
+    }
+    LocalFree(wargv);
+
+    out.ptrs.reserve(out.buf.size() + 1);
+    for (auto& s : out.buf) out.ptrs.push_back(s.data());
+    out.ptrs.push_back(nullptr);
+    return out;
+}
+#endif
+
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
+#ifdef _WIN32
+    auto utf8 = make_utf8_argv();
+    // repair argv only when it matches the process command line
+    if (static_cast<int>(utf8.buf.size()) == argc) {
+        argv = utf8.ptrs.data();
+    }
+#endif
+
     auto ctx_arg = common_params_parser_init(params, ex, print_usage);
     const common_params params_org = ctx_arg.params; // the example can modify the default params
 
@@ -1078,7 +1196,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
      * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
      */
     auto add_opt = [&](common_arg arg) {
-        if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) {
+        // download only exposes the handful of args explicitly tagged for it
+        const bool inherit_common = ex != LLAMA_EXAMPLE_DOWNLOAD;
+        if ((arg.in_example(ex) || (inherit_common && arg.in_example(LLAMA_EXAMPLE_COMMON))) && !arg.is_exclude(ex)) {
             ctx_arg.options.push_back(std::move(arg));
         }
     };
@@ -1089,7 +1209,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.usage = true;
         }
-    ));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}));
     add_opt(common_arg(
         {"--version"},
         "show version and build info",
@@ -2211,7 +2331,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, bool value) {
             params.no_mmproj = !value;
         }
-    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_AUTO"));
+    ).set_examples({LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_MMPROJ_AUTO"));
     add_opt(common_arg(
         {"--mmproj-offload"},
         {"--no-mmproj-offload"},
@@ -2610,14 +2730,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.model.path = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_MODEL"));
     add_opt(common_arg(
         {"-mu", "--model-url"}, "MODEL_URL",
         "model download url (default: unused)",
         [](common_params & params, const std::string & value) {
             params.model.url = value;
         }
-    ).set_env("LLAMA_ARG_MODEL_URL"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_MODEL_URL"));
     add_opt(common_arg(
         { "-dr", "--docker-repo" }, "[<repo>/]<model>[:quant]",
         "Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.\n"
@@ -2626,7 +2746,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.model.docker_repo = value;
         }
-    ).set_env("LLAMA_ARG_DOCKER_REPO"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_DOCKER_REPO"));
     add_opt(common_arg(
         {"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]",
         "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"
@@ -2636,14 +2756,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.model.hf_repo = value;
         }
-    ).set_env("LLAMA_ARG_HF_REPO"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_HF_REPO"));
     add_opt(common_arg(
         {"-hff", "--hf-file"}, "FILE",
         "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)",
         [](common_params & params, const std::string & value) {
             params.model.hf_file = value;
         }
-    ).set_env("LLAMA_ARG_HF_FILE"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_HF_FILE"));
     add_opt(common_arg(
         {"-hfv", "-hfrv", "--hf-repo-v"}, "<user>/<model>[:quant]",
         "Hugging Face model repository for the vocoder model (default: unused)",
@@ -2664,7 +2784,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.hf_token = value;
         }
-    ).set_env("HF_TOKEN"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("HF_TOKEN"));
+    add_opt(common_arg(
+        {"--mtp"},
+        "also download the multi-token prediction (MTP) head, if available (default: unused)",
+        [](common_params & params) {
+            params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
+        }
+    ).set_examples({LLAMA_EXAMPLE_DOWNLOAD}));
     add_opt(common_arg(
         {"--context-file"}, "FNAME",
         "file to load context from (use comma-separated values to specify multiple files)",
@@ -2874,62 +3001,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.api_prefix = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX"));
-    // Deprecated: use --ui-config instead (kept for backward compat)
     add_opt(common_arg(
-        {"--webui-config"}, "JSON",
-        "[DEPRECATED: use --ui-config] JSON that provides default WebUI settings (overrides WebUI defaults)",
-        [](common_params & params, const std::string & value) {
-            params.ui_config_json = value;
-            params.webui_config_json = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG"));
-
-    add_opt(common_arg(
-        {"--ui-config"}, "JSON",
+        {"--ui-config", "--webui-config"}, "JSON",
         "JSON that provides default UI settings (overrides UI defaults)",
         [](common_params & params, const std::string & value) {
             params.ui_config_json = value;
-            params.webui_config_json = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_CONFIG"));
-
-    // Deprecated: use --ui-config-file instead (kept for backward compat)
     add_opt(common_arg(
-        {"--webui-config-file"}, "PATH",
-        "[DEPRECATED: use --ui-config-file] JSON file that provides default WebUI settings (overrides WebUI defaults)",
-        [](common_params & params, const std::string & value) {
-            params.ui_config_json = read_file(value);
-            params.webui_config_json = params.ui_config_json;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG_FILE"));
-
-    add_opt(common_arg(
-        {"--ui-config-file"}, "PATH",
+        {"--ui-config-file", "--webui-config-file"}, "PATH",
         "JSON file that provides default UI settings (overrides UI defaults)",
         [](common_params & params, const std::string & value) {
             params.ui_config_json = read_file(value);
-            params.webui_config_json = params.ui_config_json;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_CONFIG_FILE"));
-
-    // Deprecated: use --ui-mcp-proxy instead (kept for backward compat)
     add_opt(common_arg(
-        {"--webui-mcp-proxy"},
-        {"--no-webui-mcp-proxy"},
-        "[DEPRECATED: use --ui-mcp-proxy/--no-ui-mcp-proxy] experimental: whether to enable MCP CORS proxy",
-        [](common_params & params, bool value) {
-            params.ui_mcp_proxy = value;
-            params.webui_mcp_proxy = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_MCP_PROXY"));
-
-    add_opt(common_arg(
-        {"--ui-mcp-proxy"},
-        {"--no-ui-mcp-proxy"},
+        {"--ui-mcp-proxy", "--webui-mcp-proxy"},
+        {"--no-ui-mcp-proxy", "--no-webui-mcp-proxy"},
         "experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)",
         [](common_params & params, bool value) {
             params.ui_mcp_proxy = value;
-            params.webui_mcp_proxy = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_MCP_PROXY"));
     add_opt(common_arg(
@@ -2941,24 +3032,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.server_tools = parse_csv_row(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
-    // Deprecated: use --ui/--no-ui instead (kept for backward compat)
     add_opt(common_arg(
-        {"--webui"},
-        {"--no-webui"},
-        "[DEPRECATED: use --ui/--no-ui] whether to enable the Web UI",
+        {"-ag", "--agent"},
+        {"-no-ag", "--no-agent"},
+        "whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)",
         [](common_params & params, bool value) {
-            params.ui = value;
-            params.webui = value;
+            if (value) {
+                params.server_tools = {"all"};
+                params.ui_mcp_proxy = true;
+            } else {
+                params.server_tools.clear();
+                params.ui_mcp_proxy = false;
+            }
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI"));
-
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_AGENT"));
     add_opt(common_arg(
-        {"--ui"},
-        {"--no-ui"},
+        {"--ui", "--webui"},
+        {"--no-ui", "--no-webui"},
         string_format("whether to enable the Web UI (default: %s)", params.ui ? "enabled" : "disabled"),
         [](common_params & params, bool value) {
             params.ui = value;
-            params.webui = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI"));
     add_opt(common_arg(
@@ -2989,7 +3082,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
     add_opt(common_arg(
         {"--api-key-file"}, "FNAME",
-        "path to file containing API keys (default: none)",
+        "path to file containing API keys, one per line; lines starting with a hash are treated as comments (default: none)",
         [](common_params & params, const std::string & value) {
             std::ifstream key_file(value);
             if (!key_file) {
@@ -2997,7 +3090,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
             std::string key;
             while (std::getline(key_file, key)) {
-                if (!key.empty()) {
+                if (!key.empty() && key[0] != '#') {
                     params.api_keys.push_back(key);
                 }
             }
@@ -3655,6 +3748,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "draft model for speculative decoding (default: unused)",
         [](common_params & params, const std::string & value) {
             params.speculative.draft.mparams.path = value;
+            params.speculative.draft.mparams.hf_file = value; // will be used if --spec-draft-hf is set
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_MODEL"));
     add_opt(common_arg(

common/arg.h

@@ -1,12 +1,14 @@
 #pragma once
 
 #include "common.h"
+#include "download.h"
 
 #include <set>
 #include <map>
 #include <string>
 #include <vector>
 #include <cstring>
+#include <memory>
 
 // pseudo-env variable to identify preset-only arguments
 #define COMMON_ARG_PRESET_LOAD_ON_STARTUP "__PRESET_LOAD_ON_STARTUP"
@@ -129,11 +131,21 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
 // see: https://github.com/ggml-org/llama.cpp/issues/18163
 void common_params_add_preset_options(std::vector<common_arg> & args);
 
-// populate model paths (main model, mmproj, etc) from -hf if necessary
-// return true if the model is ready to use
-// throw an exception if there is an error that prevents the model from being used (e.g. network error, model not found, etc)
-// if params.skip_download is true, no downloads will be attempted. return false if the model is invalid or missing (e.g. ETag check failed)
-bool common_params_handle_models(common_params & params, llama_example curr_ex);
+struct common_models_handler {
+    common_download_hf_plan plan;
+    common_download_hf_plan plan_spec;
+    common_download_hf_plan plan_voc;
+    common_download_opts opts;
+};
+
+// initialize downloading opts and hf_plan if needed, but does not download anything yet
+common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex);
+
+// check if the model is a preset repo (i.e. has a preset file)
+bool common_models_handler_is_preset_repo(const common_models_handler & handler);
+
+// download and update params with the downloaded model path
+void common_models_handler_apply(common_models_handler & handler, common_params & params, common_download_callback * callback = nullptr);
 
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

common/chat-auto-parser-generator.cpp

@@ -395,10 +395,11 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
                                            arguments.name_suffix) +
                            arguments.value_prefix +
                            (schema_info.resolves_to_string(param_schema) ?
-                                p.tool_arg_string_value(until_suffix) :
-                                p.tool_arg_json_value(p.schema(
-                                    p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false))) +
-                           p.tool_arg_close(p.literal(arguments.value_suffix)));
+                                p.ac(p.tool_arg_string_value(until_suffix) +
+                                    p.tool_arg_close(p.literal(arguments.value_suffix)), arguments.value_suffix) :
+                                (p.tool_arg_json_value(p.schema(
+                                    p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false)) +
+                                    p.tool_arg_close(p.literal(arguments.value_suffix)))));
 
             auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
             if (is_required) {

common/chat.cpp

@@ -90,41 +90,93 @@ std::string common_chat_msg::render_content(const std::string & delimiter) const
     return text;
 }
 
-std::vector<common_chat_msg_span> common_chat_split_by_role(const std::string & prompt, const std::vector<common_chat_msg_delimiter> & delims) {
-    if (delims.empty() || prompt.empty()) {
-        return {};
+common_chat_role common_chat_role_from_string(const std::string & role) {
+    if (role == "system")    { return COMMON_CHAT_ROLE_SYSTEM;    }
+    if (role == "assistant") { return COMMON_CHAT_ROLE_ASSISTANT; }
+    if (role == "user")      { return COMMON_CHAT_ROLE_USER;      }
+    if (role == "tool")      { return COMMON_CHAT_ROLE_TOOL;      }
+    return COMMON_CHAT_ROLE_UNKNOWN;
+}
+
+const char * common_chat_role_to_string(common_chat_role role) {
+    switch (role) {
+        case COMMON_CHAT_ROLE_SYSTEM:    return "system";
+        case COMMON_CHAT_ROLE_ASSISTANT: return "assistant";
+        case COMMON_CHAT_ROLE_USER:      return "user";
+        case COMMON_CHAT_ROLE_TOOL:      return "tool";
+        case COMMON_CHAT_ROLE_UNKNOWN:   return "";
+    }
+    return "";
+}
+
+json common_chat_msg_delimiters::to_json() const {
+    json result = json::array();
+    for (const auto & d : delimiters) {
+        result.push_back({
+            { "role",      common_chat_role_to_string(d.role) },
+            { "delimiter", d.delimiter                        },
+        });
+    }
+    return result;
+}
+
+common_chat_msg_delimiters common_chat_msg_delimiters_parse(const json & delimiters) {
+    common_chat_msg_delimiters result;
+
+    if (!delimiters.is_array()) {
+        return result;
     }
 
-    auto parser = build_peg_parser([&](common_peg_parser_builder & p) {
-        std::vector<std::string>       all_delims;
-        std::vector<common_peg_parser> tagged_messages;
-
-        all_delims.reserve(delims.size());
-        tagged_messages.reserve(delims.size());
-        for (const auto & d : delims) {
-            all_delims.push_back(d.delimiter);
+    result.delimiters.reserve(delimiters.size());
+    for (const auto & d : delimiters) {
+        if (!d.is_object()) {
+            continue;
         }
-
-        auto any_delim = p.until_one_of(all_delims);
-        for (const auto & d : delims) {
-            tagged_messages.push_back(p.tag(d.role, p.literal(d.delimiter) + any_delim));
-        }
-
-        return any_delim + p.zero_or_more(p.choice(tagged_messages)) + p.end();
-    });
-
-    common_peg_parse_context ctx(prompt);
-    const auto result = parser.parse(ctx);
-    if (!result.success()) {
-        return {};
+        result.delimiters.push_back({
+            common_chat_role_from_string(d.value("role", std::string())),
+            d.value("delimiter", std::string()),
+        });
     }
 
-    std::vector<common_chat_msg_span> spans;
-    ctx.ast.visit(result, [&](const common_peg_ast_node & node) {
-        if (!node.tag.empty()) {
-            spans.push_back({ node.tag, node.start, node.end - node.start });
+    return result;
+}
+
+void common_chat_msg_delimiters::tokenize(const llama_vocab * vocab) {
+    for (auto & d : delimiters) {
+        d.tokens = common_tokenize(vocab, d.delimiter, false, true);
+    }
+}
+
+common_chat_msg_spans common_chat_msg_delimiters::split(const llama_tokens & tokens, const std::map<size_t, size_t> & skips) const {
+    std::vector<std::pair<common_chat_role, size_t>> matches;
+
+    auto skip = skips.begin();
+    for (size_t i = 0; i < tokens.size();) {
+        if (skip != skips.end() && i == skip->first) {
+            i += skip->second;
+            ++skip;
+            continue;
         }
-    });
+        for (const auto & d : delimiters) {
+            if (i + d.tokens.size() > tokens.size()) {
+                continue;
+            }
+            if (std::equal(d.tokens.begin(), d.tokens.end(), tokens.begin() + i)) {
+                matches.emplace_back(d.role, i);
+                break;
+            }
+        }
+        i++;
+    }
+
+    matches.emplace_back(COMMON_CHAT_ROLE_UNKNOWN, tokens.size());
+
+    common_chat_msg_spans spans;
+    for (size_t i = 0; i + 1 < matches.size(); i++) {
+        const auto & curr = matches[i];
+        const auto & next = matches[i + 1];
+        spans.add(curr.first, curr.second, next.second - curr.second);
+    }
 
     return spans;
 }
@@ -1081,13 +1133,13 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
 
     data.prompt            = prompt;
     data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override= */ adjusted_messages);
-    data.message_spans = common_chat_split_by_role(prompt, {
-        { "assistant", "<|start|>assistant" },
-        { "user",      "<|start|>user"      },
-        { "system",    "<|start|>developer" },
-        { "system",    "<|start|>system"    },
-        { "tool",      "<|start|>functions" },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
+        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>developer" },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
+        { COMMON_CHAT_ROLE_TOOL,      "<|start|>functions" },
+    };
 
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = true;
@@ -1228,10 +1280,10 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
         data.prompt += data.generation_prompt;
     }
 
-    data.message_spans = common_chat_split_by_role(data.prompt, {
-        { "user",      "<|turn>user\n"  },
-        { "assistant", "<|turn>model\n" },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_USER,      "<|turn>user"  },
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|turn>model" },
+    };
 
     data.format            = COMMON_CHAT_FORMAT_PEG_GEMMA4;
     data.supports_thinking  = true;
@@ -2030,15 +2082,15 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t
         RESULT_START, RESULT_END,
     };
 
-    // Split the rendered prompt into per-role message spans. Tool results are rendered with the
+    // Declare per-role message delimiters. Tool results are rendered with the
     // system token followed by <|START_TOOL_RESULT|>, so the "tool" delimiter must be listed before
     // the plain "system" one (it is a strict superset, and the role split tries delimiters in order).
-    data.message_spans = common_chat_split_by_role(data.prompt, {
-        { "assistant", GEN_PREFIX },
-        { "user",      TURN_START + USER },
-        { "tool",      TURN_START + SYSTEM + RESULT_START },
-        { "system",    TURN_START + SYSTEM },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, GEN_PREFIX },
+        { COMMON_CHAT_ROLE_USER,      TURN_START + USER },
+        { COMMON_CHAT_ROLE_TOOL,      TURN_START + SYSTEM + RESULT_START },
+        { COMMON_CHAT_ROLE_SYSTEM,    TURN_START + SYSTEM },
+    };
 
     auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
     auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
@@ -2526,17 +2578,15 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         autoparser.analyze_template(tmpl);
         auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser);
 
-        std::vector<common_chat_msg_delimiter> delimiters;
+        common_chat_msg_delimiters delimiters;
         if (!autoparser.assistant_start.empty()) {
-            delimiters.push_back({ "assistant", autoparser.assistant_start });
+            delimiters.add(COMMON_CHAT_ROLE_ASSISTANT, autoparser.assistant_start);
         }
         if (!autoparser.user_start.empty()) {
-            delimiters.push_back({ "user", autoparser.user_start });
+            delimiters.add(COMMON_CHAT_ROLE_USER, autoparser.user_start);
         }
 
-        if (!delimiters.empty()) {
-            auto_params.message_spans = common_chat_split_by_role(auto_params.prompt, delimiters);
-        }
+        auto_params.message_delimiters = std::move(delimiters);
 
         auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE;
         if (auto_params.supports_thinking) {
@@ -2708,5 +2758,9 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
 std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_templates * chat_templates) {
     GGML_ASSERT(chat_templates != nullptr);
     GGML_ASSERT(chat_templates->template_default != nullptr);
+    if (chat_templates->template_tool_use != nullptr) {
+        // take the more expressive template when available
+        return chat_templates->template_tool_use->caps.to_map();
+    }
     return chat_templates->template_default->caps.to_map();
 }

common/chat.h

@@ -143,15 +143,75 @@ struct common_chat_msg_diff {
     }
 };
 
+enum common_chat_role {
+    COMMON_CHAT_ROLE_UNKNOWN,
+    COMMON_CHAT_ROLE_SYSTEM,
+    COMMON_CHAT_ROLE_ASSISTANT,
+    COMMON_CHAT_ROLE_USER,
+    COMMON_CHAT_ROLE_TOOL
+};
+
+common_chat_role common_chat_role_from_string(const std::string & role);
+const char *     common_chat_role_to_string(common_chat_role role);
+
 struct common_chat_msg_span {
-    std::string role;
+    common_chat_role role = COMMON_CHAT_ROLE_UNKNOWN;
     std::size_t pos = 0;
     std::size_t len = 0;
+
+    bool valid() const {
+        return role != COMMON_CHAT_ROLE_UNKNOWN;
+    }
+};
+
+struct common_chat_msg_spans {
+    std::vector<common_chat_msg_span> spans;
+
+    void add(common_chat_role role, size_t pos, size_t len) {
+        spans.push_back({ role, pos, len });
+    }
+
+    bool is_user_start(int32_t pos) const {
+        for (auto it = spans.begin(); it != spans.end(); ++it) {
+            if (it->role == COMMON_CHAT_ROLE_USER && pos == (int32_t) it->pos) {
+                return true;
+            }
+        }
+        return false;
+    }
+
+    int32_t last_user_message_pos() const {
+        for (auto it = spans.rbegin(); it != spans.rend(); ++it) {
+            if (it->role == COMMON_CHAT_ROLE_USER) {
+                return (int32_t) it->pos;
+            }
+        }
+        return -1;
+    }
 };
 
 struct common_chat_msg_delimiter {
-    std::string role;
-    std::string delimiter;
+    common_chat_role role = COMMON_CHAT_ROLE_UNKNOWN;
+    std::string      delimiter;
+    llama_tokens     tokens = {};
+};
+
+struct common_chat_msg_delimiters {
+    std::vector<common_chat_msg_delimiter> delimiters;
+
+    common_chat_msg_delimiters() = default;
+    common_chat_msg_delimiters(std::initializer_list<common_chat_msg_delimiter> delims) : delimiters(delims) {}
+
+    void add(common_chat_role role, const std::string & delimiter) {
+        delimiters.push_back({ role, delimiter });
+    }
+
+    void tokenize(const llama_vocab * vocab);
+
+    // 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;
 };
 
 struct common_chat_tool {
@@ -219,7 +279,7 @@ struct common_chat_params {
     std::vector<std::string>            preserved_tokens;
     std::vector<std::string>            additional_stops;
     std::string                         parser;
-    std::vector<common_chat_msg_span>   message_spans;
+    common_chat_msg_delimiters          message_delimiters;
 };
 
 // per-message parsing syntax
@@ -325,5 +385,4 @@ struct common_chat_prompt_preset {
 
 common_chat_prompt_preset common_chat_get_asr_prompt(const common_chat_templates * chat_templates);
 
-std::vector<common_chat_msg_span> common_chat_split_by_role(const std::string & prompt, const std::vector<common_chat_msg_delimiter> & delims);
-
+common_chat_msg_delimiters common_chat_msg_delimiters_parse(const nlohmann::ordered_json & delimiters);

common/common.cpp

@@ -1074,6 +1074,18 @@ std::vector<common_file_info> fs_list(const std::string & path, bool include_dir
     return files;
 }
 
+std::ifstream fs_open_ifstream(const std::string & fname, std::ios_base::openmode mode) {
+#ifdef _WIN32
+    int wlen = MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, NULL, 0);
+    if (!wlen) { return std::ifstream(); }
+    std::vector<wchar_t> wfname(wlen);
+    (void)MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, wfname.data(), wlen);
+    return std::ifstream(wfname.data(), mode);
+#else
+    return std::ifstream(fname, mode);
+#endif
+}
+
 //
 // TTY utils
 //
@@ -2034,7 +2046,7 @@ bool common_prompt_batch_decode(
 }
 
 size_t common_prompt_checkpoint::size() const {
-    return data_tgt.size() + data_dft.size();
+    return data_tgt.size() + data_dft.size() + data_spec.size();
 }
 
 bool common_prompt_checkpoint::empty() const {
@@ -2049,6 +2061,7 @@ void common_prompt_checkpoint::clear() {
 
     data_tgt.clear();
     data_dft.clear();
+    data_spec.clear();
 }
 
 void common_prompt_checkpoint::update_pos(
@@ -2138,4 +2151,5 @@ void common_prompt_checkpoint::clear_tgt() {
 
 void common_prompt_checkpoint::clear_dft() {
     data_dft.clear();
+    data_spec.clear();
 }

common/common.h

@@ -96,6 +96,7 @@ enum llama_example {
     LLAMA_EXAMPLE_FIT_PARAMS,
     LLAMA_EXAMPLE_RESULTS,
     LLAMA_EXAMPLE_EXPORT_GRAPH_OPS,
+    LLAMA_EXAMPLE_DOWNLOAD,
 
     LLAMA_EXAMPLE_COUNT,
 };
@@ -290,12 +291,25 @@ struct common_params_sampling {
 };
 
 struct common_params_model {
-    std::string path        = ""; // model local path                                       // NOLINT
-    std::string url         = ""; // model url to download                                  // NOLINT
-    std::string hf_repo     = ""; // HF repo                                                // NOLINT
-    std::string hf_file     = ""; // HF file                                                // NOLINT
-    std::string docker_repo = ""; // Docker repo                                            // NOLINT
-    std::string name        = ""; // in format <user>/<model>[:<tag>] (tag is optional)     // NOLINT
+    std::string path        = ""; // model local path
+    std::string url         = ""; // model url to download
+    std::string hf_repo     = ""; // HF repo
+    std::string hf_file     = ""; // HF file
+    std::string docker_repo = ""; // Docker repo
+
+    std::string get_name() const {
+        if (!hf_repo.empty()) {
+            return hf_repo;
+        }
+        if (!docker_repo.empty()) {
+            return docker_repo;
+        }
+        return path;
+    }
+
+    bool empty() const {
+        return get_name().empty();
+    }
 };
 
 // draft-model-based speculative decoding parameters
@@ -358,12 +372,12 @@ struct common_params_speculative {
     common_params_speculative_ngram_cache ngram_cache;
 
     bool has_dft() const {
-        return !draft.mparams.path.empty() || !draft.mparams.hf_repo.empty();
+        return !draft.mparams.empty();
     }
 
     uint32_t need_n_rs_seq() const {
         bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
-            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP;
+            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3;
         });
 
         return needs_rs_seq ? draft.n_max : 0u;
@@ -510,7 +524,6 @@ struct common_params {
     int32_t control_vector_layer_start = -1; // layer range for control vector
     int32_t control_vector_layer_end   = -1; // layer range for control vector
     bool    offline                    = false;
-    bool    skip_download              = false; // skip model file downloading
 
     int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
     int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
@@ -600,7 +613,7 @@ struct common_params {
     bool    cache_prompt        = true;  // whether to enable prompt caching
     bool    cache_idle_slots    = true;  // save and clear idle slots upon starting a new task
     int32_t n_ctx_checkpoints   = 32;    // max number of context checkpoints per slot
-    int32_t checkpoint_min_step = 256;   // minimum spacing between context checkpoints
+    int32_t checkpoint_min_step = 8192;  // minimum spacing between context checkpoints
     int32_t cache_ram_mib       = 8192;  // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
 
     std::string hostname      = "127.0.0.1";
@@ -624,12 +637,6 @@ struct common_params {
 
     // UI configs
     bool ui = true;
-
-    // Deprecated: use ui, ui_mcp_proxy, ui_config_json instead
-    bool webui = ui;
-    bool webui_mcp_proxy = false;
-    std::string webui_config_json;
-
     bool ui_mcp_proxy = false;
     std::string ui_config_json;
 
@@ -642,10 +649,11 @@ struct common_params {
     std::vector<std::string> server_tools;
 
     // router server configs
-    std::string models_dir    = ""; // directory containing models for the router server
-    std::string models_preset = ""; // directory containing model presets for the router server
-    int models_max = 4;             // maximum number of models to load simultaneously
-    bool models_autoload = true;    // automatically load models when requested via the router server
+    std::string models_dir    = "";     // directory containing models for the router server
+    std::string models_preset = "";     // directory containing model presets for the router server
+    int models_max = 4;                 // maximum number of models to load simultaneously
+    bool models_autoload = true;        // automatically load models when requested via the router server
+    std::string models_preset_hf = "";  // show a warning about remote presets on router loaded (if not empty)
 
     bool log_json = false;
 
@@ -847,6 +855,9 @@ struct common_file_info {
 };
 std::vector<common_file_info> fs_list(const std::string & path, bool include_directories);
 
+// fs open, also handle UTF8 on Windows
+std::ifstream fs_open_ifstream(const std::string & fname, std::ios_base::openmode mode);
+
 //
 // TTY utils
 //
@@ -1064,6 +1075,10 @@ struct common_prompt_checkpoint {
     std::vector<uint8_t> data_tgt;
     std::vector<uint8_t> data_dft;
 
+    // (optional) speculative-decoding implementation state stashed with the checkpoint
+    // (e.g. eagle3's deferred-boundary g_embd row)
+    std::vector<uint8_t> data_spec;
+
     size_t size() const;
 
     bool empty() const;

common/download.cpp

@@ -292,10 +292,6 @@ static int common_download_file_single_online(const std::string & url,
 
     const bool file_exists = std::filesystem::exists(path);
 
-    if (!file_exists && opts.skip_download) {
-        return -2; // file is missing and download is disabled
-    }
-
     if (file_exists && skip_etag) {
         LOG_DBG("%s: using cached file: %s\n", __func__, path.c_str());
         return 304; // 304 Not Modified - fake cached response
@@ -362,9 +358,6 @@ static int common_download_file_single_online(const std::string & url,
             return 304; // 304 Not Modified - fake cached response
         }
         // pass this point, the file exists but is different from the server version, so we need to redownload it
-        if (opts.skip_download) {
-            return -2; // special code to indicate that the download was skipped due to etag mismatch
-        }
         if (remove(path.c_str()) != 0) {
             LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
             return -1;
@@ -691,18 +684,8 @@ static void list_available_gguf_files(const hf_cache::hf_files & files) {
     }
 }
 
-struct hf_plan {
-    hf_cache::hf_file primary;
-    hf_cache::hf_files model_files;
-    hf_cache::hf_file mmproj;
-    hf_cache::hf_file mtp;
-};
-
-static hf_plan get_hf_plan(const common_params_model  & model,
-                           const common_download_opts & opts,
-                           bool download_mmproj,
-                           bool download_mtp) {
-    hf_plan plan;
+common_download_hf_plan common_download_get_hf_plan(const common_params_model & model, const common_download_opts & opts) {
+    common_download_hf_plan plan;
     hf_cache::hf_files all;
 
     auto [repo, tag] = common_download_split_repo_tag(model.hf_repo);
@@ -717,6 +700,14 @@ static hf_plan get_hf_plan(const common_params_model  & model,
         return plan;
     }
 
+    // if preset.ini exists in the repo root, download only that file
+    for (const auto & f : all) {
+        if (f.path == "preset.ini") {
+            plan.preset = f;
+            return plan;
+        }
+    }
+
     hf_cache::hf_file primary;
 
     if (!model.hf_file.empty()) {
@@ -743,115 +734,49 @@ static hf_plan get_hf_plan(const common_params_model  & model,
     plan.primary = primary;
     plan.model_files = get_split_files(all, primary);
 
-    if (download_mmproj) {
+    if (opts.download_mmproj) {
         plan.mmproj = find_best_mmproj(all, primary.path);
     }
-
-    if (download_mtp) {
+    if (opts.download_mtp) {
         plan.mtp = find_best_mtp(all, primary.path);
     }
 
     return plan;
 }
 
-struct download_task {
-    std::string url;
-    std::string path;
-};
-
-static std::vector<download_task> get_url_tasks(const common_params_model & model) {
-    auto split = get_gguf_split_info(model.url);
-
-    if (split.count <= 1) {
-        return {{model.url, model.path}};
-    }
-
-    auto filename = split.prefix;
-    if (auto pos = split.prefix.rfind('/'); pos != std::string::npos) {
-        filename = split.prefix.substr(pos + 1);
-    }
-
-    auto parent_path = std::filesystem::path(model.path).parent_path();
-    auto prefix_path = (parent_path / filename).string();
-
-    std::vector<download_task> tasks;
-    for (int i = 1; i <= split.count; i++) {
-        auto suffix = string_format("-%05d-of-%05d.gguf", i, split.count);
-        tasks.push_back({split.prefix + suffix, prefix_path + suffix});
-    }
-    return tasks;
-}
-
-common_download_model_result common_download_model(const common_params_model  & model,
-                                                   const common_download_opts & opts) {
-    common_download_model_result result;
-    std::vector<download_task> tasks;
-    hf_plan hf;
-
-    bool download_mmproj = opts.download_mmproj;
-    bool download_mtp = opts.download_mtp;
-    bool is_hf = !model.hf_repo.empty();
-
-    if (is_hf) {
-        hf = get_hf_plan(model, opts, download_mmproj, download_mtp);
-        for (const auto & f : hf.model_files) {
-            tasks.push_back({f.url, f.local_path});
-        }
-        if (!hf.mmproj.path.empty()) {
-            tasks.push_back({hf.mmproj.url, hf.mmproj.local_path});
-        }
-        if (!hf.mtp.path.empty()) {
-            tasks.push_back({hf.mtp.url, hf.mtp.local_path});
-        }
-    } else if (!model.url.empty()) {
-        tasks = get_url_tasks(model);
-    } else {
-        result.model_path = model.path;
-        return result;
-    }
-
-    if (tasks.empty()) {
-        return result;
-    }
-
+void common_download_run_tasks(const std::vector<common_download_task> & tasks) {
     std::vector<std::future<int>> futures;
     for (const auto & task : tasks) {
         futures.push_back(std::async(std::launch::async,
-            [&task, &opts, is_hf]() {
-                return common_download_file_single(task.url, task.path, opts, is_hf);
+            [&task]() {
+                return common_download_file_single(task.url, task.local_path, task.opts, task.is_hf);
             }
         ));
     }
 
-    for (auto & f : futures) {
-        int status = f.get();
-        if (status == -2 && opts.skip_download) {
-            throw common_skip_download_exception();
-        }
+    for (size_t i = 0; i < futures.size(); ++i) {
+        std::string url = tasks[i].url;
+        int status = futures[i].get();
         bool is_ok = is_http_status_ok(status);
         if (!is_ok) {
-            return {};
+            throw std::runtime_error(string_format("Download '%s' failed with status code: %d", url.c_str(), status));
         }
     }
+}
 
-    if (is_hf) {
-        for (const auto & f : hf.model_files) {
-            hf_cache::finalize_file(f);
-        }
-        result.model_path = hf.primary.final_path;
+std::vector<std::string> common_download_get_all_parts(const std::string & url) {
+    auto split = get_gguf_split_info(url);
 
-        if (!hf.mmproj.path.empty()) {
-            result.mmproj_path = hf_cache::finalize_file(hf.mmproj);
-        }
-
-        if (!hf.mtp.path.empty()) {
-            result.mtp_path = hf_cache::finalize_file(hf.mtp);
-        }
-    } else {
-        result.model_path = model.path;
+    if (split.count <= 1) {
+        return {url};
     }
 
-    return result;
+    std::vector<std::string> parts;
+    for (int i = 1; i <= split.count; i++) {
+        auto suffix = string_format("-%05d-of-%05d.gguf", i, split.count);
+        parts.push_back(split.prefix + suffix);
+    }
+    return parts;
 }
 
 //
@@ -997,3 +922,87 @@ std::vector<common_cached_model_info> common_list_cached_models() {
 
     return result;
 }
+
+bool common_download_remove(const std::string & hf_repo_with_tag) {
+    namespace fs = std::filesystem;
+
+    auto [repo_id, tag] = common_download_split_repo_tag(hf_repo_with_tag);
+
+    if (tag.empty()) {
+        return hf_cache::remove_cached_repo(repo_id);
+    }
+
+    std::string tag_upper = tag;
+    for (char & c : tag_upper) {
+        c = (char) std::toupper((unsigned char) c);
+    }
+
+    auto files = hf_cache::get_cached_files(repo_id);
+    if (files.empty()) {
+        return false;
+    }
+
+    // collect snapshot entries whose tag matches
+    std::vector<fs::path> to_remove;
+    for (const auto & f : files) {
+        auto split = get_gguf_split_info(f.path);
+        if (split.tag == tag_upper) {
+            to_remove.emplace_back(f.local_path);
+        }
+    }
+
+    if (to_remove.empty()) {
+        return false;
+    }
+
+    // resolve blob paths from symlinks before deleting snapshot entries
+    std::vector<fs::path> blobs_to_check;
+    for (const auto & p : to_remove) {
+        std::error_code ec;
+        if (fs::is_symlink(p, ec)) {
+            auto target = fs::read_symlink(p, ec);
+            if (!ec) {
+                blobs_to_check.push_back((p.parent_path() / target).lexically_normal());
+            }
+        }
+    }
+
+    // remove snapshot entries
+    for (const auto & p : to_remove) {
+        std::error_code ec;
+        fs::remove(p, ec);
+        if (ec) {
+            LOG_WRN("%s: failed to remove %s: %s\n", __func__, p.string().c_str(), ec.message().c_str());
+        }
+    }
+
+    if (blobs_to_check.empty()) {
+        return true;
+    }
+
+    // collect blobs still referenced by remaining snapshot entries
+    std::unordered_set<std::string> still_referenced;
+    for (const auto & f : hf_cache::get_cached_files(repo_id)) {
+        fs::path p(f.local_path);
+        std::error_code ec;
+        if (fs::is_symlink(p, ec)) {
+            auto target = fs::read_symlink(p, ec);
+            if (!ec) {
+                still_referenced.insert((p.parent_path() / target).lexically_normal().string());
+            }
+        }
+    }
+
+    // remove orphaned blobs
+    for (const auto & blob : blobs_to_check) {
+        if (still_referenced.find(blob.string()) == still_referenced.end()) {
+            std::error_code ec;
+            fs::remove(blob, ec);
+            if (ec) {
+                LOG_WRN("%s: failed to remove blob %s: %s\n", __func__, blob.string().c_str(), ec.message().c_str());
+            }
+        }
+    }
+
+    return true;
+}

common/download.h

@@ -1,7 +1,10 @@
 #pragma once
 
+#include "hf-cache.h"
+
 #include <string>
 #include <vector>
+#include <functional>
 
 struct common_params_model;
 
@@ -47,65 +50,40 @@ struct common_cached_model_info {
     }
 };
 
-// Options for common_download_model and common_download_file_single
+// Options for common_download_file_single
 struct common_download_opts {
     std::string bearer_token;
     common_header_list headers;
     bool offline = false;
-    bool skip_download = false; // if true, only validation is performed, common_skip_download_exception may be thrown if the file is missing or invalid
     bool download_mmproj = false;
     bool download_mtp = false;
     common_download_callback * callback = nullptr;
 };
 
-// Result of common_download_model
-struct common_download_model_result {
-    std::string model_path;
-    std::string mmproj_path;
-    std::string mtp_path;
+struct common_download_task {
+    common_download_opts opts;
+    std::string url;
+    std::string local_path;
+    std::function<void()> on_done;
+    bool is_hf = false;
+
+    common_download_task() = default;
+    common_download_task(hf_cache::hf_file f,
+            const common_download_opts & opts,
+            std::function<void()> on_done = nullptr)
+        : opts(opts), url(f.url), local_path(f.local_path), on_done(on_done), is_hf(true) {}
 };
 
-// throw if the file is missing or invalid (e.g. ETag check failed)
-struct common_skip_download_exception : public std::runtime_error {
-    common_skip_download_exception() : std::runtime_error("skip download") {}
-};
+void common_download_run_tasks(const std::vector<common_download_task> & tasks);
 
-// Download model from HuggingFace repo or URL
-//
-// input (via model struct):
-// - model.hf_repo: HF repo with optional tag, see common_download_split_repo_tag
-// - model.hf_file: specific file in the repo (requires hf_repo)
-// - model.url: simple download (used if hf_repo is empty)
-// - model.path: local file path
-//
-// tag matching (for HF repos without model.hf_file):
-// - if tag is specified, searches for GGUF matching that quantization
-// - if no tag, searches for Q4_K_M, then Q4_0, then first available GGUF
-//
-// split GGUF: multi-part files like "model-00001-of-00003.gguf" are automatically
-// detected and all parts are downloaded
-//
-// caching:
-// - HF repos: uses HuggingFace cache
-// - URLs: uses ETag-based caching
-//
-// when opts.offline=true, no network requests are made
-// when download_mmproj=true, searches for mmproj in same directory as model or any parent directory
-// then with the closest quantization bits
-// when download_mtp=true, applies the same sibling search for an MTP-head GGUF
-//
-// returns result with model_path, mmproj_path and mtp_path (empty when not found / on failure)
-common_download_model_result common_download_model(
-    const common_params_model & model,
-    const common_download_opts & opts = {}
-);
+// if url is a multi-part GGUF file, returns all parts, otherwise returns the single file
+std::vector<std::string> common_download_get_all_parts(const std::string & url);
 
 // returns list of cached models
 std::vector<common_cached_model_info> common_list_cached_models();
 
 // download single file from url to local path
 // returns status code or -1 on error
-// returns -2 if the download was skipped due to ETag mismatch (file outdated, skip_download=true)
 // skip_etag: if true, don't read/write .etag files (for HF cache where filename is the hash)
 int common_download_file_single(const std::string & url,
                                 const std::string & path,
@@ -115,3 +93,19 @@ int common_download_file_single(const std::string & url,
 // resolve and download model from Docker registry
 // return local path to downloaded model file
 std::string common_docker_resolve_model(const std::string & docker);
+
+// Remove a cached model from disk
+// input format: "user/model" or "user/model:tag"
+// - if tag is omitted, removes the entire repo cache directory
+// - if tag is present, removes only files matching that tag (and orphaned blobs)
+// returns true if anything was removed
+bool common_download_remove(const std::string & hf_repo_with_tag);
+
+struct common_download_hf_plan {
+    hf_cache::hf_file primary;
+    hf_cache::hf_files model_files;
+    hf_cache::hf_file mmproj;
+    hf_cache::hf_file mtp;
+    hf_cache::hf_file preset; // if set, only this file is downloaded
+};
+common_download_hf_plan common_download_get_hf_plan(const common_params_model & model, const common_download_opts & opts);

common/hf-cache.cpp

@@ -495,4 +495,19 @@ std::string finalize_file(const hf_file & file) {
     return file.final_path;
 }
 
+bool remove_cached_repo(const std::string & repo_id) {
+    if (!is_valid_repo_id(repo_id)) {
+        LOG_WRN("%s: invalid repository: %s\n", __func__, repo_id.c_str());
+        return false;
+    }
+    fs::path repo_path = get_repo_path(repo_id);
+    std::error_code ec;
+    auto removed = fs::remove_all(repo_path, ec);
+    if (ec) {
+        LOG_ERR("%s: failed to remove repo cache %s: %s\n", __func__, repo_path.string().c_str(), ec.message().c_str());
+        return false;
+    }
+    return removed > 0;
+}
+
 } // namespace hf_cache

common/hf-cache.h

@@ -29,4 +29,7 @@ hf_files get_cached_files(const std::string & repo_id = {});
 // Create snapshot path (link or move/copy) and return it
 std::string finalize_file(const hf_file & file);
 
+// Remove the entire cached directory for a repo, returns true if removed
+bool remove_cached_repo(const std::string & repo_id);
+
 } // namespace hf_cache

common/jinja/runtime.cpp

@@ -686,59 +686,62 @@ value set_statement::execute_impl(context & ctx) {
     return mk_val<value_undefined>();
 }
 
+static inline void bind_parameters(const std::string & name, const statements & this_args, const func_args & args, context & ctx) {
+    const size_t expected_count = this_args.size();
+    const size_t input_count = args.count();
+
+    JJ_DEBUG("Invoking '%s' with %zu input arguments (expected %zu)", name.c_str(), input_count, expected_count);
+    for (size_t i = 0; i < expected_count; ++i) {
+        if (i < input_count) {
+            if (is_stmt<identifier>(this_args[i])) {
+                // normal parameter
+                std::string param_name = cast_stmt<identifier>(this_args[i])->val;
+                value param_value = args.get_kwarg_or_pos(param_name, i);
+                JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
+                ctx.set_val(param_name, param_value);
+            } else if (is_stmt<keyword_argument_expression>(this_args[i])) {
+                // default argument used as normal parameter
+                auto kwarg = cast_stmt<keyword_argument_expression>(this_args[i]);
+                if (!is_stmt<identifier>(kwarg->key)) {
+                    throw std::runtime_error("Keyword argument key must be an identifier in '" + name + "'");
+                }
+                std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
+                value param_value = args.get_kwarg_or_pos(param_name, i);
+                JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
+                ctx.set_val(param_name, param_value);
+            } else {
+                throw std::runtime_error("Invalid parameter type in '" + name + "'");
+            }
+        } else {
+            auto & default_arg = this_args[i];
+            if (is_stmt<keyword_argument_expression>(default_arg)) {
+                auto kwarg = cast_stmt<keyword_argument_expression>(default_arg);
+                if (!is_stmt<identifier>(kwarg->key)) {
+                    throw std::runtime_error("Keyword argument key must be an identifier in '" + name + "'");
+                }
+                std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
+                JJ_DEBUG("  Binding parameter '%s' to default argument of type %s", param_name.c_str(), kwarg->val->type().c_str());
+                ctx.set_val(param_name, kwarg->val->execute(args.ctx));
+            } else {
+                throw std::runtime_error("Not enough arguments provided to '" + name + "'");
+            }
+            //std::string param_name = cast_stmt<identifier>(default_args[i])->val;
+            //JJ_DEBUG("  Binding parameter '%s' to default", param_name.c_str());
+            //ctx.var[param_name] = default_args[i]->execute(ctx);
+        }
+    }
+}
+
 value macro_statement::execute_impl(context & ctx) {
     if (!is_stmt<identifier>(this->name)) {
         throw std::runtime_error("Macro name must be an identifier");
     }
     std::string name = cast_stmt<identifier>(this->name)->val;
 
-    const func_handler func = [this, name, &ctx](const func_args & args) -> value {
-        size_t expected_count = this->args.size();
-        size_t input_count = args.count();
+    const func_handler func = [this, name](const func_args & args) -> value {
+        context macro_ctx(args.ctx); // new scope for macro execution
 
-        JJ_DEBUG("Invoking macro '%s' with %zu input arguments (expected %zu)", name.c_str(), input_count, expected_count);
-        context macro_ctx(ctx); // new scope for macro execution
-
-        // bind parameters
-        for (size_t i = 0; i < expected_count; ++i) {
-            if (i < input_count) {
-                if (is_stmt<identifier>(this->args[i])) {
-                    // normal parameter
-                    std::string param_name = cast_stmt<identifier>(this->args[i])->val;
-                    value param_value = args.get_kwarg_or_pos(param_name, i);
-                    JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
-                    macro_ctx.set_val(param_name, param_value);
-                } else if (is_stmt<keyword_argument_expression>(this->args[i])) {
-                    // default argument used as normal parameter
-                    auto kwarg = cast_stmt<keyword_argument_expression>(this->args[i]);
-                    if (!is_stmt<identifier>(kwarg->key)) {
-                        throw std::runtime_error("Keyword argument key must be an identifier in macro '" + name + "'");
-                    }
-                    std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
-                    value param_value = args.get_kwarg_or_pos(param_name, i);
-                    JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
-                    macro_ctx.set_val(param_name, param_value);
-                } else {
-                    throw std::runtime_error("Invalid parameter type in macro '" + name + "'");
-                }
-            } else {
-                auto & default_arg = this->args[i];
-                if (is_stmt<keyword_argument_expression>(default_arg)) {
-                    auto kwarg = cast_stmt<keyword_argument_expression>(default_arg);
-                    if (!is_stmt<identifier>(kwarg->key)) {
-                        throw std::runtime_error("Keyword argument key must be an identifier in macro '" + name + "'");
-                    }
-                    std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
-                    JJ_DEBUG("  Binding parameter '%s' to default argument of type %s", param_name.c_str(), kwarg->val->type().c_str());
-                    macro_ctx.set_val(param_name, kwarg->val->execute(ctx));
-                } else {
-                    throw std::runtime_error("Not enough arguments provided to macro '" + name + "'");
-                }
-                //std::string param_name = cast_stmt<identifier>(default_args[i])->val;
-                //JJ_DEBUG("  Binding parameter '%s' to default", param_name.c_str());
-                //macro_ctx.var[param_name] = default_args[i]->execute(ctx);
-            }
-        }
+        bind_parameters(name, this->args, args, macro_ctx);
 
         // execute macro body
         JJ_DEBUG("Executing macro '%s' body with %zu statements", name.c_str(), this->body.size());
@@ -752,6 +755,46 @@ value macro_statement::execute_impl(context & ctx) {
     return mk_val<value_undefined>();
 }
 
+value call_statement::execute_impl(context & ctx) {
+    auto call_expr = cast_stmt<call_expression>(this->call);
+    if (!call_expr) {
+        throw std::runtime_error("Call statement requires a valid call expression");
+    }
+
+    value callee_val = call_expr->callee->execute(ctx);
+    if (!is_val<value_func>(callee_val)) {
+        throw std::runtime_error("Callee is not a function: got " + callee_val->type());
+    }
+    auto * callee_func = cast_val<value_func>(callee_val);
+
+    context caller_ctx(ctx); // new scope for caller execution
+
+    const func_handler func = [this, caller_ctx = std::move(caller_ctx)](const func_args & args) -> value {
+        context block_ctx(caller_ctx); // new scope for block execution
+
+        bind_parameters("caller", this->caller_args, args, block_ctx);
+
+        JJ_DEBUG("Executing call body with %zu statements", this->body.size());
+        auto res = exec_statements(this->body, block_ctx);
+        JJ_DEBUG("Call body execution complete, result: %s", res->val_str.str().c_str());
+        return res;
+    };
+
+    context call_ctx(ctx);
+    call_ctx.set_val("caller", mk_val<value_func>("caller", func));
+
+    func_args args(call_ctx);
+
+    for (const auto & arg_expr : call_expr->args) {
+        auto arg_val = arg_expr->execute(ctx);
+        JJ_DEBUG("  Argument type: %s", arg_val->type().c_str());
+        args.push_back(arg_val);
+    }
+
+    JJ_DEBUG("Calling macro '%s' with %zu arguments", callee_func->name.c_str(), args.count());
+    return callee_func->invoke(args);
+}
+
 value member_expression::execute_impl(context & ctx) {
     value object = this->object->execute(ctx);
 

common/jinja/runtime.h

@@ -552,6 +552,7 @@ struct call_statement : public statement {
         for (const auto & arg : this->caller_args) chk_type<expression>(arg);
     }
     std::string type() const override { return "CallStatement"; }
+    value execute_impl(context & ctx) override;
 };
 
 struct ternary_expression : public expression {

common/json-partial.cpp

@@ -1,324 +0,0 @@
-#include "json-partial.h"
-
-#include "log.h"
-
-#include <nlohmann/json.hpp>
-
-#include <string>
-#include <regex>
-
-using json = nlohmann::ordered_json;
-
-enum common_json_stack_element_type {
-    COMMON_JSON_STACK_ELEMENT_OBJECT,
-    COMMON_JSON_STACK_ELEMENT_KEY,
-    COMMON_JSON_STACK_ELEMENT_ARRAY,
-};
-
-struct common_json_stack_element {
-    common_json_stack_element_type type;
-    std::string key;
-};
-
-bool common_json_parse(
-    const std::string & input,
-    const std::string & healing_marker,
-    common_json & out)
-{
-    std::string::const_iterator it = input.begin();
-    const auto end = input.end();
-    return common_json_parse(it, end, healing_marker, out);
-}
-
-bool common_json_parse(
-    std::string::const_iterator & it,
-    const std::string::const_iterator & end,
-    const std::string & healing_marker,
-    common_json & out)
-{
-    // // https://json.nlohmann.me/features/parsing/sax_interface/
-    struct json_error_locator : public nlohmann::json_sax<json> {
-        std::size_t position;
-        bool found_error;
-        std::string last_token;
-        std::string exception_message;
-        std::vector<common_json_stack_element> stack;
-
-        json_error_locator() : position(0), found_error(false) {}
-
-        bool parse_error(std::size_t position, const std::string & last_token, const json::exception & ex) override { // NOLINT
-            this->position = position - 1;
-            this->found_error = true;
-            this->last_token = last_token;
-            this->exception_message = ex.what();
-            return false;
-        }
-        void close_value() {
-            if (!stack.empty() && (stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY)) {
-                stack.pop_back();
-            }
-        }
-        bool null() override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool boolean(bool) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_integer(number_integer_t) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_unsigned(number_unsigned_t) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_float(number_float_t, const string_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool string(string_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool binary(binary_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool start_object(std::size_t) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_OBJECT, ""});
-            return true;
-        }
-        bool end_object() override {
-            GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT);
-            stack.pop_back();
-            close_value();
-            return true;
-        }
-        bool key(string_t & key) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_KEY, key});
-            return true;
-        }
-        bool start_array(std::size_t) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_ARRAY, ""});
-            return true;
-        }
-        bool end_array() override {
-            GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY);
-            stack.pop_back();
-            close_value();
-            return true;
-        }
-    };
-    json_error_locator err_loc;
-    auto start = it;
-    json::sax_parse(it, end, &err_loc);
-
-    if (err_loc.found_error) {
-        it = start;
-        auto temptative_end = it + err_loc.position;
-        // LOG_DBG("Error at position %zu (is_end = %s): %s\n", err_loc.position, temptative_end == end ? "true" : "false", err_loc.exception_message.c_str());
-
-        auto input = std::string(it, temptative_end);
-        try {
-            out.json = json::parse(input);
-            // out.json = json::parse(it, temptative_end);
-            it = temptative_end;
-            return true;
-        } catch (const std::exception & ex) {
-            // No, needs healing.
-            LOG_DBG("Failed to parse up to error: %s: <<<%s>>>\n", ex.what(), std::string(it, temptative_end).c_str());
-        }
-        auto can_parse = [](const std::string & str) {
-            try {
-                auto _ = json::parse(str); // NOLINT
-                return true;
-            } catch (const std::exception &) {
-                return false;
-            }
-        };
-        if (!healing_marker.empty() && !err_loc.stack.empty()) {
-            std::string str(it, temptative_end);
-            auto last_non_sp_pos = str.find_last_not_of(" \n\r\t");
-            if (last_non_sp_pos == std::string::npos) {
-                throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
-            }
-            auto last_non_sp_char = str[last_non_sp_pos];
-            // Used to detect stops on a number, which may not be complete.
-            auto was_maybe_number = [&]() {
-                if (!str.empty() && std::isspace(str.back())) {
-                    return false;
-                }
-                return std::isdigit(last_non_sp_char) ||
-                    last_non_sp_char == '.' ||
-                    last_non_sp_char == 'e' ||
-                    last_non_sp_char == 'E' ||
-                    last_non_sp_char == '-';
-            };
-
-            std::string closing;
-            for (size_t i = err_loc.stack.size(); i > 0; i--) {
-                auto & el = err_loc.stack[i - 1];
-                if (el.type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
-                    closing += "}";
-                } else if (el.type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
-                    closing += "]";
-                } else if (el.type != COMMON_JSON_STACK_ELEMENT_KEY) {
-                    throw std::runtime_error("Unexpected stack element type");
-                }
-            }
-
-            // Matches a potentially partial unicode escape sequence, e.g. \u, \uX, \uXX, \uXXX, \uXXXX
-            static const std::regex partial_unicode_regex(R"(\\u(?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F])?)?)?)?$)");
-
-            auto is_high_surrogate = [&](const std::string & s) {
-                // Check if a partial of a high surrogate (U+D800-U+DBFF)
-                return s.length() >= 4 &&
-                    s[0] == '\\' && s[1] == 'u' &&
-                    std::tolower(s[2]) == 'd' &&
-                    (s[3] == '8' || s[3] == '9' || std::tolower(s[3]) == 'a' || std::tolower(s[3]) == 'b');
-            };
-
-            // Initialize the unicode marker to a low surrogate to handle the edge case
-            // where a high surrogate (U+D800-U+DBFF) is immediately followed by a
-            // backslash (\)
-            std::string unicode_marker_padding = "udc00";
-            std::smatch last_unicode_seq;
-
-            if (std::regex_search(str, last_unicode_seq, partial_unicode_regex)) {
-                std::smatch second_last_seq;
-                std::string prelude = str.substr(0, last_unicode_seq.position());
-
-                // Pad the escape sequence with 0s until it forms a complete sequence of 6 characters
-                unicode_marker_padding = std::string(6 - last_unicode_seq.length(), '0');
-
-                if (is_high_surrogate(last_unicode_seq.str())) {
-                    // If the sequence is a partial match for a high surrogate, add a low surrogate (U+DC00-U+UDFF)
-                    unicode_marker_padding += "\\udc00";
-                } else if (std::regex_search(prelude, second_last_seq, partial_unicode_regex)) {
-                    if (is_high_surrogate(second_last_seq.str())) {
-                        // If this follows a high surrogate, pad it to be a low surrogate
-                        if (last_unicode_seq.length() == 2) {
-                            unicode_marker_padding = "dc00";
-                        } else if (last_unicode_seq.length() == 3) {
-                            unicode_marker_padding = "c00";
-                        } else {
-                            // The original unicode_marker_padding is already padded with 0s
-                        }
-                    }
-                }
-            }
-
-            const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$";
-
-            if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) {
-                // We're inside an object value
-                if (last_non_sp_char == ':' && can_parse(str + "1" + closing)) {
-                    // Was about to create an object value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + ": 1" + closing)) {
-                    str += (out.healing_marker.json_dump_marker = ":\"" + magic_seed) + "\"" + closing;
-                } else if (last_non_sp_char == '{' && can_parse(str + closing)) {
-                    // Was about to create an object
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + "\"" + closing)) {
-                    // Was inside an object value string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
-                    // Was inside an object value string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) {
-                    // Was inside an object value string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing;
-                } else {
-                    // find last :
-                    auto last_pos = str.find_last_of(':');
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
-                    }
-                    // Cutting back to opening : for object value
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
-                if ((last_non_sp_char == ',' || last_non_sp_char == '[') && can_parse(str + "1" + closing)) {
-                    // Was about to create an array value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + "\"" + closing)) {
-                    // Was inside an array value string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
-                    // Was inside an array value string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) {
-                    // Was inside an array value string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing;
-                } else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) {
-                    // Had just finished a value
-                    str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing;
-                } else {
-                    auto last_pos = str.find_last_of("[,");
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON array stopped in an unknown location");
-                    }
-                    // Cutting back to last [ or , for array value
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
-                if ((last_non_sp_char == '{' && can_parse(str + closing)) ||
-                        (last_non_sp_char == ',' && can_parse(str + "\"\": 1" + closing))) {
-                    // Was about to create an object key+value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
-                } else if (!was_maybe_number() && can_parse(str + ",\"\": 1" + closing)) {
-                    // Was about to create an object key+value
-                    str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + "\": 1" + closing)) {
-                    // Was inside an object key string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\": 1" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) {
-                    // Was inside an object key string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\": 1" + closing)) {
-                    // Was inside an object key string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\": 1" + closing;
-                } else {
-                    auto last_pos = str.find_last_of(':');
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
-                    }
-                    // fprintf(stderr, "Cutting back to last : for object key+value\n");
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else {
-                throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
-            }
-            // fprintf(stderr, "HEALED:\nSTRING <<<\n%s\n>>>\n\nmagic_cut: <<<\n%s\n>>>\n\n", str.c_str(), out.healing_marker.json_dump_marker.c_str());
-            out.json = json::parse(str);
-            it = temptative_end;
-            return true;
-        }
-        // handle unclosed top-level primitive
-        if (err_loc.position != 0 && !healing_marker.empty() && err_loc.stack.empty()) {
-            std::string str(it, temptative_end);
-            const auto & magic_seed = out.healing_marker.marker = healing_marker;
-            if (can_parse(str + "\"")) {
-                // Was inside an string
-                str += (out.healing_marker.json_dump_marker = magic_seed) + "\"";
-            } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"")) {
-                // Was inside an string after an escape
-                str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"";
-            } else {
-                // TODO: handle more unclosed top-level primitive if the stack was empty but we got an error (e.g. "tru", "\"", etc...)
-                // fprintf(stderr, "Closing: TODO\n");
-                return false;
-            }
-            out.json = json::parse(str);
-            it = temptative_end;
-            return true;
-        }
-        return false;
-    }
-    out.json = json::parse(it, end);
-    it = end;
-    return true;
-}

common/json-partial.h

@@ -1,39 +0,0 @@
-#pragma once
-
-// TODO: use json_fwd.hpp when possible
-#include <nlohmann/json.hpp>
-
-// Healing marker (empty if the JSON was fully parsed / wasn't healed).
-struct common_healing_marker {
-    // Raw marker.
-    std::string marker;
-
-    // Cutting the `common_json.json.dump()` string at the (only) occurrence of this marker should yield the original partial JSON string (modulo spaces / if it had the same dump format).
-    std::string json_dump_marker;
-};
-
-// Represents a parsed JSON object, with its optional healing marker (a JSON dump fragment that can be used to find the position of healing in the JSON dump string)
-struct common_json {
-    nlohmann::ordered_json json;
-
-    common_healing_marker healing_marker;
-};
-
-// Parse the JSON string, healing (closing) any partial JSON if `healing_marker` is not empty.
-//
-// Healing completes partial JSON strings by adding a (possibly modified) healing marker, then whatever is needed to close the JSON.
-// This allows to parse the resulting healed JSON string, yet be able to cut it again if needed at the healing marker.
-// (this is used when parsing JSON outputs from the models, then crafting partial JSONs for the partial tool calls in OAI format).
-//
-// For instance, parsing `{` with a healing marker `foo` will produce a healed JSON `{"foo":1}`, w/ json_dump_marker = `"foo"` (which can be used to break the JSON again).
-bool common_json_parse(
-    const std::string & input,
-    const std::string & healing_marker,
-    common_json & out);
-
-// Parse the JSON string (see overload above), but advancing an iterator to the end of the input when the (potentially partial) parsing succeeds.
-bool common_json_parse(
-    std::string::const_iterator & it,
-    const std::string::const_iterator & end,
-    const std::string & healing_marker,
-    common_json & out);

common/json-schema-to-grammar.cpp

@@ -233,27 +233,27 @@ struct BuiltinRule {
 };
 
 static std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
-    {"boolean", {"(\"true\" | \"false\") space", {}}},
+    {"boolean", {"(\"true\" | \"false\")", {}}},
     {"decimal-part", {"[0-9]{1,16}", {}}},
     {"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}},
-    {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}},
-    {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}},
+    {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)?", {"integral-part", "decimal-part"}}},
+    {"integer", {"(\"-\"? integral-part)", {"integral-part"}}},
     {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}},
-    {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
-    {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
-    {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}},
+    {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? space \"}\"", {"string", "value"}}},
+    {"array", {"\"[\" space ( value (\",\" space value)* )? space \"]\"", {"value"}}},
+    {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\"", {}}},
     {"char",   {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}},
-    {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
-    {"null", {"\"null\" space", {}}},
+    {"string", {"\"\\\"\" char* \"\\\"\"", {"char"}}},
+    {"null", {"\"null\"", {}}},
 };
 
 static std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
     {"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
     {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
     {"date-time", {"date \"T\" time", {"date", "time"}}},
-    {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}},
-    {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}},
-    {"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}}
+    {"date-string", {"\"\\\"\" date \"\\\"\"", {"date"}}},
+    {"time-string", {"\"\\\"\" time \"\\\"\"", {"time"}}},
+    {"date-time-string", {"\"\\\"\" date-time \"\\\"\"", {"date-time"}}}
 };
 
 static bool is_reserved_name(const std::string & name) {
@@ -551,16 +551,16 @@ private:
             }
             return join_seq();
         };
-        return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space");
+        return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\"");
     }
 
     /*
         Returns a rule that matches a JSON string that is none of the provided strings
 
         not_strings({"a"})
-            -> ["] ( [a] char+ | [^"a] char* )? ["] space
+            -> ["] ( [a] char+ | [^"a] char* )? ["]
         not_strings({"and", "also"})
-            -> ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["] space
+            -> ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["]
     */
     std::string _not_strings(const std::vector<std::string> & strings) {
 
@@ -619,7 +619,7 @@ private:
         if (!trie.is_end_of_string) {
             out << "?";
         }
-        out << " [\"] space";
+        out << " [\"]";
         return out.str();
     }
 
@@ -725,7 +725,7 @@ private:
             rule += " )?";
         }
 
-        rule += " \"}\" space";
+        rule += " space \"}\"";
 
         return rule;
     }
@@ -858,14 +858,14 @@ public:
             return _add_rule(rule_name, _generate_union_rule(name, schema_types));
         }
         if (schema.contains("const")) {
-            return _add_rule(rule_name, _generate_constant_rule(schema["const"]) + " space");
+            return _add_rule(rule_name, _generate_constant_rule(schema["const"]));
         }
         if (schema.contains("enum")) {
             std::vector<std::string> enum_values;
             for (const auto & v : schema["enum"]) {
                 enum_values.push_back(_generate_constant_rule(v));
             }
-            return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space");
+            return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ")");
         }
         if ((schema_type.is_null() || schema_type == "object")
                 && (schema.contains("properties") ||
@@ -933,7 +933,7 @@ public:
                     }
                 }
                 if (!enum_intersection.empty()) {
-                    return _add_rule(rule_name, "(" + string_join(enum_intersection, " | ") + ") space");
+                    return _add_rule(rule_name, "(" + string_join(enum_intersection, " | ") + ")");
                 }
             }
             return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json()));
@@ -948,7 +948,7 @@ public:
                     }
                     rule += visit(items[i], name + (name.empty() ? "" : "-") + "tuple-" + std::to_string(i));
                 }
-                rule += " \"]\" space";
+                rule += " space \"]\"";
                 return _add_rule(rule_name, rule);
             }
             std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item");
@@ -956,7 +956,7 @@ public:
             json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json();
             int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : std::numeric_limits<int>::max();
 
-            return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space");
+            return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " space \"]\"");
         }
         if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) {
             return _visit_pattern(schema["pattern"], rule_name);
@@ -972,7 +972,7 @@ public:
             std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char"));
             int min_len = schema.contains("minLength") ? schema["minLength"].get<int>() : 0;
             int max_len = schema.contains("maxLength") ? schema["maxLength"].get<int>() : std::numeric_limits<int>::max();
-            return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space");
+            return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\"");
         }
         if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) {
             int64_t min_value = std::numeric_limits<int64_t>::min();
@@ -990,7 +990,7 @@ public:
             std::stringstream out;
             out << "(";
             build_min_max_int(min_value, max_value, out);
-            out << ") space";
+            out << ")";
             return _add_rule(rule_name, out.str());
         }
         if (schema.empty() || schema_type == "object") {

common/peg-parser.cpp

@@ -6,13 +6,14 @@
 #include "unicode.h"
 
 #include <algorithm>
+#include <deque>
 #include <initializer_list>
 #include <map>
 #include <memory>
 #include <nlohmann/json.hpp>
 #include <regex>
+#include <set>
 #include <stdexcept>
-#include <unordered_set>
 
 // Trick to catch missing branches
 template <typename T>
@@ -88,40 +89,7 @@ struct trie {
         return match_result{match_result::NO_MATCH};
     }
 
-    struct prefix_and_next {
-        std::vector<uint32_t> prefix;
-        std::vector<uint32_t> next_chars;
-    };
-
-    std::vector<prefix_and_next> collect_prefix_and_next() {
-        std::vector<uint32_t>        prefix;
-        std::vector<prefix_and_next> result;
-        collect_prefix_and_next(0, prefix, result);
-        return result;
-    }
-
   private:
-    void collect_prefix_and_next(size_t index, std::vector<uint32_t> & prefix, std::vector<prefix_and_next> & out) {
-        if (!nodes[index].is_word) {
-            if (!nodes[index].children.empty()) {
-                std::vector<uint32_t> chars;
-                chars.reserve(nodes[index].children.size());
-                for (const auto & p : nodes[index].children) {
-                    chars.push_back(p.first);
-                }
-                out.emplace_back(prefix_and_next{prefix, chars});
-            }
-        }
-
-        for (const auto & p : nodes[index].children) {
-            uint32_t ch = p.first;
-            auto child = p.second;
-            prefix.push_back(ch);
-            collect_prefix_and_next(child, prefix, out);
-            prefix.pop_back();
-        }
-    }
-
     size_t create_node() {
         size_t index = nodes.size();
         nodes.emplace_back();
@@ -153,6 +121,65 @@ struct trie {
     }
 };
 
+// Aho-Corasick automaton
+struct aho_corasick {
+    trie                t;
+    std::vector<size_t> fail;      // failure links
+    std::vector<size_t> order;     // states in BFS order
+    std::vector<bool>   terminal;  // match states (directly or via a suffix link)
+    std::set<uint32_t>  alphabet;  // every character with a transition
+
+    aho_corasick(const std::vector<std::string> & strings) : t(strings) {
+        const auto & nodes = t.nodes;
+        const size_t n = nodes.size();
+
+        fail.assign(n, 0);
+        order.reserve(n);
+
+        std::deque<size_t> queue{ 0 };
+        while (!queue.empty()) {
+            size_t u = queue.front();
+            queue.pop_front();
+            order.push_back(u);
+            for (const auto & [ch, v] : nodes[u].children) {
+                if (u != 0) {
+                    size_t f = fail[u];
+                    while (f && nodes[f].children.find(ch) == nodes[f].children.end()) {
+                        f = fail[f];
+                    }
+                    auto it = nodes[f].children.find(ch);
+                    fail[v] = (it != nodes[f].children.end() && it->second != v) ? it->second : 0;
+                }
+                queue.push_back(v);
+            }
+        }
+
+        terminal.assign(n, false);
+        for (size_t u : order) {
+            terminal[u] = nodes[u].is_word || (u != 0 && terminal[fail[u]]);
+        }
+
+        for (const auto & node : nodes) {
+            for (const auto & [ch, v] : node.children) {
+                alphabet.insert(ch);
+            }
+        }
+    }
+
+    size_t num_states()          const { return t.nodes.size(); }
+    bool   is_terminal(size_t s) const { return terminal[s]; }
+
+    // follow failure links until a transition on `ch` exists.
+    size_t next(size_t state, uint32_t ch) const {
+        const auto & nodes = t.nodes;
+        while (state && nodes[state].children.find(ch) == nodes[state].children.end()) {
+            state = fail[state];
+        }
+        auto it = nodes[state].children.find(ch);
+        return it != nodes[state].children.end() ? it->second : 0;
+    }
+};
+
 static std::pair<uint32_t, size_t> parse_hex_escape(const std::string & str, size_t pos, int hex_count) {
     if (pos + hex_count > str.length()) {
         return {0, 0};
@@ -894,6 +921,10 @@ struct parser_executor {
     common_peg_parse_result operator()(const common_peg_gbnf_parser & p) {
         return arena.parse(p.child, ctx, start_pos);
     }
+
+    common_peg_parse_result operator()(const common_peg_ac_parser & p) {
+        return arena.parse(p.child, ctx, start_pos);
+    }
 };
 
 common_peg_parse_result common_peg_arena::parse(common_peg_parse_context & ctx, size_t start) const {
@@ -962,7 +993,8 @@ void common_peg_arena::resolve_refs() {
                                  std::is_same_v<T, common_peg_not_parser> ||
                                  std::is_same_v<T, common_peg_tag_parser> ||
                                  std::is_same_v<T, common_peg_atomic_parser> ||
-                                 std::is_same_v<T, common_peg_gbnf_parser>) {
+                                 std::is_same_v<T, common_peg_gbnf_parser> ||
+                                 std::is_same_v<T, common_peg_ac_parser>) {
                 p.child = resolve_ref(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_rule_parser>) {
                 p.child = resolve_ref(p.child);
@@ -992,12 +1024,12 @@ void common_peg_arena::resolve_refs() {
 }
 
 std::string common_peg_arena::dump(common_peg_parser_id id) const {
-    std::unordered_set<common_peg_parser_id> visited;
+    std::set<common_peg_parser_id> visited;
     return dump_impl(id, visited);
 }
 
 std::string common_peg_arena::dump_impl(common_peg_parser_id                       id,
-                                        std::unordered_set<common_peg_parser_id> & visited) const {
+                                        std::set<common_peg_parser_id> & visited) const {
     // Check for cycles
     if (visited.count(id)) {
         return "[cycle]";
@@ -1043,6 +1075,8 @@ std::string common_peg_arena::dump_impl(common_peg_parser_id
             return "Atomic(" + dump_impl(p.child, visited) + ")";
         } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
             return "Gbnf(" + p.grammar + ", " + dump_impl(p.child, visited) + ")";
+        } else if constexpr (std::is_same_v<T, common_peg_ac_parser>) {
+            return "Ac(" + string_join(p.delimiters, " | ") + ", " + dump_impl(p.child, visited) + ")";
         } else if constexpr (std::is_same_v<T, common_peg_any_parser>) {
             return "Any";
         } else if constexpr (std::is_same_v<T, common_peg_space_parser>) {
@@ -1342,7 +1376,7 @@ common_peg_parser common_peg_parser_builder::json_object() {
 common_peg_parser common_peg_parser_builder::json_array() {
     return rule("json-array", [this]() {
         auto ws = space();
-        auto elements = sequence({json(), zero_or_more(sequence({literal(","), ws, json()}))});
+        auto elements = sequence({json(), zero_or_more(sequence({ws, literal(","), ws, json()}))});
         return sequence({
             literal("["),
             ws,
@@ -1452,6 +1486,13 @@ common_peg_parser common_peg_parser_builder::json_member(const std::string & key
     });
 }
 
+common_peg_parser common_peg_parser_builder::ac(const common_peg_parser & p, const std::vector<std::string> & delimiters) {
+    if (delimiters.empty()) {
+        throw std::runtime_error("ac parser requires at least one delimiter");
+    }
+    return add(common_peg_ac_parser{p, delimiters});
+}
+
 static std::string gbnf_escape_char_class(uint32_t c) {
     if (c == '-' || c == ']' || c == '[' || c == '\\') {
         return "\\" + std::string(1, (char) c);
@@ -1502,61 +1543,118 @@ static std::string gbnf_escape_char_class(uint32_t c) {
     return std::string(buf);
 }
 
-static std::string gbnf_excluding_pattern(const std::vector<std::string> & strings) {
-    trie matcher(strings);
-    auto pieces = matcher.collect_prefix_and_next();
-
-    std::string pattern;
-    std::string trailing;  // optional proper-prefix of a delimiter, allowed only at the very end
-    for (size_t i = 0; i < pieces.size(); ++i) {
-        if (i > 0) {
-            pattern += " | ";
-        }
-
-        const auto & pre = pieces[i].prefix;
-        const auto & chars = pieces[i].next_chars;
-
-        std::string cls;
-        cls.reserve(chars.size());
-        for (uint32_t ch : chars) {
-            cls += gbnf_escape_char_class(ch);
-        }
-
-        if (!pre.empty()) {
-            std::string pre_literal = gbnf_format_literal(common_unicode_cpts_to_utf8(pre));
-            pattern += pre_literal + " [^" + cls + "]";
-            // Each interior alternative consumes a delimiter-prefix plus a disambiguating
-            // char, so the repetition alone cannot match a value that *ends* on a proper
-            // prefix of a delimiter (e.g. a trailing "\n" when the delimiter is
-            // "\n</parameter>\n"). The runtime until() (greedy first-match) accepts such
-            // values, so without this the grammar would reject input the parser accepts.
-            // Allow the value to terminate on any proper prefix as an optional tail.
-            // This makes the grammar a slight superset of the runtime language (a value
-            // may end on the longest prefix, which greedy first-match would not itself
-            // produce); harmless for constrained generation, which only needs to admit
-            // every runtime-valid string.
-            if (!trailing.empty()) {
-                trailing += " | ";
-            }
-            trailing += pre_literal;
-        } else {
-            pattern += "[^" + cls + "]";
-        }
+static std::string gbnf_char_class(const std::vector<uint32_t> & chars, bool negate) {
+    std::string s = negate ? "[^" : "[";
+    for (uint32_t ch : chars) {
+        s += gbnf_escape_char_class(ch);
     }
-
-    std::string result = "(" + pattern + ")*";
-    if (!trailing.empty()) {
-        result += " (" + trailing + ")?";
-    }
-    return result;
+    return s + "]";
 }
 
-static std::unordered_set<std::string> collect_reachable_rules(
+static std::string gbnf_ac_grammar(
+    const common_grammar_builder &   builder,
+    const std::string &              prefix,
+    const std::vector<std::string> & strings,
+    const std::function<std::string(const std::vector<uint32_t> &,
+                                    const std::map<size_t, std::vector<uint32_t>> &,
+                                    const std::vector<uint32_t> &,
+                                    const std::function<std::string(size_t)> &)> & build_rule) {
+    aho_corasick ac(strings);
+
+    auto state_name = [&](size_t s) -> std::string {
+        if (s == 0) {
+            return prefix;
+        }
+        std::string num = std::to_string(s);
+        num = num.size() == 1 ? ("0" + num) : num;
+        return prefix + "-" + num;
+    };
+
+    for (size_t q = 0; q < ac.num_states(); q++) {
+        if (ac.is_terminal(q)) {
+            continue; // match states
+        }
+
+        std::map<size_t, std::vector<uint32_t>> buckets;
+        std::vector<uint32_t> completing;  // chars that complete a delimiter
+        std::vector<uint32_t> specific;    // chars with an explicit transition
+        for (uint32_t c : ac.alphabet) {
+            size_t d = ac.next(q, c);
+            if (ac.is_terminal(d)) {
+                completing.push_back(c);
+                specific.push_back(c);
+            } else if (d != 0) {
+                buckets[d].push_back(c); // specific non-root destination
+                specific.push_back(c);
+            }
+        }
+
+        builder.add_rule(state_name(q), build_rule(completing, buckets, specific, state_name));
+    }
+
+    // An empty delimiter makes the start state terminal. Emit an entry rule
+    // that matches the empty string so the returned reference stays valid.
+    if (ac.is_terminal(0)) {
+        builder.add_rule(prefix, "|");
+    }
+
+    return state_name(0);
+}
+
+// GBNF grammar matching strings that contain no string in `strings` as a
+// substring. Emits the complement of an Aho-Corasick automaton DFA and returns
+// the start state rule name.
+//
+// ref: https://github.com/ggml-org/llama.cpp/pull/24839
+static std::string gbnf_excluding_grammar(const common_grammar_builder & builder,
+                                          const std::string &            prefix,
+                                          const std::vector<std::string> & strings) {
+    return gbnf_ac_grammar(builder, prefix, strings,
+        [](const std::vector<uint32_t> & /*completing*/,
+           const std::map<size_t, std::vector<uint32_t>> & buckets,
+           const std::vector<uint32_t> & specific,
+           const std::function<std::string(size_t)> & state_name) {
+            // every state is accepting and completing chars get no
+            // alternative, so a forbidden string can never be matched
+            std::string rhs = "|";
+            for (const auto & [d, chars] : buckets) {
+                rhs += " " + gbnf_char_class(chars, false) + " " + state_name(d) + " |";
+            }
+            rhs += " " + gbnf_char_class(specific, true) + " " + state_name(0);
+            return rhs;
+        });
+}
+
+// GBNF grammar matching everything up to and including the first occurrence of
+// any string in `strings`. Emits the Aho-Corasick automaton DFA and returns
+// the start state rule name.
+static std::string gbnf_including_grammar(const common_grammar_builder & builder,
+                                          const std::string &            prefix,
+                                          const std::vector<std::string> & strings) {
+    return gbnf_ac_grammar(builder, prefix, strings,
+        [](const std::vector<uint32_t> & completing,
+           const std::map<size_t, std::vector<uint32_t>> & buckets,
+           const std::vector<uint32_t> & specific,
+           const std::function<std::string(size_t)> & state_name) {
+            std::vector<std::string> alts;
+            if (!completing.empty()) {
+                alts.push_back(gbnf_char_class(completing, false)); // terminate on match
+            }
+            for (const auto & [d, chars] : buckets) {
+                alts.push_back(gbnf_char_class(chars, false) + " " + state_name(d));
+            }
+            // every other character keeps scanning from the start state
+            alts.push_back(gbnf_char_class(specific, true) + " " + state_name(0));
+            return string_join(alts, " | ");
+        });
+}
+
+static std::set<std::string> collect_reachable_rules(
     const common_peg_arena & arena,
     const common_peg_parser_id & rule
 ) {
-    std::unordered_set<std::string> reachable;
-    std::unordered_set<std::string> visited;
+    std::set<std::string> reachable;
+    std::set<std::string> visited;
 
     std::function<void(common_peg_parser_id)> visit = [&](common_peg_parser_id id) {
         const auto & parser = arena.get(id);
@@ -1588,6 +1686,7 @@ static std::unordered_set<std::string> collect_reachable_rules(
                                  std::is_same_v<T, common_peg_tag_parser> ||
                                  std::is_same_v<T, common_peg_atomic_parser> ||
                                  std::is_same_v<T, common_peg_gbnf_parser> ||
+                                 std::is_same_v<T, common_peg_ac_parser> ||
                                  std::is_same_v<T, common_peg_schema_parser>) {
                 visit(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_rule_parser>) {
@@ -1765,7 +1864,7 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                 if (p.delimiters.empty()) {
                     return ".*";
                 }
-                return gbnf_excluding_pattern(p.delimiters);
+                return gbnf_excluding_grammar(builder, "until-" + std::to_string(id), p.delimiters);
             } else if constexpr (std::is_same_v<T, common_peg_schema_parser>) {
                 if (schema_delegates(p)) {
                     return to_gbnf(p.child);
@@ -1782,6 +1881,8 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                 return to_gbnf(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
                 return p.grammar;
+            } else if constexpr (std::is_same_v<T, common_peg_ac_parser>) {
+                return gbnf_including_grammar(builder, "ac-" + std::to_string(id), p.delimiters);
             } else {
                 static_assert(is_always_false_v<T>);
             }
@@ -1789,7 +1890,7 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
     };
 
     // Collect reachable rules
-    std::unordered_set<std::string> reachable_rules;
+    std::set<std::string> reachable_rules;
 
     if (lazy) {
         // Collect rules reachable from trigger rules
@@ -1918,6 +2019,8 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant &
             };
         } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
             return json{{"type", "gbnf"}, {"child", p.child}, {"grammar", p.grammar}};
+        } else if constexpr (std::is_same_v<T, common_peg_ac_parser>) {
+            return json{{"type", "ac"}, {"child", p.child}, {"delimiters", p.delimiters}};
         }
     }, variant);
 }
@@ -2090,6 +2193,16 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json
         };
     }
 
+    if (type == "ac") {
+        if (!j.contains("child") || !j.contains("delimiters") || !j["delimiters"].is_array() || j["delimiters"].empty()) {
+            throw std::runtime_error("ac parser requires 'child' and a non-empty 'delimiters' array");
+        }
+        return common_peg_ac_parser{
+            j["child"].get<common_peg_parser_id>(),
+            j["delimiters"].get<std::vector<std::string>>(),
+        };
+    }
+
     throw std::runtime_error("Unknown parser type: " + type);
 }
 

common/peg-parser.h

@@ -3,8 +3,8 @@
 #include <nlohmann/json_fwd.hpp>
 
 #include <memory>
+#include <set>
 #include <unordered_map>
-#include <unordered_set>
 #include <string>
 #include <string_view>
 #include <functional>
@@ -275,6 +275,11 @@ struct common_peg_gbnf_parser {
     std::string grammar;
 };
 
+struct common_peg_ac_parser {
+    common_peg_parser_id child;
+    std::vector<std::string> delimiters;
+};
+
 // Variant holding all parser types
 using common_peg_parser_variant = std::variant<
     common_peg_epsilon_parser,
@@ -296,7 +301,8 @@ using common_peg_parser_variant = std::variant<
     common_peg_ref_parser,
     common_peg_atomic_parser,
     common_peg_tag_parser,
-    common_peg_gbnf_parser
+    common_peg_gbnf_parser,
+    common_peg_ac_parser
 >;
 
 class common_peg_arena {
@@ -335,7 +341,7 @@ class common_peg_arena {
     friend class common_peg_parser_builder;
 
   private:
-    std::string dump_impl(common_peg_parser_id id, std::unordered_set<common_peg_parser_id> & visited) const;
+    std::string dump_impl(common_peg_parser_id id, std::set<common_peg_parser_id> & visited) const;
 
     common_peg_parser_id add_parser(common_peg_parser_variant parser);
     void add_rule(const std::string & name, common_peg_parser_id id);
@@ -514,6 +520,13 @@ class common_peg_parser_builder {
     // the child's grammar. Parsing delegates entirely to the child.
     common_peg_parser gbnf(const common_peg_parser & p, const std::string & grammar) { return add(common_peg_gbnf_parser{p, grammar}); }
 
+    // Wraps a child parser but emits a GBNF grammar built from the Aho-Corasick
+    // automaton of `delimiters`, matching everything up to and including the
+    // first delimiter. Parsing delegates entirely to the child, which is
+    // responsible for consuming the delimiter (e.g. until(D) + literal(D)).
+    common_peg_parser ac(const common_peg_parser & p, const std::vector<std::string> & delimiters);
+    common_peg_parser ac(const common_peg_parser & p, const std::string & delimiter) { return ac(p, std::vector<std::string>{delimiter}); }
+
     void set_root(const common_peg_parser & p);
 
     common_peg_arena build();

common/preset.cpp

@@ -16,48 +16,6 @@ static std::string rm_leading_dashes(const std::string & str) {
     return str.substr(pos);
 }
 
-// only allow a subset of args for remote presets for security reasons
-// do not add more args unless absolutely necessary
-// args that output to files are strictly prohibited
-static std::set<std::string> get_remote_preset_whitelist(const std::map<std::string, common_arg> & key_to_opt) {
-    static const std::set<std::string> allowed_options = {
-        "model-url",
-        "hf-repo",
-        "hf-repo-draft",
-        "hf-repo-v", // vocoder
-        "hf-file-v", // vocoder
-        "mmproj-url",
-        "pooling",
-        "jinja",
-        "batch-size",
-        "ubatch-size",
-        "cache-reuse",
-        "chat-template-kwargs",
-        "mmap",
-        // note: sampling params are automatically allowed by default
-        // negated args will be added automatically if the positive arg is specified above
-    };
-
-    std::set<std::string> allowed_keys;
-
-    for (const auto & it : key_to_opt) {
-        const std::string & key = it.first;
-        const common_arg & opt = it.second;
-        if (allowed_options.find(key) != allowed_options.end() || opt.is_sampling) {
-            allowed_keys.insert(key);
-            // also add variant keys (args without leading dashes and env vars)
-            for (const auto & arg : opt.get_args()) {
-                allowed_keys.insert(rm_leading_dashes(arg));
-            }
-            for (const auto & env : opt.get_env()) {
-                allowed_keys.insert(env);
-            }
-        }
-    }
-
-    return allowed_keys;
-}
-
 std::vector<std::string> common_preset::to_args(const std::string & bin_path) const {
     std::vector<std::string> args;
 
@@ -300,16 +258,10 @@ static std::string parse_bool_arg(const common_arg & arg, const std::string & ke
     return value;
 }
 
-common_preset_context::common_preset_context(llama_example ex, bool only_remote_allowed)
+common_preset_context::common_preset_context(llama_example ex)
         : ctx_params(common_params_parser_init(default_params, ex)) {
     common_params_add_preset_options(ctx_params.options);
     key_to_opt = get_map_key_opt(ctx_params);
-
-    // setup allowed keys if only_remote_allowed is true
-    if (only_remote_allowed) {
-        filter_allowed_keys = true;
-        allowed_keys = get_remote_preset_whitelist(key_to_opt);
-    }
 }
 
 common_presets common_preset_context::load_from_ini(const std::string & path, common_preset & global) const {

common/preset.h

@@ -60,7 +60,7 @@ struct common_preset_context {
     std::set<std::string> allowed_keys;
 
     // if only_remote_allowed is true, only accept whitelisted keys
-    common_preset_context(llama_example ex, bool only_remote_allowed = false);
+    common_preset_context(llama_example ex);
 
     // load presets from INI file
     common_presets load_from_ini(const std::string & path, common_preset & global) const;

common/sampling.cpp

@@ -259,6 +259,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
              }
         }
     }
+    if (!grmr && !grammar_str.empty()) {
+        throw std::runtime_error("failed to parse grammar");
+    }
 
     // Compute prefill tokens from the generation prompt
     std::vector<llama_token> prefill_tokens;

common/speculative.cpp

@@ -161,6 +161,10 @@ struct common_speculative_impl {
 
     virtual void accept(llama_seq_id seq_id, uint16_t n_accepted, bool is_other) = 0;
 
+    // (optional) serialize/restore per-seq internal state (e.g. eagle3's deferred boundary).
+    virtual bool get_state(llama_seq_id /*seq_id*/, std::vector<uint8_t> & /*data*/) const { return false; }
+    virtual void set_state(llama_seq_id /*seq_id*/, const std::vector<uint8_t> & /*data*/) {}
+
     // true if this implementation requires the target context to extract post-norm embeddings
     virtual bool need_embd() const = 0;
 
@@ -841,6 +845,49 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
                     (size_t) n_embd_dec * sizeof(float));
     }
 
+    // we only need to stash the deferred boundary's g_embd row for recurrent/hybrid targets:
+    // their single-position checkpoints drop it on restore
+    bool need_boundary_stash() const {
+        const llama_model * model_tgt = llama_get_model(params.ctx_tgt);
+        return llama_model_is_recurrent(model_tgt) || llama_model_is_hybrid(model_tgt);
+    }
+
+    bool get_state(llama_seq_id seq_id, std::vector<uint8_t> & data) const override {
+        if (!need_boundary_stash()) {
+            return false;
+        }
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq || pending_pos_last[seq_id] < 0) {
+            return false;
+        }
+
+        const llama_pos          pos = pending_pos_last[seq_id];
+        const std::vector<float> & g = pending_g_last[seq_id];
+
+        data.resize(sizeof(llama_pos) + g.size() * sizeof(float));
+        std::memcpy(data.data(),                     &pos,     sizeof(llama_pos));
+        std::memcpy(data.data() + sizeof(llama_pos), g.data(), g.size() * sizeof(float));
+        return true;
+    }
+
+    void set_state(llama_seq_id seq_id, const std::vector<uint8_t> & data) override {
+        if (!need_boundary_stash()) {
+            return;
+        }
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return;
+        }
+        if (data.size() != sizeof(llama_pos) + (size_t) n_embd_dec * sizeof(float)) {
+            return;
+        }
+
+        llama_pos pos = -1;
+        std::memcpy(&pos, data.data(), sizeof(llama_pos));
+
+        pending_pos_last[seq_id] = pos;
+        pending_g_last[seq_id].resize(n_embd_dec);
+        std::memcpy(pending_g_last[seq_id].data(), data.data() + sizeof(llama_pos), (size_t) n_embd_dec * sizeof(float));
+    }
+
     bool need_embd() const override {
         return false;
     }
@@ -858,7 +905,13 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
     int32_t n_embd = 0;
 
-    bool is_mem_shared = false;
+    // One MTP draft driver, three modes (set once in the ctor):
+    //   is_mem_shared (gemma4): shares the target KV, runs all heads in one graph.
+    //   chain_heads (step35): n_mtp_layers trained heads, one per draft step.
+    //   neither (qwen35 / qwen35moe): a single trained MTP head.
+    int32_t n_mtp_layers  = 1;
+    bool    is_mem_shared = false;   // gemma4
+    bool    chain_heads   = false;   // derived in the ctor: n_mtp_layers > 1 && !is_mem_shared
 
     // Per-sequence cross-batch carryover: pair (h_p, x_{p+1}) at MTP pos p+1.
     // The last h-row of one process() call needs the first token of the NEXT
@@ -873,10 +926,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
     std::vector<std::vector<float>> verify_h;
     std::vector<int32_t> verify_h_rows;
 
-    // Per-seq draft length from the last draft() call, used in accept() to
-    // roll back ctx_dft's recurrent state past the AR draft's redundant
-    // pre-advancement before process() mirrored the verify batch.
-    std::vector<uint16_t> last_n_drafted;
+    std::vector<int>                i_last;
+    std::vector<std::vector<float>> chain_h;
 
     common_speculative_impl_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
@@ -889,6 +940,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         n_embd = llama_model_n_embd_out(llama_get_model(ctx_dft));
         GGML_ASSERT(n_embd == llama_model_n_embd(llama_get_model(ctx_tgt)) &&
                 "MTP input row width must match the target h_nextn width");
+        n_mtp_layers = std::max(1, (int) llama_model_n_layer_nextn(llama_get_model(ctx_dft)));
 
         LOG_INF("%s: adding speculative implementation 'draft-mtp'\n", __func__);
         LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
@@ -935,16 +987,25 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
 
         is_mem_shared = llama_get_ctx_other(ctx_dft) == ctx_tgt;
+        chain_heads   = n_mtp_layers > 1 && !is_mem_shared;
+
+        if (chain_heads) {
+            this->params.n_max = std::min(this->params.n_max, n_mtp_layers);
+
+            chain_h.assign(n_seq, {});
+            for (auto & c : chain_h) {
+                c.reserve((size_t) (this->params.n_max + 1) * n_embd);
+            }
+        }
 
         pending_h.assign(n_seq, std::vector<float>(n_embd, 0.0f));
 
+        i_last.assign(n_seq, -1);
         i_batch_beg.assign(n_seq, -1);
         i_batch_end.assign(n_seq, -1);
 
         verify_h.assign(n_seq, {});
         verify_h_rows.assign(n_seq, 0);
-
-        last_n_drafted.assign(n_seq, 0);
     }
 
     ~common_speculative_impl_draft_mtp() override {
@@ -1050,9 +1111,34 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                 set_h(i_batch_beg[seq_id], pending_h[seq_id].data());
             }
 
-            const int32_t rc = llama_decode(ctx_dft, batch);
-            if (rc != 0) {
-                LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (pos=%d)\n", __func__, (int) rc, (int) batch_in.pos[0]);
+            auto * mem_dft = llama_get_memory(ctx_dft);
+
+            bool ok = true;
+            for (int head = 0; head < n_mtp_layers; ++head) {
+                if (chain_heads) {
+                    // ref: https://github.com/ggml-org/llama.cpp/pull/24340/changes#r3413498544
+                    for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                        if (i_batch_beg[seq_id] < 0) {
+                            continue;
+                        }
+                        llama_memory_seq_rm(mem_dft, seq_id, batch_in.pos[i_batch_beg[seq_id]], -1);
+                    }
+                    llama_set_nextn_layer_offset(ctx_dft, head);
+                }
+
+                const int32_t rc = llama_decode(ctx_dft, batch);
+                if (rc != 0) {
+                    LOG_ERR("%s: llama_decode(ctx_dft) head=%d failed rc=%d (pos=%d)\n",
+                            __func__, head, (int) rc, (int) batch_in.pos[0]);
+                    ok = false;
+                    break;
+                }
+            }
+
+            if (chain_heads) {
+                llama_set_nextn_layer_offset(ctx_dft, 0); // restore default for non-draft decodes
+            }
+            if (!ok) {
                 return false;
             }
         }
@@ -1087,7 +1173,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         int n_drafting = 0;
         std::vector<bool> drafting(n_seq);
 
-        const float * h_row = nullptr;
         const size_t row_bytes = (size_t) n_embd * sizeof(float);
 
         for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
@@ -1102,22 +1187,43 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
             common_sampler_reset(smpls[seq_id].get());
 
             common_batch_add(batch, dp.id_last, dp.n_past, { seq_id }, true);
+            std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, pending_h[seq_id].data(), row_bytes);
 
-            h_row = pending_h[seq_id].data();
-            std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
-        }
+            i_last[seq_id] = batch.n_tokens - 1;
 
-        int ret = llama_decode(ctx_dft, batch);
-        if (ret != 0) {
-            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
-            return;
+            if (chain_heads) {
+                chain_h[seq_id].assign(pending_h[seq_id].begin(), pending_h[seq_id].end());
+            }
         }
 
         int i = 0;
 
         while (n_drafting > 0) {
-            int i_batch = 0;
+            // each step decodes under a different head, i.e. a different decoder layer, and
+            // KV is per layer. process() filled this layer's KV only for positions < n_past
+            // (prompt + accepted prefix) — nothing in the draft region yet. so reset the
+            // draft region (the seq_rm lower bound is n_past, leaving the prompt KV intact)
+            // and select head i so it rebuilds its own layer's KV there; decoding just the
+            // latest token would leave its attention reading cells only another head wrote.
+            if (chain_heads) {
+                auto * mem_dft = llama_get_memory(ctx_dft);
+                for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                    if (drafting[seq_id]) {
+                        llama_memory_seq_rm(mem_dft, seq_id, dparams[seq_id].n_past, -1);
+                    }
+                }
+                llama_set_nextn_layer_offset(ctx_dft, i);
+            }
 
+            int ret = llama_decode(ctx_dft, batch);
+            if (ret != 0) {
+                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                break;
+            }
+
+            // rebuild the batch for the next step: the growing-KV paths re-add only the
+            // new token (the KV already holds the prefix), while chained heads re-add the
+            // whole prefix at the next head. dropped sequences are simply not re-added.
             common_batch_clear(batch);
 
             for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
@@ -1127,9 +1233,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
                 auto * smpl = smpls[seq_id].get();
 
-                common_sampler_sample(smpl, ctx_dft, i_batch, true);
-                h_row = llama_get_embeddings_nextn_ith(ctx_dft, i_batch);
-                ++i_batch;
+                common_sampler_sample(smpl, ctx_dft, i_last[seq_id], true);
+                const float * h_row = llama_get_embeddings_nextn_ith(ctx_dft, i_last[seq_id]);
 
                 const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
@@ -1163,30 +1268,41 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                     continue;
                 }
 
-                if (is_mem_shared) {
+                if (chain_heads) {
+                    // ref: https://github.com/ggml-org/llama.cpp/pull/24340#discussion_r3448031546
+                    chain_h[seq_id].insert(chain_h[seq_id].end(), h_row, h_row + n_embd);
+
+                    const int n_rows = (int) result.size() + 1; // id_last + tokens drafted so far
+                    for (int t = 0; t < n_rows; ++t) {
+                        const llama_token tok = (t == 0) ? dp.id_last : result[t - 1];
+                        common_batch_add(batch, tok, dp.n_past + t, { seq_id }, t == n_rows - 1);
+                        std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd,
+                                    chain_h[seq_id].data() + (size_t) t * n_embd, row_bytes);
+                    }
+                } else if (is_mem_shared) {
                     // note: with shared memory (e.g. Gemma4 assistants) we use the same position for all draft tokens
                     // ref: https://github.com/huggingface/transformers/blob/effde20942e3f82a1b97449f60b3a48c5ff96145/docs/source/en/model_doc/gemma4_assistant.md?plain=1#L36-L37
                     common_batch_add(batch, id, dp.n_past, { seq_id }, true);
+                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, h_row, row_bytes);
                 } else {
                     common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
+                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, h_row, row_bytes);
                 }
-                std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
+
+                i_last[seq_id] = batch.n_tokens - 1;
             }
 
             if (batch.n_tokens == 0) {
                 break;
             }
 
-            // evaluate the drafted tokens on the draft model
-            ret = llama_decode(ctx_dft, batch);
-            if (ret != 0) {
-                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
-                break;
-            }
-
             ++i;
         }
 
+        if (chain_heads) {
+            llama_set_nextn_layer_offset(ctx_dft, 0); // restore default for non-draft decodes
+        }
+
         for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
             auto & dp = dparams[seq_id];
             if (!dp.drafting) {
@@ -1196,8 +1312,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
             if (dp.result->size() < (size_t) params.n_min) {
                 dp.result->clear();
             }
-
-            last_n_drafted[seq_id] = (uint16_t) dp.result->size();
         }
     }
 
@@ -1810,7 +1924,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
 
         bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
         bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
-        bool has_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
+        bool has_draft_mtp    = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP))    && params.draft.ctx_dft != nullptr;
 
 
 
@@ -1848,7 +1962,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         if (has_draft_eagle3) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params));
         }
-        if (has_mtp) {
+        if (has_draft_mtp) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params));
         }
     }
@@ -2118,6 +2232,31 @@ void common_speculative_accept(common_speculative * spec, llama_seq_id seq_id, u
     }
 }
 
+// TODO: support the case of more than one speculative implementations having a state
+bool common_speculative_get_state(common_speculative * spec, llama_seq_id seq_id, std::vector<uint8_t> & data) {
+    if (spec == nullptr) {
+        return false;
+    }
+
+    for (auto & impl : spec->impls) {
+        if (impl->get_state(seq_id, data)) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+void common_speculative_set_state(common_speculative * spec, llama_seq_id seq_id, const std::vector<uint8_t> & data) {
+    if (spec == nullptr) {
+        return;
+    }
+
+    for (auto & impl : spec->impls) {
+        impl->set_state(seq_id, data);
+    }
+}
+
 void common_speculative_print_stats(const common_speculative * spec) {
     if (spec == nullptr) {
         return;

common/speculative.h

@@ -68,6 +68,10 @@ void common_speculative_draft(common_speculative * spec);
 // informs the speculative context that n_accepted tokens were accepted by the target model
 void common_speculative_accept(common_speculative * spec, llama_seq_id, uint16_t n_accepted);
 
+// (optional) get/set internal state
+bool common_speculative_get_state(common_speculative * spec, llama_seq_id seq_id, std::vector<uint8_t> & data);
+void common_speculative_set_state(common_speculative * spec, llama_seq_id seq_id, const std::vector<uint8_t> & data);
+
 // print statistics about the speculative decoding
 void common_speculative_print_stats(const common_speculative * spec);
 

conversion/__init__.py

@@ -46,6 +46,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "DbrxForCausalLM": "dbrx",
     "DeciLMForCausalLM": "deci",
     "DeepseekForCausalLM": "deepseek",
+    "DeepseekOCRForCausalLM": "deepseek",
     "DeepseekV2ForCausalLM": "deepseek",
     "DeepseekV3ForCausalLM": "deepseek",
     "DeepseekV32ForCausalLM": "deepseek",
@@ -96,6 +97,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "GraniteMoeHybridForCausalLM": "granite",
     "GraniteMoeSharedForCausalLM": "granite",
     "GraniteSpeechForConditionalGeneration": "granite",
+    "GraniteSpeechPlusForConditionalGeneration": "granite",
     "Grok1ForCausalLM": "grok",
     "GrokForCausalLM": "grok",
     "GroveMoeForCausalLM": "grovemoe",
@@ -123,6 +125,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "LLaDAModelLM": "llada",
     "LLaMAForCausalLM": "llama",
     "Lfm25AudioTokenizer": "lfm2",
+    "Lfm2BidirectionalModel": "lfm2",
     "Lfm2ForCausalLM": "lfm2",
     "Lfm2Model": "lfm2",
     "Lfm2MoeForCausalLM": "lfm2",
@@ -133,6 +136,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "LlamaModel": "llama",
     "Eagle3DraftModel": "llama",
     "Eagle3Speculator": "llama",
+    "Eagle3LlamaForCausalLM": "llama",
     "LlamaForCausalLMEagle3": "llama",
     "LlavaForConditionalGeneration": "llama",
     "LlavaStableLMEpochForCausalLM": "stablelm",
@@ -231,6 +235,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "UMT5ForConditionalGeneration": "t5",
     "UMT5Model": "t5",
     "UltravoxModel": "ultravox",
+    "UnlimitedOCRForCausalLM": "deepseek",
     "VLlama3ForCausalLM": "llama",
     "VoxtralForConditionalGeneration": "llama",
     "WavTokenizerDec": "wavtokenizer",
@@ -261,6 +266,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
     "GlmasrModel": "ultravox",
     "Granite4VisionForConditionalGeneration": "granite",
     "GraniteSpeechForConditionalGeneration": "granite",
+    "GraniteSpeechPlusForConditionalGeneration": "granite",
     "HunYuanVLForConditionalGeneration": "hunyuan",
     "Idefics3ForConditionalGeneration": "smolvlm",
     "InternVisionModel": "internvl",
@@ -296,6 +302,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
     "StepVLForConditionalGeneration": "step3",
     "Step3p7ForConditionalGeneration": "step3",
     "UltravoxModel": "ultravox",
+    "UnlimitedOCRForCausalLM": "deepseek",
     "VoxtralForConditionalGeneration": "ultravox",
     "YoutuVLForConditionalGeneration": "youtuvl",
 }

conversion/bailingmoe.py

@@ -126,7 +126,7 @@ class BailingMoeV2Model(TextModel):
         if (rope_dim := hparams.get("head_dim")) is None:
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
 
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.5)))
         self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
         self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])

conversion/base.py

@@ -1119,8 +1119,10 @@ class TextModel(ModelBase):
 
         rope_theta = self.find_hparam(["global_rope_theta", "rope_global_theta", "rope_theta_global", "rope_theta", "rotary_emb_base"], optional=True)
         local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "rope_theta_local", "swa_rope_theta", "rope_local_base_freq"], optional=True)
+        partial_rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"], optional=True)
+        original_max_position_embeddings = self.find_hparam(["original_max_position_embeddings"], optional=True)
 
-        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
+        # Ensure global params are mirrored in rope_parameters
         if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
             if local_rope_theta is not None:
                 self.rope_parameters["sliding_attention"] = {"rope_theta": local_rope_theta}
@@ -1128,6 +1130,10 @@ class TextModel(ModelBase):
                 self.rope_parameters["rope_theta"] = rope_theta
             if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
                 self.rope_parameters["rope_type"] = rope_type
+            if "partial_rotary_factor" not in self.rope_parameters and partial_rotary_factor is not None:
+                self.rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+            if "original_max_position_embeddings" not in self.rope_parameters and original_max_position_embeddings is not None:
+                self.rope_parameters["original_max_position_embeddings"] = original_max_position_embeddings
 
     @classmethod
     def __init_subclass__(cls):

conversion/chatglm.py

@@ -148,7 +148,7 @@ class ChatGLMModel(TextModel):
             rope_dim = self.hparams["attention_dim"]
         else:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.5)))
         self.gguf_writer.add_add_bos_token(False)
         rope_freq = 10000
         if "rope_ratio" in self.hparams:

conversion/deci.py

@@ -161,7 +161,7 @@ class DeciModel(TextModel):
                 factor = rope_params.get("factor", 8.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor

conversion/deepseek.py

@@ -14,7 +14,7 @@ from .base import MmprojModel, ModelBase, TextModel, gguf, logger
 from .qwen import QwenModel
 
 
-@ModelBase.register("DeepseekOCRForCausalLM")
+@ModelBase.register("DeepseekOCRForCausalLM", "UnlimitedOCRForCausalLM")
 class DeepseekOCRVisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -205,6 +205,8 @@ class DeepseekModel(TextModel):
 @ModelBase.register(
     "DeepseekV2ForCausalLM",
     "DeepseekV3ForCausalLM",
+    "DeepseekOCRForCausalLM",
+    "UnlimitedOCRForCausalLM",
     "KimiVLForConditionalGeneration",
     "KimiK25ForConditionalGeneration",
     "YoutuForCausalLM",
@@ -224,7 +226,7 @@ class DeepseekV2Model(TextModel):
         self.origin_hf_arch = hparams.get('architectures', [None])[0]
 
         # special handling for Deepseek OCR
-        if self.origin_hf_arch in ("DeepseekOCRForCausalLM", "DeepseekOCR2ForCausalLM"):
+        if self.origin_hf_arch in ("DeepseekOCRForCausalLM", "DeepseekOCR2ForCausalLM", "UnlimitedOCRForCausalLM"):
             self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
             self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
             self.gguf_writer.add_architecture()
@@ -350,6 +352,12 @@ class DeepseekV2Model(TextModel):
 
         self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 
+        # Unlimited-OCR sliding window; written for metadata, the decoder ignores it (full MHA)
+        if is_ocr:
+            sliding_window = hparams.get("sliding_window_size") or hparams.get("sliding_window")
+            if sliding_window:
+                self.gguf_writer.add_sliding_window(sliding_window)
+
         if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None:
             # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
             # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul

conversion/exaone.py

@@ -24,7 +24,7 @@ class ExaoneModel(TextModel):
 
         assert (hparams["activation_function"] == "silu")
 
-        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True)
+        rotary_factor = self.rope_parameters.get("partial_rotary_factor")
         rotary_factor = rotary_factor if rotary_factor is not None else 1.0
         self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
 
@@ -39,7 +39,7 @@ class ExaoneModel(TextModel):
                 factor = rope_params.get("factor", 8.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor
@@ -104,7 +104,7 @@ class Exaone4Model(TextModel):
                 factor = rope_params.get("factor", 16.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor

conversion/gemma.py

@@ -693,7 +693,7 @@ class Gemma4Model(Gemma3Model):
             self.gguf_writer.add_head_count_kv(value_arr)
 
         # handle n_rot differently for global vs swa layers
-        partial_rotary_factor_swa = self.hparams.get("partial_rotary_factor", 1.0)
+        partial_rotary_factor_swa = self.rope_parameters.get("partial_rotary_factor", 1.0)
         n_rot_full = int(head_dim_full) # "proportional" is used, see generate_extra_tensors
         n_rot_swa = int(head_dim_swa * partial_rotary_factor_swa)
         self.gguf_writer.add_rope_dimension_count(n_rot_full)

conversion/glm.py

@@ -124,7 +124,7 @@ class Glm4MoeModel(TextModel):
                 self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
             )
         self.gguf_writer.add_rope_dimension_count(
-            int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
+            int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.5))
         )
 
         # MoE parameters - Use only routed expert count (shared experts handled separately)
@@ -226,7 +226,7 @@ class GlmMoeDsaModel(DeepseekV2Model):
         super().set_gguf_parameters()
 
         rope_dim = self.hparams["qk_rope_head_dim"]
-        partial_rotary_factor = self.hparams.get("partial_rotary_factor", 1.0)
+        partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 1.0)
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))
 
         # NextN/MTP prediction layers

conversion/granite.py

@@ -348,6 +348,34 @@ class GraniteSpeechMmprojModel(MmprojModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("GraniteSpeechPlusForConditionalGeneration")
+class GraniteSpeechPlusMmprojModel(GraniteSpeechMmprojModel):
+    """Conversion for GraniteSpeechPlus - extends GraniteSpeech with feature layer concatenation"""
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    def set_gguf_parameters(self):
+        assert self.hparams_audio is not None
+        super().set_gguf_parameters()
+
+        # Add feature_layer if present in encoder config
+        if feature_layers := self.hparams_audio.get("cat_hidden_layers"):
+            self.gguf_writer.add_audio_feature_layers(feature_layers)
+            logger.info(f"gguf: audio feature_layers = {feature_layers}")
+
+            # Validate projector dimension matches concatenated encoder output
+            hidden_dim = self.hparams_audio["hidden_dim"]
+            expected_dim = hidden_dim * (len(feature_layers) + 1)
+            projector_dim = self.global_config["projector_config"]["encoder_hidden_size"]
+
+            if projector_dim != expected_dim:
+                raise ValueError(
+                    f"Projector encoder_hidden_size ({projector_dim}) does not match "
+                    f"expected concatenated dimension ({expected_dim}). "
+                    f"Expected: hidden_dim ({hidden_dim}) * (len(feature_layers) + 1) = {expected_dim}"
+                )
+
+
 @ModelBase.register("Granite4VisionForConditionalGeneration")
 class Granite4VisionMmprojModel(MmprojModel):
     has_vision_encoder = True

conversion/lfm2.py

@@ -64,11 +64,17 @@ class LFM2Model(TextModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Lfm2Model")
+@ModelBase.register("Lfm2Model", "Lfm2BidirectionalModel")
 class LFM2ColBertModel(LFM2Model):
     model_arch = gguf.MODEL_ARCH.LFM2
     dense_tensor_name = "dense_2"
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.hf_arch == "Lfm2BidirectionalModel":
+            self.gguf_writer.add_causal_attention(False)
+        self._try_set_pooling_type()
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if not name.startswith(self.dense_tensor_name):
             name = "model." + name
@@ -76,10 +82,11 @@ class LFM2ColBertModel(LFM2Model):
         yield from super().modify_tensors(data_torch, name, bid)
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        # dense tensor is stored in a separate safetensors file
+        # optional dense tensor is stored in a separate safetensors file
         from safetensors.torch import load_file
         tensors_file = self.dir_model / "1_Dense" / "model.safetensors"
-        assert tensors_file.is_file()
+        if not tensors_file.is_file():
+            return
         tensor = load_file(tensors_file)["linear.weight"]
         self.gguf_writer.add_embedding_length_out(tensor.shape[0])
         yield f"{self.dense_tensor_name}.weight", tensor.clone()

conversion/llama.py

@@ -23,6 +23,7 @@ from .base import ModelBase, TextModel, gguf, logger
     "LlavaForConditionalGeneration",
     "VoxtralForConditionalGeneration",
     "LlamaForCausalLMEagle3",
+    "Eagle3LlamaForCausalLM",
     "Eagle3Speculator",
     "Eagle3DraftModel",
     "IQuestCoderForCausalLM",
@@ -289,7 +290,7 @@ class LlamaModel(TextModel):
                 factor = rope_params.get("factor", 8.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor

conversion/mamba.py

@@ -114,7 +114,8 @@ class Mamba2Model(TextModel):
             hparams["text_config"] = hparams["llm_config"]
         super().__init__(dir_model, *args, hparams=hparams, **kwargs)
         self.d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
-        self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or 2 * self.d_model
+        self.expand = self.find_hparam(["mamba_expand", "expand"], optional=True) or 2
+        self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or self.expand * self.d_model
         self.n_group = self.find_hparam(["n_groups"], optional=True) or 1
 
     def set_vocab(self):
@@ -144,11 +145,9 @@ class Mamba2Model(TextModel):
 
         rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
 
-        # Fail early for models which don't have a block expansion factor of 2
-        # TODO: does this really matter?
         # skip the assertion for FalconH1 Model
         if self.model_arch != gguf.MODEL_ARCH.FALCON_H1:
-            assert self.d_inner == 2 * self.d_model
+            assert self.d_inner == self.expand * self.d_model
             assert self.d_inner % head_dim == 0
 
         self.gguf_writer.add_context_length(2**20)  # arbitrary value; for those who use the default

conversion/mimo.py

@@ -154,7 +154,7 @@ class MimoV2Model(TextModel):
         self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
         self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
 
-        rope_dim = int(self.hparams["head_dim"] * self.hparams["partial_rotary_factor"])
+        rope_dim = int(self.hparams["head_dim"] * self.rope_parameters["partial_rotary_factor"])
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon", 1e-5))

conversion/minicpm.py

@@ -32,11 +32,9 @@ class MiniCPMModel(TextModel):
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
 
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is not None:
-            long_factors = rope_scaling.get('long_factor', None)
-            short_factors = rope_scaling.get('short_factor', None)
-
+        long_factors = self.rope_parameters.get('long_factor')
+        short_factors = self.rope_parameters.get('short_factor')
+        if long_factors or short_factors:
             if long_factors is None or short_factors is None:
                 raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
 
@@ -85,13 +83,11 @@ class MiniCPM3Model(TextModel):
         self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is not None:
+        long_factors = self.rope_parameters.get('long_factor')
+        short_factors = self.rope_parameters.get('short_factor')
+        if long_factors or short_factors:
             rope_dims = self.hparams["qk_rope_head_dim"]
 
-            long_factors = rope_scaling.get('long_factor', None)
-            short_factors = rope_scaling.get('short_factor', None)
-
             if long_factors is None or short_factors is None:
                 raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
 

conversion/nemotron.py

@@ -125,17 +125,18 @@ class NemotronModel(TextModel):
         self.gguf_writer.add_layer_norm_eps(f_norm_eps)
 
         # * Partial RoPE
-        rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
+        rot_pct = self.rope_parameters["partial_rotary_factor"]
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
         self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
 
         # * RopeScaling for Nemotron
-        if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None:
+        factor = self.hparams.get("factor") or self.rope_parameters.get("factor")
+        if factor is None:
             self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
         else:
             self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])
+            self.gguf_writer.add_rope_scaling_factor(factor)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side

conversion/phi.py

@@ -18,7 +18,7 @@ class Phi2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.PHI2
 
     def set_gguf_parameters(self):
-        rot_pct = self.find_hparam(["partial_rotary_factor"])
+        rot_pct = self.rope_parameters["partial_rotary_factor"]
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
 
@@ -149,8 +149,8 @@ class Phi3MiniModel(TextModel):
         n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
         rms_eps = self.find_hparam(["rms_norm_eps"])
         max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
-        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
-        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
+        orig_max_pos_embds = self.rope_parameters["original_max_position_embeddings"]
+        rot_pct = self.rope_parameters.get("partial_rotary_factor", 1.0)
         rope_dims = int(rot_pct * n_embd) // n_head
 
         self.gguf_writer.add_context_length(max_pos_embds)
@@ -174,18 +174,19 @@ class Phi3MiniModel(TextModel):
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
         max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
-        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
-        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
+        orig_max_pos_embds = self.rope_parameters["original_max_position_embeddings"]
+        rot_pct = self.rope_parameters.get("partial_rotary_factor", 1.0)
         rope_dims = int(rot_pct * n_embd) // n_head
 
         # write rope scaling for long context (128k) model
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is None:
+        long_factors = self.rope_parameters.get('long_factor')
+        short_factors = self.rope_parameters.get('short_factor')
+        if not long_factors:
             return
 
         scale = max_pos_embds / orig_max_pos_embds
 
-        rope_scaling_type = rope_scaling.get('rope_type', rope_scaling.get('type', '')).lower()
+        rope_scaling_type = self.rope_parameters.get('rope_type', '').lower()
         if len(rope_scaling_type) == 0:
             raise KeyError('Missing the required key rope_scaling.type')
 
@@ -198,9 +199,6 @@ class Phi3MiniModel(TextModel):
 
         self.gguf_writer.add_rope_scaling_attn_factors(attn_factor)
 
-        long_factors = rope_scaling.get('long_factor', None)
-        short_factors = rope_scaling.get('short_factor', None)
-
         if long_factors is None or short_factors is None:
             raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
 

conversion/qwen.py

@@ -280,7 +280,7 @@ class Qwen3NextModel(Qwen2MoeModel):
         self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
         if (rope_dim := self.hparams.get("head_dim")) is None:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.25)))
 
     @classmethod
     def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:

conversion/stablelm.py

@@ -28,7 +28,7 @@ class StableLMModel(TextModel):
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(self.block_count)
         self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
+        rotary_factor = self.rope_parameters["partial_rotary_factor"]
         self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
         self.gguf_writer.add_head_count(hparams["num_attention_heads"])
         self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])

conversion/step3.py

@@ -314,7 +314,7 @@ class Step35Model(TextModel):
         factor = float(rope_params.get("factor", 8.0))
         low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
         high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
-        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
+        old_context_len = int(rope_params.get("original_max_position_embeddings", 8192))
 
         low_freq_wavelen = old_context_len / low_freq_factor
         high_freq_wavelen = old_context_len / high_freq_factor

docs/android.md

@@ -29,7 +29,7 @@ With Termux, you can install and run `llama.cpp` as if the environment were Linu
 
 ```
 $ apt update && apt upgrade -y
-$ apt install git cmake
+$ apt install git cmake libandroid-spawn
 ```
 
 Then, follow the [build instructions](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md), specifically for CMake.

docs/backend/OPENVINO.md

@@ -237,8 +237,8 @@ chmod +x ubuntu-llamacpp-ov-install.sh
 # ============================================
 set -euo pipefail
 
-OPENVINO_VERSION_MAJOR="2026.2"
-OPENVINO_VERSION_FULL="2026.2.0.21903.52ddc073857"
+OPENVINO_VERSION_MAJOR="2026.2.1"
+OPENVINO_VERSION_FULL="2026.2.1.21919.ede283a88e3"
 
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 OPENVINO_INSTALL_DIR="/opt/intel/openvino_${OPENVINO_VERSION_MAJOR}"
@@ -334,7 +334,7 @@ echo "  ./build/ReleaseOV/bin/llama-cli -m model.gguf"
 ```
 
 > [!NOTE]
-> The script pins OpenVINO `2026.2` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
+> The script pins OpenVINO `2026.2.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
 
 </details>
 
@@ -364,8 +364,8 @@ REM ============================================
 REM llama.cpp OpenVINO Build Script (Ninja)
 REM ============================================
 
-set "OPENVINO_VERSION_MAJOR=2026.2"
-set "OPENVINO_VERSION_FULL=2026.2.0.21903.52ddc073857"
+set "OPENVINO_VERSION_MAJOR=2026.2.1"
+set "OPENVINO_VERSION_FULL=2026.2.1.21919.ede283a88e3"
 
 set "SCRIPT_DIR=%~dp0"
 set "VCPKG_DIR=C:\vcpkg"
@@ -547,7 +547,7 @@ endlocal
 ```
 
 > [!NOTE]
-> The script pins OpenVINO `2026.2` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
+> The script pins OpenVINO `2026.2.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
 
 </details>
 

docs/backend/SYCL.md

@@ -161,6 +161,64 @@ You could update your test result in it directly.
 
 Please refer to [Docker with SYCL](../docker.md#docker-with-sycl) for details.
 
+## Quick Development WOW
+
+This chapter is for quick development & try with SYCL backend on Intel GPU.
+
+You need to install following sofeware before development:
+   - Intel GPU driver
+   - oneAPI package
+   - other development tools.
+
+Please refer to [Linux](#linux) or [Windows](#windows-1) for above installation and resolve the trouble in usage. There are the detailed guide.
+
+- Linux
+
+```
+## build from source code
+./examples/sycl/build.sh
+
+## run CONV_2D_DW unit test cases
+./build/bin/test-backend-ops -b SYCL0 -o CONV_2D_DW
+
+## run all unit test cases
+./build/bin/test-backend-ops -b SYCL0
+
+## run with LLM on the first GPU
+./examples/sycl/test.sh -mg 0 -m xxxx.gguf
+
+## run service with LLM on the first GPU
+export ONEAPI_DEVICE_SELECTOR="level_zero:0"
+./examples/sycl/start-svr.sh -m xxxx.gguf
+
+## update the docs/ops.md for new/update OPs
+./examples/sycl/update-ops-doc.sh
+```
+
+- Windows
+
+```
+## build from source code
+examples\sycl\win-build-sycl.bat
+
+## run CONV_2D_DW unit test cases
+build\bin\test-backend-ops.exe -b SYCL0 -o CONV_2D_DW
+
+## run all unit test cases
+build\bin\test-backend-ops.exe -b SYCL0
+
+## run LLM on the first GPU
+examples\sycl\win-test.bat -mg 0 -m xxxx.gguf
+
+## run service with LLM on the first GPU
+set ONEAPI_DEVICE_SELECTOR="level_zero:0"
+examples\sycl\win-start-svr.bat -m xxxx.gguf
+
+## update the docs/ops.md for new/update OPs
+examples\sycl\win-update-ops-doc.bat
+```
+
+
 ## Linux
 
 ### I. Setup Environment
@@ -355,6 +413,15 @@ In two device selection modes, the default SYCL backend is level_zero, you can c
 |------------------|----------------------------------------|
 | Single device    | --split-mode none --main-gpu DEVICE_ID |
 | Multiple devices | --split-mode layer (default)           |
+| Multiple devices | --split-mode tensor (tensor parallelism) |
+
+`--split-mode tensor` (tensor parallelism) shards each layer across the selected
+GPUs. It requires flash attention, which is auto-enabled when `--flash-attn` is
+left at its default `auto`, so `--split-mode tensor` works out of the box.
+Passing `--flash-attn off` together with `--split-mode tensor` is rejected at
+context creation. The default `f16` KV cache is recommended. Tensor parallelism
+is currently optimized for 2 GPUs; other device counts fall back to a generic
+all-reduce.
 
 Examples:
 
@@ -657,6 +724,15 @@ In two device selection modes, the default SYCL backend is level_zero, you can c
 |------------------|----------------------------------------|
 | Single device    | --split-mode none --main-gpu DEVICE_ID |
 | Multiple devices | --split-mode layer (default)           |
+| Multiple devices | --split-mode tensor (tensor parallelism) |
+
+`--split-mode tensor` (tensor parallelism) shards each layer across the selected
+GPUs. It requires flash attention, which is auto-enabled when `--flash-attn` is
+left at its default `auto`, so `--split-mode tensor` works out of the box.
+Passing `--flash-attn off` together with `--split-mode tensor` is rejected at
+context creation. The default `f16` KV cache is recommended. Tensor parallelism
+is currently optimized for 2 GPUs; other device counts fall back to a generic
+all-reduce.
 
 Examples:
 
@@ -701,7 +777,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_GRAPH    | ON *(default)* \|OFF *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
 | GGML_SYCL_DNN      | ON *(default)* \|OFF *(Optional)*     | Enable build with oneDNN.                   |
 | GGML_SYCL_HOST_MEM_FALLBACK | ON *(default)* \|OFF *(Optional)* | Allow host memory fallback when device memory is full during quantized weight reorder. Enables inference to continue at reduced speed (reading over PCIe) instead of failing. Requires Linux kernel 6.8+. |
-| GGML_SYCL_SUPPORT_LEVEL_ZERO | ON *(default)* \|OFF *(Optional)* | Enable Level Zero API for device memory allocation. Requires Level Zero headers/library at build time and Intel GPU driver (Level Zero runtime) at run time. Reduces system RAM usage during multi-GPU inference. |
+| GGML_SYCL_SUPPORT_LEVEL_ZERO_API | ON *(default)* \|OFF *(Optional)* | Support to use Level Zero API for device memory allocation. Requires Level Zero headers/library at build time and Intel GPU driver (Level Zero runtime) at run time. Reduces system RAM usage during multi-GPU inference. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).|
 | CMAKE_C_COMPILER   | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path.      |
 | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
 
@@ -712,10 +788,11 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | Name              | Value            | Function                                                                                                                  |
 |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
 | GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
+| GGML_SYCL_DEV2DEV_MEMCPY | 0 (default) or 1 | Choose the SYCL or L0 API in dev2dev memory copy.<br>Value: <br>*  0: SYCL API (default)<br>* 1: L0 API -- L0 API is found to lead to abnormal crash in some case. This debug flag is used to check the issue.|
 | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
 | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for Intel devices older than Gen 10) |
 | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
-| GGML_SYCL_ENABLE_LEVEL_ZERO | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO=ON at build time. |
+| GGML_SYCL_USE_LEVEL_ZERO_API | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON at build time. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).|
 | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
 | GGML_SYCL_ENABLE_VMM | 0 or 1 (default) | Enable the virtual-memory device pool. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
@@ -731,6 +808,7 @@ Pass these via `CXXFLAGS` or add a one-off `#define` to enable a flag on the spo
 | DEBUG_SYCL_POOL | Enable device memory pool logging on teardown. Useful for profiling allocations. |
 | DEBUG_SYCL_MALLOC | Enable verbose per-call logging of device pool alloc/free operations. |
 
+
 ## Design Rule
 
 - Open to all contributors.

docs/backend/snapdragon/CMakeUserPresets.json

@@ -24,7 +24,6 @@
             "GGML_LLAMAFILE":   "OFF",
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
-            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
             "LLAMA_OPENSSL":    "OFF"
         }
     },
@@ -47,7 +46,6 @@
             "GGML_LLAMAFILE":   "OFF",
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
-            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
             "LLAMA_OPENSSL":    "OFF"
         }
     },
@@ -73,7 +71,6 @@
             "GGML_LLAMAFILE":   "OFF",
             "GGML_OPENCL":      "OFF",
             "GGML_HEXAGON":     "ON",
-            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
             "LLAMA_OPENSSL":    "OFF"
         }
     },

docs/multimodal.md

@@ -1,10 +1,11 @@
 # Multimodal
 
 llama.cpp supports multimodal input via `libmtmd`. Currently, there are 2 tools support this feature:
-- [llama-mtmd-cli](../tools/mtmd/README.md)
+- [llama-cli](../tools/cli/README.md)
 - [llama-server](../tools/server/README.md) via OpenAI-compatible `/chat/completions` API
+- [llama-mtmd-cli](../tools/mtmd/README.md), for testing and development
 
-Currently, we support **image** and **audio** input. Audio is highly experimental and may have reduced quality.
+Currently, we support **image**, **audio** and **video** input.
 
 To enable it, you can use one of the 2 methods below:
 

docs/ops.md

@@ -27,11 +27,11 @@ Legend:
 |                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
-|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              COS | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |

docs/preset.md

@@ -8,55 +8,53 @@ The INI preset feature, introduced in [PR#17859](https://github.com/ggml-org/lla
 
 When running multiple models on the server (router mode), INI preset files can be used to configure model-specific parameters. Please refer to the [server documentation](../tools/server/README.md) for more details.
 
-### Using a Remote Preset
+### Using a Hugging Face Preset
 
-> [!NOTE]
+> [!IMPORTANT]
 >
-> This feature is currently only supported via the `-hf` option.
+> Please only use presets that you can trust! Unknown presets may be unsafe
 
-For GGUF models hosted on Hugging Face, you can include a `preset.ini` file in the root directory of the repository to define specific configurations for that model.
+You can push your preset to Hugging Face Hub and share with other users by:
+1. Creating an empty model repository on Hugging Face
+2. Creating a `preset.ini` file in the root directory of the repository
 
-Example:
+Example of a `preset.ini`:
 
 ```ini
-hf-repo-draft = username/my-draft-model-GGUF
-temp = 0.5
-top-k = 20
-top-p = 0.95
+[*]
+ctx-size             = 0
+mmap                 = 1
+kv-unified           = 1
+parallel             = 4
+spec-default         = 1
+
+[Qwen3.5-4B]
+hf                   = unsloth/Qwen3.5-4B-GGUF:Q4_K_M
+ctx-size             = 262144
+batch-size           = 2048
+ubatch-size          = 2048
+top-p                = 1.0
+top-k                = 0
+min-p                = 0.01
+temp                 = 1.0
+
+[gpt-oss-120b-hf]
+hf                   = ggml-org/gpt-oss-120b-GGUF
+ctx-size             = 262144
+batch-size           = 2048
+ubatch-size          = 2048
+top-p                = 1.0
+top-k                = 0
+min-p                = 0.01
+temp                 = 1.0
+chat-template-kwargs = {"reasoning_effort": "high"}
 ```
 
-For security reasons, only certain options are allowed. Please refer to [preset.cpp](../common/preset.cpp) for the complete list of permitted options.
-
-Example usage:
-
-Assuming your repository `username/my-model-with-preset` contains a `preset.ini` with the configuration above:
-
-```sh
-llama-cli -hf username/my-model-with-preset
-
-# This is equivalent to:
-llama-cli -hf username/my-model-with-preset \
-  --hf-repo-draft username/my-draft-model-GGUF \
-  --temp 0.5 \
-  --top-k 20 \
-  --top-p 0.95
-```
-
-You can also override preset arguments by specifying them on the command line:
+The preset will be loaded similarly to the `--models-preset` option. Therefore, you can also override certain params via CLI arguments:
 
 ```sh
 # Force temp = 0.1, overriding the preset value
-llama-cli -hf username/my-model-with-preset --temp 0.1
-```
-
-If you want to define multiple preset configurations for one or more GGUF models, you can create a blank HF repo for each preset. Each HF repo should contain a `preset.ini` file that references the actual model(s):
-
-```ini
-hf-repo = user/my-model-main
-hf-repo-draft = user/my-model-draft
-temp = 0.8
-ctx-size = 1024
-; (and other configurations)
+llama-cli -hf username/my-preset --temp 0.1
 ```
 
 ### Named presets

docs/speculative.md

@@ -13,6 +13,45 @@ The `llama-server` application supports several implementations of speculative d
 A much smaller model (called the _draft model_) generates drafts.
 A draft model is the most used approach in speculative decoding.
 
+### EAGLE-3 (`draft-eagle3`)
+
+EAGLE-3 uses a small draft model that reads the target model's hidden states to predict the next tokens, so it
+reaches higher acceptance than a standalone draft model of the same size. The draft is a one-layer transformer
+trained for a specific target model; it shares the target model's tokenizer and, optionally, uses a reduced draft
+vocabulary with its own `lm_head`, which is mapped back using a `d2t` table.
+
+Convert the EAGLE-3 checkpoint with `--target-model-dir` so it inherits the target's tokenizer and the layer
+indices to read. Both the SpecForge `LlamaForCausalLMEagle3` and the vLLM/AngelSlim `Eagle3LlamaForCausalLM`
+checkpoint formats are supported (for example [`AngelSlim/Qwen3-4B_eagle3`](https://huggingface.co/AngelSlim/Qwen3-4B_eagle3)
+for `Qwen/Qwen3-4B`):
+
+```bash
+python convert_hf_to_gguf.py AngelSlim/Qwen3-4B_eagle3 \
+    --target-model-dir Qwen/Qwen3-4B --outtype bf16 --outfile Qwen3-4B-eagle3.gguf
+
+llama-server -m Qwen3-4B.gguf -md Qwen3-4B-eagle3.gguf --spec-type draft-eagle3
+```
+
+Supported EAGLE-3 draft models include:
+
+- [yuhuili/EAGLE3-LLaMA3.1-Instruct-8B](https://huggingface.co/yuhuili/EAGLE3-LLaMA3.1-Instruct-8B)
+- [yuhuili/EAGLE3-LLaMA3.3-Instruct-70B](https://huggingface.co/yuhuili/EAGLE3-LLaMA3.3-Instruct-70B)
+- [RedHatAI/gemma-4-31B-it-speculator.eagle3](https://huggingface.co/RedHatAI/gemma-4-31B-it-speculator.eagle3)
+- [RedHatAI/gemma-4-26B-A4B-it-speculator.eagle3](https://huggingface.co/RedHatAI/gemma-4-26B-A4B-it-speculator.eagle3)
+- [Tengyunw/qwen3_8b_eagle3](https://huggingface.co/Tengyunw/qwen3_8b_eagle3)
+- [Tengyunw/qwen3_30b_moe_eagle3](https://huggingface.co/Tengyunw/qwen3_30b_moe_eagle3)
+- [AngelSlim/Qwen3-1.7B_eagle3](https://huggingface.co/AngelSlim/Qwen3-1.7B_eagle3)
+- [AngelSlim/Qwen3-4B_eagle3](https://huggingface.co/AngelSlim/Qwen3-4B_eagle3)
+- [AngelSlim/Qwen3-8B_eagle3](https://huggingface.co/AngelSlim/Qwen3-8B_eagle3)
+- [AngelSlim/Qwen3-14B_eagle3](https://huggingface.co/AngelSlim/Qwen3-14B_eagle3)
+- [AngelSlim/Qwen3-32B_eagle3](https://huggingface.co/AngelSlim/Qwen3-32B_eagle3)
+- [AngelSlim/Qwen3-a3B_eagle3](https://huggingface.co/AngelSlim/Qwen3-a3B_eagle3)
+- [RedHatAI/gpt-oss-20b-speculator.eagle3](https://huggingface.co/RedHatAI/gpt-oss-20b-speculator.eagle3)
+- [lmsys/EAGLE3-gpt-oss-120b-bf16](https://huggingface.co/lmsys/EAGLE3-gpt-oss-120b-bf16)
+- [nvidia/gpt-oss-120b-Eagle3-long-context](https://huggingface.co/nvidia/gpt-oss-120b-Eagle3-long-context)
+
+For the full and up-to-date list of supported models, see #18039.
+
 ### n-gram Cache (`ngram-cache`)
 
 An n-gram is a sequence of n tokens. The n-gram cache implementation maintains statistics about short n-gram sequences.
@@ -108,7 +147,7 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 ### General Speculative Parameters
 
 ```
---spec-type [none|draft-simple|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
+--spec-type [none|draft-simple|draft-eagle3|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
                                         comma-separated list of types of speculative decoding to use
                                         (default: none)
                                         (env: LLAMA_ARG_SPEC_TYPE)
@@ -247,6 +286,7 @@ Specifies a comma-separated list of speculative decoding types to use.
 |------|-------------|
 | `none` | No speculative decoding (default) |
 | `draft-simple` | Use a simple draft model for speculation |
+| `draft-eagle3` | Use an EAGLE-3 draft model that reads the target's hidden states |
 | `draft-mtp` | Use Multi Token Prediction (MTP) heads from the main model |
 | `ngram-cache` | Use n-gram cache lookup |
 | `ngram-simple` | Use simple n-gram pattern matching |

examples/json_schema_to_grammar.py

@@ -198,18 +198,18 @@ class BuiltinRule:
 SPACE_RULE = '| " " | "\\n"{1,2} [ \\t]{0,20}'
 
 PRIMITIVE_RULES = {
-    'boolean'      : BuiltinRule('("true" | "false") space', []),
+    'boolean'      : BuiltinRule('("true" | "false")', []),
     'decimal-part' : BuiltinRule('[0-9]{1,16}', []),
     'integral-part': BuiltinRule('[0] | [1-9] [0-9]{0,15}', []),
-    'number'       : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
-    'integer'      : BuiltinRule('("-"? integral-part) space', ['integral-part']),
+    'number'       : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?', ['integral-part', 'decimal-part']),
+    'integer'      : BuiltinRule('("-"? integral-part)', ['integral-part']),
     'value'        : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
-    'object'       : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
-    'array'        : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
-    'uuid'         : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []),
+    'object'       : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? space "}"', ['string', 'value']),
+    'array'        : BuiltinRule('"[" space ( value ("," space value)* )? space "]"', ['value']),
+    'uuid'         : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\""', []),
     'char'         : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []),
-    'string'       : BuiltinRule(r'"\"" char* "\"" space', ['char']),
-    'null'         : BuiltinRule('"null" space', []),
+    'string'       : BuiltinRule(r'"\"" char* "\""', ['char']),
+    'null'         : BuiltinRule('"null"', []),
 }
 
 # TODO: support "uri", "email" string formats
@@ -217,9 +217,9 @@ STRING_FORMAT_RULES = {
     'date'            : BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
     'time'            : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
     'date-time'       : BuiltinRule('date "T" time', ['date', 'time']),
-    'date-string'     : BuiltinRule('"\\"" date "\\"" space', ['date']),
-    'time-string'     : BuiltinRule('"\\"" time "\\"" space', ['time']),
-    'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
+    'date-string'     : BuiltinRule('"\\"" date "\\""', ['date']),
+    'time-string'     : BuiltinRule('"\\"" time "\\""', ['time']),
+    'date-time-string': BuiltinRule('"\\"" date-time "\\""', ['date-time']),
 }
 
 DOTALL = '[\\U00000000-\\U0010FFFF]'
@@ -319,7 +319,7 @@ class SchemaConverter:
                 out.append(f'[^"{"".join(rejects)}] {char_rule}*')
         visit(trie)
 
-        out.append(f' ){"" if trie.is_end_of_string else "?"} ["] space')
+        out.append(f' ){"" if trie.is_end_of_string else "?"} ["]')
         return ''.join(out)
 
     def _add_rule(self, name, rule):
@@ -549,7 +549,7 @@ class SchemaConverter:
         return self._add_rule(
             name,
             to_rule(transform()) if self._raw_pattern \
-                else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space")
+                else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\"")
 
 
     def _resolve_ref(self, ref):
@@ -580,10 +580,10 @@ class SchemaConverter:
             return self._add_rule(rule_name, self._generate_union_rule(name, [{**schema, 'type': t} for t in schema_type]))
 
         elif 'const' in schema:
-            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']) + ' space')
+            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']))
 
         elif 'enum' in schema:
-            rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) + ') space'
+            rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) + ')'
             return self._add_rule(rule_name, rule)
 
         elif schema_type in (None, 'object') and \
@@ -624,7 +624,7 @@ class SchemaConverter:
                     enum_intersection &= s
 
                 if enum_intersection:
-                    rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in sorted(enum_intersection))) + ') space'
+                    rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in sorted(enum_intersection))) + ')'
                     return self._add_rule(rule_name, rule)
 
             return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None))
@@ -638,12 +638,12 @@ class SchemaConverter:
                     ' "," space '.join(
                         self.visit(item, f'{name}{"-" if name else ""}tuple-{i}')
                         for i, item in enumerate(items)) +
-                    ' "]" space')
+                    ' space "]"')
             else:
                 item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item')
                 min_items = schema.get("minItems", 0)
                 max_items = schema.get("maxItems")
-                return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space')
+                return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' space "]"')
 
         elif schema_type in (None, 'string') and 'pattern' in schema:
             return self._visit_pattern(schema['pattern'], rule_name)
@@ -663,7 +663,7 @@ class SchemaConverter:
             min_len = schema.get('minLength', 0)
             max_len = schema.get('maxLength')
 
-            return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space')
+            return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\""')
 
         elif schema_type in (None, 'integer') and \
                 ('minimum' in schema or 'exclusiveMinimum' in schema or 'maximum' in schema or 'exclusiveMaximum' in schema):
@@ -680,7 +680,7 @@ class SchemaConverter:
 
             out = ["("]
             _generate_min_max_int(min_value, max_value, out)
-            out.append(") space")
+            out.append(")")
             return self._add_rule(rule_name, ''.join(out))
 
         elif (schema_type == 'object') or (len(schema) == 0):
@@ -765,7 +765,7 @@ class SchemaConverter:
                 rule += ' )'
             rule += ' )?'
 
-        rule += ' "}" space'
+        rule += ' space "}"'
 
         return rule
 

examples/sycl/update-ops-doc.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+
+#  MIT license
+#  Copyright (C) 2026 Intel Corporation
+#  SPDX-License-Identifier: MIT
+
+./build/bin/test-backend-ops support --output csv > docs/ops/SYCL.csv
+./scripts/create_ops_docs.py
+

examples/sycl/win-update-ops-doc.bat

@@ -0,0 +1,8 @@
+@echo off
+
+rem MIT license
+rem Copyright (C) 2026 Intel Corporation
+rem SPDX-License-Identifier: MIT
+
+build\bin\test-backend-ops support --output csv > docs\ops\SYCL.csv
+python scripts\create_ops_docs.py

ggml/CMakeLists.txt

@@ -5,7 +5,7 @@ project("ggml" C CXX ASM)
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
 set(GGML_VERSION_MINOR 15)
-set(GGML_VERSION_PATCH 1)
+set(GGML_VERSION_PATCH 3)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
@@ -249,7 +249,7 @@ option(GGML_SYCL                            "ggml: use SYCL"
 option(GGML_SYCL_F16                        "ggml: use 16 bit floats for sycl calculations"   OFF)
 option(GGML_SYCL_GRAPH                      "ggml: enable graphs in the SYCL backend"         ON)
 option(GGML_SYCL_HOST_MEM_FALLBACK          "ggml: allow host memory fallback in SYCL reorder (requires kernel 6.8+)" ON)
-option(GGML_SYCL_SUPPORT_LEVEL_ZERO         "ggml: use Level Zero API in SYCL backend"  ON)
+option(GGML_SYCL_SUPPORT_LEVEL_ZERO_API     "ggml: use Level Zero API in SYCL backend"  ON)
 option(GGML_SYCL_DNN                        "ggml: enable oneDNN in the SYCL backend"         ON)
 set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
                                             "ggml: sycl target device")
@@ -266,7 +266,6 @@ set   (GGML_OPENCL_TARGET_VERSION "300" CACHE STRING
                                             "ggml: OpenCL API version to target")
 
 option(GGML_HEXAGON                         "ggml: enable Hexagon backend"                    OFF)
-set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml: quantize group size (32, 64, or 128)")
 
 # toolchain for vulkan-shaders-gen
 set   (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen")

ggml/include/ggml-sycl.h

@@ -27,6 +27,14 @@ GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int de
 // split tensor buffer that splits matrices by rows across multiple devices
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
 
+// Tensor parallelism (--split-mode tensor): comm_init/free/allreduce_tensor
+// trio queried by the meta-backend via ggml_backend_reg_get_proc_address.
+// See typedefs in ggml/include/ggml-backend.h. Mirrors the CUDA backend's
+// pattern (ggml_backend_cuda_comm_*).
+GGML_BACKEND_API void * ggml_backend_sycl_comm_init(ggml_backend_t * backends, size_t n_backends);
+GGML_BACKEND_API void   ggml_backend_sycl_comm_free(void * comm_ctx);
+GGML_BACKEND_API bool   ggml_backend_sycl_comm_allreduce_tensor(void * comm_ctx, struct ggml_tensor ** tensors);
+
 // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
 

ggml/src/CMakeLists.txt

@@ -438,7 +438,14 @@ if (GGML_CPU_ALL_VARIANTS)
             ggml_add_cpu_backend_variant(power8_2       POWER8  VSX)
             ggml_add_cpu_backend_variant(power9         POWER9  VSX)
             ggml_add_cpu_backend_variant(power10        POWER10 VSX)
-            ggml_add_cpu_backend_variant(power11        POWER11 VSX)
+            # POWER11 backend: only if compiler supports -mcpu=power11
+            check_cxx_compiler_flag("-mcpu=power11" GGML_CXX_SUPPORTS_POWER11)
+            if (GGML_CXX_SUPPORTS_POWER11)
+                message(STATUS "Compiler supports -mcpu=power11, enabling POWER11 backend")
+                ggml_add_cpu_backend_variant(power11 POWER11 VSX)
+            else()
+                message(STATUS "Skipping POWER11 backend: compiler does not support -mcpu=power11")
+            endif()
         else()
             message(FATAL_ERROR "Unsupported PowerPC target OS: ${CMAKE_SYSTEM_NAME}")
         endif()

ggml/src/ggml-backend.cpp

@@ -1551,6 +1551,8 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
         int split_backend_id = split->backend_id;
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
+        ggml_backend_synchronize(split_backend);
+
         // copy the input tensors to the split backend
         for (int input_id = 0; input_id < split->n_inputs; input_id++) {
             ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[input_id]);
@@ -1561,15 +1563,15 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
-                } else {
+                } else if (!split_backend->iface.cpy_tensor_async) {
                     ggml_backend_synchronize(split_backend);
                 }
-                ggml_backend_tensor_copy(input, input_cpy);
+                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
             } else {
                 // wait for the split backend to finish using the input before overwriting it
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
-                } else {
+                } else if (!split_backend->iface.cpy_tensor_async) {
                     ggml_backend_synchronize(split_backend);
                 }
 
@@ -1674,6 +1676,8 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
             }
         }
 
+        ggml_backend_synchronize(split_backend);
+
         if (!sched->callback_eval) {
             enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
             if (ec != GGML_STATUS_SUCCESS) {

ggml/src/ggml-cpu/CMakeLists.txt

@@ -389,7 +389,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             string(REGEX MATCHALL "POWER *([0-9]+)" MATCHED_STRING "${POWER10_M_UPPER}")
             string(REGEX REPLACE "POWER *([0-9]+)" "\\1" EXTRACTED_NUMBER "${MATCHED_STRING}")
 
-            if (EXTRACTED_NUMBER GREATER_EQUAL 10)
+            if (EXTRACTED_NUMBER EQUAL 10 OR EXTRACTED_NUMBER EQUAL 11)
                 list(APPEND ARCH_FLAGS -mcpu=power10)
             elseif (EXTRACTED_NUMBER EQUAL 9)
                 list(APPEND ARCH_FLAGS -mcpu=power9)

ggml/src/ggml-cpu/amx/mmq.cpp

@@ -2417,15 +2417,14 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te
             // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size
             GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size);
 
-            parallel_for_ggml(params, n_batch, [&](int begin, int end) {
-                for (int batch_idx = begin; batch_idx < end; ++batch_idx) {
+            parallel_for_ggml(params, n_batch * M, [&](int begin, int end) {
+                for (int idx = begin; idx < end; ++idx) {
+                    int batch_idx = idx / M;
+                    int m         = idx % M;
                     int64_t src1_offset = ggml_batch_offset(src1, batch_idx, ne2);
                     const float * A_data = (const float *)((const char *)src1->data + src1_offset);
                     char * wdata_batch = (char *)wdata + batch_idx * M * row_size_A;
-
-                    for (int m = 0; m < M; ++m) {
-                        from_float<vec_dot_type>(A_data + m * K, wdata_batch + m * row_size_A, K);
-                    }
+                    from_float<vec_dot_type>(A_data + m * K, wdata_batch + m * row_size_A, K);
                 }
             });
         });

ggml/src/ggml-cpu/llamafile/sgemm.cpp

@@ -2345,7 +2345,7 @@ class tinyBLAS_Q0_PPC {
             else if (n_aligned % 16 == 0) nc = 16;
             else                          nc = 8;
         }
-        bool can_use_tiled = n_aligned > 0 && (m % mc == 0) && (k % kc == 0);
+        bool can_use_tiled = n_aligned > 0 && (m % mc == 0);
         if (can_use_tiled) {
             matmul_tiled(m, n_aligned, mc, nc, kc);
             if (n > n_aligned) {
@@ -3063,13 +3063,14 @@ class tinyBLAS_Q0_PPC {
             int64_t ii = (job / xtiles) * mc;
             int64_t jj = (job % xtiles) * nc;
             for (int64_t kk = 0; kk < k; kk += kc) {
+                int64_t k_cur = MIN(kc, k - kk);
                 if constexpr(is_Ablock_q4) {
-                    packNormal_q4_fp16(A + ii * lda + kk, lda, mc, kc, (uint8_t *)A_pack);
+                    packNormal_q4_fp16(A + ii * lda + kk, lda, mc, k_cur, (uint8_t *)A_pack);
                 } else {
-                    packNormal_q8_fp16(A + ii * lda + kk, lda, mc, kc, (uint8_t *)A_pack);
+                    packNormal_q8_fp16(A + ii * lda + kk, lda, mc, k_cur, (uint8_t *)A_pack);
                 }
-                packNormal_q8_fp16(B + jj * ldb + kk, ldb, nc, kc, (uint8_t *)B_pack);
-                KERNEL_Q0(ii, jj, mc, nc, kc, kk, A_pack, B_pack);
+                packNormal_q8_fp16(B + jj * ldb + kk, ldb, nc, k_cur, (uint8_t *)B_pack);
+                KERNEL_Q0(ii, jj, mc, nc, k_cur, kk, A_pack, B_pack);
             }
         }
     }

ggml/src/ggml-cpu/ops.cpp

@@ -3688,8 +3688,6 @@ static void ggml_compute_forward_norm_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -3703,25 +3701,49 @@ static void ggml_compute_forward_norm_f32(
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
             for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
-                const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                const char * x = (const char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                char * y = (char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3;
 
-                float sum = 0.0;
-                ggml_vec_sum_f32(ne00, &sum, x);
-                float mean = sum/ne00;
+                if (nb00 == sizeof(float) && nb0 == sizeof(float)) {
+                    const float * xf = (const float *) x;
 
-                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
-                float variance = 0;
+                    float sum = 0.0;
+                    ggml_vec_sum_f32(ne00, &sum, xf);
+                    float mean = sum/ne00;
+
+                    float * yf = (float *) y;
+                    float variance = 0;
 
 #ifdef GGML_USE_ACCELERATE
-                mean = -mean;
-                vDSP_vsadd(x, 1, &mean, y, 1, ne00);
-                vDSP_measqv(y, 1, &variance, ne00);
+                    mean = -mean;
+                    vDSP_vsadd(xf, 1, &mean, yf, 1, ne00);
+                    vDSP_measqv(yf, 1, &variance, ne00);
 #else
-                variance = ggml_vec_cvar_f32(ne00, y, x, mean);
+                    variance = ggml_vec_cvar_f32(ne00, yf, xf, mean);
 #endif //GGML_USE_ACCELERATE
 
-                const float scale = 1.0f/sqrtf(variance + eps);
-                ggml_vec_scale_f32(ne00, y, scale);
+                    const float scale = 1.0f/sqrtf(variance + eps);
+                    ggml_vec_scale_f32(ne00, yf, scale);
+                } else {
+                    float sum = 0.0;
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        sum += *(const float *) (x + i00*nb00);
+                    }
+                    const float mean = sum/ne00;
+
+                    float variance = 0.0f;
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const float v = *(const float *) (x + i00*nb00) - mean;
+                        *(float *) (y + i00*nb0) = v;
+                        variance += v * v;
+                    }
+                    variance /= ne00;
+
+                    const float scale = 1.0f/sqrtf(variance + eps);
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        *(float *) (y + i00*nb0) *= scale;
+                    }
+                }
             }
         }
     }
@@ -4142,8 +4164,6 @@ static void ggml_compute_forward_l2_norm_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -4158,20 +4178,27 @@ static void ggml_compute_forward_l2_norm_f32(
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
             for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
-                const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                const char * x = (const char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
 
                 ggml_float sum = 0.0;
                 for (int64_t i00 = 0; i00 < ne00; i00++) {
-                    sum += (ggml_float)(x[i00] * x[i00]);
+                    const float xi = *(const float *) (x + i00*nb00);
+                    sum += (ggml_float)(xi * xi);
                 }
 
-                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
-
-                memcpy(y, x, ne00 * sizeof(float));
-
                 const float scale = 1.0f/fmaxf(sqrtf(sum), eps);
 
-                ggml_vec_scale_f32(ne00, y, scale);
+                char * y = (char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3;
+
+                if (nb00 == sizeof(float) && nb0 == sizeof(float)) {
+                    memcpy(y, x, ne00 * sizeof(float));
+                    ggml_vec_scale_f32(ne00, (float *) y, scale);
+                } else {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const float xi = *(const float *) (x + i00*nb00);
+                        *(float *) (y + i00*nb0) = xi * scale;
+                    }
+                }
             }
         }
     }

ggml/src/ggml-cpu/vec.cpp

@@ -75,12 +75,12 @@ void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * G
             ay1 = GGML_F32_VEC_LOAD(y + i);
             sum1 = GGML_F32_VEC_FMA(sum1, ax1, ay1);
         }
-        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
+        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmla on available elements only
         if (np2 < n) {
             svbool_t pg = svwhilelt_b32(np2, n);
             ax1 = svld1_f32(pg, x + np2);
             ay1 = svld1_f32(pg, y + np2);
-            sum1 = svmad_f32_m(pg, ax1, ay1, sum1);
+            sum1 = svmla_f32_m(pg, sum1, ax1, ay1);
         }
         // reduce sum1,sum2 to sum1
         GGML_F32_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8);

ggml/src/ggml-cuda/binbcast.cu

@@ -34,26 +34,26 @@ template <float (*bin_op)(const float, const float),
 static __global__ void k_bin_bcast(const src0_t *         src0,
                                    const src1_t *         src1,
                                    dst_t *                dst,
-                                   const int              ne0,
-                                   const int              ne1,
-                                   const int              ne2,
+                                   const uint32_t         ne0,
+                                   const uint32_t         ne1,
+                                   const uint32_t         ne2,
                                    const uint3            ne3,
                                    const uint3            ne10,
                                    const uint3            ne11,
                                    const uint3            ne12,
                                    const uint3            ne13,
-                                 /*const int              s0,*/
-                                   const int              s1,
-                                   const int              s2,
-                                   const int              s3,
-                                   const int              s00,
-                                   const int              s01,
-                                   const int              s02,
-                                   const int              s03,
-                                   const int              s10,
-                                   const int              s11,
-                                   const int              s12,
-                                   const int              s13,
+                                 /*const uint32_t         s0,*/
+                                   const uint32_t         s1,
+                                   const uint32_t         s2,
+                                   const uint32_t         s3,
+                                   const uint32_t         s00,
+                                   const uint32_t         s01,
+                                   const uint32_t         s02,
+                                   const uint32_t         s03,
+                                   const uint32_t         s10,
+                                   const uint32_t         s11,
+                                   const uint32_t         s12,
+                                   const uint32_t         s13,
                                    src1_ptrs... src1s) {
     ggml_cuda_pdl_lc();
     const uint32_t i0s = blockDim.x * blockIdx.x + threadIdx.x;
@@ -61,7 +61,7 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
     const uint32_t i2  = fastdiv((blockDim.z * blockIdx.z + threadIdx.z), ne3);
     const uint32_t i3  = (blockDim.z * blockIdx.z + threadIdx.z) - (i2 * ne3.z);
 
-    if (i0s >= (uint32_t)ne0 || i1 >= (uint32_t)ne1 || i2 >= (uint32_t)ne2 || i3 >= ne3.z) {
+    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3.z) {
         return;
     }
 
@@ -69,25 +69,32 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
     const uint32_t i12 = fastmodulo(i2, ne12);
     const uint32_t i13 = fastmodulo(i3, ne13);
 
-    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
+    const size_t i_src0 = size_t( i3)*s03 + size_t( i2)*s02 + size_t( i1)*s01;
+    const size_t i_src1 = size_t(i13)*s13 + size_t(i12)*s12 + size_t(i11)*s11;
+    const size_t i_dst  = size_t( i3)*s3  + size_t( i2)*s2  + size_t( i1)*s1;
 
     const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
     dst_t * dst_row = dst + i_dst;
 
+    const uint32_t s0 = blockDim.x * gridDim.x;
+
     ggml_cuda_pdl_sync();
-    for (int i0 = i0s; i0 < ne0; i0 += blockDim.x * gridDim.x) {
+    for (uint32_t i0 = i0s; i0 < ne0; i0 += s0) {
         const uint32_t i10 = fastmodulo(i0, ne10);
 
-        float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
+        float result = src0_row ? (float) src0_row[size_t(i0)*s00] : 0.0f;
         if constexpr (sizeof...(src1_ptrs) > 0) {
-            result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
+            result = (..., (result = bin_op(result, (float)src1s[i_src1 + size_t(i10)*s10])));
         } else {
-            result = bin_op(result, (float)src1[i_src1 + i10*s10]);
+            result = bin_op(result, (float)src1[i_src1 + size_t(i10)*s10]);
         }
 
         dst_row[i0] = (dst_t) result;
+
+        // protect i0 from overflow
+        if (ne0 - i0 <= s0) {
+           break;
+        }
     }
 }
 
@@ -110,19 +117,19 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
                                            const uint3            ne12,
                                            const uint3            ne13,
                                          /*const int              s0,*/
-                                           const int              s1,
-                                           const int              s2,
-                                           const int              s3,
-                                           const int              s00,
-                                           const int              s01,
-                                           const int              s02,
-                                           const int              s03,
-                                           const int              s10,
-                                           const int              s11,
-                                           const int              s12,
-                                           const int              s13,
+                                           const uint32_t         s1,
+                                           const uint32_t         s2,
+                                           const uint32_t         s3,
+                                           const uint32_t         s00,
+                                           const uint32_t         s01,
+                                           const uint32_t         s02,
+                                           const uint32_t         s03,
+                                           const uint32_t         s10,
+                                           const uint32_t         s11,
+                                           const uint32_t         s12,
+                                           const uint32_t         s13,
                                            src1_ptrs... src1s) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+    const uint32_t i  = blockDim.x*blockIdx.x + threadIdx.x;
 
     const uint32_t i3 = fastdiv(i, prod_012);
     const uint32_t i2 = fastdiv(i - i3 * prod_012.z, prod_01);
@@ -133,25 +140,25 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
         return;
     }
 
-    const int i11 = fastmodulo(i1, ne11);
-    const int i12 = fastmodulo(i2, ne12);
-    const int i13 = fastmodulo(i3, ne13);
+    const uint32_t i11 = fastmodulo(i1, ne11);
+    const uint32_t i12 = fastmodulo(i2, ne12);
+    const uint32_t i13 = fastmodulo(i3, ne13);
 
-    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
+    const size_t i_src0 = size_t( i3)*s03 + size_t( i2)*s02 + size_t( i1)*s01;
+    const size_t i_src1 = size_t(i13)*s13 + size_t(i12)*s12 + size_t(i11)*s11;
+    const size_t i_dst  = size_t( i3)*s3  + size_t( i2)*s2  + size_t( i1)*s1;
 
     const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
     dst_t * dst_row = dst + i_dst;
 
-    const int i10 = fastmodulo(i0, ne10);
+    const uint32_t i10 = fastmodulo(i0, ne10);
 
     ggml_cuda_pdl_sync();
-    float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
+    float result = src0_row ? (float) src0_row[size_t(i0)*s00] : 0.0f;
     if constexpr (sizeof...(src1_ptrs) > 0) {
-        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
+        result = (..., (result = bin_op(result, (float)src1s[i_src1 + size_t(i10)*s10])));
     } else {
-        result = bin_op(result, (float)src1[i_src1 + i10*s10]);
+        result = bin_op(result, (float)src1[i_src1 + size_t(i10)*s10]);
     }
 
     dst_row[i0] = (dst_t) result;
@@ -248,6 +255,31 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         size_t s02 = nb02 / sizeof(src0_t);
         size_t s03 = nb03 / sizeof(src0_t);
 
+        GGML_ASSERT(ne0 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(ne1 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(ne2 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(ne3 <= std::numeric_limits<uint32_t>::max());
+
+      //GGML_ASSERT(s0  <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s1  <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s2  <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s3  <= std::numeric_limits<uint32_t>::max());
+
+        GGML_ASSERT(s00 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s01 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s02 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s03 <= std::numeric_limits<uint32_t>::max());
+
+        GGML_ASSERT(s10 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s11 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s12 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s13 <= std::numeric_limits<uint32_t>::max());
+
+        GGML_ASSERT(cne1[0] <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(cne1[1] <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(cne1[2] <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(cne1[3] <= std::numeric_limits<uint32_t>::max());
+
         GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
         GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
         GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
@@ -263,6 +295,8 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
         GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
 
+        GGML_ASSERT(ne2 * ne3 <= std::numeric_limits<unsigned int>::max());
+
         const int block_size = 128;
 
         int64_t hne0 = std::max(ne0 / 2LL, 1LL);
@@ -281,7 +315,13 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         const uint3 ne13 = init_fastdiv_values((uint32_t) cne1[3]);
 
         if (block_nums.z > 65535 || block_nums.y > 65535) {
-            int         block_num  = (ne0 * ne1 * ne2 * ne3 + block_size - 1) / block_size;
+            int64_t     block_num   = (ne0 * ne1 * ne2 * ne3 + block_size - 1) / block_size;
+
+            GGML_ASSERT(block_num              <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(block_num * block_size <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(ne0 * ne1              <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(ne0 * ne1 * ne2        <= std::numeric_limits<uint32_t>::max());
+
             const uint3 prod_012    = init_fastdiv_values((uint32_t) (ne0 * ne1 * ne2));
             const uint3 prod_01     = init_fastdiv_values((uint32_t) (ne0 * ne1));
             const uint3 ne0_fastdiv = init_fastdiv_values((uint32_t) ne0);
@@ -298,6 +338,10 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
                     s10, s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
             }
         } else {
+            GGML_ASSERT(int64_t(block_nums.x) * block_dims.x <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(int64_t(block_nums.y) * block_dims.y <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(int64_t(block_nums.z) * block_dims.z <= std::numeric_limits<uint32_t>::max());
+
             const uint3 ne3_fastdiv = init_fastdiv_values((uint32_t) ne3);
             {
                 const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);

ggml/src/ggml-cuda/col2im-1d.cu

@@ -0,0 +1,81 @@
+#include "col2im-1d.cuh"
+#include "convert.cuh"
+
+// col2im_1d: scatter-add GEMM columns to 1D signal (gather approach)
+// columns: [K*OC, T_in]  ->  output: [T_out, OC]
+// Supports F32, F16, BF16 data with F32 accumulator.
+
+template <typename T>
+static __global__ void col2im_1d_kernel(
+        const T * __restrict__ col,
+        T       * __restrict__ dst,
+        const int T_in, const uint3 T_out_fd,
+        const int OC, const int K, const int K_OC,
+        const int s0, const int p0, const int total) {
+
+    const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (idx >= total) return;
+
+    // dst layout: [T_out, OC], ne[0]=T_out fastest
+    const uint2 qr  = fast_div_modulo((uint32_t)idx, T_out_fd);  // qr.x = idx / T_out, qr.y = idx % T_out
+    const int oc    = (int)qr.x;
+    const int t_out = (int)qr.y;
+    const int t_abs = t_out + p0;  // absolute position in uncropped signal
+
+    // Gather: find all (t_in, k) where t_in*s + k == t_abs, 0 <= k < K
+    int t_in_min = (t_abs - K + s0) / s0;  // ceil((t_abs - K + 1) / s)
+    if (t_in_min < 0) t_in_min = 0;
+    int t_in_max = t_abs / s0;
+    if (t_in_max >= T_in) t_in_max = T_in - 1;
+
+    float sum = 0.0f;
+    for (int t_in = t_in_min; t_in <= t_in_max; t_in++) {
+        const int k = t_abs - t_in * s0;
+        // col layout: [K*OC, T_in], column index = oc * K + k
+        sum += ggml_cuda_cast<float>(col[(oc * K + k) + t_in * K_OC]);
+    }
+
+    dst[idx] = ggml_cuda_cast<T>(sum);
+}
+
+void ggml_cuda_op_col2im_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t OC = ((const int32_t *)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
+
+    const int K_OC = (int) src0->ne[0];
+    const int T_in = (int) src0->ne[1];
+    const int K    = K_OC / OC;
+    const int T_out = (int) dst->ne[0];
+
+    const uint3 T_out_fd = init_fastdiv_values((uint32_t)T_out);
+
+    const int total = T_out * OC;
+    const int block_size = 256;
+    const int num_blocks = (total + block_size - 1) / block_size;
+
+    switch (src0->type) {
+        case GGML_TYPE_F32: {
+            col2im_1d_kernel<<<num_blocks, block_size, 0, stream>>>(
+                (const float *)src0->data, (float *)dst->data,
+                T_in, T_out_fd, OC, K, K_OC, s0, p0, total);
+        } break;
+        case GGML_TYPE_F16: {
+            col2im_1d_kernel<<<num_blocks, block_size, 0, stream>>>(
+                (const half *)src0->data, (half *)dst->data,
+                T_in, T_out_fd, OC, K, K_OC, s0, p0, total);
+        } break;
+        case GGML_TYPE_BF16: {
+            col2im_1d_kernel<<<num_blocks, block_size, 0, stream>>>(
+                (const nv_bfloat16 *)src0->data, (nv_bfloat16 *)dst->data,
+                T_in, T_out_fd, OC, K, K_OC, s0, p0, total);
+        } break;
+        default:
+            GGML_ABORT("col2im_1d: unsupported type");
+    }
+}

ggml/src/ggml-cuda/col2im-1d.cuh

@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_col2im_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/cpy.cu

@@ -53,10 +53,10 @@ static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const
     const int64_t nmat = ne / (ne00 * ne01);
     const int64_t n = ne00 * ne01;
 
-    const int x = blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.x;
-    const int y = blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
-    const int tx = blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.x;  // transpose block offset
-    const int ty = blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
+    const int64_t x  = (int64_t) blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.x;
+    const int64_t y  = (int64_t) blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
+    const int64_t tx = (int64_t) blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.x;  // transpose block offset
+    const int64_t ty = (int64_t) blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
 
     __shared__ float tile[2][CUDA_CPY_TILE_DIM_2D][CUDA_CPY_TILE_DIM_2D+1];
     int cur_tile_buf = 0;
@@ -197,7 +197,7 @@ static void ggml_cpy_scalar_contiguous_cuda(
 cudaStream_t stream) {
 
     const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream);
     ggml_cuda_kernel_launch(cpy_scalar_contiguous<src_t, dst_t>, launch_params, cx, cdst, ne);
 }
@@ -208,6 +208,14 @@ static void ggml_cpy_scalar_cuda(
     const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
+    const auto launch_scalar_generic = [&]() {
+        const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+        GGML_ASSERT(num_blocks <= INT_MAX);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream);
+        ggml_cuda_kernel_launch(cpy_scalar<cpy_1_scalar<src_t, dst_t>>, launch_params,
+            cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+    };
+
     if (transposed) {
         GGML_ASSERT(ne == ne00*ne01*ne02);  // ne[3] is 1 assumed
         int64_t ne00n, ne01n, ne02n;
@@ -224,20 +232,18 @@ static void ggml_cpy_scalar_cuda(
         int64_t grid_x = (ne01n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D;
         int64_t grid_y = (ne00n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D;
         int64_t grid_z = (ne/(ne01n*ne00n) + CUDA_CPY_BLOCK_NM - 1) / CUDA_CPY_BLOCK_NM;
-        GGML_ASSERT(grid_x < UINT_MAX);
-        GGML_ASSERT(grid_y < USHRT_MAX);
-        GGML_ASSERT(grid_z < USHRT_MAX);
-        dim3 dimGrid(grid_x, grid_y, grid_z);
-        dim3 dimBlock(CUDA_CPY_TILE_DIM_2D, CUDA_CPY_BLOCK_ROWS, 1);
-        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(dimGrid, dimBlock, 0, stream);
-        ggml_cuda_kernel_launch(cpy_scalar_transpose<dst_t>, launch_params,
-            cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+        GGML_ASSERT(grid_x <= INT_MAX);
+        if (grid_y > USHRT_MAX || grid_z > USHRT_MAX) {
+            launch_scalar_generic();
+        } else {
+            dim3 dimGrid(grid_x, grid_y, grid_z);
+            dim3 dimBlock(CUDA_CPY_TILE_DIM_2D, CUDA_CPY_BLOCK_ROWS, 1);
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(dimGrid, dimBlock, 0, stream);
+            ggml_cuda_kernel_launch(cpy_scalar_transpose<dst_t>, launch_params,
+                cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+        }
     } else {
-        const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-        GGML_ASSERT(num_blocks < UINT_MAX);
-        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream);
-        ggml_cuda_kernel_launch(cpy_scalar<cpy_1_scalar<src_t, dst_t>>, launch_params,
-            cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+        launch_scalar_generic();
     }
 }
 
@@ -248,7 +254,7 @@ static void ggml_cpy_f32_q8_0_cuda(
 
     GGML_ASSERT(ne % QK8_0 == 0);
     const int64_t num_blocks = ne / QK8_0;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -259,7 +265,7 @@ static void ggml_cpy_q8_0_f32_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q8_0_f32, QK8_0><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -271,7 +277,7 @@ static void ggml_cpy_f32_q4_0_cuda(
 
     GGML_ASSERT(ne % QK4_0 == 0);
     const int64_t num_blocks = ne / QK4_0;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -284,7 +290,7 @@ static void ggml_cpy_q4_0_f32_cuda(
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q_f32<dequantize_q4_0, QK4_0>, QK4_0><<<num_blocks, 1, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
          ne10, ne11, ne12, nb10, nb11, nb12, nb13);
@@ -297,7 +303,7 @@ static void ggml_cpy_f32_q4_1_cuda(
 
     GGML_ASSERT(ne % QK4_1 == 0);
     const int64_t num_blocks = ne / QK4_1;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -310,7 +316,7 @@ static void ggml_cpy_q4_1_f32_cuda(
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q_f32<dequantize_q4_1, QK4_1>, QK4_1><<<num_blocks, 1, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
          ne10, ne11, ne12, nb10, nb11, nb12, nb13);
@@ -323,7 +329,7 @@ static void ggml_cpy_f32_q5_0_cuda(
 
     GGML_ASSERT(ne % QK5_0 == 0);
     const int64_t num_blocks = ne / QK5_0;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -336,7 +342,7 @@ static void ggml_cpy_q5_0_f32_cuda(
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q_f32<dequantize_q5_0, QK5_0>, QK5_0><<<num_blocks, 1, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
         ne10, ne11, ne12, nb10, nb11, nb12, nb13);
@@ -349,7 +355,7 @@ static void ggml_cpy_f32_q5_1_cuda(
 
     GGML_ASSERT(ne % QK5_1 == 0);
     const int64_t num_blocks = ne / QK5_1;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -362,7 +368,7 @@ static void ggml_cpy_q5_1_f32_cuda(
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1><<<num_blocks, 1, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
         ne10, ne11, ne12, nb10, nb11, nb12, nb13);
@@ -375,7 +381,7 @@ static void ggml_cpy_f32_iq4_nl_cuda(
 
     GGML_ASSERT(ne % QK4_NL == 0);
     const int64_t num_blocks = ne / QK4_NL;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -11,6 +11,7 @@
 #include "ggml-cuda/argsort.cuh"
 #include "ggml-cuda/binbcast.cuh"
 #include "ggml-cuda/clamp.cuh"
+#include "ggml-cuda/col2im-1d.cuh"
 #include "ggml-cuda/concat.cuh"
 #include "ggml-cuda/conv-transpose-1d.cuh"
 #include "ggml-cuda/conv2d.cuh"
@@ -3051,6 +3052,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_CONV_TRANSPOSE_1D:
             ggml_cuda_op_conv_transpose_1d(ctx,dst);
             break;
+        case GGML_OP_COL2IM_1D:
+            ggml_cuda_op_col2im_1d(ctx, dst);
+            break;
         case GGML_OP_POOL_2D:
             ggml_cuda_op_pool2d(ctx, dst);
             break;
@@ -3188,11 +3192,24 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
     ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
     ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
 
-    if (!ggml_backend_is_cuda(backend_src) || !ggml_backend_is_cuda(backend_dst)) {
+    // Enables async copies from CPU to CUDA, instead of only CUDA-to-CUDA
+    // Excluding this path for HIP and MUSA as a precaution.
+    // According to the summary in https://github.com/ggml-org/llama.cpp/pull/20793#issuecomment-4275794315, this change is not beneficial for hip anyways.
+    // Additionally, there is a lot of anectodal evidence that hip/musa stream behavior might not always 1:1 match CUDA behavior.
+    // e.g. https://github.com/ROCm/rocm-systems/issues/5109
+    // It thus makes sense to exclude this path for HIP and MUSA. This PR was not aimed these backends, the majority of testing happened on CUDA.
+    // This can be revisited in the future if enabling copy_from_host benefits hip/MUSA, and if the PR author can extensively test on these backends.
+#if defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)
+    const bool copy_from_host = false;
+#else
+    const bool copy_from_host = ggml_backend_buffer_is_host(buf_src) && ggml_backend_dev_type(backend_src->device) == GGML_BACKEND_DEVICE_TYPE_CPU;
+#endif
+
+    if (!(copy_from_host || ggml_backend_is_cuda(backend_src)) || !ggml_backend_is_cuda(backend_dst)) {
         return false;
     }
 
-    if (!ggml_backend_buffer_is_cuda(buf_src) || !ggml_backend_buffer_is_cuda(buf_dst)) {
+    if (!(copy_from_host || ggml_backend_buffer_is_cuda(buf_src)) || !ggml_backend_buffer_is_cuda(buf_dst)) {
         return false;
     }
 
@@ -3203,14 +3220,17 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
     ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *) buf_src->context;
     ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *) buf_dst->context;
 
-    if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
+    if ((copy_from_host && cuda_ctx_dst->device != buf_ctx_dst->device) ||
+        !copy_from_host && (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device)) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__);
 #endif // NDEBUG
         return false;
     }
 
-    if (backend_src != backend_dst) {
+    if (copy_from_host) {
+        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyHostToDevice, cuda_ctx_dst->stream()));
+    } else if (backend_src != backend_dst) {
         // copy on src stream
         if (cuda_ctx_src->device == cuda_ctx_dst->device) {
             CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream()));
@@ -5316,13 +5336,21 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 }
                 return false;
             } break;
+        case GGML_OP_COL2IM_1D:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                return (src0_type == GGML_TYPE_F32 || src0_type == GGML_TYPE_F16 || src0_type == GGML_TYPE_BF16) &&
+                    op->type == src0_type &&
+                    ggml_is_contiguous(op->src[0]) &&
+                    ggml_is_contiguous(op);
+            } break;
         case GGML_OP_SILU_BACK:
             return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
             break;
         case GGML_OP_NORM:
         case GGML_OP_RMS_NORM:
         case GGML_OP_L2_NORM:
-            return true;
+            return ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_RMS_NORM_BACK:
             return ggml_is_contiguous(op->src[0]);
             break;

ggml/src/ggml-cuda/out-prod.cu

@@ -2,6 +2,28 @@
 
 #include <cstdint>
 
+static __global__ void k_compute_out_prod_ptrs(
+        const float * src0_d, const float * src1_d, float * dst_d,
+        const float ** ptrs_a, const float ** ptrs_b, float ** ptrs_c,
+        const int64_t ne2, const int64_t ne3,
+        const int64_t dps2, const int64_t dps3,
+        const size_t s02, const size_t s03,
+        const size_t s12, const size_t s13,
+        const size_t s2,  const size_t s3) {
+    const int64_t i2 = blockIdx.x*blockDim.x + threadIdx.x;
+    const int64_t i3 = blockIdx.y*blockDim.y + threadIdx.y;
+
+    if (i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int64_t idx = i3*ne2 + i2;
+
+    ptrs_a[idx] = src0_d + (i3/dps3)*s03 + (i2/dps2)*s02;
+    ptrs_b[idx] = src1_d +  i3      *s13 +  i2      *s12;
+    ptrs_c[idx] = dst_d  +  i3      *s3  +  i2      *s2;
+}
+
 void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
@@ -67,18 +89,39 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
                         &beta,  dst_d  +  i3     *s3,  ldc, s2,
                         batch_count));
         }
+    } else if (ne2 > 1 || ne3 > 1) {
+        // dps2 > 1 (src0 broadcast along dim 2 with non-uniform stride) or multiple GEMMs
+        // along dim 3: compute per-GEMM pointers on the device and use a single batched GEMM.
+        GGML_ASSERT(ne3 > 0);
+        GGML_ASSERT(ne2 <= (int64_t) std::numeric_limits<int>::max() / ne3);
+        const int batch_count = (int) (ne2 * ne3);
+
+        ggml_cuda_pool_alloc<const float *> ptrs_a(ctx.pool(), batch_count);
+        ggml_cuda_pool_alloc<const float *> ptrs_b(ctx.pool(), batch_count);
+        ggml_cuda_pool_alloc<      float *> ptrs_c(ctx.pool(), batch_count);
+
+        const dim3 block_dims(16, 16);
+        const dim3 grid_dims((ne2 + block_dims.x - 1)/block_dims.x, (ne3 + block_dims.y - 1)/block_dims.y);
+        k_compute_out_prod_ptrs<<<grid_dims, block_dims, 0, stream>>>(
+            src0_d, src1_d, dst_d,
+            ptrs_a.get(), ptrs_b.get(), ptrs_c.get(),
+            ne2, ne3, dps2, dps3, s02, s03, s12, s13, s2, s3);
+        CUDA_CHECK(cudaGetLastError());
+
+        CUBLAS_CHECK(
+            cublasSgemmBatched(handle, CUBLAS_OP_N, src1_cublas_op,
+                    ne0, ne1, ne01,
+                    &alpha, ptrs_a.get(), lda,
+                            ptrs_b.get(), ldb,
+                    &beta,  ptrs_c.get(), ldc,
+                    batch_count));
     } else {
-        // Fallback: ne2 == 1 (no batching benefit) or dps2 > 1 (src0 broadcast along dim 2
-        // with non-uniform stride; would need cublasSgemmBatched with pointer arrays).
-        for (int64_t i3 = 0; i3 < ne3; ++i3) {
-            for (int64_t i2 = 0; i2 < ne2; ++i2) {
-                CUBLAS_CHECK(
-                    cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
-                            ne0, ne1, ne01,
-                            &alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, lda,
-                                    src1_d +  i3      *s13 +  i2      *s12, ldb,
-                            &beta,  dst_d  +  i3      *s3  +  i2      *s2,  ldc));
-            }
-        }
+        // ne2 == 1 && ne3 == 1: single GEMM
+        CUBLAS_CHECK(
+            cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
+                    ne0, ne1, ne01,
+                    &alpha, src0_d, lda,
+                            src1_d, ldb,
+                    &beta,  dst_d,  ldc));
     }
 }

ggml/src/ggml-cuda/vendors/hip.h

@@ -48,6 +48,7 @@
 #define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
 #define cublasSetStream hipblasSetStream
 #define cublasSgemm hipblasSgemm
+#define cublasSgemmBatched hipblasSgemmBatched
 #define cublasSgemmStridedBatched hipblasSgemmStridedBatched
 #define cublasStatus_t hipblasStatus_t
 #define cublasOperation_t hipblasOperation_t

ggml/src/ggml-cuda/vendors/musa.h

@@ -32,6 +32,7 @@
 #define cublasSetMathMode mublasSetMathMode
 #define cublasSetStream mublasSetStream
 #define cublasSgemm mublasSgemm
+#define cublasSgemmBatched mublasSgemmBatched
 #define cublasSgemmStridedBatched mublasSgemmStridedBatched
 #define cublasStatus_t mublasStatus_t
 #define cublasOperation_t mublasOperation_t

ggml/src/ggml-hexagon/CMakeLists.txt

@@ -25,7 +25,6 @@ include(ExternalProject)
 option(GGML_HEXAGON_HTP_DEBUG  "ggml-hexagon: enable HTP debug output" OFF)
 option(GGML_HEXAGON_FA_EXP2_HF "ggml-hexagon: use FP16 exp2 polynomial in FA softmax instead of F32 exp round-trip" OFF)
 set(GGML_HEXAGON_HTP_CERT  "$ENV{HEXAGON_HTP_CERT}" CACHE PATH "ggml-hexagon: enable HTP library signing using certificate")
-set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml-hexagon: quantize group size (32, 64, or 128)")
 
 add_library(htp_iface OBJECT
     ${CMAKE_CURRENT_BINARY_DIR}/htp_iface_stub.c)
@@ -72,15 +71,12 @@ function(build_htp_skel V)
             -DHEXAGON_SDK_ROOT=${HEXAGON_SDK_ROOT}
             -DHEXAGON_TOOLS_ROOT=${HEXAGON_TOOLS_ROOT}
             -DHEXAGON_HTP_DEBUG=${GGML_HEXAGON_HTP_DEBUG}
-            -DGGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE}
             -DDSP_VERSION=${V}
             -DPREBUILT_LIB_DIR="toolv19_${V}")
     list(APPEND HTP_SKELS ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-${V}.so)
     set(HTP_SKELS ${HTP_SKELS} PARENT_SCOPE)
 endfunction()
 
-build_htp_skel(v68)
-build_htp_skel(v69)
 build_htp_skel(v73)
 build_htp_skel(v75)
 build_htp_skel(v79)