vulkan: fix mul_mat_vec_iq1_s formatting (#18026 )

graph: add f_attn_temp_offset (#18025 )
common : refactor common_sampler + grammar logic changes (#17937 )
2025-12-14 14:52:46 +01:00 · 2025-12-14 13:05:59 +01:00 · 2025-12-14 10:11:13 +02:00 · 2025-12-14 09:00:00 +01:00 · 2025-12-14 08:47:49 +01:00 · 2025-12-14 08:29:37 +01:00
145 changed files with 5684 additions and 3343 deletions
@@ -4,7 +4,7 @@

 # Define the CANN base image for easier version updates later
 ARG CHIP_TYPE=910b
-ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc1.alpha001-${CHIP_TYPE}-openeuler22.03-py3.11
+ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.11

 # ==============================================================================
 # BUILD STAGE
@@ -111,7 +111,7 @@ ENTRYPOINT ["/app/tools.sh"]
 # ==============================================================================
 FROM base AS light

-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app

 ENTRYPOINT [ "/app/llama-cli" ]

@@ -68,7 +68,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light

-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app

 WORKDIR /app

@@ -74,7 +74,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light

-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app

 WORKDIR /app

@@ -73,7 +73,7 @@ ENTRYPOINT ["/app/tools.sh"]
 FROM base AS light

 COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app

 WORKDIR /app

@@ -81,7 +81,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light

-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app

 WORKDIR /app

@@ -94,7 +94,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light

-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app

 WORKDIR /app

@@ -105,7 +105,7 @@ 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
+COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin/llama-completion /llama.cpp/bin

 ENTRYPOINT [ "/llama.cpp/bin/llama-cli" ]

@@ -13,6 +13,8 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
    exec ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
    exec ./llama-cli "$@"
+elif [[ "$arg1" == '--run-legacy' || "$arg1" == '-l' ]]; then
+    exec ./llama-completion "$@"
 elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then
    exec ./llama-bench "$@"
 elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then
@@ -32,8 +34,10 @@ elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
 else
    echo "Unknown command: $arg1"
    echo "Available commands: "
-    echo "  --run (-r): Run a model previously converted into ggml"
-    echo "              ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512"
+    echo "  --run (-r): Run a model (chat) previously converted into ggml"
+    echo "              ex: -m /models/7B/ggml-model-q4_0.bin"
+    echo "  --run-legacy (-l): Run a model (legacy completion) previously converted into ggml"
+    echo "              ex: -m /models/7B/ggml-model-q4_0.bin -no-cnv -p \"Building a website can be done in 10 simple steps:\" -n 512"
    echo "  --bench (-b): Benchmark the performance of the inference for various parameters."
    echo "              ex: -m model.gguf"
    echo "  --perplexity (-p): Measure the perplexity of a model over a given text."
@@ -68,7 +68,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light

-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app

 WORKDIR /app

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

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

 concurrency:
@@ -1400,25 +1402,54 @@ jobs:
        chip_type: ['910b', '310p']
        build: ['Release']
    runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
-    container: ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc1.alpha001-910b-openeuler22.03-py3.11' || '8.2.rc1-310p-openeuler22.03-py3.11' }}
    steps:
      - name: Checkout
        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0

-      - name: Dependencies
+      - name: Free up disk space
+        uses: ggml-org/free-disk-space@v1.3.1
+        with:
+          tool-cache: true
+
+      - name: Set container image
+        id: cann-image
        run: |
-          yum update -y
-          yum install -y git gcc gcc-c++ make cmake libcurl-devel
+          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
+          echo "image=${image}" >> "${GITHUB_OUTPUT}"
+
+      - name: Pull container image
+        run: docker pull "${{ steps.cann-image.outputs.image }}"

      - name: Build
+        env:
+          BUILD_TYPE: ${{ matrix.build }}
+          SOC_TYPE: ascend${{ matrix.chip_type }}
        run: |
-          export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
+          HOST_UID=$(id -u)
+          HOST_GID=$(id -g)

-          cmake -S . -B build \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build }} \
-              -DGGML_CANN=on \
-              -DSOC_TYPE=ascend${{ matrix.chip_type }}
-          cmake --build build -j $(nproc)
+          docker run --rm \
+            -v "${PWD}:/workspace" \
+            -w /workspace \
+            -e SOC_TYPE=${SOC_TYPE} \
+            -e BUILD_TYPE=${BUILD_TYPE} \
+            "${{ steps.cann-image.outputs.image }}" \
+            bash -lc '
+              set -e
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
+              yum clean all && rm -rf /var/cache/yum
+              git config --global --add safe.directory "/workspace"
+              export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
+              cmake -S . -B build \
+                  -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+                  -DGGML_CANN=on \
+                  -DSOC_TYPE=${SOC_TYPE}
+              cmake --build build -j $(nproc)
+
+              chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build
+            '

 # TODO: simplify the following workflows using a matrix
 # TODO: run lighter CI on PRs and the full CI only on master (if needed)
@@ -1770,7 +1801,7 @@ jobs:
          echo "Fetch llama2c model"
          wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin
          ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
-          ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
+          ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256

  ubuntu-cmake-sanitizer-riscv64-native:
    runs-on: RISCV64
@@ -731,6 +731,78 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz
          name: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz

+
+  openEuler-cann:
+    strategy:
+      matrix:
+        arch: [x86, aarch64]
+        chip_type: ['910b', '310p']
+        build: ['Release']
+    runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Free up disk space
+        uses: ggml-org/free-disk-space@v1.3.1
+        with:
+          tool-cache: true
+
+      - name: Set container image
+        id: cann-image
+        run: |
+          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
+          echo "image=${image}" >> "${GITHUB_OUTPUT}"
+
+      - name: Pull container image
+        run: docker pull "${{ steps.cann-image.outputs.image }}"
+
+      - name: Build
+        env:
+          BUILD_TYPE: ${{ matrix.build }}
+          SOC_TYPE: ascend${{ matrix.chip_type }}
+        run: |
+          HOST_UID=$(id -u)
+          HOST_GID=$(id -g)
+
+          docker run --rm \
+            -v "${PWD}:/workspace" \
+            -w /workspace \
+            -e SOC_TYPE=${SOC_TYPE} \
+            -e BUILD_TYPE=${BUILD_TYPE} \
+            "${{ steps.cann-image.outputs.image }}" \
+            bash -lc '
+              set -e
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
+              yum clean all && rm -rf /var/cache/yum
+              git config --global --add safe.directory "/workspace"
+              export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
+              cmake -S . -B build \
+                  -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+                  -DGGML_CANN=on \
+                  -DSOC_TYPE=${SOC_TYPE}
+              cmake --build build -j $(nproc)
+
+              chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build
+            '
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+
+      - name: Upload artifacts (tar)
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz
+          name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz
+
  release:
    if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}

@@ -752,6 +824,7 @@ jobs:
      - macOS-arm64
      - macOS-x64
      - ios-xcode-build
+      - openEuler-cann

    steps:
      - name: Clone
@@ -844,6 +917,12 @@ jobs:
            - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
            - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)

+            **openEuler:**
+            - [openEuler x86 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-x86.tar.gz)
+            - [openEuler x86 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-x86.tar.gz)
+            - [openEuler aarch64 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-aarch64.tar.gz)
+            - [openEuler aarch64 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-aarch64.tar.gz)
+
      - name: Upload release
        id: upload_release
        uses: actions/github-script@v3
@@ -54,6 +54,7 @@
 /out/
 /tmp/
 /autogen-*.md
+/common/build-info.cpp

 # Deprecated

@@ -73,6 +73,8 @@ add_library(${TARGET} STATIC
    ngram-cache.h
    peg-parser.cpp
    peg-parser.h
+    preset.cpp
+    preset.h
    regex-partial.cpp
    regex-partial.h
    sampling.cpp
@@ -47,6 +47,7 @@
 #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083

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

 static std::initializer_list<enum llama_example> mmproj_examples = {
    LLAMA_EXAMPLE_MTMD,
@@ -64,6 +65,15 @@ static std::string read_file(const std::string & fname) {
    return content;
 }

+static const std::vector<common_arg> & get_common_arg_defs() {
+    static const std::vector<common_arg> options = [] {
+        common_params params;
+        auto ctx = common_params_parser_init(params, LLAMA_EXAMPLE_SERVER, nullptr);
+        return ctx.options;
+    }();
+    return options;
+}
+
 common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) {
    this->examples = examples;
    return *this;
@@ -95,6 +105,16 @@ bool common_arg::is_exclude(enum llama_example ex) {

 bool common_arg::get_value_from_env(std::string & output) const {
    if (env == nullptr) return false;
+    if (!args_neg.empty()) {
+        // for compatibility, we need to check LLAMA_ARG_NO_ env as well
+        std::string neg_env = env;
+        string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_");
+        char * neg_value = std::getenv(neg_env.c_str());
+        if (neg_value) {
+            output = "0"; // falsey
+            return true;
+        }
+    }
    char * value = std::getenv(env);
    if (value) {
        output = value;
@@ -104,6 +124,14 @@ bool common_arg::get_value_from_env(std::string & output) const {
 }

 bool common_arg::has_value_from_env() const {
+    if (env != nullptr && !args_neg.empty()) {
+        // for compatibility, we need to check LLAMA_ARG_NO_ env as well
+        std::string neg_env = env;
+        string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_");
+        if (std::getenv(neg_env.c_str())) {
+            return true;
+        }
+    }
    return env != nullptr && std::getenv(env);
 }

@@ -134,16 +162,17 @@ static std::vector<std::string> break_str_into_lines(std::string input, size_t m
    return result;
 }

-std::string common_arg::to_string() {
+std::string common_arg::to_string() const {
    // params for printing to console
    const static int n_leading_spaces = 40;
    const static int n_char_per_line_help = 70; // TODO: detect this based on current console
    std::string leading_spaces(n_leading_spaces, ' ');

    std::ostringstream ss;
-    for (const auto arg : args) {
-        if (arg == args.front()) {
-            if (args.size() == 1) {
+    auto all_args = get_args(); // also contains args_neg
+    for (const auto & arg : all_args) {
+        if (arg == all_args.front()) {
+            if (all_args.size() == 1) {
                ss << arg;
            } else {
                // first arg is usually abbreviation, we need padding to make it more beautiful
@@ -152,7 +181,7 @@ std::string common_arg::to_string() {
                ss << tmp << spaces;
            }
        } else {
-            ss << arg << (arg != args.back() ? ", " : "");
+            ss << arg << (arg != all_args.back() ? ", " : "");
        }
    }
    if (value_hint) ss << " " << value_hint;
@@ -171,6 +200,31 @@ std::string common_arg::to_string() {
    return ss.str();
 }

+std::vector<std::string> common_arg::get_args() const {
+    std::vector<std::string> result;
+    for (const auto & arg : args) {
+        result.push_back(std::string(arg));
+    }
+    for (const auto & arg : args_neg) {
+        result.push_back(std::string(arg));
+    }
+    return result;
+}
+
+std::vector<std::string> common_arg::get_env() const {
+    std::vector<std::string> result;
+    if (env) {
+        result.push_back(std::string(env));
+    }
+    if (!args_neg.empty() && env) {
+        // for compatibility, we need to add LLAMA_ARG_NO_ variant
+        std::string neg_env = env;
+        string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_");
+        result.push_back(neg_env);
+    }
+    return result;
+}
+
 //
 // utils
 //
@@ -306,6 +360,16 @@ static std::string get_all_kv_cache_types() {
    return msg.str();
 }

+static bool parse_bool_value(const std::string & value) {
+    if (is_truthy(value)) {
+        return true;
+    } else if (is_falsey(value)) {
+        return false;
+    } else {
+        throw std::invalid_argument("invalid boolean value");
+    }
+}
+
 //
 // CLI argument parsing functions
 //
@@ -313,10 +377,13 @@ static std::string get_all_kv_cache_types() {
 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
    common_params & params = ctx_arg.params;

-    std::unordered_map<std::string, common_arg *> arg_to_options;
+    std::unordered_map<std::string, std::pair<common_arg *, bool>> arg_to_options;
    for (auto & opt : ctx_arg.options) {
        for (const auto & arg : opt.args) {
-            arg_to_options[arg] = &opt;
+            arg_to_options[arg] = {&opt, /* is_positive */ true};
+        }
+        for (const auto & arg : opt.args_neg) {
+            arg_to_options[arg] = {&opt, /* is_positive */ false};
        }
    }

@@ -325,12 +392,15 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        std::string value;
        if (opt.get_value_from_env(value)) {
            try {
-                if (opt.handler_void && (value == "1" || value == "true")) {
+                if (opt.handler_void && is_truthy(value)) {
                    opt.handler_void(params);
                }
                if (opt.handler_int) {
                    opt.handler_int(params, std::stoi(value));
                }
+                if (opt.handler_bool) {
+                    opt.handler_bool(params, parse_bool_value(value));
+                }
                if (opt.handler_string) {
                    opt.handler_string(params, value);
                    continue;
@@ -359,7 +429,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        if (arg_to_options.find(arg) == arg_to_options.end()) {
            throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
        }
-        auto opt = *arg_to_options[arg];
+        auto & tmp = arg_to_options[arg];
+        auto opt = *tmp.first;
+        bool is_positive = tmp.second;
        if (opt.has_value_from_env()) {
            fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
        }
@@ -368,6 +440,10 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
                opt.handler_void(params);
                continue;
            }
+            if (opt.handler_bool) {
+                opt.handler_bool(params, is_positive);
+                continue;
+            }

            // arg with single value
            check_arg(i);
@@ -392,7 +468,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
            throw std::invalid_argument(string_format(
                "error while handling argument \"%s\": %s\n\n"
                "usage:\n%s\n\nto show complete usage, run with -h",
-                arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str()));
+                arg.c_str(), e.what(), opt.to_string().c_str()));
        }
    }

@@ -428,7 +504,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context

    // model is required (except for server)
    // TODO @ngxson : maybe show a list of available models in CLI in this case
-    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage) {
+    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage && !params.completion) {
        throw std::invalid_argument("error: --model is required\n");
    }

@@ -563,6 +639,7 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
        "llama-batched-bench",
        "llama-bench",
        "llama-cli",
+        "llama-completion",
        "llama-convert-llama2c-to-ggml",
        "llama-cvector-generator",
        "llama-embedding",
@@ -647,6 +724,56 @@ static void add_rpc_devices(const std::string & servers) {
    }
 }

+bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map) {
+    common_params dummy_params;
+    common_params_context ctx_arg = common_params_parser_init(dummy_params, ex, nullptr);
+
+    std::unordered_map<std::string, common_arg *> arg_to_options;
+    for (auto & opt : ctx_arg.options) {
+        for (const auto & arg : opt.args) {
+            arg_to_options[arg] = &opt;
+        }
+        for (const auto & arg : opt.args_neg) {
+            arg_to_options[arg] = &opt;
+        }
+    }
+
+    // TODO @ngxson : find a way to deduplicate this code
+
+    // handle command line arguments
+    auto check_arg = [&](int i) {
+        if (i+1 >= argc) {
+            throw std::invalid_argument("expected value for argument");
+        }
+    };
+
+    for (int i = 1; i < argc; i++) {
+        const std::string arg_prefix = "--";
+
+        std::string arg = argv[i];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
+        }
+        if (arg_to_options.find(arg) == arg_to_options.end()) {
+            throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
+        }
+        auto opt = *arg_to_options[arg];
+        std::string val;
+        if (opt.value_hint != nullptr) {
+            // arg with single value
+            check_arg(i);
+            val = argv[++i];
+        }
+        if (opt.value_hint_2 != nullptr) {
+            // TODO: support arg with 2 values
+            throw std::invalid_argument("error: argument with 2 values is not yet supported\n");
+        }
+        out_map[opt] = val;
+    }
+
+    return true;
+}
+
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
    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
@@ -692,25 +819,19 @@ static std::string list_builtin_chat_templates() {
    return msg.str();
 }

-static bool is_truthy(const std::string & value) {
-    return value == "on" || value == "enabled" || value == "1";
+bool common_arg_utils::is_truthy(const std::string & value) {
+    return value == "on" || value == "enabled" || value == "true" || value == "1";
 }

-static bool is_falsey(const std::string & value) {
-    return value == "off" || value == "disabled" || value == "0";
+bool common_arg_utils::is_falsey(const std::string & value) {
+    return value == "off" || value == "disabled" || value == "false" || value == "0";
 }

-static bool is_autoy(const std::string & value) {
+bool common_arg_utils::is_autoy(const std::string & value) {
    return value == "auto" || value == "-1";
 }

 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
-    // default values specific to example
-    // note: we place it here instead of inside server.cpp to allow llama-gen-docs to pick it up
-    if (ex == LLAMA_EXAMPLE_SERVER) {
-        params.use_jinja = true;
-    }
-
    params.use_color = tty_can_use_colors();

    // load dynamic backends
@@ -788,10 +909,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ));
    add_opt(common_arg(
+        {"--display-prompt"},
        {"--no-display-prompt"},
-        string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
-        [](common_params & params) {
-            params.display_prompt = false;
+        string_format("whether to print prompt at generation (default: %s)", params.display_prompt ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.display_prompt = value;
        }
    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
    add_opt(common_arg(
@@ -1004,18 +1126,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.kv_unified = true;
        }
    ).set_env("LLAMA_ARG_KV_UNIFIED"));
-    add_opt(common_arg(
-        {"--no-context-shift"},
-        string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
-        [](common_params & params) {
-            params.ctx_shift = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
    add_opt(common_arg(
        {"--context-shift"},
-        string_format("enables context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.ctx_shift = true;
+        {"--no-context-shift"},
+        string_format("whether to use context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.ctx_shift = value;
        }
    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT"));
    add_opt(common_arg(
@@ -1055,20 +1171,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION}));
    add_opt(common_arg(
+        {"--perf"},
        {"--no-perf"},
-        string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
-        [](common_params & params) {
-            params.no_perf = true;
-            params.sampling.no_perf = true;
+        string_format("whether to enable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.no_perf = !value;
+            params.sampling.no_perf = !value;
        }
-    ).set_env("LLAMA_ARG_NO_PERF"));
+    ).set_env("LLAMA_ARG_PERF"));
    add_opt(common_arg(
+        {"--show-timings"},
        {"--no-show-timings"},
-        string_format("disable timing information after each response (default: %s)", params.show_timings ? "true" : "false"),
-        [](common_params & params) {
-            params.show_timings = false;
+        string_format("whether to show timing information after each response (default: %s)", params.show_timings ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.show_timings = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_NO_SHOW_TIMINGS"));
+    ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SHOW_TIMINGS"));
    add_opt(common_arg(
        {"-f", "--file"}, "FNAME",
        "a file containing the prompt (default: none)",
@@ -1120,16 +1238,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_excludes({LLAMA_EXAMPLE_SERVER}));
    add_opt(common_arg(
        {"-e", "--escape"},
-        string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
-        [](common_params & params) {
-            params.escape = true;
-        }
-    ));
-    add_opt(common_arg(
        {"--no-escape"},
-        "do not process escape sequences",
-        [](common_params & params) {
-            params.escape = false;
+        string_format("whether to process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.escape = value;
        }
    ));
    add_opt(common_arg(
@@ -1176,19 +1288,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}));
    add_opt(common_arg(
        {"-cnv", "--conversation"},
-        "run in conversation mode:\n"
+        {"-no-cnv", "--no-conversation"},
+        "whether to run in conversation mode:\n"
        "- does not print special tokens and suffix/prefix\n"
        "- interactive mode is also enabled\n"
        "(default: auto enabled if chat template is available)",
-        [](common_params & params) {
-            params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
-        }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
-    add_opt(common_arg(
-        {"-no-cnv", "--no-conversation"},
-        "force disable conversation mode (default: false)",
-        [](common_params & params) {
-            params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
+        [](common_params & params, bool value) {
+            params.conversation_mode = value ? COMMON_CONVERSATION_MODE_ENABLED : COMMON_CONVERSATION_MODE_DISABLED;
        }
    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
    add_opt(common_arg(
@@ -1246,10 +1352,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
    add_opt(common_arg(
+        {"--warmup"},
        {"--no-warmup"},
-        "skip warming up the model with an empty run",
-        [](common_params & params) {
-            params.warmup = false;
+        string_format("whether to perform warmup with an empty run (default: %s)", params.warmup ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.warmup = value;
        }
    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY}));
    add_opt(common_arg(
@@ -1308,7 +1415,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.sampling.top_k = value;
            params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K;
        }
-    ).set_sparam());
+    ).set_sparam().set_env("LLAMA_ARG_TOP_K"));
    add_opt(common_arg(
        {"--top-p"}, "N",
        string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p),
@@ -1651,19 +1758,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_COMPLETION}));
    add_opt(common_arg(
+        {"-kvo", "--kv-offload"},
        {"-nkvo", "--no-kv-offload"},
-        "disable KV offload",
-        [](common_params & params) {
-            params.no_kv_offload = true;
+        string_format("whether to enable KV cache offloading (default: %s)", params.no_kv_offload ? "disabled" : "enabled"),
+        [](common_params & params, bool value) {
+            params.no_kv_offload = !value;
        }
-    ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
+    ).set_env("LLAMA_ARG_KV_OFFLOAD"));
    add_opt(common_arg(
+        {"--repack"},
        {"-nr", "--no-repack"},
-        "disable weight repacking",
-        [](common_params & params) {
-            params.no_extra_bufts = true;
+        string_format("whether to enable weight repacking (default: %s)", params.no_extra_bufts ? "disabled" : "enabled"),
+        [](common_params & params, bool value) {
+            params.no_extra_bufts = !value;
        }
-    ).set_env("LLAMA_ARG_NO_REPACK"));
+    ).set_env("LLAMA_ARG_REPACK"));
    add_opt(common_arg(
        {"--no-host"},
        "bypass host buffer allowing extra buffers to be used",
@@ -1792,20 +1901,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_examples({LLAMA_EXAMPLE_PARALLEL}));
    add_opt(common_arg(
        {"-cb", "--cont-batching"},
-        string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.cont_batching = true;
+        {"-nocb", "--no-cont-batching"},
+        string_format("whether to enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.cont_batching = value;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING"));
    add_opt(common_arg(
-        {"-nocb", "--no-cont-batching"},
-        "disable continuous batching",
-        [](common_params & params) {
-            params.cont_batching = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
-    add_opt(common_arg(
-        {"--mmproj"}, "FILE",
+        {"-mm", "--mmproj"}, "FILE",
        "path to a multimodal projector file. see tools/mtmd/README.md\n"
        "note: if -hf is used, this argument can be omitted",
        [](common_params & params, const std::string & value) {
@@ -1813,26 +1916,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ"));
    add_opt(common_arg(
-        {"--mmproj-url"}, "URL",
+        {"-mmu", "--mmproj-url"}, "URL",
        "URL to a multimodal projector file. see tools/mtmd/README.md",
        [](common_params & params, const std::string & value) {
            params.mmproj.url = value;
        }
    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL"));
    add_opt(common_arg(
-        {"--no-mmproj"},
-        "explicitly disable multimodal projector, useful when using -hf",
-        [](common_params & params) {
-            params.no_mmproj = true;
+        {"--mmproj-auto"},
+        {"--no-mmproj", "--no-mmproj-auto"},
+        string_format("whether to use multimodal projector file (if available), useful when using -hf (default: %s)", params.no_mmproj ? "disabled" : "enabled"),
+        [](common_params & params, bool value) {
+            params.no_mmproj = !value;
        }
-    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ"));
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_AUTO"));
    add_opt(common_arg(
+        {"--mmproj-offload"},
        {"--no-mmproj-offload"},
-        "do not offload multimodal projector to GPU",
-        [](common_params & params) {
-            params.mmproj_use_gpu = false;
+        string_format("whether to enable GPU offloading for multimodal projector (default: %s)", params.mmproj_use_gpu ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.mmproj_use_gpu = value;
        }
-    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD"));
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_OFFLOAD"));
    add_opt(common_arg(
        {"--image", "--audio"}, "FILE",
        "path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n",
@@ -1872,12 +1977,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_env("LLAMA_ARG_MLOCK"));
    add_opt(common_arg(
+        {"--mmap"},
        {"--no-mmap"},
-        "do not memory-map model (slower load but may reduce pageouts if not using mlock)",
-        [](common_params & params) {
-            params.use_mmap = false;
+        string_format("whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.use_mmap = value;
        }
-    ).set_env("LLAMA_ARG_NO_MMAP"));
+    ).set_env("LLAMA_ARG_MMAP"));
    add_opt(common_arg(
        {"--numa"}, "TYPE",
        "attempt optimizations that help on some NUMA systems\n"
@@ -2065,10 +2171,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ));
    add_opt(common_arg(
+        {"--op-offload"},
        {"--no-op-offload"},
-        string_format("disable offloading host tensor operations to device (default: %s)", params.no_op_offload ? "true" : "false"),
-        [](common_params & params) {
-            params.no_op_offload = true;
+        string_format("whether to offload host tensor operations to device (default: %s)", params.no_op_offload ? "false" : "true"),
+        [](common_params & params, bool value) {
+            params.no_op_offload = !value;
        }
    ));
    add_opt(common_arg(
@@ -2264,10 +2371,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
    add_opt(common_arg(
+        {"--ppl"},
        {"--no-ppl"},
-        string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
-        [](common_params & params) {
-            params.compute_ppl = false;
+        string_format("whether to compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.compute_ppl = value;
        }
    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
    add_opt(common_arg(
@@ -2386,12 +2494,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX"));
    add_opt(common_arg(
+        {"--webui"},
        {"--no-webui"},
-        string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.webui = false;
+        string_format("whether to enable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.webui = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI"));
    add_opt(common_arg(
        {"--embedding", "--embeddings"},
        string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
@@ -2496,18 +2605,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS"));
    add_opt(common_arg(
        {"--slots"},
-        string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.endpoint_slots = true;
+        {"--no-slots"},
+        string_format("expose slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.endpoint_slots = value;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS"));
-    add_opt(common_arg(
-        {"--no-slots"},
-        "disables slots monitoring endpoint",
-        [](common_params & params) {
-            params.endpoint_slots = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS"));
    add_opt(common_arg(
        {"--slot-save-path"}, "PATH",
        "path to save slot kv cache (default: disabled)",
@@ -2543,6 +2646,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.models_dir = value;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_DIR"));
+    add_opt(common_arg(
+        {"--models-preset"}, "PATH",
+        "path to INI file containing model presets for the router server (default: disabled)",
+        [](common_params & params, const std::string & value) {
+            params.models_preset = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_PRESET"));
    add_opt(common_arg(
        {"--models-max"}, "N",
        string_format("for router server, maximum number of models to load simultaneously (default: %d, 0 = unlimited)", params.models_max),
@@ -2551,26 +2661,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_MAX"));
    add_opt(common_arg(
+        {"--models-autoload"},
        {"--no-models-autoload"},
-        "disables automatic loading of models (default: enabled)",
-        [](common_params & params) {
-            params.models_autoload = false;
+        string_format("for router server, whether to automatically load models (default: %s)", params.models_autoload ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.models_autoload = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_MODELS_AUTOLOAD"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_AUTOLOAD"));
    add_opt(common_arg(
        {"--jinja"},
-        string_format("use jinja template for chat (default: %s)\n", params.use_jinja ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.use_jinja = true;
+        {"--no-jinja"},
+        string_format("whether to use jinja template engine for chat (default: %s)", params.use_jinja ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.use_jinja = value;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA"));
-    add_opt(common_arg(
-        {"--no-jinja"},
-        string_format("disable jinja template for chat (default: %s)\n", params.use_jinja ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.use_jinja = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_NO_JINJA"));
    add_opt(common_arg(
        {"--reasoning-format"}, "FORMAT",
        "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
@@ -2615,15 +2720,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
    add_opt(common_arg(
+        {"--prefill-assistant"},
        {"--no-prefill-assistant"},
        string_format(
            "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n"
            "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n"
        ),
-        [](common_params & params) {
-            params.prefill_assistant = false;
+        [](common_params & params, bool value) {
+            params.prefill_assistant = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_PREFILL_ASSISTANT"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PREFILL_ASSISTANT"));
    add_opt(common_arg(
        {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
        string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
@@ -3,8 +3,10 @@
 #include "common.h"

 #include <set>
+#include <map>
 #include <string>
 #include <vector>
+#include <cstring>

 //
 // CLI argument parsing
@@ -14,6 +16,7 @@ struct common_arg {
    std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
    std::set<enum llama_example> excludes = {};
    std::vector<const char *> args;
+    std::vector<const char *> args_neg;  // for negated args like --no-xxx
    const char * value_hint   = nullptr; // help text or example for arg value
    const char * value_hint_2 = nullptr; // for second arg value
    const char * env          = nullptr;
@@ -23,6 +26,9 @@ struct common_arg {
    void (*handler_string) (common_params & params, const std::string &) = nullptr;
    void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr;
    void (*handler_int)    (common_params & params, int) = nullptr;
+    void (*handler_bool)   (common_params & params, bool) = nullptr;
+
+    common_arg() = default;

    common_arg(
        const std::initializer_list<const char *> & args,
@@ -44,6 +50,13 @@ struct common_arg {
        void (*handler)(common_params & params)
    ) : args(args), help(help), handler_void(handler) {}

+    common_arg(
+        const std::initializer_list<const char *> & args,
+        const std::initializer_list<const char *> & args_neg,
+        const std::string & help,
+        void (*handler)(common_params & params, bool)
+    ) : args(args), args_neg(args_neg), help(help), handler_bool(handler) {}
+
    // support 2 values for arg
    common_arg(
        const std::initializer_list<const char *> & args,
@@ -61,9 +74,33 @@ struct common_arg {
    bool is_exclude(enum llama_example ex);
    bool get_value_from_env(std::string & output) const;
    bool has_value_from_env() const;
-    std::string to_string();
+    std::string to_string() const;
+
+    // for using as key in std::map
+    bool operator<(const common_arg& other) const {
+        if (args.empty() || other.args.empty()) {
+            return false;
+        }
+        return strcmp(args[0], other.args[0]) < 0;
+    }
+    bool operator==(const common_arg& other) const {
+        if (args.empty() || other.args.empty()) {
+            return false;
+        }
+        return strcmp(args[0], other.args[0]) == 0;
+    }
+
+    // get all args and env vars (including negated args/env)
+    std::vector<std::string> get_args() const;
+    std::vector<std::string> get_env() const;
 };

+namespace common_arg_utils {
+    bool is_truthy(const std::string & value);
+    bool is_falsey(const std::string & value);
+    bool is_autoy(const std::string & value);
+}
+
 struct common_params_context {
    enum llama_example ex = LLAMA_EXAMPLE_COMMON;
    common_params & params;
@@ -76,7 +113,11 @@ struct common_params_context {
 // if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

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

 struct common_remote_params {
@@ -1013,31 +1013,40 @@ bool tty_can_use_colors() {
 // Model utils
 //

-static inline void common_init_sampler_from_model(
+// TODO: move to common/sampling
+static void common_init_sampler_from_model(
    const llama_model * model,
    common_params_sampling & sparams) {

    const uint64_t config = sparams.user_sampling_config;

    auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) {
-        if (config & user_config) return;
+        if (config & user_config) {
+            return;
+        }

        char buf[64] = {0};
        if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
            char * end = nullptr;
            int32_t v = strtol(buf, &end, 10);
-            if (end && end != buf) dst = v;
+            if (end && end != buf) {
+                dst = v;
+            }
        }
    };

    auto get_float = [&](const char * key, float & dst, uint64_t user_config) {
-        if (config & user_config) return;
+        if (config & user_config) {
+            return;
+        }

        char buf[128] = {0};
        if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
            char * end = nullptr;
            float v = strtof(buf, &end);
-            if (end && end != buf) dst = v;
+            if (end && end != buf) {
+                dst = v;
+            }
        }
    };

@@ -1065,31 +1074,122 @@ static inline void common_init_sampler_from_model(
    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA),    sparams.mirostat_eta,    common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA);
 }

-struct common_init_result common_init_from_params(common_params & params) {
-    common_init_result iparams;
-    auto mparams = common_model_params_to_llama(params);
+struct common_init_result::impl {
+    impl() = default;
+    ~impl() = default;
+
+    llama_model_ptr   model;
+    llama_context_ptr context;
+
+    std::vector<llama_adapter_lora_ptr> lora;
+
+    std::vector<common_sampler_ptr> samplers;
+};
+
+common_init_result::common_init_result(common_params & params) :
+    pimpl(new impl{}) {
+    const auto mparams = common_model_params_to_llama(params);

    llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
    if (model == NULL) {
-        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
-        return iparams;
+        return;
    }

-    common_init_sampler_from_model(model, params.sampling);
+    pimpl->model.reset(model);

    const llama_vocab * vocab = llama_model_get_vocab(model);

+    // updates params.sampling
+    // TODO: fix naming
+    common_init_sampler_from_model(model, params.sampling);
+
    auto cparams = common_context_params_to_llama(params);

+    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
+        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
+        params.sampling.ignore_eos = false;
+    }
+
+    // initialize once
+    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
+        if (llama_vocab_is_eog(vocab, i)) {
+            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
+            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
+        }
+    }
+
+    if (params.sampling.ignore_eos) {
+        // add EOG biases to the active set of logit biases
+        params.sampling.logit_bias.insert(
+                params.sampling.logit_bias.end(),
+                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
+    }
+
+    //if (params.sampling.penalty_last_n == -1) {
+    //    LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    params.sampling.penalty_last_n = llama_n_ctx(lctx);
+    //}
+
+    //if (params.sampling.dry_penalty_last_n == -1) {
+    //    LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
+    //}
+
+    pimpl->samplers.resize(cparams.n_seq_max);
+
+    for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
+        pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
+    }
+
    llama_context * lctx = llama_init_from_model(model, cparams);
    if (lctx == NULL) {
        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
-        llama_model_free(model);
-        return iparams;
+                __func__, params.model.path.c_str());
+        return;
    }

+    pimpl->context.reset(lctx);
+}
+
+llama_model * common_init_result::model() {
+    return pimpl->model.get();
+}
+
+llama_context * common_init_result::context() {
+    return pimpl->context.get();
+}
+
+common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
+    return pimpl->samplers[seq_id].get();
+}
+
+std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
+    return pimpl->lora;
+}
+
+void common_init_result::free_context() {
+    pimpl->context.reset();
+}
+
+common_init_result_ptr common_init_from_params(common_params & params) {
+    common_init_result_ptr res(new common_init_result(params));
+
+    llama_model * model = res->model();
+    if (model == NULL) {
+        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
+            __func__, params.model.path.c_str());
+        return res;
+    }
+
+    llama_context * lctx = res->context();
+    if (lctx == NULL) {
+        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
+            __func__, params.model.path.c_str());
+        return res;
+    }
+
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
    if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
        LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
        params.ctx_shift = false;
@@ -1101,10 +1201,7 @@ struct common_init_result common_init_from_params(common_params & params) {

        const auto cvec = common_control_vector_load(params.control_vectors);
        if (cvec.n_embd == -1) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
        }

        int err = llama_apply_adapter_cvec(
@@ -1115,10 +1212,7 @@ struct common_init_result common_init_from_params(common_params & params) {
                params.control_vector_layer_start,
                params.control_vector_layer_end);
        if (err) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
        }
    }

@@ -1142,10 +1236,7 @@ struct common_init_result common_init_from_params(common_params & params) {
        }

        if (!ok) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
        }
    }

@@ -1155,9 +1246,7 @@ struct common_init_result common_init_from_params(common_params & params) {
        lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
        if (lora == nullptr) {
            LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
-            llama_free(lctx);
-            llama_model_free(model);
-            return iparams;
+            return res;
        }

        char buf[1024];
@@ -1166,43 +1255,13 @@ struct common_init_result common_init_from_params(common_params & params) {
        la.task_name = buf;
        llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
        la.prompt_prefix = buf;
-        iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
+        res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters
    }

    if (!params.lora_init_without_apply) {
        common_set_adapter_lora(lctx, params.lora_adapters);
    }

-    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
-        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
-        params.sampling.ignore_eos = false;
-    }
-
-    // initialize once
-    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
-        if (llama_vocab_is_eog(vocab, i)) {
-            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
-            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
-        }
-    }
-
-    if (params.sampling.ignore_eos) {
-        // add EOG biases to the active set of logit biases
-        params.sampling.logit_bias.insert(
-                params.sampling.logit_bias.end(),
-                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
-    }
-
-    if (params.sampling.penalty_last_n == -1) {
-        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.penalty_last_n = llama_n_ctx(lctx);
-    }
-
-    if (params.sampling.dry_penalty_last_n == -1) {
-        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
-    }
-
    if (params.warmup) {
        LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);

@@ -1241,12 +1300,11 @@ struct common_init_result common_init_from_params(common_params & params) {
        llama_set_warmup(lctx, false);
    }

-    iparams.model.reset(model);
-    iparams.context.reset(lctx);
-
-    return iparams;
+    return res;
 }

+common_init_result::~common_init_result() = default;
+
 std::string get_model_endpoint() {
    const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
    // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
@@ -1255,7 +1313,9 @@ std::string get_model_endpoint() {
    std::string model_endpoint = "https://huggingface.co/";
    if (endpoint_env) {
        model_endpoint = endpoint_env;
-        if (model_endpoint.back() != '/') model_endpoint += '/';
+        if (model_endpoint.back() != '/') {
+            model_endpoint += '/';
+        }
    }
    return model_endpoint;
 }
@@ -195,7 +195,6 @@ struct common_params_sampling {

    std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"};     // default sequence breakers for DRY

-
    std::vector<enum common_sampler_type> samplers = {
        COMMON_SAMPLER_TYPE_PENALTIES,
        COMMON_SAMPLER_TYPE_DRY,
@@ -216,6 +215,10 @@ struct common_params_sampling {
    std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
    std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens

+    bool has_logit_bias() const {
+        return !logit_bias.empty();
+    }
+
    // print the parameters into a string
    std::string print() const;
 };
@@ -464,7 +467,7 @@ struct common_params {
    std::string public_path   = "";                                                                         // NOLINT
    std::string api_prefix    = "";                                                                         // NOLINT
    std::string chat_template = "";                                                                         // NOLINT
-    bool use_jinja = false;                                                                                 // NOLINT
+    bool use_jinja = true;                                                                                  // NOLINT
    bool enable_chat_template = true;
    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
    int reasoning_budget = -1;
@@ -484,9 +487,10 @@ struct common_params {
    bool endpoint_metrics = false;

    // router server configs
-    std::string models_dir = ""; // directory containing models 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

    bool log_json = false;

@@ -668,15 +672,29 @@ bool tty_can_use_colors();
 // Model utils
 //

-// note: defines object's lifetime
-struct common_init_result {
-    llama_model_ptr   model;
-    llama_context_ptr context;
+struct common_sampler;

-    std::vector<llama_adapter_lora_ptr> lora;
+// note: defines the model, context, samplers, ets. lifetimes
+struct common_init_result {
+    common_init_result(common_params & params);
+    ~common_init_result();
+
+    llama_model * model();
+    llama_context * context();
+    common_sampler * sampler(llama_seq_id seq_id);
+
+    std::vector<llama_adapter_lora_ptr> & lora();
+
+    void free_context();
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
 };

-struct common_init_result     common_init_from_params(common_params & params);
+using common_init_result_ptr = std::unique_ptr<common_init_result>;
+
+common_init_result_ptr common_init_from_params(common_params & params);

 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
@@ -12,6 +12,8 @@
 #include <filesystem>
 #include <fstream>
 #include <future>
+#include <map>
+#include <mutex>
 #include <regex>
 #include <string>
 #include <thread>
@@ -472,36 +474,79 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string &

 #elif defined(LLAMA_USE_HTTPLIB)

-static bool is_output_a_tty() {
+class ProgressBar {
+    static inline std::mutex mutex;
+    static inline std::map<const ProgressBar *, int> lines;
+    static inline int max_line = 0;
+
+    static void cleanup(const ProgressBar * line) {
+        lines.erase(line);
+        if (lines.empty()) {
+            max_line = 0;
+        }
+    }
+
+    static bool is_output_a_tty() {
 #if defined(_WIN32)
-    return _isatty(_fileno(stdout));
+        return _isatty(_fileno(stdout));
 #else
-    return isatty(1);
+        return isatty(1);
 #endif
-}
-
-static void print_progress(size_t current, size_t total) {
-    if (!is_output_a_tty()) {
-        return;
    }

-    if (!total) {
-        return;
+public:
+    ProgressBar() = default;
+
+    ~ProgressBar() {
+        std::lock_guard<std::mutex> lock(mutex);
+        cleanup(this);
    }

-    size_t width = 50;
-    size_t pct = (100 * current) / total;
-    size_t pos = (width * current) / total;
+    void update(size_t current, size_t total) {
+        if (!is_output_a_tty()) {
+            return;
+        }

-    std::cout << "["
-              << std::string(pos, '=')
-              << (pos < width ? ">" : "")
-              << std::string(width - pos, ' ')
-              << "] " << std::setw(3) << pct << "%  ("
-              << current / (1024 * 1024) << " MB / "
-              << total / (1024 * 1024) << " MB)\r";
-    std::cout.flush();
-}
+        if (!total) {
+            return;
+        }
+
+        std::lock_guard<std::mutex> lock(mutex);
+
+        if (lines.find(this) == lines.end()) {
+            lines[this] = max_line++;
+            std::cout << "\n";
+        }
+        int lines_up = max_line - lines[this];
+
+        size_t width = 50;
+        size_t pct = (100 * current) / total;
+        size_t pos = (width * current) / total;
+
+        std::cout << "\033[s";
+
+        if (lines_up > 0) {
+            std::cout << "\033[" << lines_up << "A";
+        }
+        std::cout << "\033[2K\r["
+            << std::string(pos, '=')
+            << (pos < width ? ">" : "")
+            << std::string(width - pos, ' ')
+            << "] " << std::setw(3) << pct << "%  ("
+            << current / (1024 * 1024) << " MB / "
+            << total / (1024 * 1024) << " MB) "
+            << "\033[u";
+
+        std::cout.flush();
+
+        if (current == total) {
+             cleanup(this);
+        }
+    }
+
+    ProgressBar(const ProgressBar &) = delete;
+    ProgressBar & operator=(const ProgressBar &) = delete;
+};

 static bool common_pull_file(httplib::Client & cli,
                             const std::string & resolve_path,
@@ -523,6 +568,7 @@ static bool common_pull_file(httplib::Client & cli,
    const char * func = __func__; // avoid __func__ inside a lambda
    size_t downloaded = existing_size;
    size_t progress_step = 0;
+    ProgressBar bar;

    auto res = cli.Get(resolve_path, headers,
        [&](const httplib::Response &response) {
@@ -554,7 +600,7 @@ static bool common_pull_file(httplib::Client & cli,
            progress_step += len;

            if (progress_step >= total_size / 1000 || downloaded == total_size) {
-                print_progress(downloaded, total_size);
+                bar.update(downloaded, total_size);
                progress_step = 0;
            }
            return true;
@@ -562,8 +608,6 @@ static bool common_pull_file(httplib::Client & cli,
        nullptr
    );

-    std::cout << "\n";
-
    if (!res) {
        LOG_ERR("%s: error during download. Status: %d\n", __func__, res ? res->status : -1);
        return false;
@@ -0,0 +1,186 @@
+#include "arg.h"
+#include "preset.h"
+#include "peg-parser.h"
+#include "log.h"
+
+#include <fstream>
+#include <sstream>
+#include <filesystem>
+
+static std::string rm_leading_dashes(const std::string & str) {
+    size_t pos = 0;
+    while (pos < str.size() && str[pos] == '-') {
+        ++pos;
+    }
+    return str.substr(pos);
+}
+
+std::vector<std::string> common_preset::to_args() const {
+    std::vector<std::string> args;
+
+    for (const auto & [opt, value] : options) {
+        args.push_back(opt.args.back()); // use the last arg as the main arg
+        if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) {
+            // flag option, no value
+            if (common_arg_utils::is_falsey(value)) {
+                // use negative arg if available
+                if (!opt.args_neg.empty()) {
+                    args.back() = opt.args_neg.back();
+                } else {
+                    // otherwise, skip the flag
+                    // TODO: maybe throw an error instead?
+                    args.pop_back();
+                }
+            }
+        }
+        if (opt.value_hint != nullptr) {
+            // single value
+            args.push_back(value);
+        }
+        if (opt.value_hint != nullptr && opt.value_hint_2 != nullptr) {
+            throw std::runtime_error(string_format(
+                "common_preset::to_args(): option '%s' has two values, which is not supported yet",
+                opt.args.back()
+            ));
+        }
+    }
+
+    return args;
+}
+
+std::string common_preset::to_ini() const {
+    std::ostringstream ss;
+
+    ss << "[" << name << "]\n";
+    for (const auto & [opt, value] : options) {
+        auto espaced_value = value;
+        string_replace_all(espaced_value, "\n", "\\\n");
+        ss << rm_leading_dashes(opt.args.back()) << " = ";
+        ss << espaced_value << "\n";
+    }
+    ss << "\n";
+
+    return ss.str();
+}
+
+static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_file(const std::string & path) {
+    std::map<std::string, std::map<std::string, std::string>> parsed;
+
+    if (!std::filesystem::exists(path)) {
+        throw std::runtime_error("preset file does not exist: " + path);
+    }
+
+    std::ifstream file(path);
+    if (!file.good()) {
+        throw std::runtime_error("failed to open server preset file: " + path);
+    }
+
+    std::string contents((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+
+    static const auto parser = build_peg_parser([](auto & p) {
+        // newline ::= "\r\n" / "\n" / "\r"
+        auto newline = p.rule("newline", p.literal("\r\n") | p.literal("\n") | p.literal("\r"));
+
+        // ws ::= [ \t]*
+        auto ws = p.rule("ws", p.chars("[ \t]", 0, -1));
+
+        // comment ::= [;#] (!newline .)*
+        auto comment = p.rule("comment", p.chars("[;#]", 1, 1) + p.zero_or_more(p.negate(newline) + p.any()));
+
+        // eol ::= ws comment? (newline / EOF)
+        auto eol = p.rule("eol", ws + p.optional(comment) + (newline | p.end()));
+
+        // ident ::= [a-zA-Z_] [a-zA-Z0-9_.-]*
+        auto ident = p.rule("ident", p.chars("[a-zA-Z_]", 1, 1) + p.chars("[a-zA-Z0-9_.-]", 0, -1));
+
+        // value ::= (!eol-start .)*
+        auto eol_start = p.rule("eol-start", ws + (p.chars("[;#]", 1, 1) | newline | p.end()));
+        auto value = p.rule("value", p.zero_or_more(p.negate(eol_start) + p.any()));
+
+        // header-line ::= "[" ws ident ws "]" eol
+        auto header_line = p.rule("header-line", "[" + ws + p.tag("section-name", p.chars("[^]]")) + ws + "]" + eol);
+
+        // kv-line ::= ident ws "=" ws value eol
+        auto kv_line = p.rule("kv-line", p.tag("key", ident) + ws + "=" + ws + p.tag("value", value) + eol);
+
+        // comment-line ::= ws comment (newline / EOF)
+        auto comment_line = p.rule("comment-line", ws + comment + (newline | p.end()));
+
+        // blank-line ::= ws (newline / EOF)
+        auto blank_line = p.rule("blank-line", ws + (newline | p.end()));
+
+        // line ::= header-line / kv-line / comment-line / blank-line
+        auto line = p.rule("line", header_line | kv_line | comment_line | blank_line);
+
+        // ini ::= line* EOF
+        auto ini = p.rule("ini", p.zero_or_more(line) + p.end());
+
+        return ini;
+    });
+
+    common_peg_parse_context ctx(contents);
+    const auto result = parser.parse(ctx);
+    if (!result.success()) {
+        throw std::runtime_error("failed to parse server config file: " + path);
+    }
+
+    std::string current_section = COMMON_PRESET_DEFAULT_NAME;
+    std::string current_key;
+
+    ctx.ast.visit(result, [&](const auto & node) {
+        if (node.tag == "section-name") {
+            const std::string section = std::string(node.text);
+            current_section = section;
+            parsed[current_section] = {};
+        } else if (node.tag == "key") {
+            const std::string key = std::string(node.text);
+            current_key = key;
+        } else if (node.tag == "value" && !current_key.empty() && !current_section.empty()) {
+            parsed[current_section][current_key] = std::string(node.text);
+            current_key.clear();
+        }
+    });
+
+    return parsed;
+}
+
+static std::map<std::string, common_arg> get_map_key_opt(common_params_context & ctx_params) {
+    std::map<std::string, common_arg> mapping;
+    for (const auto & opt : ctx_params.options) {
+        for (const auto & env : opt.get_env()) {
+            mapping[env] = opt;
+        }
+        for (const auto & arg : opt.get_args()) {
+            mapping[rm_leading_dashes(arg)] = opt;
+        }
+    }
+    return mapping;
+}
+
+common_presets common_presets_load(const std::string & path, common_params_context & ctx_params) {
+    common_presets out;
+    auto key_to_opt = get_map_key_opt(ctx_params);
+    auto ini_data = parse_ini_from_file(path);
+
+    for (auto section : ini_data) {
+        common_preset preset;
+        if (section.first.empty()) {
+            preset.name = COMMON_PRESET_DEFAULT_NAME;
+        } else {
+            preset.name = section.first;
+        }
+        LOG_DBG("loading preset: %s\n", preset.name.c_str());
+        for (const auto & [key, value] : section.second) {
+            LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
+            if (key_to_opt.find(key) != key_to_opt.end()) {
+                preset.options[key_to_opt[key]] = value;
+                LOG_DBG("accepted option: %s = %s\n", key.c_str(), value.c_str());
+            } else {
+                // TODO: maybe warn about unknown key?
+            }
+        }
+        out[preset.name] = preset;
+    }
+
+    return out;
+}
@@ -0,0 +1,32 @@
+#pragma once
+
+#include "common.h"
+#include "arg.h"
+
+#include <string>
+#include <vector>
+#include <map>
+
+//
+// INI preset parser and writer
+//
+
+constexpr const char * COMMON_PRESET_DEFAULT_NAME = "default";
+
+struct common_preset {
+    std::string name;
+    // TODO: support repeated args in the future
+    std::map<common_arg, std::string> options;
+
+    // convert preset to CLI argument list
+    std::vector<std::string> to_args() const;
+
+    // convert preset to INI format string
+    std::string to_ini() const;
+
+    // TODO: maybe implement to_env() if needed
+};
+
+// interface for multiple presets in one file
+using common_presets = std::map<std::string, common_preset>;
+common_presets common_presets_load(const std::string & path, common_params_context & ctx_params);
@@ -104,9 +104,10 @@ struct ring_buffer {
 struct common_sampler {
    common_params_sampling params;

-    struct llama_sampler * grmr;
    struct llama_sampler * chain;

+    bool grammar;
+
    ring_buffer<llama_token> prev;

    std::vector<llama_token_data> cur;
@@ -116,7 +117,6 @@ struct common_sampler {
    void reset() {
        prev.clear();

-        llama_sampler_reset(grmr);
        llama_sampler_reset(chain);
    }

@@ -167,10 +167,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co

    lparams.no_perf = params.no_perf;

-    struct llama_sampler * grmr;
+    llama_sampler * chain = llama_sampler_chain_init(lparams);
+
+    bool grammar = false;
+    std::vector<llama_sampler *> samplers;
+
    if (params.grammar.compare(0, 11, "%llguidance") == 0) {
 #ifdef LLAMA_USE_LLGUIDANCE
-        grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
+        samplers.push_back(llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()));
+        grammar = true;
 #else
        GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
 #endif // LLAMA_USE_LLGUIDANCE
@@ -217,30 +222,23 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
            trigger_patterns_c.push_back(regex.c_str());
        }

-        grmr = params.grammar_lazy
-             ? llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
-                                                        trigger_patterns_c.data(), trigger_patterns_c.size(),
-                                                        trigger_tokens.data(), trigger_tokens.size())
-             :      llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
-        if (!grmr) {
-            return nullptr;
+        if (!params.grammar.empty()) {
+             if (params.grammar_lazy) {
+                 samplers.push_back(
+                         llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
+                             trigger_patterns_c.data(), trigger_patterns_c.size(),
+                             trigger_tokens.data(),     trigger_tokens.size()));
+             } else {
+                 samplers.push_back(llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"));
+             }
+
+             grammar = true;
        }
    }

-    auto * result = new common_sampler {
-        /* .params = */ params,
-        /* .grmr   = */ grmr,
-        /* .chain  = */ llama_sampler_chain_init(lparams),
-        /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
-        /* .cur    = */ {},
-        /* .cur_p  = */ {},
-    };
-
-    llama_sampler_chain_add(result->chain,
-            llama_sampler_init_logit_bias(
-                llama_vocab_n_tokens(vocab),
-                params.logit_bias.size(),
-                params.logit_bias.data()));
+    if (params.has_logit_bias()) {
+        samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data()));
+    }

    if (params.mirostat == 0) {
        for (const auto & cnstr : params.samplers) {
@@ -253,58 +251,70 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                            c_breakers.push_back(str.c_str());
                        }

-                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        samplers.push_back(llama_sampler_init_dry    (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                    }
                    break;
                case COMMON_SAMPLER_TYPE_TOP_K:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_k       (params.top_k));
+                    samplers.push_back(llama_sampler_init_top_k      (params.top_k));
                    break;
                case COMMON_SAMPLER_TYPE_TOP_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_p       (params.top_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_top_p      (params.top_p, params.min_keep));
                    break;
                case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma));
+                    samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
                    break;
                case COMMON_SAMPLER_TYPE_MIN_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_min_p       (params.min_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_min_p      (params.min_p, params.min_keep));
                    break;
                case COMMON_SAMPLER_TYPE_XTC:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_xtc         (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                    samplers.push_back(llama_sampler_init_xtc        (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
                    break;
                case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_typical     (params.typ_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_typical    (params.typ_p, params.min_keep));
                    break;
                case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext    (params.temp, params.dynatemp_range, params.dynatemp_exponent));
+                    samplers.push_back(llama_sampler_init_temp_ext   (params.temp, params.dynatemp_range, params.dynatemp_exponent));
                    break;
                case COMMON_SAMPLER_TYPE_INFILL:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill      (vocab));
+                    samplers.push_back(llama_sampler_init_infill     (vocab));
                    break;
                case COMMON_SAMPLER_TYPE_PENALTIES:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties   (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    samplers.push_back(llama_sampler_init_penalties  (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
                    break;
                default:
                    GGML_ASSERT(false && "unknown sampler type");
            }
        }
-        llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
+
+        samplers.push_back(llama_sampler_init_dist(params.seed));
    } else if (params.mirostat == 1) {
-        llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
+        samplers.push_back(llama_sampler_init_temp(params.temp));
+        samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
    } else if (params.mirostat == 2) {
-        llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
+        samplers.push_back(llama_sampler_init_temp(params.temp));
+        samplers.push_back(llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
    } else {
        GGML_ASSERT(false && "unknown mirostat version");
    }

+    for (auto * smpl : samplers) {
+        llama_sampler_chain_add(chain, smpl);
+    }
+
+    auto * result = new common_sampler {
+        /* .params  = */ params,
+        /* .chain   = */ chain,
+        /* .grammar = */ grammar,
+        /* .prev    = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
+        /* .cur     = */ {},
+        /* .cur_p   = */ {},
+    };
+
    return result;
 }

 void common_sampler_free(struct common_sampler * gsmpl) {
    if (gsmpl) {
-        llama_sampler_free(gsmpl->grmr);
-
        llama_sampler_free(gsmpl->chain);

        delete gsmpl;
@@ -314,11 +324,24 @@ void common_sampler_free(struct common_sampler * gsmpl) {
 void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
    const auto tm = gsmpl->tm();

-    if (accept_grammar) {
-        llama_sampler_accept(gsmpl->grmr, token);
-    }
+    if (gsmpl->grammar) {
+        const int n_smpl = llama_sampler_chain_n(gsmpl->chain);

-    llama_sampler_accept(gsmpl->chain, token);
+        for (int i = 0; i < n_smpl; i++) {
+            auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
+
+            // the grammar sampler is always the first one
+            if (i == 0) {
+                if (accept_grammar) {
+                    llama_sampler_accept(smpl, token);
+                }
+            } else {
+                llama_sampler_accept(smpl, token);
+            }
+        }
+    } else {
+        llama_sampler_accept(gsmpl->chain, token);
+    }

    gsmpl->prev.push_back(token);
 }
@@ -329,12 +352,12 @@ void common_sampler_reset(struct common_sampler * gsmpl) {

 struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
    return new common_sampler {
-        /* .params = */ gsmpl->params,
-        /* .grmr   = */ llama_sampler_clone(gsmpl->grmr),
-        /* .chain  = */ llama_sampler_clone(gsmpl->chain),
-        /* .prev   = */ gsmpl->prev,
-        /* .cur    = */ gsmpl->cur,
-        /* .cur_p  = */ gsmpl->cur_p,
+        /* .params  = */ gsmpl->params,
+        /* .chain   = */ llama_sampler_clone(gsmpl->chain),
+        /* .grammar = */ gsmpl->grammar,
+        /* .prev    = */ gsmpl->prev,
+        /* .cur     = */ gsmpl->cur,
+        /* .cur_p   = */ gsmpl->cur_p,
    };
 }

@@ -383,58 +406,33 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
    }
 }

-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
+struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
+    return gsmpl->chain;
+}
+
+llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx) {
    llama_synchronize(ctx);

    // start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations
    const auto tm = gsmpl->tm();

-    gsmpl->set_logits(ctx, idx);
+    llama_token id = LLAMA_TOKEN_NULL;

-    auto & grmr  = gsmpl->grmr;
    auto & chain = gsmpl->chain;
    auto & cur_p = gsmpl->cur_p; // initialized by set_logits

-    if (grammar_first) {
-        llama_sampler_apply(grmr, &cur_p);
-    }
+    gsmpl->set_logits(ctx, idx);

    llama_sampler_apply(chain, &cur_p);

    GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");

-    const llama_token id = cur_p.data[cur_p.selected].id;
+    id = cur_p.data[cur_p.selected].id;

-    if (grammar_first) {
-        return id;
-    }
-
-    // check if it the sampled token fits the grammar
-    {
-        llama_token_data       single_token_data       = { id, 1.0f, 0.0f };
-        llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
-
-        llama_sampler_apply(grmr, &single_token_data_array);
-
-        const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
-        if (is_valid) {
-            return id;
-        }
-    }
-
-    // resampling:
-    // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
-    gsmpl->set_logits(ctx, idx);
-
-    llama_sampler_apply(grmr,  &cur_p);
-    llama_sampler_apply(chain, &cur_p);
-
-    GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration");
-
-    return cur_p.data[cur_p.selected].id;
+    return id;
 }

-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft) {
    GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");

    std::vector<llama_token> result;
@@ -442,7 +440,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample

    size_t i = 0;
    for (; i < draft.size(); i++) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
+        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);

        common_sampler_accept(gsmpl, id, true);

@@ -454,7 +452,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
    }

    if (i == draft.size()) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
+        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);

        common_sampler_accept(gsmpl, id, true);

@@ -464,13 +462,13 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
    return result;
 }

-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft) {
    std::vector<int> idxs(draft.size() + 1);
    for (size_t i = 0; i < idxs.size(); ++i) {
        idxs[i] = i;
    }

-    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
+    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft);
 }

 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
@@ -515,7 +513,8 @@ std::string common_sampler_print(const struct common_sampler * gsmpl) {

    for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
        const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
-        result += std::string("-> ") + llama_sampler_name(smpl) + " ";
+        result += std::string("-> ");
+        result += std::string(llama_sampler_name(smpl)) + " ";
    }

    return result;
@@ -48,6 +48,8 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
 // arguments can be nullptr to skip printing
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);

+struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
+
 // extended sampling implementation:
 //
 // - set logits
@@ -55,10 +57,7 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
 // - check if the token fits the grammar (if any)
 // - if not: resample by first applying the grammar constraints and then sampling again (slower path)
 //
-// if grammar_first is true, the grammar is applied before the samplers (slower)
-// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
-//
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
+llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx);

 // generalized version of common_sampler_sample
 //
@@ -76,10 +75,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
 //
 // returns at least 1 token, up to idxs.size()
 //
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft);

 // assume idxs == [ 0, 1, 2, ..., draft.size() ]
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft);

 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);

@@ -107,3 +106,9 @@ std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std:

 llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
                const char * grammar_kind, const char * grammar_data);
+
+struct common_sampler_deleter {
+    void operator()(common_sampler * s) { common_sampler_free(s); }
+};
+
+typedef std::unique_ptr<common_sampler, common_sampler_deleter> common_sampler_ptr;
@@ -315,7 +315,7 @@ llama_tokens common_speculative_gen_draft(
    for (int i = 0; i < params.n_draft; ++i) {
        common_batch_clear(batch);

-        common_sampler_sample(smpl, ctx_dft, 0, true);
+        common_sampler_sample(smpl, ctx_dft, 0);

        const auto * cur_p = common_sampler_get_candidates(smpl, true);

@@ -7286,6 +7286,10 @@ class DeepseekV2Model(TextModel):
            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+
+            # [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
+            # ref https://github.com/ggml-org/llama.cpp/pull/17945
            self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_scaling["mscale_all_dim"])

    _experts: list[dict[str, Tensor]] | None = None
@@ -10041,6 +10045,10 @@ class MistralMoeModel(DeepseekV2Model):
        MistralModel.set_mistral_config(self.gguf_writer, self.hparams)
        yarn_params = self.hparams["yarn"]
        self.gguf_writer.add_attn_temperature_length(yarn_params["original_max_position_embeddings"])
+
+        # [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
+        # ref https://github.com/ggml-org/llama.cpp/pull/17945
        self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
@@ -56,7 +56,7 @@ docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:light -m /model
 or with a server image:

 ```bash
-docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
+docker run -v /path/to/models:/models -p 8080:8080 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512
 ```

 ## Docker With CUDA
@@ -91,7 +91,7 @@ After building locally, Usage is similar to the non-CUDA examples, but you'll ne
 ```bash
 docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
 docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
-docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1
 ```

 ## Docker With MUSA
@@ -125,5 +125,5 @@ After building locally, Usage is similar to the non-MUSA examples, but you'll ne
 ```bash
 docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
 docker run -v /path/to/models:/models local/llama.cpp:light-musa -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
-docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
+docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1
 ```
@@ -2,6 +2,7 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "sampling.h"

 #include <algorithm>
 #include <cstdio>
@@ -64,17 +65,23 @@ int main(int argc, char ** argv) {
    ctx_params.n_ctx   = n_kv_req;
    ctx_params.n_batch = std::max(n_predict, n_parallel);

-    llama_context * ctx = llama_init_from_model(model, ctx_params);
-
    auto sparams = llama_sampler_chain_default_params();
    sparams.no_perf = false;

-    llama_sampler * smpl = llama_sampler_chain_init(sparams);
+    std::vector<llama_sampler *> samplers;

-    llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
-    llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
-    llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
-    llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
+    for (int32_t i = 0; i < n_parallel; ++i) {
+        llama_sampler * smpl = llama_sampler_chain_init(sparams);
+
+        llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
+        llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
+        llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
+        llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
+
+        samplers.push_back(smpl);
+    }
+
+    llama_context * ctx = llama_init_from_model(model, ctx_params);

    if (ctx == NULL) {
        LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);
@@ -173,7 +180,7 @@ int main(int argc, char ** argv) {
                continue;
            }

-            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
+            const llama_token new_token_id = llama_sampler_sample(samplers[i], ctx, i_batch[i]);

            // is it an end of generation? -> mark the stream as finished
            if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
@@ -229,14 +236,17 @@ int main(int argc, char ** argv) {
            __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));

    LOG("\n");
-    llama_perf_sampler_print(smpl);
+    llama_perf_sampler_print(samplers[0]);
    llama_perf_context_print(ctx);

    fprintf(stderr, "\n");

    llama_batch_free(batch);

-    llama_sampler_free(smpl);
+    for (auto & sampler_config : samplers) {
+        llama_sampler_free(sampler_config);
+    }
+
    llama_free(ctx);
    llama_model_free(model);

@@ -131,10 +131,10 @@ int main(int argc, char ** argv) {
    llama_numa_init(params.numa);

    // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx = llama_init->context();

    if (model == NULL) {
        LOG_ERR("%s: unable to load model\n", __func__);
@@ -202,10 +202,10 @@ int main(int argc, char ** argv) {
    params.warmup = false;

    // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    if (model == nullptr || ctx == nullptr) {
        LOG_ERR("%s : failed to init\n", __func__);
@@ -14,12 +14,13 @@ static void write_table_header(std::ofstream & file) {
 static void write_table_entry(std::ofstream & file, const common_arg & opt) {
    file << "| `";
    // args
-    for (const auto & arg : opt.args) {
-    if (arg == opt.args.front()) {
+    auto all_args = opt.get_args();
+    for (const auto & arg : all_args) {
+    if (arg == all_args.front()) {
            file << arg;
-            if (opt.args.size() > 1) file << ", ";
+            if (all_args.size() > 1) file << ", ";
        } else {
-            file << arg << (arg != opt.args.back() ? ", " : "");
+            file << arg << (arg != all_args.back() ? ", " : "");
        }
    }
    // value hint
@@ -55,10 +55,10 @@ int main(int argc, char ** argv) {
    llama_numa_init(params.numa);

    // load the target model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    auto * mem = llama_get_memory(ctx);

@@ -18,16 +18,16 @@ int main(int argc, char ** argv){
    llama_numa_init(params.numa);

    // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model_ptr & model = llama_init.model;
-    llama_context_ptr & ctx = llama_init.context;
+    auto * model = llama_init->model();
+    auto * ctx = llama_init->context();

    GGML_ASSERT(model != nullptr);

    // tokenize the prompt
    std::vector<llama_token> inp;
-    inp = common_tokenize(ctx.get(), params.prompt, true, true);
+    inp = common_tokenize(ctx, params.prompt, true, true);
    fprintf(stderr, "%s: tokenization done\n", __func__);

    common_ngram_cache ngram_cache;
@@ -28,13 +28,13 @@ int main(int argc, char ** argv){
    llama_numa_init(params.numa);

    // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_context_ptr & ctx = llama_init.context;
+    llama_context * ctx = llama_init->context();

    // tokenize the prompt
    std::vector<llama_token> inp;
-    inp = common_tokenize(ctx.get(), params.prompt, true, true);
+    inp = common_tokenize(ctx, params.prompt, true, true);

    common_ngram_cache ngram_cache_context;
    common_ngram_cache ngram_cache_dynamic;
@@ -65,7 +65,7 @@ int main(int argc, char ** argv){
    }

    const int n_input = inp.size();
-    const int n_ctx = llama_n_ctx(ctx.get());
+    const int n_ctx = llama_n_ctx(ctx);

    int n_drafted = 0;
    int n_accept  = 0;
@@ -29,10 +29,10 @@ int main(int argc, char ** argv){
    llama_numa_init(params.numa);

    // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    const llama_vocab * vocab = llama_model_get_vocab(model);

@@ -1,10 +1,13 @@
 #!/usr/bin/env python3

-import numpy as np
 import sys
-import os
+import numpy as np
 from pathlib import Path

+# Add utils directory to path for direct script execution
+sys.path.insert(0, str(Path(__file__).parent.parent / "utils"))
+from common import get_model_name_from_env_path  # type: ignore[import-not-found]
+
 def quick_logits_check(pytorch_file, llamacpp_file):
    """Lightweight sanity check before NMSE"""

@@ -32,27 +35,16 @@ def quick_logits_check(pytorch_file, llamacpp_file):
    print(f"Top 10 llama.cpp logits: {llamacpp_logits[llamacpp_top10]}")
    print(f"Max absolute difference: {max_diff:.4f}")

-    if max_diff > 1.0:
-        print(f"❌ NOK: Large differences detected - max diff: {max_diff:.4f}")
-        return False
-
    return True

 def main():
-    model_path = os.getenv('MODEL_PATH')
-    if not model_path:
-        print("Error: MODEL_PATH environment variable not set")
-        sys.exit(1)
-
-    if not os.path.exists(model_path):
-        print(f"Error: Model file not found: {model_path}")
-        sys.exit(1)
-
-    model_name = os.path.basename(model_path)
+    model_name = get_model_name_from_env_path('MODEL_PATH')
    data_dir = Path("data")
-
    pytorch_file = data_dir / f"pytorch-{model_name}.bin"
-    llamacpp_file = data_dir / f"llamacpp-{model_name}.bin"
+
+    llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL')
+    print(f"Using converted model: {llamacpp_model_name}")
+    llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin"

    if not pytorch_file.exists():
        print(f"Error: PyTorch logits file not found: {pytorch_file}")
@@ -200,7 +200,7 @@ with torch.no_grad():
    logits = outputs.logits

    # Extract logits for the last token (next token prediction)
-    last_logits = logits[0, -1, :].cpu().numpy()
+    last_logits = logits[0, -1, :].float().cpu().numpy()

    print(f"Logits shape: {logits.shape}")
    print(f"Last token logits shape: {last_logits.shape}")
@@ -5,6 +5,7 @@ import sys
 import os
 import argparse
 from pathlib import Path
+from common import get_model_name_from_env_path  # type: ignore[import-not-found]

 def calculate_nmse(reference, test):
    mse = np.mean((test - reference) ** 2)
@@ -67,11 +68,13 @@ def main():
    parser.add_argument('-m', '--model-path', required=True,  help='Path to the model directory')
    args = parser.parse_args()

-    model_name = os.path.basename(args.model_path)
+    model_name = get_model_name_from_env_path('MODEL_PATH')
    data_dir = Path("data")

    pytorch_file = data_dir / f"pytorch-{model_name}.bin"
-    llamacpp_file = data_dir / f"llamacpp-{model_name}.bin"
+
+    llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL')
+    llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin"

    print(f"Model name: {model_name}")
    print(f"PyTorch logits file: {pytorch_file}")
@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+
+def get_model_name_from_env_path(env_path_name):
+    model_path = os.getenv(env_path_name)
+    if not model_path:
+        print(f"Error: {env_path_name} environment variable not set")
+        sys.exit(1)
+
+    if not os.path.exists(model_path):
+        print(f"Error: Model file not found: {model_path}")
+        sys.exit(1)
+
+    name = os.path.basename(os.path.normpath(model_path))
+    if name.endswith(".gguf"):
+        name = name[:-5]
+
+    return name
@@ -192,10 +192,10 @@ int main(int argc, char ** argv) {
    llama_numa_init(params.numa);

    // load the target model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    auto * mem = llama_get_memory(ctx);

@@ -149,10 +149,10 @@ int main(int argc, char ** argv) {
    llama_numa_init(params.numa);

    // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    if (model == NULL) {
        LOG_ERR("%s: unable to load model\n", __func__);
@@ -34,10 +34,10 @@ int main(int argc, char ** argv) {
    std::string result2;

    // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    if (model == nullptr || ctx == nullptr) {
        fprintf(stderr, "%s : failed to init\n", __func__);
@@ -40,10 +40,10 @@ int main(int argc, char ** argv) {
    llama_context * ctx_dft = NULL;

    // load the target model
-    common_init_result llama_init_tgt = common_init_from_params(params);
+    auto llama_init_tgt = common_init_from_params(params);

-    model_tgt = llama_init_tgt.model.get();
-    ctx_tgt   = llama_init_tgt.context.get();
+    model_tgt = llama_init_tgt->model();
+    ctx_tgt   = llama_init_tgt->context();

    const llama_vocab * vocab = llama_model_get_vocab(model_tgt);

@@ -61,10 +61,10 @@ int main(int argc, char ** argv) {
    params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
    params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;

-    common_init_result llama_init_dft = common_init_from_params(params);
+    auto llama_init_dft = common_init_from_params(params);

-    //model_dft = llama_init_dft.model.get();
-    ctx_dft   = llama_init_dft.context.get();
+    //model_dft = llama_init_dft->model();
+    ctx_dft   = llama_init_dft->context();

    if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
        LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str());
@@ -255,6 +255,8 @@ int main(int argc, char ** argv) {
    LOG_INF("target:\n\n");
    common_perf_print(ctx_tgt, smpl);

+    llama_batch_free(batch_tgt);
+
    common_sampler_free(smpl);
    common_speculative_free(spec);

@@ -71,10 +71,10 @@ int main(int argc, char ** argv) {
    llama_context * ctx_dft = NULL;

    // load the target model
-    common_init_result llama_init_tgt = common_init_from_params(params);
+    auto llama_init_tgt = common_init_from_params(params);

-    model_tgt = llama_init_tgt.model.get();
-    ctx_tgt   = llama_init_tgt.context.get();
+    model_tgt = llama_init_tgt->model();
+    ctx_tgt   = llama_init_tgt->context();

    // load the draft model
    params.devices = params.speculative.devices;
@@ -87,10 +87,10 @@ int main(int argc, char ** argv) {
    params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
    params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;

-    common_init_result llama_init_dft = common_init_from_params(params);
+    auto llama_init_dft = common_init_from_params(params);

-    model_dft = llama_init_dft.model.get();
-    ctx_dft   = llama_init_dft.context.get();
+    model_dft = llama_init_dft->model();
+    ctx_dft   = llama_init_dft->context();

    const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
    const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
@@ -242,7 +242,7 @@ int main(int argc, char ** argv) {
                bool accept = false;
                if (params.sampling.temp > 0) {
                    // stochastic verification
-                    common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
+                    common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);

                    auto & dist_tgt = *common_sampler_get_candidates(smpl, true);

@@ -491,7 +491,7 @@ int main(int argc, char ** argv) {
                    continue;
                }

-                common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
+                common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft);

                const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl, true);

@@ -39,9 +39,10 @@ int main(int argc, char ** argv) {
    llama_backend_init();
    llama_numa_init(params.numa);
    // load the model and apply lora adapter, if any
-    common_init_result   llama_init = common_init_from_params(params);
-    llama_model_ptr    & model      = llama_init.model;
-    llama_context_ptr  & ctx        = llama_init.context;
+    auto llama_init = common_init_from_params(params);
+
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    if (model == NULL) {
        LOG_ERR("%s: unable to load model\n", __func__);
@@ -54,8 +55,8 @@ int main(int argc, char ** argv) {
        LOG_INF("%s\n", common_params_get_system_info(params).c_str());
    }

-    std::vector<llama_token> tokens  = common_tokenize(ctx.get(), params.prompt, true);
-    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get()) / 2);
+    std::vector<llama_token> tokens  = common_tokenize(ctx, params.prompt, true);
+    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx, tokens, llama_n_ctx(ctx) / 2);

    struct lr_opt & lr = params.lr;
    LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n",
@@ -70,7 +71,7 @@ int main(int argc, char ** argv) {
        /*get_opt_pars_ud =*/&params.lr,
        /*optimizer_type  =*/params.optimizer,
    };
-    llama_opt_init(ctx.get(), model.get(), lopt_params);
+    llama_opt_init(ctx, model, lopt_params);

    const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split);

@@ -78,7 +79,7 @@ int main(int argc, char ** argv) {
    ggml_opt_result_t result_eval  = ggml_opt_result_init();

    for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) {
-        llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
+        llama_opt_epoch(ctx, dataset, result_train, result_eval, idata_split,
                        ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
        fprintf(stderr, "\n");

@@ -88,7 +89,7 @@ int main(int argc, char ** argv) {
    ggml_opt_result_free(result_train);
    ggml_opt_result_free(result_eval);

-    llama_model_save_to_file(model.get(), params.out_file.c_str());
+    llama_model_save_to_file(model, params.out_file.c_str());

    llama_backend_free();

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

 if (EMSCRIPTEN)
@@ -99,6 +99,7 @@ extern "C" {
    GGML_BACKEND_API int ggml_cpu_has_sme        (void);
    // other
    GGML_BACKEND_API int ggml_cpu_has_riscv_v    (void);
+    GGML_BACKEND_API int ggml_cpu_get_rvv_vlen   (void);  // risc-v vector length in bytes
    GGML_BACKEND_API int ggml_cpu_has_vsx        (void);
    GGML_BACKEND_API int ggml_cpu_has_vxe        (void);
    GGML_BACKEND_API int ggml_cpu_has_wasm_simd  (void);
@@ -312,16 +312,9 @@ static struct buffer_address ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * al
 }

 // this is a very naive implementation, but for our case the number of free blocks should be very small
-static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size, const struct ggml_tensor * tensor) {
+static void ggml_dyn_tallocr_free_bytes(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size) {
    size = aligned_offset(NULL, size, alloc->alignment);

-    AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n",
-        __func__, tensor->name, addr.chunk, addr.offset, size, alloc->chunks[addr.chunk]->n_free_blocks);
-
-#ifdef GGML_ALLOCATOR_DEBUG
-    remove_allocated_tensor(alloc, addr, tensor);
-#endif
-
    struct tallocr_chunk * chunk = alloc->chunks[addr.chunk];

    // see if we can merge with an existing block
@@ -357,8 +350,6 @@ static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, struct
    }
    // otherwise, add a new block
    ggml_dyn_tallocr_insert_block(chunk, addr.offset, size);
-
-    GGML_UNUSED(tensor);
 }

 static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) {
@@ -616,13 +607,17 @@ static void ggml_gallocr_free_extra_space(ggml_gallocr_t galloc, struct ggml_ten

    GGML_ASSERT(parent_size >= node_size);

+    // note: we want after the freeing the chunks to continue to be aligned
+    struct ggml_dyn_tallocr * p_alloc = galloc->buf_tallocs[p_hn->buffer_id];
+    parent_size = aligned_offset(NULL, parent_size, p_alloc->alignment);
+    node_size = aligned_offset(NULL, node_size, p_alloc->alignment);
+
    if (parent_size > node_size) {
-        struct ggml_dyn_tallocr * p_alloc = galloc->buf_tallocs[p_hn->buffer_id];
        struct buffer_address p_addr = p_hn->addr;
        p_addr.offset += node_size;
        size_t extra_size = parent_size - node_size;
        AT_PRINTF("freeing extra %zu bytes from parent %s for %s\n", extra_size, parent->name, node->name);
-        ggml_dyn_tallocr_free_tensor(p_alloc, p_addr, extra_size, parent);
+        ggml_dyn_tallocr_free_bytes(p_alloc, p_addr, extra_size);
    }
 }

@@ -706,7 +701,14 @@ static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * n
    struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
    ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
    size_t size = ggml_backend_buft_get_alloc_size(buft, node);
-    ggml_dyn_tallocr_free_tensor(alloc, hn->addr, size, node);
+
+    AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n",
+        __func__, node->name, hn->addr.chunk, hn->addr.offset, size, alloc->chunks[hn->addr.chunk]->n_free_blocks);
+#ifdef GGML_ALLOCATOR_DEBUG
+    remove_allocated_tensor(alloc, hn->addr, node);
+#endif
+
+    ggml_dyn_tallocr_free_bytes(alloc, hn->addr, size);
    hn->allocated = false;
 }

@@ -2548,6 +2548,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
        case GGML_OP_ARGSORT:
        case GGML_OP_ACC:
        case GGML_OP_GROUP_NORM:
+            return true;
        case GGML_OP_PAD:
            // TODO: add circular padding support for cann, see https://github.com/ggml-org/llama.cpp/pull/16985
            return ggml_get_op_params_i32(op, 8) == 0;
@@ -24,6 +24,7 @@

 #define UNUSED GGML_UNUSED

+#if defined(__aarch64__) && defined(__ARM_NEON) && (defined(__ARM_FEATURE_MATMUL_INT8) || defined(__ARM_FEATURE_DOTPROD))
 static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in,
                                             int16x8_t *     out_mins,
                                             int8_t *        out_scales) {
@@ -46,6 +47,7 @@ static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in,
    scales_u32[1] = (sm[2] & kmask2) | (((sm[0] >> 6) & kmask3) << 4);
    memcpy(out_scales, scales_u32, 8);
 }
+#endif

 void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
    assert(QK8_0 == 32);
@@ -81,6 +81,11 @@ struct ggml_arm_arch_features_type {
 } ggml_arm_arch_features = { 0 };
 #endif

+#if defined(__riscv)
+struct ggml_riscv_arch_features_type {
+    int rvv_vlen;
+} ggml_riscv_arch_features = { 0 };
+#endif

 #if defined(_WIN32)

@@ -187,6 +192,9 @@ typedef void * thread_ret_t;

 typedef pthread_t ggml_thread_t;

+#define GGML_THREADPOOL_N_THREADS_MASK (0xffffU)
+#define GGML_THREADPOOL_N_THREADS_BITS (16)
+
 #if defined(__APPLE__)
 #include <unistd.h>
 #include <mach/mach.h>
@@ -449,7 +457,7 @@ struct ggml_threadpool {
    struct ggml_cplan  * cplan;

    // synchronization primitives
-    atomic_int n_graph;       // incremented when there is work to be done (i.e each graph)
+    atomic_int n_graph;       // updated when there is work to be done (i.e each graph) holds graph and active thread counts.
    atomic_int GGML_CACHE_ALIGN n_barrier;
    atomic_int GGML_CACHE_ALIGN n_barrier_passed;
    atomic_int GGML_CACHE_ALIGN current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
@@ -457,12 +465,10 @@ struct ggml_threadpool {
    // these are atomic as an annotation for thread-sanitizer
    atomic_bool stop;         // Used for stopping the threadpool altogether
    atomic_bool pause;        // Used for pausing the threadpool or individual threads
-    atomic_int abort;         // Used for aborting processing of a graph
+    atomic_int  abort;        // Used for aborting processing of a graph

    struct ggml_compute_state * workers;   // per thread state
-    int          n_threads_max; // number of threads in the pool
-    atomic_int   n_threads_cur; // number of threads used in the current graph
-
+    int          n_threads;   // Number of threads in the pool
    int32_t      prio;        // Scheduling priority
    uint32_t     poll;        // Polling level (0 - no polling)

@@ -539,7 +545,7 @@ struct ggml_state {
 static struct ggml_state g_state = {0};

 void ggml_barrier(struct ggml_threadpool * tp) {
-    int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed);
+    int n_threads = atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK;
    if (n_threads == 1) {
        return;
    }
@@ -556,7 +562,7 @@ void ggml_barrier(struct ggml_threadpool * tp) {
        // last thread
        atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed);

-        // exit barrier (fill seq-cst fence)
+        // exit barrier (full seq-cst fence)
        atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst);
        return;
    }
@@ -702,6 +708,15 @@ static void ggml_init_arm_arch_features(void) {}
 #endif
 #endif // __ARM_ARCH

+#if defined(__riscv) && defined(__riscv_v_intrinsic)
+#include <riscv_vector.h>
+static void ggml_init_riscv_arch_features(void) {
+    ggml_riscv_arch_features.rvv_vlen = __riscv_vlenb();
+}
+#else
+static void ggml_init_riscv_arch_features(void) {}
+#endif
+
 struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) {
    GGML_ASSERT(!ggml_get_no_alloc(ctx));

@@ -2628,7 +2643,7 @@ static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask
 void ggml_threadpool_free(struct ggml_threadpool* threadpool) {
    if (!threadpool) return;

-    const int n_threads = threadpool->n_threads_max;
+    const int n_threads = threadpool->n_threads;

 #ifndef GGML_USE_OPENMP
    struct ggml_compute_state* workers = threadpool->workers;
@@ -2704,7 +2719,7 @@ struct ggml_cplan ggml_graph_plan(
        //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads);
    }
    if (n_threads <= 0) {
-        n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS;
+        n_threads = threadpool ? threadpool->n_threads : GGML_DEFAULT_N_THREADS;
    }

 #if defined(__EMSCRIPTEN__) && !defined(__EMSCRIPTEN_PTHREADS__)
@@ -2912,12 +2927,14 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {

    struct ggml_compute_params params = {
        /*.ith       =*/ state->ith,
-        /*.nth       =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed),
+        /*.nth       =*/ atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK,
        /*.wsize     =*/ cplan->work_size,
        /*.wdata     =*/ cplan->work_data,
        /*.threadpool=*/ tp,
    };

+    GGML_PRINT_DEBUG("thread #%d compute-start cplan %p last-graph %d \n", state->ith, cplan, state->last_graph);
+
    for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) {
        struct ggml_tensor * node = cgraph->nodes[node_n];

@@ -2939,6 +2956,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
        }
    }

+    GGML_PRINT_DEBUG("thread #%d compute-done cplan %p last-graph %d \n", state->ith, cplan, state->last_graph);
+
    ggml_barrier(state->threadpool);

    return 0;
@@ -2946,27 +2965,23 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {

 #ifndef GGML_USE_OPENMP

-// check if thread is active
-static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) {
-    struct ggml_threadpool * threadpool = state->threadpool;
-    int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed);
-    return (state->ith < n_threads);
-}
-
 // check if thread is ready to proceed (exit from polling or sleeping)
+// returns true if loops should exit, sets state->pending to indicate new work
 static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) {
    struct ggml_threadpool * threadpool = state->threadpool;

    if (state->pending || threadpool->stop || threadpool->pause) { return true; }

    // check for new graph/work
-    int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed);
-    if (new_graph != state->last_graph) {
-        state->pending    = ggml_graph_compute_thread_active(state);
-        state->last_graph = new_graph;
+    int n_graph   = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed);
+    int n_threads = n_graph & GGML_THREADPOOL_N_THREADS_MASK;
+    if (n_graph != state->last_graph) {
+        state->pending    = (state->ith < n_threads);
+        state->last_graph = n_graph;
+        return true;
    }

-    return state->pending;
+    return false;
 }

 // sync thread state after polling
@@ -2983,11 +2998,6 @@ static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * st
 static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) {
    struct ggml_threadpool * threadpool = state->threadpool;

-    // Skip polling for unused threads
-    if (!ggml_graph_compute_thread_active(state)) {
-        return state->pending;
-    }
-
    // This seems to make 0 ... 100 a decent range for polling level across modern processors.
    // Perhaps, we can adjust it dynamically based on load and things.
    const uint64_t n_rounds = 1024UL * 128 * threadpool->poll;
@@ -3049,7 +3059,6 @@ static thread_ret_t ggml_graph_compute_secondary_thread(void* data) {
        ggml_graph_compute_check_for_work(state);
        if (state->pending) {
            state->pending = false;
-
            ggml_graph_compute_thread(state);
        }
    }
@@ -3064,14 +3073,15 @@ static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int

    ggml_mutex_lock(&threadpool->mutex);

-    GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads);
+    // Update the number of active threads and the graph count
+    int n_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed) >> GGML_THREADPOOL_N_THREADS_BITS;
+    n_graph = ((n_graph + 1) << GGML_THREADPOOL_N_THREADS_BITS) | (n_threads & GGML_THREADPOOL_N_THREADS_MASK);

-    // Update the number of active threads
-    atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed);
+    GGML_PRINT_DEBUG("compute-kickoff: n_threads %d n_graph %d\n", n_threads, n_graph);

    // Indicate the graph is ready to be processed
    // We need the full seq-cst fence here because of the polling threads (used in thread_sync)
-    atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst);
+    atomic_store_explicit(&threadpool->n_graph, n_graph, memory_order_seq_cst);

    if (threadpool->pause) {
       // Update main thread prio and affinity to match the threadpool settings
@@ -3109,8 +3119,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl(
        threadpool->pause            = tpp->paused;
        threadpool->abort            = -1;
        threadpool->workers          = NULL;
-        threadpool->n_threads_max    = tpp->n_threads;
-        threadpool->n_threads_cur    = tpp->n_threads;
+        threadpool->n_threads        = tpp->n_threads;
        threadpool->poll             = tpp->poll;
        threadpool->prio             = tpp->prio;
        threadpool->ec               = GGML_STATUS_SUCCESS;
@@ -3205,7 +3214,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
            {
                // update the number of threads from the actual number of threads that we got from OpenMP
                n_threads = omp_get_num_threads();
-                atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed);
+                atomic_store_explicit(&threadpool->n_graph, n_threads, memory_order_relaxed);
            }

            // Apply thread CPU mask and priority
@@ -3218,13 +3227,13 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
            ggml_graph_compute_thread(&threadpool->workers[ith]);
        }
    } else {
-        atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed);
+        atomic_store_explicit(&threadpool->n_graph, 1, memory_order_relaxed);
        ggml_graph_compute_thread(&threadpool->workers[0]);
    }
 #else
-    if (n_threads > threadpool->n_threads_max) {
-        GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max);
-        n_threads = threadpool->n_threads_max;
+    if (n_threads > threadpool->n_threads) {
+        GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads);
+        n_threads = threadpool->n_threads;
    }

    // Kick all threads to start the new graph
@@ -3464,6 +3473,14 @@ int ggml_cpu_has_riscv_v(void) {
 #endif
 }

+int ggml_cpu_get_rvv_vlen(void) {
+#if defined(__riscv) && defined(__riscv_v_intrinsic)
+    return ggml_riscv_arch_features.rvv_vlen;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_f16c(void) {
 #if defined(__F16C__)
    return 1;
@@ -3630,6 +3647,10 @@ void ggml_cpu_init(void) {
        ggml_init_arm_arch_features();
 #endif

+#if defined(__riscv)
+        ggml_init_riscv_arch_features();
+#endif
+
        is_first_call = false;
    }

@@ -583,6 +583,10 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r
        if (ggml_cpu_has_riscv_v()) {
            features.push_back({ "RISCV_V", "1" });
        }
+        if (ggml_cpu_get_rvv_vlen() > 0) {
+            static std::string rvv_vlen = std::to_string(ggml_cpu_get_rvv_vlen());
+            features.push_back({ "RVV_VLEN", rvv_vlen.c_str() });
+        }
        if (ggml_cpu_has_vsx()) {
            features.push_back({ "VSX", "1" });
        }
@@ -2169,7 +2169,8 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
    static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0> iq4_nl_8x8_q8_0;

    if (cur->type == GGML_TYPE_Q4_0) {
-        if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) {
+        if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)
+            || (ggml_cpu_has_riscv_v() && (ggml_cpu_get_rvv_vlen() >= QK4_0))) {
            if (cur->ne[1] % 8 == 0) {
                return &q4_0_8x8_q8_0;
            }
@@ -67,19 +67,22 @@
 #define GGML_CUDA_CC_RDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x1010) // RX 5000
 #define GGML_CUDA_CC_RDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a
 #define GGML_CUDA_CC_RDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA
+#define GGML_CUDA_CC_RDNA3_5    (GGML_CUDA_CC_OFFSET_AMD + 0x1150) // AI 370, AI Max 395 laptops.
 #define GGML_CUDA_CC_RDNA4      (GGML_CUDA_CC_OFFSET_AMD + 0x1200) // RX 9000

-#define GGML_CUDA_CC_IS_AMD(cc)   (cc >= GGML_CUDA_CC_OFFSET_AMD)
-#define GGML_CUDA_CC_IS_RDNA(cc)  (cc >= GGML_CUDA_CC_RDNA1)
-#define GGML_CUDA_CC_IS_RDNA1(cc) (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
-#define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
-#define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA4)
-#define GGML_CUDA_CC_IS_RDNA4(cc) (cc >= GGML_CUDA_CC_RDNA4)
-#define GGML_CUDA_CC_IS_GCN(cc)   (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1)
-#define GGML_CUDA_CC_IS_CDNA(cc)  (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1)
-#define GGML_CUDA_CC_IS_CDNA1(cc) (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_CDNA2)
-#define GGML_CUDA_CC_IS_CDNA2(cc) (cc >= GGML_CUDA_CC_CDNA2 && cc < GGML_CUDA_CC_CDNA3)
-#define GGML_CUDA_CC_IS_CDNA3(cc) (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_AMD(cc)     (cc >= GGML_CUDA_CC_OFFSET_AMD)
+#define GGML_CUDA_CC_IS_RDNA(cc)    (cc >= GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_RDNA1(cc)   (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
+#define GGML_CUDA_CC_IS_RDNA2(cc)   (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
+#define GGML_CUDA_CC_IS_RDNA3_0(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA3_5)
+#define GGML_CUDA_CC_IS_RDNA3_5(cc) (cc >= GGML_CUDA_CC_RDNA3_5 && cc < GGML_CUDA_CC_RDNA4)
+#define GGML_CUDA_CC_IS_RDNA3(cc)   (GGML_CUDA_CC_IS_RDNA3_0(cc) || GGML_CUDA_CC_IS_RDNA3_5(cc))
+#define GGML_CUDA_CC_IS_RDNA4(cc)   (cc >= GGML_CUDA_CC_RDNA4)
+#define GGML_CUDA_CC_IS_GCN(cc)     (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1)
+#define GGML_CUDA_CC_IS_CDNA(cc)    (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_CDNA1(cc)   (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_CDNA2)
+#define GGML_CUDA_CC_IS_CDNA2(cc)   (cc >= GGML_CUDA_CC_CDNA2 && cc < GGML_CUDA_CC_CDNA3)
+#define GGML_CUDA_CC_IS_CDNA3(cc)   (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)

 // Moore Threads
 #define MUSART_HMASK 40300 // MUSA rc4.3, min. ver. for half2 -> uint mask comparisons
@@ -642,8 +642,8 @@ static __global__ void flash_attn_stream_k_fixup(
    const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa;
    const int iter_j = (ne01 + (ncols1    - 1)) / ncols1;

-    const int kbc0      = (bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
-    const int kbc0_stop = (bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+    const int kbc0      = int64_t(bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+    const int kbc0_stop = int64_t(bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;

    const bool did_not_have_any_data   = kbc0 == kbc0_stop;
    const bool wrote_beginning_of_tile = kbc0 % iter_k == 0;
@@ -679,7 +679,7 @@ static __global__ void flash_attn_stream_k_fixup(
    int bidx = bidx0 - 1;
    int kbc_stop = kbc0;
    while(true) {
-        const int kbc = bidx*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+        const int kbc = int64_t(bidx)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
        if (kbc == kbc_stop) { // Did not have any data.
            bidx--;
            kbc_stop = kbc;
@@ -1380,8 +1380,8 @@ static __global__ void flash_attn_ext_f16(
    const int iter_j = (ne01.z + (ncols1    - 1)) / ncols1;

    // kbc == k block continuous, current index in continuous ijk space.
-    int       kbc      = (blockIdx.x + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
-    const int kbc_stop = (blockIdx.x + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+    int       kbc      = int64_t(blockIdx.x + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+    const int kbc_stop = int64_t(blockIdx.x + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;

    // If the seams of 2 CUDA blocks fall within an output tile their results need to be combined.
    // For this we need to track both the block that starts the tile (needs_fixup) and the block that finishes the tile (is_fixup).
@@ -1401,7 +1401,7 @@ static __global__ void flash_attn_ext_f16(
        const float2 * Q_f2   = (const float2 *) (Q + nb03*sequence + nb02* head0);
        const half2  * K_h2   = (const half2  *) (K + nb13*sequence + nb12*(head0 / gqa_ratio));
        const half   * mask_h = ncols2 == 1 && !mask ? nullptr :
-            (const half  *) (mask + nb33*(sequence % ne33));
+            (const half *) (mask + nb33*(sequence % ne33));
        float2       * dstk   = ((float2 *) dst) + (sequence*ne01.z*ne02 + head0) * (DV/2);

        const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio));
@@ -4630,9 +4630,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
        case GGML_OP_CUMSUM:
        case GGML_OP_TRI:
        case GGML_OP_DIAG:
-            return true;
        case GGML_OP_SOLVE_TRI:
-            return op->src[0]->ne[0] <= 64 && op->src[1]->ne[0] <= 32;
+            return true;
+
        default:
            return false;
    }
@@ -189,6 +189,9 @@ namespace ggml_cuda_mma {
                return 8 * (threadIdx.x / 16) + l;
 #elif defined(RDNA3)
                return 2 * l + (threadIdx.x / 16);
+#else
+                NO_DEVICE_CODE;
+                return -1;
 #endif // defined(RDNA4)
            } else {
                NO_DEVICE_CODE;
@@ -290,8 +293,12 @@ namespace ggml_cuda_mma {
            }
        }
 #elif defined(AMD_WMMA_AVAILABLE)
-
+#if defined(RDNA3)
+        // RDNA3 has duplicated data as input.
+        static constexpr int ne = I * J / 32 * 2;
+#else
        static constexpr int ne = I * J / 32;
+#endif // defined(RDNA3)
        half2 x[ne] = {{0.0f, 0.0f}};

        static constexpr __device__ bool supported() {
@@ -310,7 +317,14 @@ namespace ggml_cuda_mma {

        static __device__ __forceinline__ int get_j(const int l) {
            if constexpr (I == 16 && J == 8) {
+#if defined(RDNA4)
                return 4 * (threadIdx.x / 16) + l;
+#elif defined(RDNA3)
+                return l;
+#else
+                NO_DEVICE_CODE;
+                return -1;
+#endif // defined(RDNA4)
            } else {
                NO_DEVICE_CODE;
                return -1;
@@ -366,11 +380,16 @@ namespace ggml_cuda_mma {
        static constexpr int         I  = I_;
        static constexpr int         J  = J_;
        static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR;
-        static constexpr int         ne = I * J / WARP_SIZE;
-
-        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};

 #if defined(AMD_WMMA_AVAILABLE)
+#if defined(RDNA3)
+        // RDNA3 has duplicated data as input.
+        static constexpr int ne = I * J / 32 * 2;
+#else
+        static constexpr int ne = I * J / 32;
+#endif // defined(RDNA3)
+        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
+
        static constexpr __device__ bool supported() {
            if (I == 16 && J == 8) return true;
            return false;
@@ -387,13 +406,23 @@ namespace ggml_cuda_mma {

        static __device__ __forceinline__ int get_j(const int l) {
            if constexpr (I == 16 && J == 8) {
+#if defined(RDNA4)
                return 4 * (threadIdx.x / 16) + l;
+#elif defined(RDNA3)
+                return l;
+#else
+                NO_DEVICE_CODE;
+                return -1;
+#endif // defined(RDNA4)
            } else {
                NO_DEVICE_CODE;
                return -1;
            }
        }
 #else
+        static constexpr int ne = I * J / WARP_SIZE;
+        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
+
        static constexpr __device__ bool supported() {
            if (I ==  8 && J ==  8) return true;
            if (I == 16 && J ==  4) return true;
@@ -546,8 +575,14 @@ namespace ggml_cuda_mma {
        }
 #elif defined(AMD_WMMA_AVAILABLE)
        if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
-            ggml_cuda_memcpy_1<sizeof(t.x)>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
-
+#if defined(RDNA4)
+                ggml_cuda_memcpy_1<sizeof(t.x)>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
+#elif defined(RDNA3)
+                ggml_cuda_memcpy_1<sizeof(t.x)/2>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
+                ggml_cuda_memcpy_1<sizeof(t.x)/2>(t.x + t.ne/2, xs0 + t.get_i(0) * stride + t.get_j(t.ne/2));
+#else
+                NO_DEVICE_CODE;
+#endif // defined(RDNA4)
        } else if constexpr (std::is_same_v<T, int>) {
            if constexpr (I == 16 && J == 4) {
                int64_t * xi = (int64_t *) t.x;
@@ -888,6 +923,16 @@ namespace ggml_cuda_mma {
        const halfx8_t& a_frag = reinterpret_cast<const halfx8_t&>(A.x[0]);
        const halfx8_t& b_frag = reinterpret_cast<const halfx8_t&>(B.x[0]);
        acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(a_frag, b_frag, acc_frag);
+#elif defined(RDNA3)
+        using halfx16_t = __attribute__((ext_vector_type(16))) _Float16;
+        using floatx8_t = __attribute__((ext_vector_type(8))) float;
+        floatx8_t& acc_frag = reinterpret_cast<floatx8_t&>(D.x[0]);
+        const halfx16_t& a_frag = reinterpret_cast<const halfx16_t&>(A.x[0]);
+        const halfx16_t& b_frag = reinterpret_cast<const halfx16_t&>(B.x[0]);
+        acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32(a_frag, b_frag, acc_frag);
+#else
+        GGML_UNUSED_VARS(D, A, B);
+        NO_DEVICE_CODE;
 #endif // RDNA4
 #else
        GGML_UNUSED_VARS(D, A, B);
@@ -905,6 +950,16 @@ namespace ggml_cuda_mma {
        const bf16x8_t& a_frag = reinterpret_cast<const bf16x8_t&>(A.x[0]);
        const bf16x8_t& b_frag = reinterpret_cast<const bf16x8_t&>(B.x[0]);
        acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_bf16_w32_gfx12(a_frag, b_frag, acc_frag);
+#elif defined(RDNA3)
+        using bf16x16_t = __attribute__((ext_vector_type(16))) __bf16;
+        using floatx8_t = __attribute__((ext_vector_type(8))) float;
+        floatx8_t& acc_frag = reinterpret_cast<floatx8_t&>(D.x[0]);
+        const bf16x16_t& a_frag = reinterpret_cast<const bf16x16_t&>(A.x[0]);
+        const bf16x16_t& b_frag = reinterpret_cast<const bf16x16_t&>(B.x[0]);
+        acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_bf16_w32(a_frag, b_frag, acc_frag);
+#else
+        GGML_UNUSED_VARS(D, A, B);
+        NO_DEVICE_CODE;
 #endif // RDNA4
 #else
        GGML_UNUSED_VARS(D, A, B);
@@ -151,7 +151,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
            return false;
        }
    } else {
-        if (src1_ncols > 16) {
+        if (GGML_CUDA_CC_IS_RDNA3_0(cc) && src1_ncols > 8) {
+            return false;
+        } else if (src1_ncols > 16) {
            return false;
        }
    }
@@ -160,9 +162,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
        case GGML_TYPE_F32:
            return ampere_mma_available(cc);
        case GGML_TYPE_F16:
-            return volta_mma_available(cc) || turing_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc));
+            return volta_mma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc);
        case GGML_TYPE_BF16:
-            return ampere_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc));
+            return ampere_mma_available(cc) || amd_wmma_available(cc);
        default:
            return false;
    }
@@ -765,7 +765,10 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0
                return ne11 <= 8;
            } else if (GGML_CUDA_CC_IS_AMD(cc)) {
                if (fp16_mma_hardware_available(cc)) {
-                    if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
+                    if (GGML_CUDA_CC_IS_RDNA3(cc)) {
+                        return ne11 <= 3;
+                    }
+                    if (GGML_CUDA_CC_IS_RDNA4(cc)) {
                        return ne11 <= 5;
                    }
                    return ne11 <= 2;
@@ -3,6 +3,80 @@
 #include "solve_tri.cuh"

 #define MAX_N_FAST 64
+#define MAX_K_FAST 32
+
+static __global__ void get_batch_pointers(const float *  A,
+                                          float *        X,
+                                          const float ** A_ptrs,
+                                          float **       X_ptrs,
+                                          int64_t        ne02,
+                                          int64_t        total_batches,
+                                          size_t         s02,
+                                          size_t         s03,
+                                          size_t         s2,
+                                          size_t         s3) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx >= total_batches) {
+        return;
+    }
+
+    const int64_t i3 = idx / ne02;
+    const int64_t i2 = idx % ne02;
+
+    A_ptrs[idx] = A + i3 * s03 + i2 * s02;
+    X_ptrs[idx] = X + i3 * s3 + i2 * s2;
+}
+
+static void solve_tri_f32_cublas(ggml_backend_cuda_context & ctx,
+                                 const float *               A,
+                                 const float *               B,
+                                 float *                     X,
+                                 int                         n,
+                                 int                         k,
+                                 int64_t                     ne02,
+                                 int64_t                     ne03,
+                                 size_t                      s02,
+                                 size_t                      s03,
+                                 size_t                      s12,
+                                 size_t                      s13,
+                                 size_t                      s2,
+                                 size_t                      s3,
+                                 cudaStream_t                stream) {
+    const float   alpha         = 1.0f;
+    const int64_t total_batches = ne02 * ne03;
+    if (total_batches == 0) {
+        return;
+    }
+
+    // Bulk copy B -> X (contiguous tensors)
+    if (X != B) {
+        const int64_t total_elements_BX = n * k * total_batches;
+        CUDA_CHECK(cudaMemcpyAsync(X, B, total_elements_BX * sizeof(float), cudaMemcpyDeviceToDevice, stream));
+    }
+
+    const int id = ggml_cuda_get_device();
+
+    ggml_cuda_pool_alloc<const float *> A_ptrs_alloc(ctx.pool(id), total_batches);
+    ggml_cuda_pool_alloc<float *>       X_ptrs_alloc(ctx.pool(id), total_batches);
+
+    const float ** A_ptrs_dev = A_ptrs_alloc.get();
+    float **       X_ptrs_dev = X_ptrs_alloc.get();
+
+    get_batch_pointers<<<(total_batches + 255) / 256, 256, 0, stream>>>(A, X, A_ptrs_dev, X_ptrs_dev, ne02,
+                                                                        total_batches, s02, s03, s2, s3);
+
+    CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
+
+    // Yes, this is necessary, without this we get RMSE errors
+    CUBLAS_CHECK(cublasSetMathMode(ctx.cublas_handle(id), CUBLAS_DEFAULT_MATH));
+    CUBLAS_CHECK(cublasStrsmBatched(ctx.cublas_handle(id), CUBLAS_SIDE_RIGHT, CUBLAS_FILL_MODE_UPPER, CUBLAS_OP_N,
+                                    CUBLAS_DIAG_NON_UNIT, k, n, &alpha, A_ptrs_dev, n, X_ptrs_dev, k, total_batches));
+
+    // revert to standard mode from common.cuh
+    CUBLAS_CHECK(cublasSetMathMode(ctx.cublas_handle(id), CUBLAS_TF32_TENSOR_OP_MATH));
+
+    GGML_UNUSED_VARS(s12, s13);
+}

 // ======================
 // Fast Kernel (n <= 64, k <= 32) - Warp-based parallel reduction
@@ -63,7 +137,7 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A,
    float x_low  = (lane < n) ? B_batch[lane * k + col_idx] : 0.0f;
    float x_high = (WARP_SIZE + lane < n) ? B_batch[(WARP_SIZE + lane) * k + col_idx] : 0.0f;

-    const int half = WARP_SIZE;
+    const int half      = WARP_SIZE;
    const int nrows_low = (n < half) ? n : half;

 #pragma unroll
@@ -81,8 +155,8 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A,

 #pragma unroll
    for (int row = half; row < n; ++row) {
-        float sum = sA[row * n + lane] * x_low;
-        const int j = half + lane;
+        float     sum = sA[row * n + lane] * x_low;
+        const int j   = half + lane;
        if (j < row) {
            sum += sA[row * n + j] * x_high;
        }
@@ -97,7 +171,7 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A,
    for (int rr = 0; rr < 2; ++rr) {
        const int row = rr * WARP_SIZE + lane;
        if (row < n) {
-            const float val = (row < half) ? x_low : x_high;
+            const float val            = (row < half) ? x_low : x_high;
            X_batch[row * k + col_idx] = val;
        }
    }
@@ -176,20 +250,26 @@ static void solve_tri_f32_cuda(const float * A,
 }

 void ggml_cuda_op_solve_tri(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];  // A (triangular n x x matrix)
-    const ggml_tensor * src1 = dst->src[1];  // B (right hand side of n x k equation columns)
+    const ggml_tensor * src0 = dst->src[0];  // A (n×n, lower triangular)
+    const ggml_tensor * src1 = dst->src[1];  // B (n×k)

    ggml_is_contiguous(src0);
    ggml_is_contiguous(src1);

-    const int64_t n = src0->ne[0];
-    const int64_t k = src1->ne[0];
+    const int64_t n    = src0->ne[0];
+    const int64_t k    = src1->ne[0];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];

-    GGML_ASSERT(n <= 64);
-    GGML_ASSERT(k <= 32);
-
-    solve_tri_f32_cuda((const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k, src0->ne[2],
-                       src0->ne[3], src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float),
-                       src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float),
-                       dst->nb[3] / sizeof(float), ctx.stream());
+    if (n <= MAX_N_FAST && k <= MAX_K_FAST) {
+        solve_tri_f32_cuda((const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k,
+                           src0->ne[2], src0->ne[3], src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float),
+                           src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float),
+                           dst->nb[3] / sizeof(float), ctx.stream());
+    } else {
+        solve_tri_f32_cublas(ctx, (const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k,
+                             ne02, ne03, src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float),
+                             src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float),
+                             dst->nb[3] / sizeof(float), ctx.stream());
+    }
 }
@@ -19,6 +19,9 @@
 #define CUDA_R_16F  HIPBLAS_R_16F
 #define CUDA_R_16BF HIPBLAS_R_16B
 #define CUDA_R_32F  HIPBLAS_R_32F
+#define CUBLAS_SIDE_RIGHT HIPBLAS_SIDE_RIGHT
+#define CUBLAS_FILL_MODE_UPPER HIPBLAS_FILL_MODE_UPPER
+#define CUBLAS_DIAG_NON_UNIT HIPBLAS_DIAG_NON_UNIT
 #define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED hipDeviceAttributeVirtualMemoryManagementSupported
 #define CU_MEM_ALLOC_GRANULARITY_RECOMMENDED hipMemAllocationGranularityRecommended
 #define CU_MEM_ALLOCATION_TYPE_PINNED hipMemAllocationTypePinned
@@ -30,6 +33,7 @@
 #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
 #define __all_sync(mask, var) __all(var)
 #define __any_sync(mask, var) __any(var)
+#define cublasStrsmBatched hipblasStrsmBatched
 #define cublasCreate hipblasCreate
 #define cublasDestroy hipblasDestroy
 #define cublasGemmEx hipblasGemmEx
@@ -12,11 +12,16 @@
 #define CUBLAS_GEMM_DEFAULT_TENSOR_OP MUBLAS_GEMM_DEFAULT
 #define CUBLAS_OP_N MUBLAS_OP_N
 #define CUBLAS_OP_T MUBLAS_OP_T
+#define CUBLAS_DEFAULT_MATH MUBLAS_DEFAULT_MATH
+#define CUBLAS_SIDE_RIGHT MUBLAS_SIDE_RIGHT
+#define CUBLAS_FILL_MODE_UPPER MUBLAS_FILL_MODE_UPPER
+#define CUBLAS_DIAG_NON_UNIT MUBLAS_DIAG_NON_UNIT
 #define CUBLAS_STATUS_SUCCESS MUBLAS_STATUS_SUCCESS
 #define CUBLAS_TF32_TENSOR_OP_MATH MUBLAS_TENSOR_OP_MATH
 #define CUDA_R_16F  MUSA_R_16F
 #define CUDA_R_16BF MUSA_R_16BF
 #define CUDA_R_32F  MUSA_R_32F
+#define cublasStrsmBatched mublasStrsmBatched
 #define cublasComputeType_t cudaDataType_t
 #define cublasCreate mublasCreate
 #define cublasDestroy mublasDestroy
@@ -73,15 +73,15 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) {
    return (1 - MIN(1, MAX(0, y)));
 }

-static void rope_cache_init(const float   theta_base,
-                            float         freq_scale,
-                            const float * freq_factors,
-                            float *       corr_dims,
-                            uint32_t      ne0,
-                            float         ext_factor,
-                            float         mscale,
-                            float *       cache,
-                            float         theta_scale) {
+static void rope_cache_init(const float    theta_base,
+                            const float    freq_scale,
+                            const float *  freq_factors,
+                            float *        corr_dims,
+                            const uint32_t ne0,
+                            const float    ext_factor,
+                            const float    mscale,
+                            float *        cache,
+                            const float    theta_scale) {
    // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
    float theta = theta_base;

@@ -92,18 +92,19 @@ static void rope_cache_init(const float   theta_base,

        // Get n-d rotational scaling corrected for extrapolation
        float theta_interp = freq_scale * theta_extrap;
-        float theta2       = theta_interp;
+        float theta_final  = theta_interp;
+        float mscale_final = mscale;

        if (ext_factor != 0.0f) {
            float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
-            theta2         = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+            theta_final    = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;

            // Get n-d magnitude scaling corrected for interpolation
-            mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+            mscale_final *= 1.0f + 0.1f * logf(1.0f / freq_scale);
        }

-        cache[i0 + 0] = cosf(theta2) * mscale;
-        cache[i0 + 1] = sinf(theta2) * mscale;
+        cache[i0 + 0] = cosf(theta_final) * mscale_final;
+        cache[i0 + 1] = sinf(theta_final) * mscale_final;

        theta *= theta_scale;
    }
@@ -151,9 +152,9 @@ static void init_rope_ctx(struct rope_th_ctx * rope_ctx, struct htp_ops_context
 }

 static void hvx_calc_rope_neox_f32(const float * restrict src0,
-                              float * restrict dst,
-                              const int num_elems,
-                              const float * restrict theta_cache) {
+                                   float * restrict dst,
+                                   const int num_elems,
+                                   const float * restrict theta_cache) {
    // for (int i = 0; i < num_elems; i += 2) {
    //const float cos_theta = theta_cache[i + 0];
    //const float sin_theta = theta_cache[i + 1];
@@ -192,7 +193,7 @@ static void hvx_calc_rope_neox_f32(const float * restrict src0,
        HVX_Vector v4 = Q6_Vqf32_vsub_Vqf32Vqf32(vx0_c, vx1_s);
        HVX_Vector v5 = Q6_Vqf32_vadd_Vqf32Vqf32(vx0_s, vx1_c);

-        *(HVX_Vector *) dst_curr          = Q6_Vsf_equals_Vqf32(v4);
+        *(HVX_Vector *) dst_curr               = Q6_Vsf_equals_Vqf32(v4);
        *(HVX_Vector *) (dst_curr + half_size) = Q6_Vsf_equals_Vqf32(v5);

        src0_curr += VLEN;
@@ -259,7 +260,7 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
                         const uint32_t       ir1,
                         int                  nth,
                         int                  ith,
-                         int                  opt_path) {
+                         const int            opt_path) {
    struct htp_ops_context * octx = rope_ctx->octx;

    const struct htp_tensor * src0 = &octx->src0;
@@ -267,8 +268,8 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
    const struct htp_tensor * src2 = &octx->src2;
    struct htp_tensor *       dst  = &octx->dst;

-    const int32_t mode  = rope_ctx->mode;
-    const bool is_neox  = mode & HTP_ROPE_TYPE_NEOX;
+    const int32_t mode    = rope_ctx->mode;
+    const bool    is_neox = mode & HTP_ROPE_TYPE_NEOX;

    htp_rope_preamble;

@@ -281,8 +282,9 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
        freq_factors = (const float *) src2->data;
    }

-    int ir = 0;
-
+    const uint32_t i1_end       = MIN(ir1, ne1);
+    const int32_t  half_dims    = rope_ctx->n_dims / 2;
+    const size_t   remain_bytes = (ne0 - rope_ctx->n_dims) * sizeof(float);
    for (uint32_t i3 = 0; i3 < ne3; i3++) {      // batch
        for (uint32_t i2 = 0; i2 < ne2; i2++) {  // seq-len
            const int32_t p = pos[i2];
@@ -290,14 +292,7 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
            rope_cache_init(p, rope_ctx->freq_scale, freq_factors, rope_ctx->corr_dims, ne0, rope_ctx->ext_factor,
                            rope_ctx->attn_factor, wp0, rope_ctx->theta_scale);

-            for (uint32_t i1 = 0; i1 < ne1; i1++) {  // attn-heads
-                if (ir++ < ir0) {
-                    continue;
-                }
-                if (ir > ir1) {
-                    break;
-                }
-
+            for (uint32_t i1 = ir0; i1 < i1_end; i1++) {  // attn-heads
                const float * src      = (float *) ((char *) src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01);
                float *       dst_data = (float *) ((char *) dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);

@@ -310,6 +305,9 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
                    } else {
                        hvx_calc_rope_f32(src_loc, dst_data_loc, rope_ctx->n_dims, wp0);
                    }
+
+                    src_loc += rope_ctx->n_dims;
+                    dst_data_loc += rope_ctx->n_dims;
                } else {
                    for (uint32_t i0 = 0; i0 < rope_ctx->n_dims; i0 += 2) {
                        const float cos_theta = wp0[i0 + 0];
@@ -317,10 +315,10 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,

                        if (is_neox) {
                            const float x0 = src_loc[0];
-                            const float x1 = src_loc[rope_ctx->n_dims/2];
+                            const float x1 = src_loc[half_dims];

-                            dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta;
-                            dst_data_loc[rope_ctx->n_dims/2] = x0 * sin_theta + x1 * cos_theta;
+                            dst_data_loc[0]         = x0 * cos_theta - x1 * sin_theta;
+                            dst_data_loc[half_dims] = x0 * sin_theta + x1 * cos_theta;

                            src_loc += 1;
                            dst_data_loc += 1;
@@ -335,15 +333,13 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
                            dst_data_loc += 2;
                        }
                    }
+
+                    src_loc += (is_neox ? half_dims : 0);
+                    dst_data_loc += (is_neox ? half_dims : 0);
                }

-                for (uint32_t i0 = rope_ctx->n_dims; i0 < ne0; i0 += 2) {
-                    dst_data_loc[0] = src_loc[0];
-                    dst_data_loc[1] = src_loc[1];
-
-                    src_loc += 2;
-                    dst_data_loc += 2;
-                }
+                // TODO: use simd to speed up the remaining elements copy
+                memcpy(dst_data_loc, src_loc, remain_bytes);
            }
        }
    }
@@ -659,6 +659,7 @@ struct vk_device_struct {
    vk_pipeline pipeline_cos_f32;
    vk_pipeline pipeline_log[2];
    vk_pipeline pipeline_tri[2];
+    vk_pipeline pipeline_diag[2];
    vk_pipeline pipeline_clamp_f32;
    vk_pipeline pipeline_pad_f32;
    vk_pipeline pipeline_roll_f32;
@@ -722,6 +723,11 @@ struct vk_device_struct {
    vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
    vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512;
    vk_pipeline pipeline_soft_max_back_f32;
+
+    vk_pipeline pipeline_soft_max_large1_f32, pipeline_soft_max_large1_f32_f16;
+    vk_pipeline pipeline_soft_max_large2_f32, pipeline_soft_max_large2_f32_f16;
+    vk_pipeline pipeline_soft_max_large3_f32, pipeline_soft_max_large3_f32_f16;
+
    vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16, pipeline_rope_norm_f32_f16;
    vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16, pipeline_rope_neox_f32_f16;
    vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16;
@@ -757,7 +763,8 @@ struct vk_device_struct {

    vk_pipeline pipeline_flash_attn_split_k_reduce;

-    vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT];
+    // [2] is for whether to take n_experts from spec constant (0) or push constant (1)
+    vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT][2];

    std::vector<vk_pipeline_ref> all_pipelines;

@@ -1149,6 +1156,7 @@ static_assert(sizeof(vk_op_multi_add_push_constants) <= 256);

 struct vk_op_topk_moe_push_constants {
    uint32_t n_rows;
+    uint32_t n_experts_push;
    uint32_t n_expert_used;
    float clamp_min;
    float clamp_max;
@@ -3730,6 +3738,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs",  get_rows_iq4_xs_len,  get_rows_iq4_xs_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl",  get_rows_iq4_nl_len,  get_rows_iq4_nl_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4],   "get_rows_mxfp4",   get_rows_mxfp4_len,   get_rows_mxfp4_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_I32],     "get_rows_i32",     get_rows_i32_len,     get_rows_i32_data,     "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -3917,6 +3926,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_tri[0], "tri_f32", tri_f32_len, tri_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_tri[1], "tri_f16", tri_f16_len, tri_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

+    ggml_vk_create_pipeline(device, device->pipeline_diag[0], "diag_f32", diag_f32_len, diag_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
    ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1);
@@ -3996,6 +4008,13 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_back_f32, "soft_max_back_f32", soft_max_back_f32_len, soft_max_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1, true);

+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32,     "soft_max_large1_f32",     soft_max_large1_f32_len,     soft_max_large1_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32,     "soft_max_large2_f32",     soft_max_large2_f32_len,     soft_max_large2_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32,     "soft_max_large3_f32",     soft_max_large3_f32_len,     soft_max_large3_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32_f16, "soft_max_large1_f32_f16", soft_max_large1_f32_f16_len, soft_max_large1_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32_f16, "soft_max_large2_f32_f16", soft_max_large2_f32_f16_len, soft_max_large2_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32_f16, "soft_max_large3_f32_f16", soft_max_large3_f32_f16_len, soft_max_large3_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+
    ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32, "rope_multi_f32", rope_multi_f32_len, rope_multi_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
@@ -4204,10 +4223,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);

-    for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) {
-        ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX],      "topk_moe_f32_early_softmax_"+std::to_string(i),       topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 0}, 1, true, true, device->subgroup_size);
-        ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM], "topk_moe_f32_early_softmax_norm"+std::to_string(i),   topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 1, 0}, 1, true, true, device->subgroup_size);
-        ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX],       "topk_moe_f32_late_softmax"+std::to_string(i),         topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 1}, 1, true, true, device->subgroup_size);
+    for (uint32_t use_push = 0; use_push < 2; ++use_push) {
+        for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) {
+            ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX][use_push],      "topk_moe_f32_early_softmax_"+std::to_string(i),       topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 0, use_push}, 1, true, true, device->subgroup_size);
+            ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM][use_push], "topk_moe_f32_early_softmax_norm"+std::to_string(i),   topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 1, 0, use_push}, 1, true, true, device->subgroup_size);
+            ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX][use_push],       "topk_moe_f32_late_softmax"+std::to_string(i),         topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 1, use_push}, 1, true, true, device->subgroup_size);
+        }
    }

    for (auto &c : compiles) {
@@ -8274,6 +8295,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
    switch (op) {
    case GGML_OP_GET_ROWS:
        GGML_ASSERT(src1->type == GGML_TYPE_I32);
+        if (src0->type == GGML_TYPE_I32) {
+            // i32 src only supports i32 result
+            GGML_ASSERT(dst->type == GGML_TYPE_I32);
+            return ctx->device->pipeline_get_rows[src0->type];
+        }
        if (dst->type == GGML_TYPE_F16) {
            return ctx->device->pipeline_get_rows[src0->type];
        }
@@ -8400,6 +8426,12 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
            return ctx->device->pipeline_tri[dst->type == GGML_TYPE_F16];
        }
        return nullptr;
+    case GGML_OP_DIAG:
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_diag[dst->type == GGML_TYPE_F16];
+        }
+        return nullptr;
    case GGML_OP_CLAMP:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_clamp_f32;
@@ -8554,7 +8586,9 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
            uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0])));
            GGML_ASSERT(idx < num_topk_moe_pipelines);
            topk_moe_mode mode = ggml_vk_num_additional_ops_to_topk_moe_mode(ctx->num_additional_fused_ops);
-            return ctx->device->pipeline_topk_moe[idx][mode];
+            // use n_experts from push constant if it's not equal to the power of two spec constant
+            bool use_push = dst->ne[0] != (1u << idx);
+            return ctx->device->pipeline_topk_moe[idx][mode][use_push];
        }

        if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
@@ -9091,6 +9125,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
    case GGML_OP_COS:
    case GGML_OP_LOG:
    case GGML_OP_TRI:
+    case GGML_OP_DIAG:
    case GGML_OP_CLAMP:
    case GGML_OP_PAD:
    case GGML_OP_ROLL:
@@ -9778,6 +9813,12 @@ static void ggml_vk_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TRI, std::move(p));
 }

+static void ggml_vk_diag(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst));
+
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_DIAG, std::move(p));
+}
+
 static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
    p.param1 = ggml_get_op_params_f32(dst, 0);
@@ -10111,7 +10152,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

-    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, {
+    vk_op_soft_max_push_constants pc {
        ncols,
        src1 != nullptr ? nrows_y : (uint32_t)0,
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],
@@ -10122,7 +10163,55 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
        n_head_log2,
        nrows_x,
        src2 != nullptr
-    });
+    };
+
+    if (ncols <= 16384) {
+        ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, std::move(pc));
+    } else {
+
+        vk_subbuffer buf_a = ggml_vk_tensor_subbuffer(ctx, src0);
+        vk_subbuffer buf_b = src1 ? ggml_vk_tensor_subbuffer(ctx, src1) : buf_a;
+        vk_subbuffer buf_c = src2 ? ggml_vk_tensor_subbuffer(ctx, src2) : buf_a;
+        vk_subbuffer buf_d = ggml_vk_tensor_subbuffer(ctx, dst);
+
+        uint32_t elems_per_wg = 128 * 4;
+        uint32_t num_wgs = CEIL_DIV(ncols, elems_per_wg);
+        size_t tmp_size = num_wgs * nrows_x * sizeof(float);
+
+        if (ctx->prealloc_size_x < tmp_size) {
+            ctx->prealloc_size_x = tmp_size;
+            ggml_vk_preallocate_buffers(ctx, subctx);
+        }
+        if (ctx->prealloc_size_y < tmp_size) {
+            ctx->prealloc_size_y = tmp_size;
+            ggml_vk_preallocate_buffers(ctx, subctx);
+        }
+        if (ctx->prealloc_x_need_sync || ctx->prealloc_y_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+
+        vk_subbuffer buf_x = { ctx->prealloc_x, 0, tmp_size };
+        vk_subbuffer buf_y = { ctx->prealloc_y, 0, tmp_size };
+
+        std::array<uint32_t, 3> elements = { num_wgs, nrows_x, 1 };
+
+        vk_pipeline pipeline1 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large1_f32_f16 : ctx->device->pipeline_soft_max_large1_f32;
+        vk_pipeline pipeline2 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large2_f32_f16 : ctx->device->pipeline_soft_max_large2_f32;
+        vk_pipeline pipeline3 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large3_f32_f16 : ctx->device->pipeline_soft_max_large3_f32;
+
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline3, 1);
+
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);
+        ggml_vk_sync_buffers(ctx, subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);
+        ggml_vk_sync_buffers(ctx, subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline3, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);
+
+        ctx->prealloc_x_need_sync = true;
+        ctx->prealloc_y_need_sync = true;
+    }
 }

 static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -10158,6 +10247,7 @@ static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx,

    vk_op_topk_moe_push_constants pc {};
    pc.n_rows = n_rows;
+    pc.n_experts_push = n_experts;
    pc.n_expert_used = n_expert_used;
    if (mode == TOPK_MOE_EARLY_SOFTMAX_NORM) {
        ggml_tensor * clamp = cgraph->nodes[node_idx + 7];
@@ -11857,6 +11947,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
    case GGML_OP_TRI:
        ggml_vk_tri(ctx, compute_ctx, src0, node);

+        break;
+    case GGML_OP_DIAG:
+        ggml_vk_diag(ctx, compute_ctx, src0, node);
+
        break;
    case GGML_OP_CLAMP:
        ggml_vk_clamp(ctx, compute_ctx, src0, node);
@@ -12832,8 +12926,7 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc
    }

    const int n_expert = softmax->ne[0];
-    // n_expert must be a power of 2
-    if (!is_pow2(n_expert) || n_expert > (1 << (num_topk_moe_pipelines-1))) {
+    if (n_expert > (1 << (num_topk_moe_pipelines-1))) {
        return false;
    }

@@ -13877,6 +13970,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                    case GGML_TYPE_IQ4_XS:
                    case GGML_TYPE_IQ4_NL:
                    case GGML_TYPE_MXFP4:
+                    case GGML_TYPE_I32:
                        return true;
                    default:
                        return false;
@@ -14001,6 +14095,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_LOG:
        case GGML_OP_TRI:
+        case GGML_OP_DIAG:
            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                   op->type == op->src[0]->type;
        case GGML_OP_ARGSORT:
@@ -14591,6 +14686,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
            tensor_clone = ggml_log(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_TRI) {
            tensor_clone = ggml_tri(ggml_ctx, src_clone[0], ggml_get_op_params_i32(tensor, 0));
+        } else if (tensor->op == GGML_OP_DIAG) {
+            tensor_clone = ggml_diag(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_CLAMP) {
            const float * params = (const float *)tensor->op_params;
            tensor_clone = ggml_clamp(ggml_ctx, src_clone[0], params[0], params[1]);
@@ -0,0 +1,29 @@
+#version 450
+
+#include "rte.glsl"
+#include "types.glsl"
+#include "generic_unary_head.glsl"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12_offset = i12*p.ne11*p.ne10;
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
+
+    if (i10 == i11) {
+        const float val = float(data_a[get_aoffset() + i13*p.nb03 + i12*p.nb02 + 0*p.nb01 + i10*p.nb00]);
+        data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val);
+    } else {
+        data_d[get_doffset() + dst_idx(idx)] = D_TYPE(0);
+    }
+}
@@ -256,6 +256,9 @@ void main() {
        barrier();
    }

+    // prevent race on tmpsh
+    barrier();
+
    // reduce across threads

    [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
@@ -302,6 +302,9 @@ void main() {
        barrier();
    }

+    // prevent race on tmpsh
+    barrier();
+
    // reduce across threads

    float rowmaxf[rows_per_thread], eMf[rows_per_thread], Moldf[rows_per_thread];
@@ -26,9 +26,9 @@ void main() {
            const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23;

 #if defined(DATA_A_BF16)
-            FLOAT_TYPE v = FLOAT_TYPE(bf16_to_fp32(data_a[a_offset + i00]));
+            TEMP_TYPE v = TEMP_TYPE(bf16_to_fp32(data_a[a_offset + i00]));
 #else
-            FLOAT_TYPE v = FLOAT_TYPE(data_a[a_offset + i00]);
+            TEMP_TYPE v = TEMP_TYPE(data_a[a_offset + i00]);
 #endif
 #ifndef OPTIMIZATION_ERROR_WORKAROUND
            data_d[d_offset + i00] = D_TYPE(v);
@@ -7,34 +7,50 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;

 FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];

-void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
-    const uint y_idx = i * QUANT_K + 32 * ib32;
-
-    uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const float d = float(data_a[ibi].d);
-        const uint qh = data_a[ibi].qh[ib32];
-        const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1);
-        const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
-
+void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i,
+                     const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
+    const uint y_idx_base = i * QUANT_K + 32 * ib32;
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx_base) / 4;
        [[unroll]] for (uint l = 0; l < 4; ++l) {
-            const uint qs = data_a[ibi].qs[4 * ib32 + l];
-            const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3);
-            const int16_t grid = int16_t(iq1s_grid[qs | (idxhi << 8)]);
+            const vec4 b_val_0 = vec4(data_b_v4[base_b_idx + 2 * l]);
+            const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]);

-            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]);
-                vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]);
+            // index for data_a
+            uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                const float d = float(data_a[ibi].d);
+                const uint qh = data_a[ibi].qh[ib32];
+
+                const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1);
+                const uint qs = data_a[ibi].qs[4 * ib32 + l];
+                const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3);
+                const uint16_t grid = uint16_t(iq1s_grid[qs | (idxhi << 8)]);
+
+                const float delta_val = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
+                const vec4 delta_v = vec4(delta_val);
+                const vec4 fbits0 = vec4(
+                    float(bitfieldExtract(grid, 0, 2)),
+                    float(bitfieldExtract(grid, 2, 2)),
+                    float(bitfieldExtract(grid, 4, 2)),
+                    float(bitfieldExtract(grid, 6, 2))
+                );
+                const vec4 fbits1 = vec4(
+                    float(bitfieldExtract(grid, 8, 2)),
+                    float(bitfieldExtract(grid, 10, 2)),
+                    float(bitfieldExtract(grid, 12, 2)),
+                    float(bitfieldExtract(grid, 14, 2))
+                );
+
+                vec4 sum_v = fma(b_val_0, fbits0 + delta_v, vec4(0.0));
+                sum_v      = fma(b_val_1, fbits1 + delta_v, sum_v);
+                FLOAT_TYPE sum = dot(sum_v, vec4(1.0));

-                FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-                [[unroll]] for (int k = 0; k < 4; ++k) {
-                    sum = fma(FLOAT_TYPE(b0[k]), bitfieldExtract(grid, 2 * k, 2) + delta,
-                          fma(FLOAT_TYPE(b4[k]), bitfieldExtract(grid, 8 + 2 * k, 2) + delta, sum));
-                }
                temp[j][n] = fma(dl, sum, temp[j][n]);
+                ibi += num_blocks_per_row;
            }
        }
-        ibi += num_blocks_per_row;
    }
 }

@@ -244,17 +244,20 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
            const uint iqs = idx % 128;                 // 0..127

            const uint n = iqs / 64;                    // 0,1
-            const uint b = (iqs % 64) / 32;             // 0,1
+            const uint b = ((iqs % 64) / 32) * 4;       // 0,4
            const uint is_b = (iqs % 16) / 8;           // 0,1
            const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
            const uint is = 8 * n + qhshift + is_b;     // 0..15
-            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
-            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+            const uint qsi = n * 32 + (iqs % 32);       // 0..63
+            const uint qhi = n * 16 + (iqs % 16);       // 0..31

            const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);

-            buf_a[buf_idx] = FLOAT_TYPE_VEC2(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32),
-                                             dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));
+            const uint ql = (uint(data_a_packed16[ib].ql[qsi]) >> b) & 0x0F0F;
+            const uint qh = (uint(data_a_packed16[ib].qh[qhi]) >> qhshift) & 0x0303;
+            const vec2 q = (vec2(unpack8(ql | (qh << 4)).xy) - 32) * dscale;
+
+            buf_a[buf_idx] = FLOAT_TYPE_VEC2(q.x, q.y);
 #elif defined(DATA_A_IQ1_S)
            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
@@ -0,0 +1,62 @@
+#version 450
+
+#include "soft_max_large_common.glsl"
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.y;
+    const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
+
+    const uint32_t i03 = rowx / (p.ne01 * p.ne02);
+    const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
+    const uint32_t i01 = rowx % p.ne01;
+
+    uint rowy_start = 0;
+    if (p.KY > 0) {
+        rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
+    }
+
+    if (rowx >= p.nrows_x) {
+        return;
+    }
+
+    float slope = get_slope(rowx);
+
+    // Find max
+    FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
+
+    [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
+        const uint col = col0 + tid;
+
+        FLOAT_TYPE a = FLOAT_TYPE(0);
+        if (col < p.KX) {
+            a = data_a[rowx * p.KX + col];
+        }
+
+        FLOAT_TYPE b = FLOAT_TYPE(0);
+        if (p.KY > 0 && col < p.KX) {
+            b = data_b[rowy_start + col];
+        }
+
+        FLOAT_TYPE v = a * p.scale + slope * b;
+
+        if (col < p.KX) {
+            max_val = max(max_val, v);
+        }
+    }
+
+    // reduce across the workgroup
+    vals[tid] = max_val;
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(vals[tid], vals[tid + s]);
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        max_val = vals[0];
+        data_m[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = max_val;
+    }
+}
@@ -0,0 +1,79 @@
+#version 450
+
+#include "soft_max_large_common.glsl"
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.y;
+    const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
+
+    const uint32_t i03 = rowx / (p.ne01 * p.ne02);
+    const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
+    const uint32_t i01 = rowx % p.ne01;
+
+    uint rowy_start = 0;
+    if (p.KY > 0) {
+        rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
+    }
+
+    if (rowx >= p.nrows_x) {
+        return;
+    }
+
+    float slope = get_slope(rowx);
+
+    // Find max
+    FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
+
+    [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) {
+        if (i + tid < gl_NumWorkGroups.x) {
+            max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]);
+        }
+    }
+
+    // reduce across the workgroup
+    vals[tid] = max_val;
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(max_val, vals[tid + s]);
+        }
+        barrier();
+    }
+
+    max_val = vals[0];
+    barrier();
+
+    FLOAT_TYPE sum = FLOAT_TYPE(0.0f);
+
+    // Compute sum{exp(x - max)}
+    [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            break;
+        }
+
+        // compute exp(a*scale+b*slope), add it to sum
+        const uint i = rowx * p.KX + col;
+        FLOAT_TYPE val;
+        val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy_start + col]) : FLOAT_TYPE(0.0f)) - max_val);
+        sum += val;
+        data_d[i] = D_TYPE(val);
+    }
+
+    // reduce across the workgroup
+    vals[tid] = sum;
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] += vals[tid + s];
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        sum = vals[0];
+        data_s[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = sum;
+    }
+}
@@ -0,0 +1,65 @@
+#version 450
+
+#include "soft_max_large_common.glsl"
+
+shared FLOAT_TYPE sumsh[BLOCK_SIZE];
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.y;
+    const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
+
+    const uint32_t i03 = rowx / (p.ne01 * p.ne02);
+    const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
+    const uint32_t i01 = rowx % p.ne01;
+
+    uint rowy_start = 0;
+    if (p.KY > 0) {
+        rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
+    }
+
+    if (rowx >= p.nrows_x) {
+        return;
+    }
+
+    FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
+    FLOAT_TYPE sum = FLOAT_TYPE(0.0f);
+
+    [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) {
+        if (i + tid < gl_NumWorkGroups.x) {
+            max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]);
+            sum += data_s[rowx * gl_NumWorkGroups.x + i + tid];
+        }
+    }
+
+    // reduce across the workgroup
+    vals[tid] = max_val;
+    sumsh[tid] = sum;
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(max_val, vals[tid + s]);
+            sumsh[tid] += sumsh[tid + s];
+        }
+        barrier();
+    }
+
+    max_val = vals[0];
+    sum = sumsh[0];
+
+    if (p.has_sinks != 0) {
+        sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val));
+    }
+
+    FLOAT_TYPE rcpdivisor = 1.0/sum;
+
+    [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            continue;
+        }
+
+        data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor);
+    }
+}
@@ -0,0 +1,53 @@
+#extension GL_EXT_control_flow_attributes : enable
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    uint ne00;
+    uint ne01;
+    uint ne02;
+    uint ne12;
+    uint ne13;
+    uint nb11;
+    uint nb12;
+    uint nb13;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint n_head_log2;
+    uint nrows_x;
+    uint has_sinks;
+} p;
+
+#include "types.glsl"
+
+layout(constant_id = 0) const uint BLOCK_SIZE = 128;
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+layout(constant_id = 1) const uint num_iters = 4;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
+layout (binding = 2) readonly buffer Z {float data_c[];};
+layout (binding = 3) buffer D {D_TYPE data_d[];};
+layout (binding = 4) buffer M {float data_m[];};
+layout (binding = 5) buffer S {float data_s[];};
+
+shared FLOAT_TYPE vals[BLOCK_SIZE];
+
+float get_slope(uint rowx) {
+    float slope = 1.0f;
+
+    // ALiBi
+    if (p.max_bias > 0.0f) {
+        const uint h = (rowx / p.ne01) % p.ne02; // head index
+
+        const float base = h < p.n_head_log2 ? p.m0 : p.m1;
+        const uint   exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    return slope;
+}
@@ -10,6 +10,7 @@
 layout (push_constant) uniform parameter
 {
    uint n_rows;
+    uint n_experts_push;
    uint n_expert_used;
    float clamp_min;
    float clamp_max;
@@ -18,11 +19,16 @@ layout (push_constant) uniform parameter
 layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in;

 layout(constant_id = 0) const uint WARP_SIZE = 32;
-layout(constant_id = 1) const uint n_experts = 512;
+layout(constant_id = 1) const uint n_experts_spec = 512;
 layout(constant_id = 2) const bool with_norm = true;
 layout(constant_id = 3) const bool late_softmax = false;
+layout(constant_id = 4) const bool nexperts_use_push = false;

-const uint experts_per_thread = (n_experts > WARP_SIZE) ? n_experts / WARP_SIZE : 1;
+uint n_experts = nexperts_use_push ? n_experts_push : n_experts_spec;
+
+#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
+
+const uint experts_per_thread = CEIL_DIV(n_experts_spec, WARP_SIZE);

 layout (binding = 0, std430) readonly buffer Logits {float logits[];};
 layout (binding = 1, std430) writeonly buffer Weights {float weights[];};
@@ -94,7 +100,7 @@ void main() {
    }

    if (!late_softmax) {
-        softmax_warp_inplace(wt, n_experts, lane, false);
+        softmax_warp_inplace(wt, n_experts, lane, nexperts_use_push);
    }

    // at this point, each thread holds a portion of softmax,
@@ -704,13 +704,15 @@ void process_shaders() {
        shader = (tname == "f32" || tname == "f16" || tname == "bf16") ? "get_rows.comp" : "get_rows_quant.comp";

        if (tname == "f16") {
-            string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}));
+            string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}));
        } else {
-            string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}));
+            string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}));
        }
-        string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}));
+        string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}));
    }

+    string_to_spv("get_rows_i32", "get_rows.comp", {{"TEMP_TYPE", "uint"}, {"A_TYPE", "uint"}, {"B_TYPE", "int"}, {"D_TYPE", "uint"}});
+
    string_to_spv("mul_mat_vec_p021_f16_f32_subgroup_add", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}});
    string_to_spv("mul_mat_vec_p021_f16_f32",              "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}});
    string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}});
@@ -854,6 +856,8 @@ void process_shaders() {

    string_to_spv("tri_f16",        "tri.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
    string_to_spv("tri_f32",        "tri.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("diag_f16",       "diag.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
+    string_to_spv("diag_f32",       "diag.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});

    string_to_spv("softplus_f16",   "softplus.comp",    {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
    string_to_spv("softplus_f32",   "softplus.comp",    {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
@@ -899,6 +903,13 @@ void process_shaders() {
    string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
    string_to_spv("soft_max_back_f32", "soft_max_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));

+    string_to_spv("soft_max_large1_f32", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large2_f32", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large3_f32", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large1_f32_f16", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large2_f32_f16", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large3_f32_f16", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+
    string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
    string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
    string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
@@ -1,5 +1,5 @@
 {
-  "extraPaths": ["gguf-py"],
+  "extraPaths": ["gguf-py", "examples/model-conversion/scripts"],
  "pythonVersion": "3.9",
  "pythonPlatform": "All",
  "reportUnusedImport": "warning",
@@ -0,0 +1,281 @@
+import argparse
+import requests
+import json
+from pathlib import Path
+import logging
+
+logger = logging.getLogger("compare-logprobs")
+logging.basicConfig(level=logging.INFO)
+
+
+DESCRIPTION = """
+Compare logits between llama.cpp and another inference engine using OpenAI-compatible server endpoints.
+
+Unlike compare-logits.py, it allows dumping logits from a hosted API endpoint. Useful when it's not possible to run both models locally.
+
+Example usage:
+    Step 1: Dump logits from two different servers
+        python scripts/compare-logprobs.py dump logits_llama.log http://localhost:8080/v1/completions
+        python scripts/compare-logprobs.py dump logits_other.log http://other-engine:8000/v1/completions
+
+        (optionally, you can add --api-key <key> if the endpoint requires authentication)
+
+    Step 2: Compare the dumped logits
+        python scripts/compare-logprobs.py compare logits_llama.log logits_other.log report.md
+"""
+
+
+def generate_input_prompt(length: int) -> list[str]:
+    CORPUS = """
+    You are an advanced AI assistant capable of using tools to gather information, perform calculations, or execute tasks. Always think step by step before responding. If a user's query requires external data, computation, or actions beyond your internal knowledge, use the appropriate tools via function calls.
+
+    ### Tool Call Format:
+    When you need to use a tool, output the call in this exact XML format. Include the opening and closing tags. Do not escape arguments; they will be parsed as plain text.
+
+    You can make multiple calls in one go by placing them one after another.
+    """
+    words = [w.strip() for w in CORPUS.strip().split(" ")]
+    words = [w for w in words if len(w) > 0]  # filter out empty strings
+    while len(words) < length:
+        words += words
+    return words[:length]
+
+
+def dump_logits(
+    endpoint: str,
+    output_path: Path,
+    input_words: list[str],
+    pattern: list[tuple[bool, int]],
+    api_key=None,
+):
+    logger.info(f"Dumping logits to {output_path} from endpoint {endpoint}...")
+    words = input_words
+    curr_text = ""
+    n_total = sum(n for get, n in pattern if get)
+    n_done = 0
+    i_cur = 0
+    i_total = len(words)
+    with output_path.open("w") as f:
+        for get, n in pattern:
+            if not get:
+                # skip n words
+                for i in range(n):
+                    curr_text += words.pop(0) + " "
+                    i_cur += 1
+                continue
+            # get n words
+            for i in range(n):
+                curr_text += words.pop(0) + " "
+                payload = {
+                    "prompt": curr_text.strip(),
+                    "temperature": 0.0,
+                    "top_k": 1,
+                    "max_tokens": 1,
+                    "logprobs": 1,
+                    "stream": False,
+                }
+                response = requests.post(
+                    endpoint,
+                    json=payload,
+                    headers={"Authorization": f"Bearer {api_key}"} if api_key else {},
+                )
+                response.raise_for_status()
+                data = response.json()
+                data["__index"] = i_cur  # add index for easier debugging later
+                data = json.dumps(data)
+                f.write(f"{data}\n")
+                n_done += 1
+                i_cur += 1
+                logger.info(
+                    f"\n\n{data}\n\n[Step: {n_done}/{n_total} | Word: {i_cur}/{i_total}]"
+                )
+    logger.info(f"Logits dumped to {output_path}")
+
+
+def get_token_logprobs(data: dict):
+    logprobs = data["choices"][0]["logprobs"]
+    if "content" in logprobs:
+        # llama.cpp case
+        top = logprobs["content"][0]["top_logprobs"][0]
+        return top["token"], top["logprob"]
+    else:
+        # vllm case
+        tokens = logprobs["tokens"]
+        token_logprobs = logprobs["token_logprobs"]
+        return tokens[0], token_logprobs[0]
+
+
+def clean_text(text: str) -> str:
+    return (
+        "'"
+        + text.replace("\n", "\\n")
+        .replace("\t", "\\t")
+        .replace("\r", "\\r")
+        .replace("|", "\\|")
+        + "'"
+    )
+
+
+def compare_logits(input1: Path, input2: Path, output_path: Path):
+    with input1.open("r") as f1, input2.open("r") as f2, output_path.open("w") as fout:
+        lines1 = f1.readlines()
+        lines2 = f2.readlines()
+
+        tab_header = [
+            "idx",
+            input1.name,
+            "logprob_1",
+            input2.name,
+            "logprob_2",
+            "diff (abs)",
+        ]
+        tab_entries = []
+        tab_max_widths = [len(h) for h in tab_header]
+
+        assert len(lines1) == len(
+            lines2
+        ), "Input files must have the same number of lines."
+
+        fout.write("# Logits Comparison Report\n\n")
+        for i, (line1, line2) in enumerate(zip(lines1, lines2)):
+            if not line1.strip() or not line2.strip():
+                continue  # skip empty lines
+
+            data1 = json.loads(line1)
+            data2 = json.loads(line2)
+
+            idx1 = data1.get("__index", -1)
+            idx2 = data2.get("__index", -1)
+            if idx1 != idx2:
+                logger.warning(
+                    f"Warning: Mismatched indices at line {i}: {idx1} vs {idx2}"
+                )
+
+            token1, logprob1 = get_token_logprobs(data1)
+            token2, logprob2 = get_token_logprobs(data2)
+
+            token1 = clean_text(token1)
+            token2 = clean_text(token2)
+            abs_diff = abs(logprob1 - logprob2)
+
+            tab_entries.append(
+                (
+                    str(idx1 + 1),
+                    token1,
+                    f"{logprob1:.4f}",
+                    token2,
+                    f"{logprob2:.4f}",
+                    f"{(abs_diff):.4f}",
+                )
+            )
+
+        for i in range(len(tab_entries)):
+            for j in range(len(tab_header)):
+                tab_max_widths[j] = max(tab_max_widths[j], len(tab_entries[i][j]))
+
+        output = ""
+        for j in range(len(tab_header)):
+            output += f"| {tab_header[j]:<{tab_max_widths[j]}} "
+        output += "|\n"
+        for j in range(len(tab_header)):
+            output += f"|{'-' * (tab_max_widths[j] + 2)}"
+        output += "|\n"
+        for entry in tab_entries:
+            for j in range(len(tab_header)):
+                output += f"| {entry[j]:<{tab_max_widths[j]}} "
+            output += "|\n"
+
+        logger.info("\n" + output)
+        fout.write(output)
+        logger.info(f"Report written to {output_path}")
+
+
+def parse_pattern(pattern: str) -> list[tuple[bool, int]]:
+    parts = pattern.split(",")
+    result = []
+    for i, part in enumerate(parts):
+        n = int(part)
+        if i % 2 == 0:
+            result.append((True, n))  # get n words
+        else:
+            result.append((False, n))  # skip n words
+    return result
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=DESCRIPTION, formatter_class=argparse.RawTextHelpFormatter
+    )
+    subparsers = parser.add_subparsers(
+        dest="verb", required=True, help="action to perform"
+    )
+
+    # dump subcommand
+    parser_dump = subparsers.add_parser("dump", help="dump logits from an endpoint")
+    parser_dump.add_argument(
+        "output", type=Path, help="output path for dumped logits (.log)"
+    )
+    parser_dump.add_argument(
+        "endpoint", type=str, help="OAI-compat /completions endpoint"
+    )
+    parser_dump.add_argument(
+        "--api-key",
+        type=str,
+        default=None,
+        help="API key for authentication (if required)",
+    )
+    parser_dump.add_argument(
+        "--file",
+        type=Path,
+        default=None,
+        help="File containing prompt to use instead of the default",
+    )
+    parser_dump.add_argument(
+        "--pattern",
+        type=str,
+        default="10,1000,10,4000,10",
+        help="Pattern n_get,n_skip,... where n_get is number of words to get and n_skip is number of words to skip (num of words, NOT num of tokens)",
+    )
+
+    # compare subcommand
+    parser_compare = subparsers.add_parser(
+        "compare", help="compare two dumped logits files"
+    )
+    parser_compare.add_argument("input1", type=Path, help="first input file (.log)")
+    parser_compare.add_argument("input2", type=Path, help="second input file (.log)")
+    parser_compare.add_argument(
+        "output", type=Path, help="output path for comparison report (.md)"
+    )
+
+    try:
+        return parser.parse_args()
+    except Exception as e:
+        parser.print_help()
+        raise e
+
+
+def main():
+    args = parse_args()
+
+    if args.verb == "dump":
+        pattern = parse_pattern(args.pattern)
+        input_length = sum(n for _, n in pattern)
+        input_words = generate_input_prompt(input_length)
+        if args.file is not None:
+            with args.file.open("r") as f:
+                input_words = f.read().strip().split(" ")
+                if input_length < sum(n for _, n in pattern):
+                    raise ValueError(
+                        f"Input file has only {input_length} words, but pattern requires at least {input_length} words."
+                    )
+                input_length = len(input_words)
+        logger.info(f"Using {input_length} words")
+        dump_logits(args.endpoint, args.output, input_words, pattern, args.api_key)
+    elif args.verb == "compare":
+        compare_logits(args.input1, args.input2, args.output)
+    else:
+        raise ValueError(f"Unknown verb: {args.verb}")
+
+
+if __name__ == "__main__":
+    main()
@@ -1 +1 @@
-55bc9320a4aae82af18e23eefd5de319a755d7b9
+130bc125a88bb57664b88932c48c38a1cb316fac
@@ -695,6 +695,8 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
    udata->seq_idx   .resize(LLAMA_MAX_SEQ, -1);
    udata->output    .resize(n_tokens);

+    udata->seq_id_data.reserve(n_tokens);
+
    seq_set_t seq_set_unq;

    for (size_t i = 0; i < idxs.size(); ++i) {
@@ -716,11 +718,13 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
        }

        udata->n_seq_id[i] = batch.n_seq_id[idxs[i]];
-        udata->seq_id[i]   = batch.seq_id[idxs[i]];
        udata->output[i]   = batch.logits[idxs[i]];

        for (int s = 0; s < udata->n_seq_id[i]; ++s) {
-            seq_set_unq.set(udata->seq_id[i][s]);
+            const llama_seq_id seq_id = batch.seq_id[idxs[i]][s];
+
+            udata->seq_id_data.push_back(seq_id);
+            seq_set_unq.set(seq_id);
        }

        if (udata->output[i]) {
@@ -728,6 +732,12 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
        }
    }

+    llama_seq_id * seq_id_ptr = udata->seq_id_data.data();
+    for (size_t i = 0; i < idxs.size(); ++i) {
+        udata->seq_id[i] = seq_id_ptr;
+        seq_id_ptr += udata->n_seq_id[i];
+    }
+
    for (uint32_t s = 0; s < n_seq_max; ++s) {
        if (seq_set_unq.test(s)) {
            udata->seq_idx[s] = udata->seq_id_unq.size();
@@ -56,13 +56,15 @@ struct llama_ubatch {
        std::vector<float>          embd;
        std::vector<llama_pos>      pos;
        std::vector<int32_t>        n_seq_id;
-        std::vector<llama_seq_id *> seq_id;
+        std::vector<llama_seq_id *> seq_id;      // these point into the seq_id_data below
        std::vector<llama_seq_id>   seq_id_unq;
        std::vector<int32_t>        seq_idx;
        std::vector<int8_t>         output;
+
+        std::vector<llama_seq_id> seq_id_data;
    };

-    // the llama_ubatch pointers above point to this data if set. otherwise - points to non-owning data
+    // the llama_ubatch pointers above point to this data if set. otherwise - point to external non-owning data
    std::shared_ptr<data_t> data;
 };

@@ -9,6 +9,7 @@
 #include "llama-model.h"

 #include <cinttypes>
+#include <cmath>
 #include <cstring>
 #include <limits>
 #include <stdexcept>
@@ -72,6 +73,43 @@ llama_context::llama_context(
        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
    }

+    if (cparams.yarn_ext_factor != 0) {
+        static auto get_mscale = [](float scale, float mscale) {
+            return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
+        };
+
+        const float factor = 1.0f / cparams.rope_freq_scale;
+
+        // ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348
+        if (hparams.rope_yarn_log_mul != 0.0f) {
+            // note: here we assume `mscale == 1.0f`
+            // TODO: start reading the actual value of mscale and handle the case where it is not 1.0f
+                  float mscale          = 1.0f;
+            const float mscale_all_dims = hparams.rope_yarn_log_mul;
+
+            // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+            // special-case DEEPSEEK v2:
+            // https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43
+            if (model.arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) {
+                mscale = mscale_all_dims;
+            }
+
+            cparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
+
+            LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n",
+                    __func__, cparams.yarn_attn_factor, mscale, mscale_all_dims);
+        } else {
+            cparams.yarn_attn_factor = get_mscale(factor, 1.0f);
+        }
+
+        // when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor:
+        // https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544
+        //
+        // ref: https://github.com/ggml-org/llama.cpp/discussions/7416
+        //      https://github.com/ggml-org/llama.cpp/pull/17945
+        cparams.yarn_attn_factor *= 1.0f / (1.0f + 0.1f * logf(factor));
+    }
+
    cparams.yarn_attn_factor *= hparams.rope_attn_factor;

    if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
@@ -1318,6 +1356,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
            // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
            LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
+            synchronize();
            buf_output = nullptr;
            logits = nullptr;
            embd = nullptr;
@@ -78,7 +78,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
        for (int i = 0; i < n_tokens; ++i) {
            const float pos = ubatch->pos[i];
            attn_scale_data[i] = std::log(
-                std::floor((pos + 1.0f) / n_attn_temp_floor_scale) + 1.0
+                std::floor((pos + f_attn_temp_offset) / n_attn_temp_floor_scale) + 1.0
            ) * f_attn_temp_scale + 1.0;
        }

@@ -1203,7 +1203,7 @@ ggml_tensor * llm_graph_context::build_inp_pos() const {
 }

 ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
-    auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
+    auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale, hparams.f_attn_temp_offset);

    auto & cur = inp->attn_scale;

@@ -132,8 +132,8 @@ public:
 // temperature tuning, used by llama4
 class llm_graph_input_attn_temp : public llm_graph_input_i {
 public:
-    llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
-        : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
+    llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale, float f_attn_temp_offset)
+        : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale), f_attn_temp_offset(f_attn_temp_offset) {}
    virtual ~llm_graph_input_attn_temp() = default;

    void set_input(const llama_ubatch * ubatch) override;
@@ -142,6 +142,7 @@ public:

    const uint32_t n_attn_temp_floor_scale;
    const float    f_attn_temp_scale;
+    const float    f_attn_temp_offset;
 };

 class llm_graph_input_pos_bucket : public llm_graph_input_i {
@@ -1,6 +1,7 @@
 #include "llama-hparams.h"

 #include "ggml.h"
+
 #include <cassert>

 void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
@@ -107,6 +107,7 @@ struct llama_hparams {
    float    rope_freq_base_train_swa;
    float    rope_freq_scale_train;
    float    rope_freq_scale_train_swa;
+
    uint32_t n_ctx_orig_yarn;
    float    rope_yarn_log_mul = 0.0f;

@@ -164,6 +165,7 @@ struct llama_hparams {
    uint32_t n_no_rope_layer_step    = 4;
    uint32_t n_attn_temp_floor_scale = 0;
    float    f_attn_temp_scale       = 0.0f;
+    float    f_attn_temp_offset      = 0.0f; // offset position index

    // gemma3n altup
    uint32_t n_altup      = 4; // altup_num_inputs
@@ -270,4 +272,3 @@ struct llama_hparams {
 };

 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
-
@@ -1369,9 +1369,10 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
                      float   freq_scale) const {
    const auto & n_ctx_orig = cparams.n_ctx_orig_yarn;

-    const auto & yarn_ext_factor = cparams.yarn_ext_factor;
-    const auto & yarn_beta_fast  = cparams.yarn_beta_fast;
-    const auto & yarn_beta_slow  = cparams.yarn_beta_slow;
+    const auto & yarn_ext_factor  = cparams.yarn_ext_factor;
+    const auto & yarn_beta_fast   = cparams.yarn_beta_fast;
+    const auto & yarn_beta_slow   = cparams.yarn_beta_slow;
+    const auto & yarn_attn_factor = cparams.yarn_attn_factor;

    const auto & n_rot     = hparams.n_rot;
    const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE
@@ -1382,12 +1383,6 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
                                ? LLAMA_ROPE_TYPE_NEOX
                                : hparams.rope_type;

-    // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly.
-    // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
-    const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2
-                                    ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale))
-                                    : cparams.yarn_attn_factor;
-
    ggml_tensor * tmp;

    if (ggml_is_quantized(cur->type)) {
@@ -668,6 +668,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                    hparams.n_swa                   = 8192;
                    hparams.n_attn_temp_floor_scale = 8192;
                    hparams.f_attn_temp_scale       = 0.1f;
+                    hparams.f_attn_temp_offset      = 1.0f;
                    hparams.set_swa_pattern(4);   // pattern: 3 chunked - 1 full
                }

@@ -1635,12 +1636,19 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                    // that have no expert_gating_func model parameter set
                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
                }
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
+
+                if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) {
+                    // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+                    // cancel the factor from the convert script
+                    hparams.rope_yarn_log_mul /= 0.1f;
+                }

                // (optional) temperature tuning - used by mistral-large
                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE,  hparams.f_attn_temp_scale,       false);
                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false);

+                hparams.f_attn_temp_offset = 0.0f;
+
                switch (hparams.n_layer) {
                    case 27: type = LLM_TYPE_16B; break;
                    case 60: type = LLM_TYPE_236B; break;
@@ -2267,9 +2275,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);

-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST,   hparams.yarn_beta_fast, false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,   hparams.yarn_beta_slow, false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     hparams.rope_yarn_log_mul, false);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast,    false);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow,    false);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,   hparams.rope_yarn_log_mul, 0.0f);
+
+                hparams.f_attn_temp_offset = 0.0f;

                // TODO: maybe add n_attn_temp_floor_scale as a separate KV?
                if (hparams.f_attn_temp_scale != 0.0f) {
@@ -2279,18 +2289,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                    }
                }

-                // TODO: this seems to be correct with the case of mscale == mscale_all_dims == 1.0f
-                //       but may need further verification with other values
-                if (hparams.rope_yarn_log_mul != 0.0f) {
-                    float factor = 1.0f / hparams.rope_freq_scale_train;
-                    float mscale = 1.0f;
-                    float mscale_all_dims = hparams.rope_yarn_log_mul;
-                    static auto get_mscale = [](float scale, float mscale) {
-                        return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
-                    };
-                    hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
-                }
-
                switch (hparams.n_layer) {
                    case 26: type = LLM_TYPE_3B; break;
                    case 34: type = LLM_TYPE_8B; break;
@@ -6806,6 +6804,7 @@ void llama_model::print_info() const {
        LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
        LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
        LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
+        LLAMA_LOG_INFO("%s: rope_yarn_log_mul= %.4f\n",   __func__, hparams.rope_yarn_log_mul);
        LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
        // MRoPE (Multi-axis Rotary Position Embedding) sections
        if (const auto & s = hparams.rope_sections; s[0] || s[1] || s[2] || s[3]) {
@@ -6869,7 +6868,6 @@ void llama_model::print_info() const {
        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
        LLAMA_LOG_INFO("%s: expert_weights_norm  = %d\n",     __func__, hparams.expert_weights_norm);
        LLAMA_LOG_INFO("%s: expert_gating_func   = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
-        LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
    }

    if (arch == LLM_ARCH_QWEN2MOE) {
@@ -1,7 +1,5 @@
 #include "models.h"

-
-
 llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
    llm_graph_context(params) {
    // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
@@ -20,9 +18,15 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr

    // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
    // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
-    const float mscale      = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
-    const float kq_scale    = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
-    const float attn_factor = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
+    // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+
+    // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor
+    GGML_ASSERT(ext_factor >= 0.0f);
+    const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));
+
+    // use the original attn_factor to pre-scale the kq_scale
+    const float mscale   = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
+    const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));

    ggml_tensor * cur;
    ggml_tensor * inpL;
@@ -20,20 +20,20 @@ int main(void) {
            std::unordered_set<std::string> seen_env_vars;
            for (const auto & opt : ctx_arg.options) {
                // check for args duplications
-                for (const auto & arg : opt.args) {
+                for (const auto & arg : opt.get_args()) {
                    if (seen_args.find(arg) == seen_args.end()) {
                        seen_args.insert(arg);
                    } else {
-                        fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg);
+                        fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg.c_str());
                        exit(1);
                    }
                }
                // check for env var duplications
-                if (opt.env) {
-                    if (seen_env_vars.find(opt.env) == seen_env_vars.end()) {
-                        seen_env_vars.insert(opt.env);
+                for (const auto & env : opt.get_env()) {
+                    if (seen_env_vars.find(env) == seen_env_vars.end()) {
+                        seen_env_vars.insert(env);
                    } else {
-                        fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", opt.env);
+                        fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", env.c_str());
                        exit(1);
                    }
                }
@@ -72,6 +72,10 @@ int main(void) {
    argv = {"binary_name", "--draft", "123"};
    assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING));

+    // negated arg
+    argv = {"binary_name", "--no-mmap"};
+    assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+

    printf("test-arg-parser: test valid usage\n\n");

@@ -115,6 +119,14 @@ int main(void) {
    assert(params.model.path == "blah.gguf");
    assert(params.cpuparams.n_threads == 1010);

+    printf("test-arg-parser: test negated environment variables\n\n");
+
+    setenv("LLAMA_ARG_MMAP", "0", true);
+    setenv("LLAMA_ARG_NO_PERF", "1", true); // legacy format
+    argv = {"binary_name"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.use_mmap == false);
+    assert(params.no_perf == true);

    printf("test-arg-parser: test environment variables being overwritten\n\n");

@@ -7652,6 +7652,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  true,  GGML_TYPE_F32, {1, 1}, 0.1f, 8.0f));
    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  true,  GGML_TYPE_F16, {1, 1}, 0.1f, 8.0f));

+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {200001, 2, 3, 1}, true,  true,  GGML_TYPE_F32, {1, 1}, 0.1f, 8.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {200001, 2, 3, 1}, true,  true,  GGML_TYPE_F16, {1, 1}, 0.1f, 8.0f));
+
    for (float max_bias : {0.0f, 8.0f}) {
        for (float scale : {1.0f, 0.1f}) {
            for (int64_t ne0 : {16, 1024}) {
@@ -7861,9 +7864,24 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 30, 30, 7, 1 }, { 8, 30, 7, 1 }));
    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 42, 42, 5, 2 }, { 10, 42, 5, 2 }));
    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 10, 64, 2, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 64, 64, 2, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 79, 79, 5, 3 }, { 417, 79, 5, 3 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 80, 80, 2, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 79, 80, 2, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 81, 80, 2, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 80, 80, 8, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 79, 80, 8, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 81, 80, 8, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 84, 84, 4, 4 }, { 32, 84, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 95, 95, 8, 8 }, { 40, 95, 8, 8 }));
    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 100, 100, 4, 4 }, { 41, 100, 4, 4 }));
    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 4 }, { 31, 128, 4, 4 }));
-    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 300, 64, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 4 }, { 32, 128, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 3, 4 }, { 32, 128, 3, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 32, 128, 4, 1 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 200, 64, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 384, 64, 4, 4 }));

    for (bool v : {false, true}) {
        for (bool circular : {false, true}) {
@@ -7956,8 +7974,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {

    for (bool with_norm : {false, true}) {
        test_cases.emplace_back(new test_topk_moe({8, 22, 1, 1}, 4, with_norm));
+        test_cases.emplace_back(new test_topk_moe({31, 22, 1, 1}, 8, with_norm));
        test_cases.emplace_back(new test_topk_moe({32, 22, 1, 1}, 8, with_norm));
+        test_cases.emplace_back(new test_topk_moe({40, 22, 1, 1}, 8, with_norm));
+        test_cases.emplace_back(new test_topk_moe({71, 22, 1, 1}, 8, with_norm));
        test_cases.emplace_back(new test_topk_moe({128, 1, 1, 1}, 128, with_norm));
+        test_cases.emplace_back(new test_topk_moe({129, 1, 1, 1}, 128, with_norm));
    }

    test_cases.emplace_back(new test_topk_moe({ 8, 22, 1, 1 }, 4, /*with_norm*/ false, /*delayed_softmax*/ true));
@@ -8064,12 +8086,13 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16416, 1, 128, {8,  1}, {4, 1}, {0, 2, 1, 3}));
    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8,  1}, {4, 1}, {0, 1, 2, 3}, 2*16416));

-    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 2 }, { 6, 64, 4, 2 }));
-    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 8, 128, 4, 1 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 32, 64, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 }));
    // qwen3next with CHUNK_SIZE 64
    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 8, 32 }, { 64, 64, 8, 32 }));
    // qwen3next with CHUNK_SIZE 128
    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 32 }, { 128, 128, 4, 32 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 256, 256, 4, 2 }, { 128, 256, 4, 2 }));

    test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_LOWER, GGML_TYPE_F32, { 256, 256, 4, 4 }));
    test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_UPPER_DIAG, GGML_TYPE_F32, { 1024, 1024, 8, 4 }));
@@ -11,19 +11,7 @@

 #define MAX_NARGS 2

-int main(int argc, char *argv[]) {
-
-    int n_threads = std::max(1, std::min(4, (int) std::thread::hardware_concurrency()));
-    int n_rounds  = 100;
-
-    if (argc > 1) {
-        n_threads = std::atoi(argv[1]);
-    }
-
-    if (argc > 2) {
-        n_rounds  = std::atoi(argv[2]);
-    }
-
+static void test_barrier(int n_threads, int n_rounds) {
    struct ggml_init_params params = {
        /* .mem_size   = */ 1024*1024*1024,
        /* .mem_buffer = */ NULL,
@@ -56,7 +44,7 @@ int main(int argc, char *argv[]) {
        exit(1);
    }

-    // Create compute plan
+    // The test runs with constant number of threads
    struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads, threadpool);

    std::vector<uint8_t> work_data(cplan.work_size);
@@ -89,6 +77,160 @@ int main(int argc, char *argv[]) {

    ggml_threadpool_free(threadpool);
    ggml_free(ctx);
+}
+
+static void test_active(int n_threads, int n_rounds) {
+    struct ggml_init_params params = {
+        /* .mem_size   = */ 1024*1024*1024,
+        /* .mem_buffer = */ NULL,
+        /* .no_alloc   = */ false,
+    };
+
+    struct ggml_context * ctx = ggml_init(params);
+
+    // Create graph
+    struct ggml_cgraph * gf = ggml_new_graph(ctx);
+
+    // Small graph with, parallel ops with barriers
+    struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,  64);
+    for (int i = 0; i < 2; i++) {
+        struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128);
+        out = ggml_mul_mat(ctx, a, out);
+
+        struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64);
+        out = ggml_mul_mat(ctx, d, out);
+    }
+
+    ggml_build_forward_expand(gf, out);
+    int n_nodes = ggml_graph_n_nodes(gf);
+
+    // Create threadpool
+    struct ggml_threadpool_params tpp  = ggml_threadpool_params_default(n_threads);
+    struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
+    if (!threadpool) {
+        fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads);
+        exit(1);
+    }
+
+    std::cerr << "graph-compute with"
+              << "\n n_threads: " << n_threads
+              << "\n   n_nodes: " << n_nodes
+              << "\n  n_rounds: " << n_rounds
+              << "\n";
+    // ggml_graph_print(gf);
+
+    // In this test we keep changing the number of threads every 4th iteration
+    // to test for race conditions in that path
+
+    for (int i=0; i < n_rounds; i++) {
+        struct ggml_cplan cplan = ggml_graph_plan(gf, (i % 4) == 0 ? 1 : n_threads, threadpool);
+
+        std::vector<uint8_t> work_data(cplan.work_size);
+        cplan.work_data = work_data.data();
+
+        ggml_graph_compute(gf, &cplan);
+    }
+
+    ggml_threadpool_free(threadpool);
+    ggml_free(ctx);
+}
+
+static void test_multi_graph(int n_threads, int n_rounds) {
+    struct ggml_init_params params = {
+        /* .mem_size   = */ 1024*1024*1024,
+        /* .mem_buffer = */ NULL,
+        /* .no_alloc   = */ false,
+    };
+
+    struct ggml_context * ctx = ggml_init(params);
+
+    // Create graphs
+    struct ggml_cgraph * gf0 = ggml_new_graph(ctx);
+    {
+        // Small graph with parallel ops with barriers
+        struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,  64);
+        for (int i = 0; i < 2; i++) {
+            struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128);
+            out = ggml_mul_mat(ctx, a, out);
+
+            struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64);
+            out = ggml_mul_mat(ctx, d, out);
+        }
+
+        ggml_build_forward_expand(gf0, out);
+    }
+
+    struct ggml_cgraph * gf1 = ggml_new_graph(ctx);
+    {
+        // Small graph with parallel ops with barriers
+        // Use larger tensors to make sure work_data size is larger than gf0
+        struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,  256);
+        for (int i = 0; i < 4; i++) {
+            struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 256, 128);
+            out = ggml_mul_mat(ctx, a, out);
+
+            struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 256);
+            out = ggml_mul_mat(ctx, d, out);
+        }
+
+        ggml_build_forward_expand(gf1, out);
+    }
+
+
+    // Create threadpool
+    struct ggml_threadpool_params tpp  = ggml_threadpool_params_default(n_threads);
+    struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
+    if (!threadpool) {
+        fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads);
+        exit(1);
+    }
+
+    std::cerr << "graph-compute with"
+              << "\n gf0 n_nodes: " << ggml_graph_n_nodes(gf0)
+              << "\n gf1 n_nodes: " << ggml_graph_n_nodes(gf1)
+              << "\n   n_threads: " << n_threads
+              << "\n    n_rounds: " << n_rounds
+              << "\n";
+
+    // In this test we keep changing the number of threads every 4th iteration
+    // and we compute two graphs back to back to test graph frequent graph switching
+
+    for (int i=0; i < n_rounds; i++) {
+        struct ggml_cplan cplan0 = ggml_graph_plan(gf0, (i % 4) == 0 ? 1 : n_threads, threadpool);
+        std::vector<uint8_t> work_data0(cplan0.work_size);
+        cplan0.work_data = work_data0.data();
+
+        struct ggml_cplan cplan1 = ggml_graph_plan(gf1, (i % 4) == 0 ? 1 : n_threads, threadpool);
+        std::vector<uint8_t> work_data1(cplan1.work_size);
+        cplan1.work_data = work_data1.data();
+
+        ggml_graph_compute(gf0, &cplan0);
+        ggml_graph_compute(gf1, &cplan1);
+    }
+
+    ggml_threadpool_free(threadpool);
+    ggml_free(ctx);
+}
+
+
+int main(int argc, char *argv[]) {
+
+    int n_threads = std::max(1, std::min(4, (int) std::thread::hardware_concurrency()));
+    int n_rounds  = 100;
+
+    if (argc > 1) {
+        n_threads = std::atoi(argv[1]);
+    }
+
+    if (argc > 2) {
+        n_rounds  = std::atoi(argv[2]);
+    }
+
+    test_barrier(n_threads, n_rounds);
+
+    test_active(n_threads,  n_rounds * 100);
+
+    test_multi_graph(n_threads,  n_rounds * 10);

    return 0;
 }
@@ -86,6 +86,10 @@ static void sigint_handler(int signo) {
 int main(int argc, char ** argv) {
    common_params params;
    g_params = &params;
+
+    // disable jinja by default
+    params.use_jinja = false;
+
    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) {
        return 1;
    }
@@ -137,13 +141,15 @@ int main(int argc, char ** argv) {

    // load the model and apply lora adapter, if any
    LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
-    common_init_result llama_init = common_init_from_params(params);

-    model = llama_init.model.get();
-    ctx = llama_init.context.get();
+    auto llama_init = common_init_from_params(params);

-    if (model == NULL) {
-        LOG_ERR("%s: error: unable to load model\n", __func__);
+    ctx   = llama_init->context();
+    model = llama_init->model();
+    smpl  = llama_init->sampler(0);
+
+    if (ctx == NULL) {
+        LOG_ERR("%s: error: unable to create context\n", __func__);
        return 1;
    }

@@ -470,12 +476,6 @@ int main(int argc, char ** argv) {
        }
    }

-    smpl = common_sampler_init(model, sparams);
-    if (!smpl) {
-        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
-        return 1;
-    }
-
    LOG_INF("sampler seed: %u\n",     common_sampler_get_seed(smpl));
    LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
    LOG_INF("sampler chain: %s\n",    common_sampler_print(smpl).c_str());
@@ -989,8 +989,6 @@ int main(int argc, char ** argv) {
    LOG("\n\n");
    common_perf_print(ctx, smpl);

-    common_sampler_free(smpl);
-
    llama_backend_free();

    ggml_threadpool_free_fn(threadpool);
@@ -419,10 +419,10 @@ int main(int argc, char ** argv) {
    llama_numa_init(params.numa);

    // load the model to get hparams
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    // int n_ctx = llama_n_ctx(ctx);
    int n_layers = llama_model_n_layer(model);
@@ -1265,10 +1265,10 @@ int main(int argc, char ** argv) {
    params.warmup = false;

    // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);

-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();

    if (model == nullptr || ctx == nullptr) {
        LOG_ERR("%s : failed to init\n", __func__);
@@ -6,11 +6,25 @@ add_library(mtmd
            mtmd.cpp
            mtmd-audio.cpp
            mtmd.h
+            mtmd-helper.cpp
+            mtmd-helper.h
            clip.cpp
            clip.h
            clip-impl.h
-            mtmd-helper.cpp
-            mtmd-helper.h
+            clip-model.h
+            clip-graph.h
+            models/models.h
+            models/cogvlm.cpp
+            models/internvl.cpp
+            models/kimivl.cpp
+            models/llama4.cpp
+            models/llava.cpp
+            models/minicpmv.cpp
+            models/pixtral.cpp
+            models/qwen2vl.cpp
+            models/qwen3vl.cpp
+            models/siglip.cpp
+            models/whisper-enc.cpp
            )

 set_target_properties(mtmd PROPERTIES
@@ -53,6 +67,15 @@ if (TARGET BUILD_INFO)
    add_dependencies(mtmd-helper BUILD_INFO)
 endif()

+# if mtmd is linked against common, we throw an error
+if (TARGET mtmd)
+    get_target_property(libs mtmd LINK_LIBRARIES)
+    if (libs AND "common" IN_LIST libs)
+        message(FATAL_ERROR "mtmd is designed to be a public library.\n"
+                            "It must not link against common")
+    endif()
+endif()
+
 add_executable(llama-llava-cli    deprecation-warning.cpp)
 add_executable(llama-gemma3-cli   deprecation-warning.cpp)
 add_executable(llama-minicpmv-cli deprecation-warning.cpp)
@@ -0,0 +1,115 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-cpp.h"
+#include "clip.h"
+#include "clip-impl.h"
+#include "clip-model.h"
+
+#include <vector>
+#include <functional>
+
+struct clip_graph {
+    const clip_model & model;
+    const clip_hparams & hparams;
+    projector_type proj_type;
+
+    // we only support single image per batch
+    const clip_image_f32 & img;
+
+    const int patch_size;
+    const int n_patches_x;
+    const int n_patches_y;
+    const int n_patches;
+    const int n_embd;
+    const int n_head;
+    const int d_head;
+    const int n_layer;
+    const int n_mmproj_embd;
+    const float eps;
+    const float kq_scale;
+    const clip_flash_attn_type flash_attn_type;
+
+    // for debugging
+    const bool debug_graph;
+    std::vector<ggml_tensor *> & debug_print_tensors;
+
+    ggml_context_ptr ctx0_ptr;
+    ggml_context * ctx0;
+    ggml_cgraph * gf;
+
+    clip_graph(clip_ctx * ctx, const clip_image_f32 & img);
+
+    virtual ~clip_graph() = default;
+    virtual ggml_cgraph * build() = 0;
+
+    //
+    // utility functions
+    //
+    void cb(ggml_tensor * cur0, const char * name, int il) const;
+
+    // siglip2 naflex
+    ggml_tensor * resize_position_embeddings();
+
+    // build vision transformer (ViT) cgraph
+    // this function should cover most of the models
+    // if your model has specific features, you should probably duplicate this function
+    ggml_tensor * build_vit(
+                ggml_tensor * inp,
+                int64_t n_pos,
+                norm_type norm_t,
+                ffn_op_type ffn_t,
+                ggml_tensor * learned_pos_embd,
+                std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos);
+
+    // build the input after conv2d (inp_raw --> patches)
+    // returns tensor with shape [n_embd, n_patches]
+    ggml_tensor * build_inp();
+
+    ggml_tensor * build_inp_raw(int channels = 3);
+
+    ggml_tensor * build_norm(
+            ggml_tensor * cur,
+            ggml_tensor * mw,
+            ggml_tensor * mb,
+            norm_type type,
+            float norm_eps,
+            int il) const;
+
+    ggml_tensor * build_ffn(
+            ggml_tensor * cur,
+            ggml_tensor * up,
+            ggml_tensor * up_b,
+            ggml_tensor * gate,
+            ggml_tensor * gate_b,
+            ggml_tensor * down,
+            ggml_tensor * down_b,
+            ffn_op_type type_op,
+            int il) const;
+
+    ggml_tensor * build_attn(
+            ggml_tensor * wo,
+            ggml_tensor * wo_b,
+            ggml_tensor * q_cur,
+            ggml_tensor * k_cur,
+            ggml_tensor * v_cur,
+            ggml_tensor * kq_mask,
+            float kq_scale,
+            int il) const;
+
+    // implementation of the 2D RoPE without adding a new op in ggml
+    // this is not efficient (use double the memory), but works on all backends
+    // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065
+    ggml_tensor * build_rope_2d(
+        ggml_context * ctx0,
+        ggml_tensor * cur,
+        ggml_tensor * pos_a, // first half
+        ggml_tensor * pos_b, // second half
+        const float freq_base,
+        const bool interleave_freq
+    );
+
+    // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
+    // support dynamic resolution
+    ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor);
+};
@@ -1,3 +1,5 @@
+#pragma once
+
 #include "ggml.h"
 #include "gguf.h"
 #include "clip.h"
@@ -13,6 +15,8 @@

 // Internal header for clip.cpp

+#define MTMD_INTERNAL_HEADER
+
 #define KEY_FTYPE               "general.file_type"
 #define KEY_NAME                "general.name"
 #define KEY_DESCRIPTION         "general.description"
@@ -132,6 +136,10 @@
 // align x to upper multiple of n
 #define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))

+// forward declaration
+// TODO: improve this later
+struct clip_ctx;
+
 enum projector_type {
    PROJECTOR_TYPE_MLP,
    PROJECTOR_TYPE_MLP_NORM,
@@ -0,0 +1,279 @@
+#pragma once
+
+#include "ggml.h"
+#include "clip.h"
+#include "clip-impl.h"
+
+#include <vector>
+#include <unordered_set>
+#include <cstdint>
+#include <cmath>
+
+enum ffn_op_type {
+    FFN_GELU,
+    FFN_GELU_ERF,
+    FFN_SILU,
+    FFN_GELU_QUICK,
+};
+
+enum norm_type {
+    NORM_TYPE_NORMAL,
+    NORM_TYPE_RMS,
+};
+
+enum patch_merge_type {
+    PATCH_MERGE_FLAT,
+    PATCH_MERGE_SPATIAL_UNPAD,
+};
+
+struct clip_hparams {
+    int32_t image_size = 0;
+    int32_t patch_size = 0;
+    int32_t n_embd = 0;
+    int32_t n_ff = 0;
+    int32_t projection_dim = 0;
+    int32_t n_head = 0;
+    int32_t n_layer = 0;
+    // idefics3
+    int32_t image_longest_edge = 0;
+    int32_t image_min_pixels = -1;
+    int32_t image_max_pixels = -1;
+    int32_t n_merge = 0; // number of patch merges **per-side**
+
+    float image_mean[3];
+    float image_std[3];
+
+    // for models using dynamic image size, we need to have a smaller image size to warmup
+    // otherwise, user will get OOM everytime they load the model
+    int32_t warmup_image_size = 0;
+    int32_t warmup_audio_size = 3000;
+
+    ffn_op_type ffn_op = FFN_GELU;
+
+    patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT;
+
+    float eps = 1e-6;
+    float rope_theta = 0.0;
+
+    std::vector<clip_image_size> image_res_candidates; // for llava-uhd style models
+    int32_t image_crop_resolution;
+    std::unordered_set<int32_t> vision_feature_layer;
+    int32_t attn_window_size = 0;
+    int32_t n_wa_pattern = 0;
+
+    // audio
+    int32_t n_mel_bins = 0; // whisper preprocessor
+    int32_t proj_stack_factor = 0; // ultravox
+
+    // legacy
+    bool has_llava_projector = false;
+    int minicpmv_version = 0;
+    int32_t minicpmv_query_num = 0;         // MiniCPM-V query number
+
+    // custom value provided by user, can be undefined if not set
+    int32_t custom_image_min_tokens = -1;
+    int32_t custom_image_max_tokens = -1;
+
+    void set_limit_image_tokens(int n_tokens_min, int n_tokens_max) {
+        const int cur_merge = n_merge == 0 ? 1 : n_merge;
+        const int patch_area = patch_size * patch_size * cur_merge * cur_merge;
+        image_min_pixels = (custom_image_min_tokens > 0 ? custom_image_min_tokens : n_tokens_min) * patch_area;
+        image_max_pixels = (custom_image_max_tokens > 0 ? custom_image_max_tokens : n_tokens_max) * patch_area;
+        warmup_image_size = static_cast<int>(std::sqrt(image_max_pixels));
+    }
+
+    void set_warmup_n_tokens(int n_tokens) {
+        int n_tok_per_side = static_cast<int>(std::sqrt(n_tokens));
+        GGML_ASSERT(n_tok_per_side * n_tok_per_side == n_tokens && "n_tokens must be n*n");
+        const int cur_merge = n_merge == 0 ? 1 : n_merge;
+        warmup_image_size = n_tok_per_side * patch_size * cur_merge;
+        // TODO: support warmup size for custom token numbers
+    }
+};
+
+struct clip_layer {
+    // attention
+    ggml_tensor * k_w = nullptr;
+    ggml_tensor * k_b = nullptr;
+    ggml_tensor * q_w = nullptr;
+    ggml_tensor * q_b = nullptr;
+    ggml_tensor * v_w = nullptr;
+    ggml_tensor * v_b = nullptr;
+    ggml_tensor * qkv_w = nullptr;
+    ggml_tensor * qkv_b = nullptr;
+
+    ggml_tensor * o_w = nullptr;
+    ggml_tensor * o_b = nullptr;
+
+    ggml_tensor * k_norm = nullptr;
+    ggml_tensor * q_norm = nullptr;
+
+    // layernorm 1
+    ggml_tensor * ln_1_w = nullptr;
+    ggml_tensor * ln_1_b = nullptr;
+
+    ggml_tensor * ff_up_w = nullptr;
+    ggml_tensor * ff_up_b = nullptr;
+    ggml_tensor * ff_gate_w = nullptr;
+    ggml_tensor * ff_gate_b = nullptr;
+    ggml_tensor * ff_down_w = nullptr;
+    ggml_tensor * ff_down_b = nullptr;
+
+    // layernorm 2
+    ggml_tensor * ln_2_w = nullptr;
+    ggml_tensor * ln_2_b = nullptr;
+
+    // layer scale (no bias)
+    ggml_tensor * ls_1_w = nullptr;
+    ggml_tensor * ls_2_w = nullptr;
+
+    // qwen3vl deepstack merger
+    ggml_tensor * deepstack_norm_w = nullptr;
+    ggml_tensor * deepstack_norm_b = nullptr;
+    ggml_tensor * deepstack_fc1_w = nullptr;
+    ggml_tensor * deepstack_fc1_b = nullptr;
+    ggml_tensor * deepstack_fc2_w = nullptr;
+    ggml_tensor * deepstack_fc2_b = nullptr;
+
+    bool has_deepstack() const {
+        return deepstack_fc1_w != nullptr;
+    }
+};
+
+struct clip_model {
+    clip_modality modality = CLIP_MODALITY_VISION;
+    projector_type proj_type = PROJECTOR_TYPE_MLP;
+    clip_hparams hparams;
+
+    // embeddings
+    ggml_tensor * class_embedding = nullptr;
+    ggml_tensor * patch_embeddings_0 = nullptr;
+    ggml_tensor * patch_embeddings_1 = nullptr;  // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
+    ggml_tensor * patch_bias = nullptr;
+    ggml_tensor * position_embeddings = nullptr;
+
+    ggml_tensor * pre_ln_w = nullptr;
+    ggml_tensor * pre_ln_b = nullptr;
+
+    std::vector<clip_layer> layers;
+
+    int32_t n_deepstack_layers = 0; // used by Qwen3-VL, calculated from clip_layer
+
+    ggml_tensor * post_ln_w;
+    ggml_tensor * post_ln_b;
+
+    ggml_tensor * projection; // TODO: rename it to fc (fully connected layer)
+    ggml_tensor * mm_fc_w;
+    ggml_tensor * mm_fc_b;
+
+    // LLaVA projection
+    ggml_tensor * mm_input_norm_w = nullptr;
+    ggml_tensor * mm_input_norm_b = nullptr;
+    ggml_tensor * mm_0_w = nullptr;
+    ggml_tensor * mm_0_b = nullptr;
+    ggml_tensor * mm_2_w = nullptr;
+    ggml_tensor * mm_2_b = nullptr;
+
+    ggml_tensor * image_newline = nullptr;
+
+    // Yi type models with mlp+normalization projection
+    ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4
+    ggml_tensor * mm_1_b = nullptr;
+    ggml_tensor * mm_3_w = nullptr;
+    ggml_tensor * mm_3_b = nullptr;
+    ggml_tensor * mm_4_w = nullptr;
+    ggml_tensor * mm_4_b = nullptr;
+
+    // GLMV-Edge projection
+    ggml_tensor * mm_model_adapter_conv_w = nullptr;
+    ggml_tensor * mm_model_adapter_conv_b = nullptr;
+
+    // MobileVLM projection
+    ggml_tensor * mm_model_mlp_1_w = nullptr;
+    ggml_tensor * mm_model_mlp_1_b = nullptr;
+    ggml_tensor * mm_model_mlp_3_w = nullptr;
+    ggml_tensor * mm_model_mlp_3_b = nullptr;
+    ggml_tensor * mm_model_block_1_block_0_0_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_0_1_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_0_1_b = nullptr;
+    ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr;
+    ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr;
+    ggml_tensor * mm_model_block_1_block_2_0_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_2_1_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_2_1_b = nullptr;
+    ggml_tensor * mm_model_block_2_block_0_0_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_0_1_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_0_1_b = nullptr;
+    ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr;
+    ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr;
+    ggml_tensor * mm_model_block_2_block_2_0_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_2_1_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_2_1_b = nullptr;
+
+    // MobileVLM_V2 projection
+    ggml_tensor * mm_model_mlp_0_w = nullptr;
+    ggml_tensor * mm_model_mlp_0_b = nullptr;
+    ggml_tensor * mm_model_mlp_2_w = nullptr;
+    ggml_tensor * mm_model_mlp_2_b = nullptr;
+    ggml_tensor * mm_model_peg_0_w = nullptr;
+    ggml_tensor * mm_model_peg_0_b = nullptr;
+
+    // MINICPMV projection
+    ggml_tensor * mm_model_pos_embed_k = nullptr;
+    ggml_tensor * mm_model_query = nullptr;
+    ggml_tensor * mm_model_proj = nullptr;
+    ggml_tensor * mm_model_kv_proj = nullptr;
+    ggml_tensor * mm_model_attn_q_w = nullptr;
+    ggml_tensor * mm_model_attn_q_b = nullptr;
+    ggml_tensor * mm_model_attn_k_w = nullptr;
+    ggml_tensor * mm_model_attn_k_b = nullptr;
+    ggml_tensor * mm_model_attn_v_w = nullptr;
+    ggml_tensor * mm_model_attn_v_b = nullptr;
+    ggml_tensor * mm_model_attn_o_w = nullptr;
+    ggml_tensor * mm_model_attn_o_b = nullptr;
+    ggml_tensor * mm_model_ln_q_w = nullptr;
+    ggml_tensor * mm_model_ln_q_b = nullptr;
+    ggml_tensor * mm_model_ln_kv_w = nullptr;
+    ggml_tensor * mm_model_ln_kv_b = nullptr;
+    ggml_tensor * mm_model_ln_post_w = nullptr;
+    ggml_tensor * mm_model_ln_post_b = nullptr;
+
+    // gemma3
+    ggml_tensor * mm_input_proj_w = nullptr;
+    ggml_tensor * mm_soft_emb_norm_w = nullptr;
+
+    // pixtral
+    ggml_tensor * token_embd_img_break = nullptr;
+    ggml_tensor * mm_patch_merger_w = nullptr;
+
+    // ultravox / whisper encoder
+    ggml_tensor * conv1d_1_w = nullptr;
+    ggml_tensor * conv1d_1_b = nullptr;
+    ggml_tensor * conv1d_2_w = nullptr;
+    ggml_tensor * conv1d_2_b = nullptr;
+    ggml_tensor * mm_norm_pre_w = nullptr;
+    ggml_tensor * mm_norm_mid_w = nullptr;
+
+    // cogvlm
+    ggml_tensor * mm_post_fc_norm_w = nullptr;
+    ggml_tensor * mm_post_fc_norm_b = nullptr;
+    ggml_tensor * mm_h_to_4h_w = nullptr;
+    ggml_tensor * mm_gate_w = nullptr;
+    ggml_tensor * mm_4h_to_h_w = nullptr;
+    ggml_tensor * mm_boi = nullptr;
+    ggml_tensor * mm_eoi = nullptr;
+
+    bool audio_has_avgpool() const {
+        return proj_type == PROJECTOR_TYPE_QWEN2A
+            || proj_type == PROJECTOR_TYPE_VOXTRAL;
+    }
+
+    bool audio_has_stack_frames() const {
+        return proj_type == PROJECTOR_TYPE_ULTRAVOX
+            || proj_type == PROJECTOR_TYPE_VOXTRAL;
+    }
+};
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Ruben Ortlam	9e6649ecf2	vulkan: fix mul_mat_vec_iq1_s formatting (#18026 )	2025-12-14 14:52:46 +01:00
Xuan-Son Nguyen	0759b09c90	graph: add f_attn_temp_offset (#18025 )	2025-12-14 13:05:59 +01:00
Georgi Gerganov	254098a279	common : refactor common_sampler + grammar logic changes (#17937 ) * common : refactor common_sampler + grammar logic changes * tests : increase max_tokens to get needed response * batched : fix uninitialized samplers	2025-12-14 10:11:13 +02:00
Jeff Bolz	3238b1400c	vulkan: Fix data race/hang in scalar/cm1 flash attention (#17887 )	2025-12-14 09:00:00 +01:00
lovedheart	4722671641	vulkan: improve mul_mat_vec_iq1_s speed (#17874 )	2025-12-14 08:47:49 +01:00
Eve	d15d177f43	vulkan: faster q6_k matmul (#17813 ) * q6_k faster mul mat * 8 values * fix comment * switch to two at a time * start ci for .glsl files	2025-12-14 08:29:37 +01:00
Georgi Gerganov	77ad8542bd	model-conversion : cast logits to float32 (#18009 )	2025-12-14 08:58:13 +02:00
Georgi Gerganov	609a2d0268	models : fix YaRN regression + consolidate logic (#18006 ) * models : fix YaRN regression + consolidate logic * cont : fix the fix * cont : remove header * cont : add header	2025-12-14 08:34:56 +02:00
Georgi Gerganov	a63cbafbbc	ggml : arm repack fix build	2025-12-14 08:33:51 +02:00
Georgi Gerganov	0e59224990	sync : ggml	2025-12-14 08:33:51 +02:00
Georgi Gerganov	71fdcf0616	ggml : arm repack fix build (whisper/0)	2025-12-14 08:33:51 +02:00
Congcong Cai	615655aafe	cmake : set `CMAKE_RUNTIME_OUTPUT_DIRECTORY` for non standalone build (ggml/1394) Some backend depends on CMAKE_RUNTIME_OUTPUT_DIRECTORY to create temporary file like metal backened. Missing CMAKE_RUNTIME_OUTPUT_DIRECTORY will cause some cmake error like permission denied (try to copy file to root). This PR wants to setup a default path for CMAKE_RUNTIME_OUTPUT_DIRECTORY when it does not exist.	2025-12-14 08:33:51 +02:00
Xuan-Son Nguyen	c00ff929dc	scripts: add script to compare logprobs of llama.cpp against other frameworks (#17947 ) * scripts: add script to compare logits of llama.cpp against other frameworks * accept custom prompt file * fix code style * clarify endpoint * fix displaying * use abs for diff * fix vllm case * rm output file * rename to compare-logprobs * add "pattern"	2025-12-13 22:33:29 +01:00
Sergey Fedorov	4ed2bae50d	server-models.cpp: add missing <filesystem> (#18000 ) Fixes: https://github.com/ggml-org/llama.cpp/issues/17999	2025-12-13 22:02:43 +01:00
Jeff Bolz	5266379bca	llama_context: synchronize before reallocating output buffer (#17974 )	2025-12-13 09:19:51 -06:00
Xuan-Son Nguyen	4d5ae24c0a	arg: fix common_params_parse not accepting negated arg (#17991 )	2025-12-13 12:53:37 +01:00
Gustavo Rocha Dias	66ba51252e	cmake: correct scope - link ws2_32 for MinGW/w64devkit builds in cpp-httplib (#17972 ) * fix - w64devkit build * fix - w64devkit build private scope	2025-12-13 12:46:36 +01:00
Jeff Bolz	36255a2268	vulkan: support get_rows for i32 (#17941 )	2025-12-13 10:12:53 +01:00
Jeff Bolz	3229a23fa6	vulkan: support GGML_OP_DIAG (#17893 )	2025-12-13 10:07:49 +01:00
Jeff Bolz	303f8615e9	vulkan: Multi-pass softmax for large number of cols (#17892 ) When the number of cols is large, split each row across multiple workgroups. There are three phases that communicate partial results through temp buffers: (1) compute max partials (2) take max of partials, compute sum(exp(x-max)) partials (3) sum partials, compute scaled result	2025-12-13 10:04:29 +01:00
Georgi Gerganov	3c6391e748	speculative-simple : free batch on exit (#17985 )	2025-12-13 09:48:34 +02:00
Sigbjørn Skjæret	8e4d678528	common : skip model validation when --completion-bash is requested (#17975 )	2025-12-13 08:40:50 +01:00
Jeff Bolz	07a10c1090	vulkan: Allow non-pow2 n_experts in topk_moe (#17872 )	2025-12-13 08:40:04 +01:00
Sigbjørn Skjæret	2bc94e7928	add llama-completion to completion-bash executables (#17976 )	2025-12-13 08:35:50 +01:00
Daniel Bevenius	fd1085ffb7	model-conversion : use CONVERTED_MODEL value for converted model [no ci] (#17984 ) * model-conversion : use CONVERTED_MODEL value for converted model [no ci] This commit updates the model verification scripts to use the CONVERTED_MODEL environment variable instead of using the MODEL_PATH (the original model path) as the basis for the converted model file name. The motivation for this that currently if the converted model file name differs from the original model directory/name the verification scripts will look for the wrong .bin files that were generating when running the models. For example, the following steps were not possible: ```console (venv) $ huggingface-cli download google/gemma-3-270m-it --local-dir ggml-org/gemma-3-270m (venv) $ python3 convert_hf_to_gguf.py ggml-org/gemma-3-270m --outfile test-bf16.gguf --outtype bf16 (venv) $ cd examples/model-conversion/ (venv) $ export MODEL_PATH=../../ggml-org/gemma-3-270m (venv) $ export CONVERTED_MODEL=../../test-bf16.gguf (venv) $ make causal-verify-logits ... Data saved to data/llamacpp-test-bf16.bin Data saved to data/llamacpp-test-bf16.txt Error: llama.cpp logits file not found: data/llamacpp-gemma-3-270m.bin Please run scripts/run-converted-model.sh first to generate this file. make: *** [Makefile:62: causal-verify-logits] Error 1 ``` With the changes in this commit, the above steps will now work as expected.	2025-12-13 08:34:26 +01:00
Xuan-Son Nguyen	380b4c984e	common: support negated args (#17919 ) * args: support negated args * update docs * fix typo * add more neg options * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * rm duplicated arg * fix LLAMA_ARG_NO_HOST * add test --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>	2025-12-12 23:58:53 +01:00
Xuan-Son Nguyen	e39a2ce66d	clip: move model cgraphs into their own files (#17965 ) * clip: move model cgraphs into their own files * more explicit enums * fix linux build * fix naming * missing headers * nits: add comments for contributors	2025-12-12 21:14:48 +01:00
jiahao su	a8c7f33d79	ci : change the cann version and the container pull method (#17953 ) fix error format Update build.yml Remove unnecessary zip files fix update	2025-12-12 20:43:00 +01:00
Sigbjørn Skjæret	b7f5f46e03	docker : include legacy llama-completion binary (#17964 )	2025-12-12 19:39:23 +01:00
Johannes Gäßler	482211438d	CUDA: fix overflow in MMA kernel without stream-k (#17939 )	2025-12-12 17:43:58 +01:00
Georgi Gerganov	7bed317f53	models : fix the attn_factor for mistral3 graphs + improve consistency (#17945 ) * models : fix the attn_factor for mistral3 graphs * cont : rework attn_factor correction logic * cont : make deepseek2 consistent * cont : add TODO * cont : special-case DSv2 * cont : revert Mistral 3 Large changes * cont : fix DS2 to use the original attn_factor * cont : minor comments	2025-12-12 17:12:40 +02:00
Sigbjørn Skjæret	dcb7d17758	cann : fix ops broken by circular padding guard (#17825 )	2025-12-12 15:49:27 +01:00
ixgbe	51604435e8	ggml-cpu : fix RISC-V Q4_0 repack select and RVV feature reporting (#17951 ) * ggml-cpu:fix RISC-V Q4_0 repack select and RVV feature reporting Signed-off-by: Wang Yang <yangwang@iscas.ac.cn> * using the name VLEN instead of CNT * Update ggml/include/ggml-cpu.h --------- Signed-off-by: Wang Yang <yangwang@iscas.ac.cn> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2025-12-12 16:26:03 +02:00
Xuan-Son Nguyen	17158965ac	mtmd: explicitly forbidden inclusion of private header and libcommon (#17946 )	2025-12-12 15:16:06 +01:00
Aleksander Grygier	12280ae905	webui: Fix parsing non-LaTeX occurrencies of `\(` or `\)` (#17810 ) * fix: Improve latex protection logic to prevent turning non-latex `\(` into `$` * chore: update webui build output	2025-12-12 15:13:36 +01:00
Xuan-Son Nguyen	54a0fee4b7	arg: add -mm and -mmu as short form of --mmproj and --mmproj-url (#17958 ) * arg: add -mm and -mmu as short form of --mmproj and --mmproj-url * correct order * update docs	2025-12-12 14:06:06 +01:00
Daniel Bevenius	dada4c846d	model-conversion : remove max diff check in compare-logits [no ci] (#17954 ) This commit removes the maximum difference check from the compare-logits.py which would stop early if the difference between the logits exceeded a threshold. The motivation for removing this is that it can be useful to be able to get the complete log for debugging/reporting purposes.	2025-12-12 13:25:16 +01:00
Adrien Gallouët	b8ee22cfde	common : add minimalist multi-thread progress bar (#17602 ) Signed-off-by: Adrien Gallouët <angt@huggingface.co>	2025-12-12 12:44:35 +01:00
Gustavo Rocha Dias	2eaa2c65cb	cmake: link ws2_32 for MinGW/w64devkit builds in cpp-httplib (#17949 )	2025-12-12 12:02:28 +01:00
yulo	c33a58bced	HIP: enable mmf for RDNA3 (#17879 ) * enable mmf for RDNA3 * disable mmf for some shape * move some mmvf to mmf * more mmfv to mmf * 3 is good in mmvf --------- Co-authored-by: zhang hui <you@example.com>	2025-12-12 11:34:33 +01:00
Pascal	a81a569577	Add a search field on model selector / improve mobile display (#17765 ) * webui: add search field to model selector and fixes mobile viewport overflow * webui: simplify model search style and code * refacor: Search Input component & consistent UI for Models Selector search * feat: Use Popover component + improve interactions * fix: Fetching props for only loaded models in ROUTER mode * webui: prevent models selector popover from overflowing viewport Use Floating UI's auto-positioning with 50dvh height limit and proper collision detection instead of forcing top positioning. Fixes overflow on desktop and mobile keyboard issues * webui: keep search field near trigger in models selector Place search at the 'near end' (closest to trigger) by swapping layout with CSS flexbox order based on popover direction. Prevents input from moving during typing as list shrinks * chore: update webui build output --------- Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>	2025-12-11 18:21:21 +01:00
Piotr Wilkin (ilintar)	53ecd4fdb9	SOLVE_TRI extension to more dimensions (#17793 ) * Extended TRI * Fix whitespace * chore: update webui build output * Just use cuBLAS for everything... * Merge both versions * Remove incorrect imports causing failures for CI * Still failing... remove all direct cublas imports and rely on common imports from "common.cuh" * Defines for hipBlas * Aaaand MUSA defines... * I hate this job... * Stupid typo... * Update ggml/src/ggml-cuda/solve_tri.cu Co-authored-by: Johannes Gäßler <johannesg@5d6.de> --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>	2025-12-11 17:20:43 +01:00
Georgi Gerganov	c6f6e4f96a	ggml-alloc : fix reuse-parent logic for misaligned sizes (#17884 )	2025-12-11 14:30:10 +02:00
Georgi Gerganov	d9f8f60618	batch : fix sequence id ownership (#17915 ) * batch : fix sequence id ownage * cont : reduce allocations	2025-12-11 14:29:47 +02:00
Yuichiro Utsumi	e4ae383317	docs: use port 8080 in Docker examples (#17903 )	2025-12-11 17:12:07 +08:00
nullname	34ce48d97a	ggml-hexagon: fix `rope` failure at `test-backend-ops` (#17565 ) * fix test failure * fix: correct scaling calculations in rope_cache_init * fix: optimize element copying in rope_hex_f32 using memcpy * fix: optimize loop boundaries in rope_hex_f32 for better performance * feat: add profiling macros for performance measurement in operations	2025-12-10 14:45:43 -08:00
Sigbjørn Skjæret	45e350e3d3	ci: fix riscv64-native build (#17916 )	2025-12-10 23:24:31 +01:00
Xuan-Son Nguyen	c6b2c9310c	mtmd: some small clean up (#17909 ) * clip: add support for fused qkv in build_vit * use bulid_ffn whenever possible * fix internvl * mtmd-cli: move image to beginning * test script: support custom args	2025-12-10 22:20:06 +01:00
Xuan-Son Nguyen	34a6d86982	cli: enable jinja by default (#17911 ) * cli: enable jinja by default * Update common/arg.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>	2025-12-10 22:19:42 +01:00
Pascal	f32ca51bfe	server: add presets (config) when using multiple models (#17859 ) * llama-server: recursive GGUF loading Replace flat directory scan with recursive traversal using std::filesystem::recursive_directory_iterator. Support for nested vendor/model layouts (e.g. vendor/model/.gguf). Model name now reflects the relative path within --models-dir instead of just the filename. Aggregate files by parent directory via std::map before constructing local_model server : router config POC (INI-based per-model settings) * server: address review feedback from @aldehir and @ngxson PEG parser usage improvements: - Simplify parser instantiation (remove arena indirection) - Optimize grammar usage (ws instead of zero_or_more, remove optional wrapping) - Fix last line without newline bug (+ operator instead of <<) - Remove redundant end position check Feature scope: - Remove auto-reload feature (will be separate PR per @ngxson) - Keep config.ini auto-creation and template generation - Preserve per-model customization logic Co-authored-by: aldehir <aldehir@users.noreply.github.com> Co-authored-by: ngxson <ngxson@users.noreply.github.com> * server: adopt aldehir's line-oriented PEG parser Complete rewrite of INI parser grammar and visitor: - Use p.chars(), p.negate(), p.any() instead of p.until() - Support end-of-line comments (key=value # comment) - Handle EOF without trailing newline correctly - Strict identifier validation ([a-zA-Z_][a-zA-Z0-9_.-]) - Simplified visitor (no pending state, no trim needed) - Grammar handles whitespace natively via eol rule Business validation preserved: - Reject section names starting with LLAMA_ARG_ - Accept only keys starting with LLAMA_ARG_* - Require explicit section before key-value pairs Co-authored-by: aldehir <aldehir@users.noreply.github.com> * server: fix CLI/env duplication in child processes Children now receive minimal CLI args (executable, model, port, alias) instead of inheriting all router args. Global settings pass through LLAMA_ARG_* environment variables only, eliminating duplicate config warnings. Fixes: Router args like -ngl, -fa were passed both via CLI and env, causing 'will be overwritten' warnings on every child spawn * add common/preset.cpp * fix compile * cont * allow custom-path models * add falsey check * server: fix router model discovery and child process spawning - Sanitize model names: replace / and \ with _ for display - Recursive directory scan with relative path storage - Convert relative paths to absolute when spawning children - Filter router control args from child processes - Refresh args after port assignment for correct port value - Fallback preset lookup for compatibility - Fix missing argv[0]: store server binary path before base_args parsing * Revert "server: fix router model discovery and child process spawning" This reverts commit e3832b42eeea7fcb108995966c7584479f745857. * clarify about "no-" prefix * correct render_args() to include binary path * also remove arg LLAMA_ARG_MODELS_PRESET for child * add co-author for ini parser code Co-authored-by: aldehir <hello@alde.dev> * also set LLAMA_ARG_HOST * add CHILD_ADDR * Remove dead code --------- Co-authored-by: aldehir <aldehir@users.noreply.github.com> Co-authored-by: ngxson <ngxson@users.noreply.github.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: aldehir <hello@alde.dev>	2025-12-10 22:18:21 +01:00
Max Krasnyansky	e1f4921980	Fix race conditions in threadpool when dealing with dynamic/frequent n_threads changes (#17748 ) * tests: update barrier test to check for race condition in active threads * cpu: combine n_graph and n_threads into a single atomic update * tests: add multi-graph test for test_barrier	2025-12-10 12:32:23 -08:00