server : add arg for disabling prompt caching (#18776)

* server : add arg for disabling prompt caching

Disabling prompt caching is useful for clients who are restricted to
sending only OpenAI-compat requests and want deterministic
responses.

* address review comments

* address review comments

.github/workflows/build.yml

@@ -1463,12 +1463,14 @@ jobs:
             "${{ steps.cann-image.outputs.image }}" \
             bash -lc '
               set -e
-              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel
               yum clean all && rm -rf /var/cache/yum
               git config --global --add safe.directory "/workspace"
               export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
               cmake -S . -B build \
                   -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+                  -DLLAMA_CURL=OFF \
+                  -DLLAMA_OPENSSL=ON \
                   -DGGML_CANN=on \
                   -DSOC_TYPE=${SOC_TYPE}
               cmake --build build -j $(nproc)

CMakeLists.txt

@@ -182,6 +182,9 @@ if (NOT MSVC)
     endif()
 endif()
 
+include("cmake/license.cmake")
+license_add_file("llama.cpp" "LICENSE")
+
 #
 # 3rd-party
 #
@@ -235,6 +238,19 @@ if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS)
     add_subdirectory(tools)
 endif()
 
+# Automatically add all files from the 'licenses' directory
+file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")
+
+foreach(FILE_PATH ${EXTRA_LICENSES})
+    get_filename_component(FILE_NAME "${FILE_PATH}" NAME)
+    string(REGEX REPLACE "^LICENSE-" "" NAME "${FILE_NAME}")
+    license_add_file("${NAME}" "${FILE_PATH}")
+endforeach()
+
+if (LLAMA_BUILD_COMMON)
+    license_generate(common)
+endif()
+
 #
 # install
 #

README.md

@@ -200,6 +200,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
 
 - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
+- [BonzAI App](https://apps.apple.com/us/app/bonzai-your-local-ai-agent/id6752847988) (proprietary)
 - [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
 - [Dot](https://github.com/alexpinel/Dot) (GPL)
 - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)

SECURITY.md

@@ -1,12 +1,52 @@
 # Security Policy
 
+ - [**Reporting a vulnerability**](#reporting-a-vulnerability)
+ - [**Requirements**](#requirements)
+ - [**Covered Topics**](#covered-topics)
  - [**Using llama.cpp securely**](#using-llamacpp-securely)
    - [Untrusted models](#untrusted-models)
    - [Untrusted inputs](#untrusted-inputs)
    - [Data privacy](#data-privacy)
    - [Untrusted environments or networks](#untrusted-environments-or-networks)
    - [Multi-Tenant environments](#multi-tenant-environments)
- - [**Reporting a vulnerability**](#reporting-a-vulnerability)
+
+## Reporting a vulnerability
+
+If you have discovered a security vulnerability in this project that falls inside the [covered topics](#covered-topics), please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
+
+Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
+
+A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
+
+> [!IMPORTANT]
+> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080
+
+## Requirements
+
+Before submitting your report, ensure you meet the following requirements:
+
+- You have read this policy and fully understand it.
+- AI is only permitted in an assistive capacity as stated in [AGENTS.md](AGENTS.md). We do not accept reports that are written exclusively by AI.
+- Your report must include a working Proof-of-Concept in the form of a script and/or attached files.
+
+Maintainers reserve the right to close the report if these requirements are not fulfilled.
+
+## Covered Topics
+
+Only vulnerabilities that fall within these parts of the project are considered valid. For problems falling outside of this list, please report them as issues.
+
+- `src/**/*`
+- `ggml/**/*`
+- `gguf-py/**/*`
+- `tools/server/*`, **excluding** the following topics:
+    - Web UI
+    - Features marked as experimental
+    - Features not recommended for use in untrusted environments (e.g., router, MCP)
+    - Bugs that can lead to Denial-of-Service attack
+
+Note that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities in LLaMA C++.
+
+For vulnerabilities that fall within the `vendor` directory, please report them directly to the third-party project.
 
 ## Using llama.cpp securely
 
@@ -55,19 +95,3 @@ If you intend to run multiple models in parallel with shared memory, it is your
 3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
 
 4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
-
-## Reporting a vulnerability
-
-Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++.
-
-<!-- normal version -->
-However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
-
-Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
-
-Please note that using AI to identify vulnerabilities and generate reports is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before submitting the report.
-
-A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
-
-> [!IMPORTANT]
-> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080

ci/run.sh

@@ -297,7 +297,8 @@ function gg_sum_test_scripts {
 }
 
 function gg_get_model {
-    local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
+    #local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
+    local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-q4_0.gguf"
     if [[ -s $gguf_0 ]]; then
         echo -n "$gguf_0"
     else

cmake/license.cmake

@@ -0,0 +1,40 @@
+define_property(GLOBAL PROPERTY LICENSE_TEXT
+    BRIEF_DOCS "Embedded licenses"
+    FULL_DOCS  "Global string containing all aggregated licenses"
+)
+
+function(license_add_file NAME FILE)
+    if(NOT IS_ABSOLUTE "${FILE}")
+        set(FILE "${CMAKE_CURRENT_SOURCE_DIR}/${FILE}")
+    endif()
+    if(EXISTS "${FILE}")
+        set(TITLE "License for ${NAME}")
+        string(REGEX REPLACE "." "=" UNDERLINE "${TITLE}")
+        file(READ "${FILE}" TEXT)
+        get_property(TMP GLOBAL PROPERTY LICENSE_TEXT)
+        string(APPEND TMP "R\"=L=(${TITLE}\n${UNDERLINE}\n\n${TEXT})=L=\",\n")
+        set_property(GLOBAL PROPERTY LICENSE_TEXT "${TMP}")
+    else()
+        message(WARNING "License file '${FILE}' not found")
+    endif()
+endfunction()
+
+function(license_generate TARGET_NAME)
+    message(STATUS "Generating embedded license file for target: ${TARGET_NAME}")
+    get_property(TEXT GLOBAL PROPERTY LICENSE_TEXT)
+
+    set(CPP_CONTENT "// Generated by CMake\n\n")
+    string(APPEND CPP_CONTENT "const char* LICENSES[] = {\n")
+    string(APPEND CPP_CONTENT "${TEXT}")
+    string(APPEND CPP_CONTENT "nullptr\n")
+    string(APPEND CPP_CONTENT "};\n")
+
+    set(CPP_FILE "${CMAKE_BINARY_DIR}/license.cpp")
+    file(WRITE "${CPP_FILE}" "${CPP_CONTENT}")
+
+    if(TARGET ${TARGET_NAME})
+        target_sources(${TARGET_NAME} PRIVATE "${CPP_FILE}")
+    else()
+        message(FATAL_ERROR "Target '${TARGET_NAME}' does not exist")
+    endif()
+endfunction()

common/CMakeLists.txt

@@ -155,27 +155,3 @@ if (LLAMA_LLGUIDANCE)
 endif ()
 
 target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
-
-
-#
-# copy the license files
-#
-
-# Check if running in GitHub Actions
-if (DEFINED ENV{GITHUB_ACTIONS} AND "$ENV{GITHUB_ACTIONS}" STREQUAL "true")
-    message(STATUS "Running inside GitHub Actions - copying license files")
-
-    # Copy all files from licenses/ to build/bin/
-    file(GLOB LICENSE_FILES "${CMAKE_SOURCE_DIR}/licenses/*")
-    foreach(LICENSE_FILE ${LICENSE_FILES})
-        get_filename_component(FILENAME ${LICENSE_FILE} NAME)
-        add_custom_command(
-            POST_BUILD
-            TARGET ${TARGET}
-            COMMAND ${CMAKE_COMMAND} -E copy_if_different
-                "${LICENSE_FILE}"
-                "$<TARGET_FILE_DIR:llama>/${FILENAME}"
-            COMMENT "Copying ${FILENAME} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}")
-        message(STATUS "Copying ${LICENSE_FILE} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${FILENAME}")
-    endforeach()
-endif()

common/arg.cpp

@@ -2,10 +2,10 @@
 
 #include "chat.h"
 #include "common.h"
+#include "download.h"
 #include "json-schema-to-grammar.h"
 #include "log.h"
 #include "sampling.h"
-#include "download.h"
 #include "preset.h"
 
 // fix problem with std::min and std::max
@@ -48,6 +48,8 @@
 
 #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
 
+extern const char * LICENSES[];
+
 using json = nlohmann::ordered_json;
 using namespace common_arg_utils;
 
@@ -279,12 +281,20 @@ static std::string clean_file_name(const std::string & fname) {
 static bool common_params_handle_remote_preset(common_params & params, llama_example ex) {
     GGML_ASSERT(!params.model.hf_repo.empty());
 
+    // the returned hf_repo is without tag
+    auto [hf_repo, hf_tag] = common_download_split_repo_tag(params.model.hf_repo);
+
+    // "latest" tag (default if not specified) is translated to "default" preset
+    if (hf_tag == "latest") {
+        hf_tag = "default";
+    }
+
     const bool offline = params.offline;
     std::string model_endpoint = get_model_endpoint();
-    auto preset_url = model_endpoint + params.model.hf_repo + "/resolve/main/preset.ini";
+    auto preset_url = model_endpoint + hf_repo + "/resolve/main/preset.ini";
 
     // prepare local path for caching
-    auto preset_fname = clean_file_name(params.model.hf_repo + "_preset.ini");
+    auto preset_fname = clean_file_name(hf_repo + "_preset.ini");
     auto preset_path = fs_get_cache_file(preset_fname);
     const int status = common_download_file_single(preset_url, preset_path, params.hf_token, offline);
     const bool has_preset = status >= 200 && status < 400;
@@ -293,14 +303,15 @@ static bool common_params_handle_remote_preset(common_params & params, llama_exa
     if (has_preset) {
         LOG_INF("applying remote preset from %s\n", preset_url.c_str());
         common_preset_context ctx(ex, /* only_remote_allowed */ true);
-        common_preset global; // unused for now
+        common_preset global;
         auto remote_presets = ctx.load_from_ini(preset_path, global);
-        if (remote_presets.find(COMMON_PRESET_DEFAULT_NAME) != remote_presets.end()) {
-            common_preset & preset = remote_presets.at(COMMON_PRESET_DEFAULT_NAME);
+        remote_presets = ctx.cascade(global, remote_presets);
+        if (remote_presets.find(hf_tag) != remote_presets.end()) {
+            common_preset preset = remote_presets.at(hf_tag);
             LOG_INF("\n%s", preset.to_ini().c_str()); // to_ini already added trailing newline
             preset.apply_to_params(params);
         } else {
-            throw std::runtime_error("Remote preset.ini does not contain [" + std::string(COMMON_PRESET_DEFAULT_NAME) + "] section");
+            throw std::runtime_error("Remote preset.ini does not contain [" + std::string(hf_tag) + "] section");
         }
     } else {
         LOG_INF("%s", "no remote preset found, skipping\n");
@@ -1030,6 +1041,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             exit(0);
         }
     ));
+    add_opt(common_arg(
+        {"--license"},
+        "show source code license and dependencies",
+        [](common_params &) {
+            for (int i = 0; LICENSES[i]; ++i) {
+                printf("%s\n", LICENSES[i]);
+            }
+            exit(0);
+        }
+    ));
     add_opt(common_arg(
         {"-cl", "--cache-list"},
         "show list of models in cache",
@@ -1274,7 +1295,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.kv_unified = true;
         }
-    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY}));
+    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED}));
     add_opt(common_arg(
         {"--context-shift"},
         {"--no-context-shift"},
@@ -2856,10 +2877,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_threads_http = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
+    add_opt(common_arg(
+        {"--cache-prompt"},
+        {"--no-cache-prompt"},
+        string_format("whether to enable prompt caching (default: %s)", params.cache_prompt ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.cache_prompt = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_PROMPT"));
     add_opt(common_arg(
         {"--cache-reuse"}, "N",
         string_format(
-            "min chunk size to attempt reusing from the cache via KV shifting (default: %d)\n"
+            "min chunk size to attempt reusing from the cache via KV shifting, requires prompt caching to be enabled (default: %d)\n"
             "[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse
         ),
         [](common_params & params, int value) {

common/common.h

@@ -80,6 +80,7 @@ int32_t cpu_get_num_math();
 //
 
 enum llama_example {
+    LLAMA_EXAMPLE_BATCHED,
     LLAMA_EXAMPLE_DEBUG,
     LLAMA_EXAMPLE_COMMON,
     LLAMA_EXAMPLE_SPECULATIVE,
@@ -475,6 +476,7 @@ struct common_params {
     int32_t timeout_write     = timeout_read; // http write timeout in seconds
     int32_t n_threads_http    = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
     int32_t n_cache_reuse     = 0;            // min chunk size to reuse from the cache via KV shifting
+    bool    cache_prompt      = true;         // whether to enable prompt caching
     int32_t n_ctx_checkpoints = 8;            // max number of context checkpoints per slot
     int32_t cache_ram_mib     = 8192;         // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
 

common/download.cpp

@@ -161,6 +161,16 @@ static bool is_http_status_ok(int status) {
     return status >= 200 && status < 400;
 }
 
+std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag) {
+    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
+    std::string tag = parts.size() > 1 ? parts.back() : "latest";
+    std::string hf_repo = parts[0];
+    if (string_split<std::string>(hf_repo, '/').size() != 2) {
+        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
+    }
+    return {hf_repo, tag};
+}
+
 #ifdef LLAMA_USE_CURL
 
 //
@@ -922,12 +932,8 @@ common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag,
                                       const std::string & bearer_token,
                                       bool offline,
                                       const common_header_list & custom_headers) {
-    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
-    std::string tag = parts.size() > 1 ? parts.back() : "latest";
-    std::string hf_repo = parts[0];
-    if (string_split<std::string>(hf_repo, '/').size() != 2) {
-        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
-    }
+    // the returned hf_repo is without tag
+    auto [hf_repo, tag] = common_download_split_repo_tag(hf_repo_with_tag);
 
     std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag;
 

common/download.h

@@ -17,6 +17,12 @@ struct common_remote_params {
 // get remote file content, returns <http_code, raw_response_body>
 std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params);
 
+// split HF repo with tag into <repo, tag>
+// for example: "user/model:tag" -> <"user/model", "tag">
+// if tag is not present, default to "latest"
+// example: "user/model" -> <"user/model", "latest">
+std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag);
+
 struct common_cached_model_info {
     std::string manifest_path;
     std::string user;

common/preset.cpp

@@ -32,8 +32,10 @@ static std::set<std::string> get_remote_preset_whitelist(const std::map<std::str
         "batch-size",
         "ubatch-size",
         "cache-reuse",
+        "chat-template-kwargs",
+        "mmap",
         // note: sampling params are automatically allowed by default
-        // negated args will be added automatically
+        // negated args will be added automatically if the positive arg is specified above
     };
 
     std::set<std::string> allowed_keys;
@@ -318,6 +320,11 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
         }
         LOG_DBG("loading preset: %s\n", preset.name.c_str());
         for (const auto & [key, value] : section.second) {
+            if (key == "version") {
+                // skip version key (reserved for future use)
+                continue;
+            }
+
             LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
             if (filter_allowed_keys && allowed_keys.find(key) == allowed_keys.end()) {
                 throw std::runtime_error(string_format(
@@ -334,7 +341,10 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
                 }
                 LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
             } else {
-                // TODO: maybe warn about unknown key?
+                throw std::runtime_error(string_format(
+                    "option '%s' not recognized in preset '%s'",
+                    key.c_str(), preset.name.c_str()
+                ));
             }
         }
 

convert_hf_to_gguf.py

@@ -528,7 +528,11 @@ class ModelBase:
         return ()
 
     def prepare_tensors(self):
-        max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
+        # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
+        if self.tensor_map.mapping:
+            max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
+        else:
+            max_name_len = len("vision_encoder.weight,")  # Default reasonable length
 
         for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()):
             # we don't need these
@@ -4363,7 +4367,37 @@ class Qwen3NextModel(Qwen2MoeModel):
         elif name.endswith("norm.weight") and not name.endswith("linear_attn.norm.weight"):
             data_torch = data_torch + 1
 
-        yield from super().modify_tensors(data_torch, name, bid)
+        if "in_proj_qkvz.weight" in name:
+            # original order:  [q, k, v, z] * head_count
+            # corrected order: [q * head_count, k * head_count, v * head_count, z * head_count]
+            head_k_dim = self.hparams["linear_key_head_dim"]
+            head_v_dim = self.hparams["linear_value_head_dim"]
+            num_v_heads = self.hparams["linear_num_value_heads"]
+            num_k_heads = self.hparams["linear_num_key_heads"]
+            hidden_size = self.hparams["hidden_size"]
+            split_arg_list_qkvz = [
+                head_k_dim, # q partition
+                head_k_dim, # k partition
+                (num_v_heads // num_k_heads * head_v_dim), # v partition
+                (num_v_heads // num_k_heads * head_v_dim), # z partition
+            ]
+            # view as (n_embd, head_count, [q+k+v+z])
+            data_torch = data_torch.permute(1, 0).contiguous()
+            data_torch = data_torch.view(-1, num_k_heads, sum(split_arg_list_qkvz))
+            # split into q, k, v, z
+            q, k, v, z = torch.split(data_torch, split_arg_list_qkvz, dim=-1)
+            # flatten dim + head_count
+            q = q.contiguous().view(hidden_size, -1)
+            k = k.contiguous().view(hidden_size, -1)
+            v = v.contiguous().view(hidden_size, -1)
+            z = z.contiguous().view(hidden_size, -1)
+            # stack back
+            qkv = torch.cat([q, k, v], dim=-1).permute(1, 0).contiguous()
+            z = z.permute(1, 0).contiguous()
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV,  bid, ".weight"), qkv)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, bid, ".weight"), z)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
 
 
 @ModelBase.register("RND1")
@@ -6038,7 +6072,175 @@ class Gemma3VisionModel(MmprojModel):
         return [] # skip other tensors
 
 
+class ConformerAudioModel(MmprojModel):
+    _batch_norm_tensors: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def is_audio_tensor(name: str):
+        return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ConformerAudioModel.is_audio_tensor(name):
+            if ".conv" in name or "_conv" in name and ".weight" in name:
+                return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # fold running_mean, running_var and eps into weight and bias for batch_norm
+        if "batch_norm" in name:
+            if self._batch_norm_tensors is None:
+                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
+            assert bid is not None
+            self._batch_norm_tensors[bid][name] = data_torch
+
+            if len(self._batch_norm_tensors[bid]) < 5:
+                return []
+
+            weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
+            bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
+            running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
+            running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
+            eps = 1e-5 # default value
+
+            a = weight / torch.sqrt(running_var + eps)
+            b = bias - running_mean * a
+            return [
+                (self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.weight"), a),
+                (self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.bias"), b),
+            ]
+
+        # reshape conv weights
+        if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
+            data_torch = data_torch[:, None, None]
+        if "conv.depthwise_conv" in name and name.endswith(".weight"):
+            assert data_torch.shape[1] == 1
+            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
+        if "conv.pointwise_conv" in name and name.endswith(".weight"):
+            assert data_torch.shape[2] == 1
+            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 @ModelBase.register("Gemma3nForConditionalGeneration")
+class Gemma3nVisionAudioModel(ConformerAudioModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    # Double indexed mapping for MobileNetV5 blocks (not supported by tensor_mapping.py)
+    # This is the only known model having this, so we prefer implementing it outside of tensor_mapping.py
+    block_tensor_mapping = {
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_exp.weight":             "v.blk.{bid}.{sid}.conv_exp.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn1.weight":                  "v.blk.{bid}.{sid}.bn1.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_pwl.weight":             "v.blk.{bid}.{sid}.conv_pwl.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn2.weight":                  "v.blk.{bid}.{sid}.bn2.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.conv.weight":        "v.blk.{bid}.{sid}.dw_start.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.bn.weight":          "v.blk.{bid}.{sid}.dw_start.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.conv.weight":          "v.blk.{bid}.{sid}.dw_mid.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.bn.weight":            "v.blk.{bid}.{sid}.dw_mid.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.conv.weight":          "v.blk.{bid}.{sid}.pw_exp.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.bn.weight":            "v.blk.{bid}.{sid}.pw_exp.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.conv.weight":         "v.blk.{bid}.{sid}.pw_proj.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.bn.weight":           "v.blk.{bid}.{sid}.pw_proj.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.layer_scale.gamma":           "v.blk.{bid}.{sid}.layer_scale.gamma",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.query.proj.weight":      "v.blk.{bid}.{sid}.attn.query.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.proj.weight":        "v.blk.{bid}.{sid}.attn.key.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.proj.weight":      "v.blk.{bid}.{sid}.attn.value.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.output.proj.weight":     "v.blk.{bid}.{sid}.attn.output.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.down_conv.weight":   "v.blk.{bid}.{sid}.attn.key.down_conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.norm.weight":        "v.blk.{bid}.{sid}.attn.key.norm.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.down_conv.weight": "v.blk.{bid}.{sid}.attn.value.down_conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.norm.weight":      "v.blk.{bid}.{sid}.attn.value.norm.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.norm.weight":                 "v.blk.{bid}.{sid}.norm.weight",
+    }
+
+    def __init__(self, *args, **kwargs):
+        # Parent init will call find_hparam which now returns 0 for empty keys
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["n_layers"] = 128 # fake value for audio encoder, vision encoder doesn't use it
+        self.hparams_vision["intermediate_size"] = self.hparams_vision.get("intermediate_size", 2048) * 4
+        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_attention_heads", 8)
+
+        # MobileNetV5 does not use image_mean/std
+        self.preprocessor_config["image_mean"] = [0.0 ,0.0 , 0.0]
+        self.preprocessor_config["image_std"] = [1.0 ,1.0 ,1.0]
+        self.hparams_vision["image_size"] = self.preprocessor_config.get(
+            "size", {"height": 768, "width": 768}
+        )["height"]
+
+        # Image sequence length (256 tokens = 16x16 for Gemma3n)
+        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
+        image_size = self.hparams_vision["image_size"]
+        self.hparams_vision["patch_size"] = image_size // image_seq_length
+
+        # remap audio hparams
+        assert self.hparams_audio is not None
+        self.hparams_audio["n_layers"] = self.hparams_audio["conf_num_hidden_layers"]
+        self.hparams_audio["num_attention_heads"] = self.hparams_audio["conf_num_attention_heads"]
+        self.hparams_audio["feat_in"] = self.hparams_audio["input_feat_size"]
+        self.hparams_audio["intermediate_size"] = self.hparams_audio.get("intermediate_size", 6144)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # vision params
+        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA3NV)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
+
+        # audio params
+        assert self.hparams_audio is not None
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA3NA)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # Force quantization settings for specific tensor types
+        if "input_projection" in name or "input_proj" in name:
+            return gguf.GGMLQuantizationType.F16
+        if ".embeddings." in name or "stem" in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def custom_map(self, name: str) -> str:
+        """Parses names like model.vision_tower.timm_model.blocks.1.2.suffix and applies template mapping."""
+        parts = name.split(".")
+        # MobileNet blocks have at least 7 parts: model, vision_tower, timm_model, blocks, bid, sid, and suffix
+        if len(parts) >= 7:
+            bid, sid = parts[4], parts[5]
+            suffix = ".".join(parts[6:])
+            template = f"model.vision_tower.timm_model.blocks.{{bid}}.{{sid}}.{suffix}"
+            if template in self.block_tensor_mapping:
+                return self.block_tensor_mapping[template].format(bid=bid, sid=sid)
+
+        raise ValueError(f"Unknown name: {name}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if (ConformerAudioModel.is_audio_tensor(name)):
+            name = name.replace("model.audio_tower.conformer.", "conformer.layers.")
+            return super().modify_tensors(data_torch, name, bid)
+
+        # Gemma3n uses
+        # - model.embed_vision.* for projection layers
+        # - model.vision_tower.* for vision encoder
+        # Skip non-vision tensors
+        if not (name.startswith("model.embed_vision.") or name.startswith("model.vision_tower.")):
+            return []
+
+        if name.startswith("model.vision_tower.timm_model.blocks."):
+            # Double-indexed block tensors through custom logic
+            new_name = self.custom_map(name)
+        else:
+            # Route non-repeating (conv_stem, msfa, embedding, etc.) and un-catched through tensor_mapping.py
+            new_name = self.map_tensor_name(name)
+
+        if new_name.endswith("conv_stem.conv.bias") or new_name.endswith("layer_scale.gamma"):
+            data_torch = data_torch.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) # [1, C, 1, 1]
+
+        return [(new_name, data_torch)]
+
+
+@ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
 class Gemma3NModel(Gemma3Model):
     model_arch = gguf.MODEL_ARCH.GEMMA3N
     norm_shift = 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code
@@ -6061,8 +6263,25 @@ class Gemma3NModel(Gemma3Model):
         ]
 
     def set_vocab(self):
+        # For Gemma3n multimodal models, we need the FULL vocab_size (262400)
+        # which includes special tokens from 262144-262399 for vision/audio.
+        # The vocab_size_per_layer_input (262144) is only the embedding size per layer.
+        # Temporarily override the hparams lookup order to prioritize vocab_size.
+
+        # Store original vocab_size_per_layer_input if it exists
+        vocab_size_per_layer_input = self.hparams.get("vocab_size_per_layer_input")
+
+        # Temporarily remove vocab_size_per_layer_input to force using vocab_size
+        if vocab_size_per_layer_input is not None:
+            del self.hparams["vocab_size_per_layer_input"]
+
+        # Call parent set_vocab which will now use vocab_size (262400)
         super().set_vocab()
 
+        # Restore vocab_size_per_layer_input for later use
+        if vocab_size_per_layer_input is not None:
+            self.hparams["vocab_size_per_layer_input"] = vocab_size_per_layer_input
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         self.gguf_writer.add_altup_active_idx(self.hparams["altup_active_idx"])
@@ -6098,8 +6317,32 @@ class Gemma3NModel(Gemma3Model):
         if "language_model." not in name:
             return [] # skip non-language model tensors
 
+        # Pad token embeddings for vision/audio special tokens (262144-262399)
+        if "embed_tokens.weight" in name or "embed_tokens_per_layer" in name:
+            # Move to CPU to avoid meta device issues during padding
+            data_torch = data_torch.to(device="cpu")
+
+            vocab_size = self.hparams.get("vocab_size", 262400)
+            current_size = data_torch.shape[0]  # First dimension is vocab_size
+
+            if current_size < vocab_size:
+                # Pad with zeros for vision/audio tokens (they get embeddings from vision tower)
+                padding_size = vocab_size - current_size
+                tensor_type = "per-layer embeddings" if "per_layer" in name else "token embeddings"
+                logger.info(f"Padding {tensor_type} shape {list(data_torch.shape)} from {current_size} to {vocab_size} (adding {padding_size} vision/audio token slots)")
+
+                # Create padding with zeros (vision tokens won't use these embeddings)
+                padding = torch.zeros((padding_size, data_torch.shape[1]), dtype=data_torch.dtype, device=data_torch.device)
+                data_torch = torch.cat([data_torch, padding], dim=0)
+
+            # Continue with normal processing
+            name = name.replace("language_model.", "")
+            return [(self.map_tensor_name(name), data_torch)]
+
         if "altup_unembed_projections" in name:
             data_torch = data_torch.to(device="cpu")
+            # altup_unembed matrices are [hidden_size, hidden_size], NOT vocab-based
+            # They should NOT be padded
             if ".0." in name:
                 self._altup_unembd[0] = data_torch
             elif ".1." in name:
@@ -9936,7 +10179,7 @@ class LFM2Model(TextModel):
         self._add_feed_forward_length()
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if self._is_vision_tensor(name) or self._is_audio_tensor(name):
+        if self._is_vision_tensor(name) or ConformerAudioModel.is_audio_tensor(name):
             # skip multimodal tensors
             return []
 
@@ -9952,9 +10195,6 @@ class LFM2Model(TextModel):
     def _is_vision_tensor(self, name: str) -> bool:
         return "vision_tower" in name or "multi_modal_projector" in name
 
-    def _is_audio_tensor(self, name: str):
-        return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])
-
 
 @ModelBase.register("Lfm2Model")
 class LFM2ColBertModel(LFM2Model):
@@ -10082,13 +10322,11 @@ class LFM2VLModel(MmprojModel):
 
 
 @ModelBase.register("Lfm2AudioForConditionalGeneration")
-class LFM2AudioModel(MmprojModel):
+class LFM2AudioModel(ConformerAudioModel):
     has_vision_encoder = False
     has_audio_encoder = True
     model_name = "Lfm2AudioEncoder"
 
-    _batch_norm_tensors: list[dict[str, Tensor]] | None = None
-
     def get_audio_config(self) -> dict[str, Any] | None:
         return self.global_config.get("encoder")
 
@@ -10102,12 +10340,7 @@ class LFM2AudioModel(MmprojModel):
         self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
         self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
 
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".conv" in name and ".weight" in name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+    def modify_tensors(self, data_torch, name, bid):
         # skip language model tensors
         if name.startswith("lfm."):
             return []
@@ -10120,40 +10353,7 @@ class LFM2AudioModel(MmprojModel):
         if any(p in name for p in ["codebook_offsets", "depth_embeddings", "depth_linear", "depthformer"]):
             return []
 
-        # fold running_mean, running_var and eps into weight and bias for batch_norm
-        if "batch_norm" in name:
-            if self._batch_norm_tensors is None:
-                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
-            assert bid is not None
-            self._batch_norm_tensors[bid][name] = data_torch
-
-            if len(self._batch_norm_tensors[bid]) < 5:
-                return []
-
-            weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
-            bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
-            running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
-            running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
-            eps = 1e-5 # default value
-
-            a = weight / torch.sqrt(running_var + eps)
-            b = bias - running_mean * a
-            return [
-                (self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.weight"), a),
-                (self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.bias"), b),
-            ]
-
-        # reshape conv weights
-        if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
-            data_torch = data_torch[:, None, None]
-        if "conv.depthwise_conv" in name and name.endswith(".weight"):
-            assert data_torch.shape[1] == 1
-            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
-        if "conv.pointwise_conv" in name and name.endswith(".weight"):
-            assert data_torch.shape[2] == 1
-            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
-
-        return [(self.map_tensor_name(name), data_torch)]
+        return super().modify_tensors(data_torch, name, bid)
 
 
 @ModelBase.register("SmallThinkerForCausalLM")

docs/ops.md

@@ -57,7 +57,6 @@ Legend:
 |                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|               GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -71,10 +70,9 @@ Legend:
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
+|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 |
 |                              PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -99,7 +97,6 @@ Legend:
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |

docs/ops/BLAS.csv

@@ -965,6 +965,7 @@
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,1,2560],ne_kernel=[3,3,1,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
+"BLAS","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
 "BLAS","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","BLAS"
@@ -4964,6 +4965,7 @@
 "BLAS","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","BLAS"
 "BLAS","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","BLAS"
 "BLAS","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","BLAS"
+"BLAS","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","BLAS"
 "BLAS","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","BLAS"
 "BLAS","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","BLAS"
 "BLAS","ARGMAX","type=f32,ne=[32,1,1,1]","support","0","no","BLAS"
@@ -5715,15 +5717,15 @@
 "BLAS","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","BLAS"
 "BLAS","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","0","no","BLAS"
 "BLAS","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
-"BLAS","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[6,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,1536,4,1],ne_b=[3,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[6,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[3,2048,4,1],ne_b=[3,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","0","no","BLAS"
@@ -5733,6 +5735,15 @@
 "BLAS","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,1024,1,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[18,1024,1,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,1024,4,1],ne_b=[9,1024,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,1536,1,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[18,1536,1,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,1536,4,1],ne_b=[9,1536,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,2048,1,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[18,2048,1,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
+"BLAS","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
 "BLAS","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","BLAS"
@@ -6592,6 +6603,30 @@
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=67,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f32,type_b=f32,m=64,n=77,k=77,bs=[12,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","BLAS"
 "BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=576,n=512,k=576,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","BLAS"
+"BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=2048,k=8192,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=f32,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=bf16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q4_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q5_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q8_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q2_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q3_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q4_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q5_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=q6_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq2_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq2_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq1_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq1_m,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq4_nl,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq3_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
+"BLAS","MUL_MAT","type_a=iq4_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","BLAS"
 "BLAS","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","BLAS"
@@ -8916,6 +8951,11 @@
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[200000,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[200000,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
+"BLAS","SOFT_MAX","type=f32,ne=[643251,3,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[16,16,1,1],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[15,15,1,1],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
 "BLAS","SOFT_MAX_BACK","type=f32,ne=[16,16,2,3],scale=1.000000,max_bias=0.000000","support","0","no","BLAS"
@@ -8968,6 +9008,7 @@
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -8977,6 +9018,7 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -8987,11 +9029,13 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9001,6 +9045,7 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9011,11 +9056,13 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9025,6 +9072,7 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9035,11 +9083,13 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9049,6 +9099,7 @@
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9059,6 +9110,7 @@
 "BLAS","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9184,6 +9236,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9193,6 +9246,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9203,11 +9257,13 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9217,6 +9273,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
@@ -9227,11 +9284,13 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9241,6 +9300,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
@@ -9251,11 +9311,13 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9265,6 +9327,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
@@ -9275,6 +9338,7 @@
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
+"BLAS","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
 "BLAS","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","BLAS"
@@ -9542,333 +9606,333 @@
 "BLAS","ARGSORT","type=f32,ne=[2048,2,1,3],order=1","support","0","no","BLAS"
 "BLAS","ARGSORT","type=f32,ne=[2049,2,1,3],order=1","support","0","no","BLAS"
 "BLAS","ARGSORT","type=f32,ne=[2,8,8192,1],order=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[1,1,1,1],k=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[12,1,2,1],k=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[2,1,1,1],k=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[13,1,2,1],k=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[2,1,1,1],k=2","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[13,1,2,1],k=2","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[4,1,1,1],k=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[15,1,2,1],k=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[4,1,1,1],k=2","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[15,1,2,1],k=2","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[4,1,1,1],k=3","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[15,1,2,1],k=3","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[8,1,1,1],k=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[19,1,2,1],k=1","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[8,1,1,1],k=2","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[19,1,2,1],k=2","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[8,1,1,1],k=3","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[19,1,2,1],k=3","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[8,1,1,1],k=7","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[19,1,2,1],k=7","support","0","no","BLAS"
-"BLAS","TOP_K","type=f32,ne=[16,1,1,1],k=1","support","0","no","BLAS"
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 "BLAS","SUM","type=f32,ne=[10,5,4,3]","support","0","no","BLAS"
 "BLAS","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","0","no","BLAS"
 "BLAS","SUM","type=f32,ne=[11,5,6,3],permute=[0,2,1,3]","support","0","no","BLAS"
@@ -9891,8 +9955,9 @@
 "BLAS","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","0","no","BLAS"
 "BLAS","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","0","no","BLAS"
 "BLAS","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","0","no","BLAS"
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 "BLAS","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","BLAS"
 "BLAS","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","BLAS"
 "BLAS","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","BLAS"
@@ -9914,6 +9979,7 @@
 "BLAS","CUMSUM","type=f32,ne=[2048,5,4,3]","support","0","no","BLAS"
 "BLAS","CUMSUM","type=f32,ne=[242004,1,1,1]","support","0","no","BLAS"
 "BLAS","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","BLAS"
+"BLAS","CUMSUM","type=f32,ne=[20481,4,1,1]","support","0","no","BLAS"
 "BLAS","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","BLAS"
 "BLAS","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","BLAS"
 "BLAS","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","BLAS"
@@ -9923,17 +9989,41 @@
 "BLAS","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","BLAS"
 "BLAS","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","BLAS"
 "BLAS","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","BLAS"
+"BLAS","DIAG","type=f32,ne=[10,1,4,3]","support","0","no","BLAS"
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 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[79,79,5,3],ne_rhs=[417,79,5,3]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,2],ne_rhs=[32,128,4,2]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[80,80,2,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[79,80,2,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[81,80,2,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[80,80,8,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[79,80,8,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[81,80,8,8]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[84,84,4,4],ne_rhs=[32,84,4,4]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[95,95,8,8],ne_rhs=[40,95,8,8]","support","0","no","BLAS"
 "BLAS","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","BLAS"
-"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[32,128,4,4]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,3,4],ne_rhs=[32,128,3,4]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,1],ne_rhs=[32,128,4,1]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[200,64,4,4]","support","0","no","BLAS"
+"BLAS","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","BLAS"
+"BLAS","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","BLAS"
 "BLAS","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","BLAS"

docs/preset.md

@@ -58,3 +58,40 @@ temp = 0.8
 ctx-size = 1024
 ; (and other configurations)
 ```
+
+### Named presets
+
+If you want to define multiple preset configurations for one or more GGUF models, you can create a blank HF repo containing a single `preset.ini` file that references the actual model(s):
+
+```ini
+[*]
+mmap = 1
+
+[gpt-oss-20b-hf]
+hf          = ggml-org/gpt-oss-20b-GGUF
+batch-size  = 2048
+ubatch-size = 2048
+top-p       = 1.0
+top-k       = 0
+min-p       = 0.01
+temp        = 1.0
+chat-template-kwargs = {"reasoning_effort": "high"}
+
+[gpt-oss-120b-hf]
+hf          = ggml-org/gpt-oss-120b-GGUF
+batch-size  = 2048
+ubatch-size = 2048
+top-p       = 1.0
+top-k       = 0
+min-p       = 0.01
+temp        = 1.0
+chat-template-kwargs = {"reasoning_effort": "high"}
+```
+
+You can then use it via `llama-cli` or `llama-server`, example:
+
+```sh
+llama-server -hf user/repo:gpt-oss-120b-hf
+```
+
+Please make sure to provide the correct `hf-repo` for each child preset. Otherwise, you may get error: `The specified tag is not a valid quantization scheme.`

examples/batched/batched.cpp

@@ -21,7 +21,7 @@ int main(int argc, char ** argv) {
     params.prompt = "Hello my name is";
     params.n_predict = 32;
 
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BATCHED, print_usage)) {
         return 1;
     }
 

examples/debug/debug.cpp

@@ -57,11 +57,21 @@ struct callback_data {
     }
 };
 
+static bool has_pooling(llama_context * ctx) {
+    switch (llama_pooling_type(ctx)) {
+        case LLAMA_POOLING_TYPE_NONE:
+        case LLAMA_POOLING_TYPE_UNSPECIFIED:
+            return false;
+        default:
+            return true;
+    }
+}
+
 struct output_data {
     float *                  data_ptr    = nullptr;
     int                      data_size   = 0;
     std::string              type_suffix;
-    std::vector<float>       storage;
+    std::vector<float>       embd_norm;
     std::string              prompt;
     std::vector<llama_token> tokens;
 
@@ -73,24 +83,32 @@ struct output_data {
         prompt = params.prompt;
 
         if (params.embedding) {
-            const int  n_embd          = llama_model_n_embd_out(model);
-            const bool pooling_enabled = llama_pooling_type(ctx) != LLAMA_POOLING_TYPE_NONE;
-            const int  n_embd_count    = pooling_enabled ? 1 : tokens.size();
-            const int  n_embeddings    = n_embd * n_embd_count;
+            const int n_embd       = llama_model_n_embd_out(model);
+            const bool pooling     = has_pooling(ctx);
+            const int n_embd_count = pooling ? 1 : tokens.size();
+            const int n_floats     = n_embd * n_embd_count;
 
-            float * embeddings;
-            if (pooling_enabled) {
-                embeddings = llama_get_embeddings_seq(ctx, 0);
-                storage.resize(n_embeddings);
-                common_embd_normalize(embeddings, storage.data(), n_embeddings, params.embd_normalize);
-                embeddings = storage.data();
-            } else {
-                embeddings = llama_get_embeddings(ctx);
+            float * embd_raw = pooling ? llama_get_embeddings_seq(ctx, 0) : llama_get_embeddings(ctx);
+            if (embd_raw == nullptr) {
+                throw std::runtime_error("failed to get embeddings from the model");
             }
 
-            data_ptr = embeddings;
-            data_size = n_embeddings;
+            LOG_DBG("pooling_enabled: %s\n", pooling ? "true" : "false");
+            LOG_DBG("n_embd: %d\n", n_embd);
+            LOG_DBG("n_floats: %d\n", n_floats);
+            LOG_DBG("n_embd_count: %d\n", n_embd_count);
+
+            data_ptr    = embd_raw;
+            data_size   = n_floats;
             type_suffix = "-embeddings";
+
+            if (params.embd_normalize >= 0) {
+                embd_norm.resize(n_floats);
+                for (int i = 0; i < n_embd_count; i++) {
+                    common_embd_normalize(embd_raw+i*n_embd, embd_norm.data()+i*n_embd, n_embd, params.embd_normalize);
+                }
+                data_ptr = embd_norm.data();
+            }
         } else {
             const float * logits = llama_get_logits_ith(ctx, tokens.size() - 1);
             const int n_logits = llama_vocab_n_tokens(vocab);

ggml/src/ggml-blas/CMakeLists.txt

@@ -32,14 +32,12 @@ if (BLAS_FOUND)
                 pkg_check_modules(DepBLAS openblas)
             endif()
         elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME")
-            add_compile_definitions(GGML_BLAS_USE_BLIS)
             pkg_check_modules(DepBLAS blis)
         elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS")
             pkg_check_modules(DepBLAS blas-atlas)
         elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS")
             pkg_check_modules(DepBLAS flexiblas_api)
         elseif (${GGML_BLAS_VENDOR} MATCHES "Intel")
-            add_compile_definitions(GGML_BLAS_USE_MKL)
             # all Intel* libraries share the same include path
             pkg_check_modules(DepBLAS mkl-sdl)
         elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC")
@@ -74,10 +72,26 @@ if (BLAS_FOUND)
 
     target_compile_options(ggml-blas PRIVATE ${BLAS_LINKER_FLAGS})
 
-    if ("${BLAS_INCLUDE_DIRS}" MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel"))
+    if ("${GGML_BLAS_VENDOR}" STREQUAL "")
+        message(WARNING "GGML_BLAS_VENDOR is not set; some methods may not link properly.")
+    endif()
+
+    if ("${GGML_BLAS_VENDOR}" MATCHES "Intel" OR ("${BLAS_INCLUDE_DIRS}" MATCHES "mkl" AND "${GGML_BLAS_VENDOR}" MATCHES "Generic"))
         add_compile_definitions(GGML_BLAS_USE_MKL)
     endif()
 
+    if ("${GGML_BLAS_VENDOR}" MATCHES "OpenBLAS")
+        add_compile_definitions(GGML_BLAS_USE_OPENBLAS)
+    endif()
+
+    if ("${GGML_BLAS_VENDOR}" MATCHES "FLAME" OR "${GGML_BLAS_VENDOR}" MATCHES "AOCL" OR "${GGML_BLAS_VENDOR}" MATCHES "AOCL_mt")
+        add_compile_definitions(GGML_BLAS_USE_BLIS)
+    endif()
+
+    if ("${GGML_BLAS_VENDOR}" MATCHES "NVPL")
+        add_compile_definitions(GGML_BLAS_USE_NVPL)
+    endif()
+
     target_link_libraries     (ggml-blas PRIVATE ${BLAS_LIBRARIES})
     target_include_directories(ggml-blas PRIVATE ${BLAS_INCLUDE_DIRS})
 else()

ggml/src/ggml-blas/ggml-blas.cpp

@@ -115,15 +115,11 @@ static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct gg
 #endif
     }
 
-#if defined(OPENBLAS_VERSION)
+#if defined(GGML_BLAS_USE_OPENBLAS)
     openblas_set_num_threads(ctx->n_threads);
-#endif
-
-#if defined(GGML_BLAS_USE_BLIS)
+#elif defined(GGML_BLAS_USE_BLIS)
     bli_thread_set_num_threads(ctx->n_threads);
-#endif
-
-#if defined(GGML_BLAS_USE_NVPL)
+#elif defined(GGML_BLAS_USE_NVPL)
     nvpl_blas_set_num_threads(ctx->n_threads);
 #endif
 
@@ -288,7 +284,7 @@ ggml_backend_t ggml_backend_blas_init(void) {
         /* .context = */ ctx,
     };
 
-#if defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
+#if defined(GGML_BLAS_USE_OPENBLAS) && defined(GGML_USE_OPENMP)
     if (openblas_get_parallel() != OPENBLAS_OPENMP) {
         GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
     }
@@ -329,7 +325,7 @@ static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t
         return "BLIS";
     #elif defined(GGML_BLAS_USE_NVPL)
         return "NVPL";
-    #elif defined(OPENBLAS_VERSION)
+    #elif defined(GGML_BLAS_USE_OPENBLAS)
         return "OpenBLAS";
     #else
         return "BLAS";

ggml/src/ggml-cuda/mmq.cu

@@ -190,7 +190,7 @@ void ggml_cuda_mul_mat_q(
     {
         const int64_t s11 = src1->nb[1] / ts_src1;
         const int64_t s12 = src1->nb[2] / ts_src1;
-        const int64_t s13 = src1->nb[2] / ts_src1;
+        const int64_t s13 = src1->nb[3] / ts_src1;
 
         if (use_native_mxfp4) {
             quantize_mmq_mxfp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
@@ -333,28 +333,31 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
     }
 
     if (amd_wmma_available(cc)) {
-        // RDNA 4 is consistently worse on rocblas
-        // https://github.com/ggml-org/llama.cpp/pull/18537#issuecomment-3706422301
         if (GGML_CUDA_CC_IS_RDNA3(cc)) {
-            // High expert counts almost always better on MMQ
-            // due to a large amount of graph splits
+            // High expert counts are almost always better on MMQ due to
+            //     the synchronization overhead in the cuBLAS/hipBLAS path:
             // https://github.com/ggml-org/llama.cpp/pull/18202
             if (n_experts >= 64) {
                 return true;
             }
 
+            // For some quantization types MMQ can have lower peak TOPS than hipBLAS
+            //     so it's only faster for sufficiently small batch sizes:
             switch (type) {
-                // These quants are really bad on MMQ
                 case GGML_TYPE_Q2_K:
+                    return ne11 <= 128;
                 case GGML_TYPE_Q6_K:
-                // These quants are usually worse but not always
+                    return ne11 <= (GGML_CUDA_CC_IS_RDNA3_0(cc) ? 128 : 256);
                 case GGML_TYPE_IQ2_XS:
                 case GGML_TYPE_IQ2_S:
-                    return ne11 <= 128;
+                    return GGML_CUDA_CC_IS_RDNA3_5(cc) || ne11 <= 128;
                 default:
                     return true;
             }
         }
+
+        // For RDNA4 MMQ is consistently faster than dequantization + hipBLAS:
+        // https://github.com/ggml-org/llama.cpp/pull/18537#issuecomment-3706422301
         return true;
     }
 

ggml/src/ggml-opencl/CMakeLists.txt

@@ -121,6 +121,8 @@ set(GGML_OPENCL_KERNELS
     tsembd
     upscale
     tanh
+    expm1
+    softplus
     pad
     repeat
     mul_mat_f16_f32

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -538,6 +538,10 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_pad;
     cl_kernel kernel_tanh_f32_nd;
     cl_kernel kernel_tanh_f16_nd;
+    cl_kernel kernel_expm1_f32_nd;
+    cl_kernel kernel_expm1_f16_nd;
+    cl_kernel kernel_softplus_f32_nd;
+    cl_kernel kernel_softplus_f16_nd;
     cl_kernel kernel_upscale;
     cl_kernel kernel_upscale_bilinear;
     cl_kernel kernel_concat_f32_contiguous;
@@ -1799,6 +1803,56 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         }
     }
 
+    // expm1
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "expm1.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("expm1.cl");
+#endif
+        cl_program prog;
+        if (!kernel_src.empty()) {
+            prog =
+                build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+            CL_CHECK((backend_ctx->kernel_expm1_f32_nd = clCreateKernel(prog, "kernel_expm1_f32_nd", &err), err));
+            CL_CHECK((backend_ctx->kernel_expm1_f16_nd = clCreateKernel(prog, "kernel_expm1_f16_nd", &err), err));
+            GGML_LOG_CONT(".");
+        } else {
+            GGML_LOG_WARN("ggml_opencl: expm1 kernel source not found or empty. Expm1 operation will not be available.\n");
+            prog = nullptr;
+            backend_ctx->kernel_expm1_f32_nd = nullptr;
+            backend_ctx->kernel_expm1_f16_nd = nullptr;
+        }
+        CL_CHECK(clReleaseProgram(prog));
+    }
+
+    // softplus
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "softplus.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("softplus.cl");
+#endif
+        cl_program prog;
+        if (!kernel_src.empty()) {
+            prog =
+                build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+            CL_CHECK((backend_ctx->kernel_softplus_f32_nd = clCreateKernel(prog, "kernel_softplus_f32_nd", &err), err));
+            CL_CHECK((backend_ctx->kernel_softplus_f16_nd = clCreateKernel(prog, "kernel_softplus_f16_nd", &err), err));
+            GGML_LOG_CONT(".");
+        } else {
+            GGML_LOG_WARN("ggml_opencl: softplus kernel source not found or empty. Softplus operation will not be available.\n");
+            prog = nullptr;
+            backend_ctx->kernel_softplus_f32_nd = nullptr;
+            backend_ctx->kernel_softplus_f16_nd = nullptr;
+        }
+        CL_CHECK(clReleaseProgram(prog));
+    }
+
     // upscale
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -3108,6 +3162,12 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 case GGML_UNARY_OP_TANH:
                    return (op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) ||
                           (op->src[0]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16);
+                case GGML_UNARY_OP_EXPM1:
+                   return (op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) ||
+                          (op->src[0]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16);
+                case GGML_UNARY_OP_SOFTPLUS:
+                   return (op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) ||
+                          (op->src[0]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16);
                 default:
                     return false;
             }
@@ -6464,6 +6524,210 @@ static void ggml_cl_tanh(ggml_backend_t backend, const ggml_tensor * src0, const
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst);
 }
 
+static void ggml_cl_expm1(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0_abs = extra0->offset + src0->view_offs;
+    cl_ulong offsetd_abs = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel;
+    if (dst->type == GGML_TYPE_F32) {
+        kernel = backend_ctx->kernel_expm1_f32_nd;
+    } else if (dst->type == GGML_TYPE_F16) {
+        kernel = backend_ctx->kernel_expm1_f16_nd;
+    } else {
+        GGML_ASSERT(false && "Unsupported type for ggml_cl_expm1");
+    }
+    GGML_ASSERT(kernel != nullptr);
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];
+
+    const cl_ulong nb00 = src0->nb[0];
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+    const cl_ulong nb03 = src0->nb[3];
+
+    const int ne10 = dst->ne[0];
+    const int ne11 = dst->ne[1];
+    const int ne12 = dst->ne[2];
+    const int ne13 = dst->ne[3];
+
+    const cl_ulong nb10 = dst->nb[0];
+    const cl_ulong nb11 = dst->nb[1];
+    const cl_ulong nb12 = dst->nb[2];
+    const cl_ulong nb13 = dst->nb[3];
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0_abs));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd_abs));
+
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),      &ne01));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne02));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int),      &ne03));
+    CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb00));
+    CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong),&nb02));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong),&nb03));
+
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),     &ne10));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),     &ne11));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),     &ne12));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),     &ne13));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong),&nb10));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong),&nb11));
+    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong),&nb12));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong),&nb13));
+
+    size_t global_work_size[3];
+    if (ne10 == 0 || ne11 == 0 || ne12 == 0 || ne13 == 0) { // Handle case of 0 elements
+        return;
+    }
+    global_work_size[0] = (size_t)ne10;
+    global_work_size[1] = (size_t)ne11;
+    global_work_size[2] = (size_t)ne12;
+
+    size_t lws0 = 16, lws1 = 4, lws2 = 1;
+    if (ne10 < 16) lws0 = ne10;
+    if (ne11 < 4) lws1 = ne11;
+    if (ne12 < 1) lws2 = ne12 > 0 ? ne12 : 1;
+
+    while (lws0 * lws1 * lws2 > 256 && lws0 > 1) lws0 /= 2;
+    while (lws0 * lws1 * lws2 > 256 && lws1 > 1) lws1 /= 2;
+    while (lws0 * lws1 * lws2 > 256 && lws2 > 1) lws2 /= 2;
+
+
+    size_t local_work_size[] = {lws0, lws1, lws2};
+
+    size_t* local_work_size_ptr = local_work_size;
+    if (!backend_ctx->non_uniform_workgroups) {
+        if (global_work_size[0] % local_work_size[0] != 0 ||
+            global_work_size[1] % local_work_size[1] != 0 ||
+            global_work_size[2] % local_work_size[2] != 0) {
+            local_work_size_ptr = NULL;
+        }
+    }
+    if (global_work_size[0] == 0 || global_work_size[1] == 0 || global_work_size[2] == 0) return;
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst);
+}
+
+static void ggml_cl_softplus(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0_abs = extra0->offset + src0->view_offs;
+    cl_ulong offsetd_abs = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel;
+    if (dst->type == GGML_TYPE_F32) {
+        kernel = backend_ctx->kernel_softplus_f32_nd;
+    } else if (dst->type == GGML_TYPE_F16) {
+        kernel = backend_ctx->kernel_softplus_f16_nd;
+    } else {
+        GGML_ASSERT(false && "Unsupported type for ggml_cl_softplus");
+    }
+    GGML_ASSERT(kernel != nullptr);
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];
+
+    const cl_ulong nb00 = src0->nb[0];
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+    const cl_ulong nb03 = src0->nb[3];
+
+    const int ne10 = dst->ne[0];
+    const int ne11 = dst->ne[1];
+    const int ne12 = dst->ne[2];
+    const int ne13 = dst->ne[3];
+
+    const cl_ulong nb10 = dst->nb[0];
+    const cl_ulong nb11 = dst->nb[1];
+    const cl_ulong nb12 = dst->nb[2];
+    const cl_ulong nb13 = dst->nb[3];
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0_abs));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd_abs));
+
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),      &ne01));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne02));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int),      &ne03));
+    CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb00));
+    CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong),&nb02));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong),&nb03));
+
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),     &ne10));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),     &ne11));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),     &ne12));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),     &ne13));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong),&nb10));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong),&nb11));
+    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong),&nb12));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong),&nb13));
+
+    size_t global_work_size[3];
+    if (ne10 == 0 || ne11 == 0 || ne12 == 0 || ne13 == 0) { // Handle case of 0 elements
+        return;
+    }
+    global_work_size[0] = (size_t)ne10;
+    global_work_size[1] = (size_t)ne11;
+    global_work_size[2] = (size_t)ne12;
+
+    size_t lws0 = 16, lws1 = 4, lws2 = 1;
+    if (ne10 < 16) lws0 = ne10;
+    if (ne11 < 4) lws1 = ne11;
+    if (ne12 < 1) lws2 = ne12 > 0 ? ne12 : 1;
+
+    while (lws0 * lws1 * lws2 > 256 && lws0 > 1) lws0 /= 2;
+    while (lws0 * lws1 * lws2 > 256 && lws1 > 1) lws1 /= 2;
+    while (lws0 * lws1 * lws2 > 256 && lws2 > 1) lws2 /= 2;
+
+
+    size_t local_work_size[] = {lws0, lws1, lws2};
+
+    size_t* local_work_size_ptr = local_work_size;
+    if (!backend_ctx->non_uniform_workgroups) {
+        if (global_work_size[0] % local_work_size[0] != 0 ||
+            global_work_size[1] % local_work_size[1] != 0 ||
+            global_work_size[2] % local_work_size[2] != 0) {
+            local_work_size_ptr = NULL;
+        }
+    }
+    if (global_work_size[0] == 0 || global_work_size[1] == 0 || global_work_size[2] == 0) return;
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst);
+}
+
 static void ggml_cl_repeat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1_shape_def, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -9637,6 +9901,18 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
                     }
                     func = ggml_cl_tanh;
                     break;
+                case GGML_UNARY_OP_EXPM1:
+                    if (!any_on_device) {
+                        return false;
+                    }
+                    func = ggml_cl_expm1;
+                    break;
+                case GGML_UNARY_OP_SOFTPLUS:
+                    if (!any_on_device) {
+                        return false;
+                    }
+                    func = ggml_cl_softplus;
+                    break;
                 default:
                     return false;
             } break;

ggml/src/ggml-opencl/kernels/expm1.cl

@@ -0,0 +1,82 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// expm1
+//------------------------------------------------------------------------------
+kernel void kernel_expm1_f32_nd(
+        global void * p_src0_base,
+        ulong off_src0_abs,
+        global void * p_dst_base,
+        ulong off_dst_abs,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13
+) {
+    int i0 = get_global_id(0);
+    int i1 = get_global_id(1);
+    int i2 = get_global_id(2);
+
+    if (i0 < ne10 && i1 < ne11 && i2 < ne12) {
+        for (int i3 = 0; i3 < ne13; ++i3) {
+            ulong src_offset_in_tensor = (ulong)i0*nb00 + (ulong)i1*nb01 + (ulong)i2*nb02 + (ulong)i3*nb03;
+            global const float *src_val_ptr = (global const float *)((global char *)p_src0_base + off_src0_abs + src_offset_in_tensor);
+
+            ulong dst_offset_in_tensor = (ulong)i0*nb10 + (ulong)i1*nb11 + (ulong)i2*nb12 + (ulong)i3*nb13;
+            global float *dst_val_ptr = (global float *)((global char *)p_dst_base + off_dst_abs + dst_offset_in_tensor);
+
+            *dst_val_ptr = exp(*src_val_ptr) - 1;
+        }
+    }
+}
+
+kernel void kernel_expm1_f16_nd(
+        global void * p_src0_base,
+        ulong off_src0_abs,
+        global void * p_dst_base,
+        ulong off_dst_abs,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13
+) {
+    int i0 = get_global_id(0);
+    int i1 = get_global_id(1);
+    int i2 = get_global_id(2);
+
+    if (i0 < ne10 && i1 < ne11 && i2 < ne12) {
+        for (int i3 = 0; i3 < ne13; ++i3) {
+            ulong src_offset_in_tensor = (ulong)i0*nb00 + (ulong)i1*nb01 + (ulong)i2*nb02 + (ulong)i3*nb03;
+            global const half *src_val_ptr = (global const half *)((global char *)p_src0_base + off_src0_abs + src_offset_in_tensor);
+
+            ulong dst_offset_in_tensor = (ulong)i0*nb10 + (ulong)i1*nb11 + (ulong)i2*nb12 + (ulong)i3*nb13;
+            global half *dst_val_ptr = (global half *)((global char *)p_dst_base + off_dst_abs + dst_offset_in_tensor);
+
+            *dst_val_ptr = exp(*src_val_ptr) - 1;
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/softplus.cl

@@ -0,0 +1,88 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// softplus
+//------------------------------------------------------------------------------
+inline float softplus_f32(float x){
+    float ax = fabs(x);
+    float m = fmax(x, 0.0f);
+    return log1p(exp(-ax)) + m;
+}
+
+kernel void kernel_softplus_f32_nd(
+        global void * p_src0_base,
+        ulong off_src0_abs,
+        global void * p_dst_base,
+        ulong off_dst_abs,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13
+) {
+    int i0 = get_global_id(0);
+    int i1 = get_global_id(1);
+    int i2 = get_global_id(2);
+
+    if (i0 < ne10 && i1 < ne11 && i2 < ne12) {
+        for (int i3 = 0; i3 < ne13; ++i3) {
+            ulong src_offset_in_tensor = (ulong)i0*nb00 + (ulong)i1*nb01 + (ulong)i2*nb02 + (ulong)i3*nb03;
+            global const float *src_val_ptr = (global const float *)((global char *)p_src0_base + off_src0_abs + src_offset_in_tensor);
+
+            ulong dst_offset_in_tensor = (ulong)i0*nb10 + (ulong)i1*nb11 + (ulong)i2*nb12 + (ulong)i3*nb13;
+            global float *dst_val_ptr = (global float *)((global char *)p_dst_base + off_dst_abs + dst_offset_in_tensor);
+
+            *dst_val_ptr = softplus_f32(*src_val_ptr);
+        }
+    }
+}
+
+kernel void kernel_softplus_f16_nd(
+        global void * p_src0_base,
+        ulong off_src0_abs,
+        global void * p_dst_base,
+        ulong off_dst_abs,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13
+) {
+    int i0 = get_global_id(0);
+    int i1 = get_global_id(1);
+    int i2 = get_global_id(2);
+
+    if (i0 < ne10 && i1 < ne11 && i2 < ne12) {
+        for (int i3 = 0; i3 < ne13; ++i3) {
+            ulong src_offset_in_tensor = (ulong)i0*nb00 + (ulong)i1*nb01 + (ulong)i2*nb02 + (ulong)i3*nb03;
+            global const half *src_val_ptr = (global const half *)((global char *)p_src0_base + off_src0_abs + src_offset_in_tensor);
+
+            ulong dst_offset_in_tensor = (ulong)i0*nb10 + (ulong)i1*nb11 + (ulong)i2*nb12 + (ulong)i3*nb13;
+            global half *dst_val_ptr = (global half *)((global char *)p_dst_base + off_dst_abs + dst_offset_in_tensor);
+
+            *dst_val_ptr = (half)(softplus_f32((float)(*src_val_ptr)));
+        }
+    }
+}

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -119,6 +119,8 @@ struct ggml_backend_vk_context;
 // Max number of adds that can be fused without exceeding MAX_PARAMETER_COUNT.
 #define MAX_FUSED_ADDS (MAX_PARAMETER_COUNT - 3)
 
+typedef std::shared_ptr<struct vk_pipeline_struct> vk_pipeline;
+
 struct vk_pipeline_struct {
     std::string name;
     vk::ShaderModule shader_module;
@@ -136,9 +138,15 @@ struct vk_pipeline_struct {
     std::atomic<bool> compiled {};
     // number of registers used, extracted from pipeline executable properties
     uint32_t register_count {};
+
+#if defined(VK_EXT_shader_64bit_indexing)
+    bool is_64b_indexing {};
+#endif
+    // linked list of pipelines for multiple compilation variants.
+    // currently only used to compile a 64-bit indexing variant.
+    vk_pipeline next;
 };
 
-typedef std::shared_ptr<vk_pipeline_struct> vk_pipeline;
 typedef std::weak_ptr<vk_pipeline_struct> vk_pipeline_ref;
 
 static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
@@ -230,9 +238,7 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
     /* .is_host          = */ NULL,
 };
 
-#ifdef GGML_VULKAN_MEMORY_DEBUG
 class vk_memory_logger;
-#endif
 class vk_perf_logger;
 static void ggml_vk_destroy_buffer(vk_buffer& buf);
 static void ggml_vk_synchronize(ggml_backend_vk_context * ctx);
@@ -584,6 +590,8 @@ struct vk_device_struct {
     bool add_rms_fusion;
     uint32_t partials_binding_alignment;
 
+    bool shader_64b_indexing;
+
     bool integer_dot_product;
     // 0: default, 1: force mmvq, -1: disable mmvq
     int32_t mmvq_mode;
@@ -815,9 +823,7 @@ struct vk_device_struct {
     bool allow_sysmem_fallback;
     bool disable_graph_optimize;
 
-#ifdef GGML_VULKAN_MEMORY_DEBUG
     std::unique_ptr<vk_memory_logger> memory_logger;
-#endif
 
     ~vk_device_struct() {
         VK_LOG_DEBUG("destroy device " << name);
@@ -1553,8 +1559,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_contex
 static void ggml_vk_load_shaders(vk_device& device);
 static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx);
 
-#if defined(GGML_VULKAN_MEMORY_DEBUG) || defined(GGML_VULKAN_DEBUG)
-#define VK_LOG_MEMORY(msg) std::cerr << "ggml_vulkan memory: " << msg << std::endl
+static bool vk_memory_logger_enabled = false;
+
+#define VK_LOG_MEMORY(msg) if (vk_memory_logger_enabled) { std::cerr << "ggml_vulkan memory: " << msg << std::endl; }
 
 static std::string format_size(size_t size) {
     const size_t kib = 1024;
@@ -1587,10 +1594,10 @@ private:
     std::map<vk::Buffer, size_t> allocations; // Track allocations
     size_t total_device;
     size_t total_host;
+    static std::mutex log_mutex;
 };
-#else
-#define VK_LOG_MEMORY(msg) ((void) 0)
-#endif // GGML_VULKAN_MEMORY_DEBUG
+
+std::mutex vk_memory_logger::log_mutex;
 
 static bool vk_perf_logger_enabled = false;
 static bool vk_perf_logger_concurrent = false;
@@ -1897,10 +1904,10 @@ struct ggml_backend_vk_buffer_context {
     }
 };
 
-#ifdef GGML_VULKAN_MEMORY_DEBUG
-static std::mutex log_mutex;
-
 void vk_memory_logger::log_allocation(vk_buffer_ref buf_ref, size_t size) {
+    if (!vk_memory_logger_enabled) {
+        return;
+    }
     std::lock_guard<std::mutex> guard(log_mutex);
     vk_buffer buf = buf_ref.lock();
     const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal);
@@ -1912,7 +1919,7 @@ void vk_memory_logger::log_allocation(vk_buffer_ref buf_ref, size_t size) {
 }
 
 void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) {
-    if (buf_ref.expired() || buf_ref.lock()->size == 0) {
+    if (buf_ref.expired() || buf_ref.lock()->size == 0 || !vk_memory_logger_enabled) {
         return;
     }
 
@@ -1930,7 +1937,6 @@ void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) {
         VK_LOG_MEMORY("ERROR " << buf->device->name << ": Attempted to deallocate unknown " << type << " memory at " << buf->buffer);
     }
 }
-#endif // GGML_VULKAN_MEMORY_DEBUG
 
 struct vk_instance_t {
     vk::Instance instance;
@@ -2080,6 +2086,19 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
         compute_pipeline_create_info.setPNext(&rci);
     }
 
+#if defined(VK_EXT_shader_64bit_indexing)
+    vk::PipelineCreateFlags2CreateInfo pipelineFlags2CreateInfo;
+    if (pipeline->is_64b_indexing)
+    {
+        pipelineFlags2CreateInfo.flags = vk::PipelineCreateFlagBits2::e64BitIndexingEXT;
+        if (device->pipeline_executable_properties_support) {
+            pipelineFlags2CreateInfo.flags |= vk::PipelineCreateFlagBits2::eCaptureStatisticsKHR;
+        }
+        pipelineFlags2CreateInfo.setPNext(compute_pipeline_create_info.pNext);
+        compute_pipeline_create_info.setPNext(&pipelineFlags2CreateInfo);
+    }
+#endif
+
     try {
         pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
     } catch (const vk::SystemError& e) {
@@ -2570,9 +2589,7 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std
         buf->bda_addr = device->device.getBufferAddress(addressInfo);
     }
 
-#ifdef GGML_VULKAN_MEMORY_DEBUG
     device->memory_logger->log_allocation(buf, size);
-#endif
 
     return buf;
 }
@@ -2629,11 +2646,9 @@ static void ggml_vk_destroy_buffer(vk_buffer& buf) {
         return;
     }
 
-#ifdef GGML_VULKAN_MEMORY_DEBUG
     if (buf->device != nullptr) {
         buf->device->memory_logger->log_deallocation(buf);
     }
-#endif
 
     buf.reset();
 }
@@ -3002,6 +3017,11 @@ static void ggml_vk_load_shaders(vk_device& device) {
         if ((device->architecture == AMD_GCN) && (device->driver_id != vk::DriverId::eAmdProprietary)) {
             m_warptile_mmq = m_warptile_mmq_int = { 256, 64, 64, 32, 16, 16, 2, 2, 2, 1, 16 };
             m_warptile_mmqid = m_warptile_mmqid_int = { 256, 64, 64, 32, 16, 16, 2, 2, 2, 1, 16 };
+        } else if (device->vendor_id == VK_VENDOR_ID_AMD && device->coopmat_support && device->driver_id != vk::DriverId::eAmdProprietary) {
+            // This is intentionally using tx_m values, slight performance increase
+            l_warptile = { 256, 128, 128, 16, subgroup_size_8, 64, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
+            l_warptile_mmq = l_warptile_mmq_int = { 256, 128, 128, 32, subgroup_size_8, 64, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
+            l_warptile_mmq_int_k = { 256, 128, 128, 32, subgroup_size_16, 64, 1, 4, 2, 1, subgroup_size_16 };
         } else if (device->vendor_id == VK_VENDOR_ID_INTEL && device->coopmat_support && device->architecture == INTEL_XE2) {
             // Xe2/Xe3 with coopmat enabled - warptile performance tuning
             l_warptile = { 512, 128, 128, 16, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
@@ -3061,7 +3081,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     }
 
     std::vector<std::future<void>> compiles;
-    auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const char *name, size_t spv_size, const void* spv_data, const char *entrypoint,
+    auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& base_pipeline, const char *name, size_t spv_size, const void* spv_data, const char *entrypoint,
                                               uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants,
                                               uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) {
 
@@ -3069,35 +3089,49 @@ static void ggml_vk_load_shaders(vk_device& device) {
             required_subgroup_size = get_subgroup_size(name, device->architecture);
         }
 
-        if (!pipeline) {
-            pipeline = std::make_shared<vk_pipeline_struct>();
-        }
-        if (!pipeline->initialized) {
-            pipeline->name = name;
-            pipeline->parameter_count = parameter_count;
-            pipeline->push_constant_size = push_constant_size;
-            pipeline->wg_denoms = wg_denoms;
-            pipeline->align = align;
-            pipeline->initialized = true;
-        }
+        vk_pipeline *ptr = &base_pipeline;
 
-        if (!pipeline->needed || pipeline->compiled) {
-            return;
+        int num_pipelines = 1;
+#if defined(VK_EXT_shader_64bit_indexing)
+        if (device->shader_64b_indexing) {
+            num_pipelines = 2;
         }
-        // TODO: We're no longer benefitting from the async compiles (shaders are
-        // compiled individually, as needed) and this complexity can be removed.
-        {
-            // wait until fewer than N compiles are in progress
-            uint32_t N = std::max(1u, std::thread::hardware_concurrency());
-            std::unique_lock<std::mutex> guard(compile_count_mutex);
-            while (compile_count >= N) {
-                compile_count_cond.wait(guard);
+#endif
+        for (int i = 0; i < num_pipelines; ++i, ptr = &(*ptr)->next) {
+            vk_pipeline &pipeline = *ptr;
+            if (!pipeline) {
+                pipeline = std::make_shared<vk_pipeline_struct>();
+            }
+            if (!pipeline->initialized) {
+                pipeline->name = name;
+                pipeline->parameter_count = parameter_count;
+                pipeline->push_constant_size = push_constant_size;
+                pipeline->wg_denoms = wg_denoms;
+                pipeline->align = align;
+                pipeline->initialized = true;
+#if defined(VK_EXT_shader_64bit_indexing)
+                pipeline->is_64b_indexing = (i == 1);
+#endif
             }
-            compile_count++;
-        }
 
-        compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), spv_size, spv_data, entrypoint,
-                                      parameter_count, wg_denoms, specialization_constants, disable_robustness, require_full_subgroups, required_subgroup_size));
+            if (!pipeline->needed || pipeline->compiled) {
+                continue;
+            }
+            // TODO: We're no longer benefitting from the async compiles (shaders are
+            // compiled individually, as needed) and this complexity can be removed.
+            {
+                // wait until fewer than N compiles are in progress
+                uint32_t N = std::max(1u, std::thread::hardware_concurrency());
+                std::unique_lock<std::mutex> guard(compile_count_mutex);
+                while (compile_count >= N) {
+                    compile_count_cond.wait(guard);
+                }
+                compile_count++;
+            }
+
+            compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), spv_size, spv_data, entrypoint,
+                                          parameter_count, wg_denoms, specialization_constants, disable_robustness, require_full_subgroups, required_subgroup_size));
+        }
     };
 
     auto const &ggml_vk_create_pipeline2 = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const char *entrypoint,
@@ -4435,9 +4469,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
         vk_device device = std::make_shared<vk_device_struct>();
         vk_instance.devices[idx] = device;
 
-#ifdef GGML_VULKAN_MEMORY_DEBUG
         device->memory_logger = std::unique_ptr<vk_memory_logger>(new vk_memory_logger());
-#endif
 
         size_t dev_num = vk_instance.device_indices[idx];
 
@@ -4475,6 +4507,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
         bool pipeline_executable_properties_support = false;
         device->coopmat_support = false;
         device->integer_dot_product = false;
+        device->shader_64b_indexing = false;
         bool bfloat16_support = false;
 
         for (const auto& properties : ext_props) {
@@ -4522,6 +4555,10 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 device->memory_priority = true;
             } else if (strcmp("VK_EXT_external_memory_host", properties.extensionName) == 0) {
                 device->external_memory_host = true;
+#if defined(VK_EXT_shader_64bit_indexing)
+            } else if (strcmp("VK_EXT_shader_64bit_indexing", properties.extensionName) == 0) {
+                device->shader_64b_indexing = true;
+#endif
             }
         }
 
@@ -4812,6 +4849,16 @@ static vk_device ggml_vk_get_device(size_t idx) {
             device_extensions.push_back("VK_EXT_external_memory_host");
         }
 
+#if defined(VK_EXT_shader_64bit_indexing)
+        VkPhysicalDeviceShader64BitIndexingFeaturesEXT shader_64bit_indexing_features {};
+        shader_64bit_indexing_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_64_BIT_INDEXING_FEATURES_EXT;
+        if (device->shader_64b_indexing) {
+            last_struct->pNext = (VkBaseOutStructure *)&shader_64bit_indexing_features;
+            last_struct = (VkBaseOutStructure *)&shader_64bit_indexing_features;
+            device_extensions.push_back("VK_EXT_shader_64bit_indexing");
+        }
+#endif
+
         vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2);
 
         device->pipeline_executable_properties_support = pipeline_executable_properties_support;
@@ -5078,7 +5125,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
             switch (device->vendor_id) {
 #ifndef GGML_VULKAN_RUN_TESTS
             case VK_VENDOR_ID_AMD:
-                device->mul_mat_l[i]    = false;
+                device->mul_mat_l[i]    = device->coopmat_support && device->driver_id != vk::DriverId::eAmdProprietary;
                 device->mul_mat_m[i]    = true;
                 device->mul_mat_s[i]    = true;
                 device->mul_mat_id_l[i] = false;
@@ -5419,6 +5466,7 @@ static void ggml_vk_instance_init() {
     vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr;
     vk_perf_logger_concurrent = getenv("GGML_VK_PERF_LOGGER_CONCURRENT") != nullptr;
     vk_enable_sync_logger = getenv("GGML_VK_SYNC_LOGGER") != nullptr;
+    vk_memory_logger_enabled = getenv("GGML_VK_MEMORY_LOGGER") != nullptr;
     const char* GGML_VK_PERF_LOGGER_FREQUENCY = getenv("GGML_VK_PERF_LOGGER_FREQUENCY");
 
     if (GGML_VK_PERF_LOGGER_FREQUENCY != nullptr) {
@@ -6897,6 +6945,20 @@ static void ggml_vk_quantize_q8_1(ggml_backend_vk_context * ctx, vk_context& sub
     ggml_vk_sync_buffers(ctx, subctx);
 }
 
+static vk_pipeline ggml_vk_get_64b_indexing_pipeline(ggml_backend_vk_context * ctx, vk_pipeline &pipeline) {
+    GGML_UNUSED(ctx);
+#if defined(VK_EXT_shader_64bit_indexing)
+    vk_pipeline *ptr = &pipeline;
+    while (*ptr) {
+        if ((*ptr)->is_64b_indexing) {
+            return *ptr;
+        }
+        ptr = &(*ptr)->next;
+    }
+#endif
+    return pipeline;
+}
+
 static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool disable_split_k) {
     VK_LOG_DEBUG("ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << ggml_type_name(src0->type) << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
     std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << ggml_type_name(src1->type) << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
@@ -6980,6 +7042,10 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
 
     vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? f16_type : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type));
 
+    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
+        pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
+    }
+
     // Reserve extra storage in the N dimension for the Y matrix, so we can avoid bounds-checking
     uint32_t padded_n = qy_needs_dequant ? ROUNDUP_POW2(ne11, pipeline->wg_denoms[1]) : ne11;
     const uint64_t x_ne = ggml_nelements(src0);
@@ -7289,6 +7355,10 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
     }
 
+    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
+        dmmv = ggml_vk_get_64b_indexing_pipeline(ctx, dmmv);
+    }
+
     const bool qx_needs_dequant = x_non_contig;
     const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig);
 
@@ -7484,9 +7554,15 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
         gqa_ratio = 1;
     }
 
+    vk_pipeline pipeline = ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1];
+
+    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
+        pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
+    }
+
     {
         // Request descriptor sets
-        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1], 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
     }
 
     vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops], true);
@@ -7528,7 +7604,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
         workgroups_z /= gqa_ratio;
     }
 
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1],
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
         {
             d_Qx,
             d_Qy,
@@ -7578,9 +7654,14 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
     const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t);
     const uint32_t channel_stride_y = nb12 / sizeof(float);
 
+    vk_pipeline pipeline = ctx->device->pipeline_mul_mat_vec_nc_f16_f32;
+    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
+        pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
+    }
+
     {
         // Request descriptor sets
-        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
     }
 
     vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops], true);
@@ -7617,7 +7698,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
 
     init_pushconst_tensor_offsets(ctx, pc, src0, src1, nullptr, nullptr, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]);
 
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32,
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
         {
             d_Qx,
             d_Qy,
@@ -7636,8 +7717,9 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, c
     // Handle huge A matrix by splitting the M dimensions. This works well for convolution use cases
     // where the M dimension is very large.
     // Split_k doesn't work with M splitting.
+    // This only supports batchsize == 1.
     const size_t nbytes = ggml_nbytes(src0);
-    const bool needs_split = nbytes > ctx->device->properties.limits.maxStorageBufferRange;
+    const bool needs_split = dst->ne[2] == 1 && dst->ne[3] == 1 && nbytes > ctx->device->properties.limits.maxStorageBufferRange;
     if (needs_split) {
         // Choose the number of rows that can fit (and divide by two, to allow for any additional offsets)
         const uint32_t M_split = ctx->device->properties.limits.maxStorageBufferRange / (2 * src0->nb[1]);
@@ -7779,6 +7861,9 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
 
     vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? f16_type : src0->type);
 
+    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
+        pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
+    }
     // Reserve extra storage in the N dimension for the Y matrix, so we can avoid bounds-checking
     uint32_t padded_n = qy_needs_dequant ? ROUNDUP_POW2(ne11, pipeline->wg_denoms[1]) :ne11;
     const uint64_t x_ne = ggml_nelements(src0);
@@ -8040,6 +8125,10 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     const bool qx_needs_dequant = x_non_contig;
     const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig);
 
+    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
+        dmmv = ggml_vk_get_64b_indexing_pipeline(ctx, dmmv);
+    }
+
     // Not implemented
     GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
     GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp

@@ -87,7 +87,6 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint tid = gl_LocalInvocationID.x;
 
     get_offsets(a_offset, b_offset, d_offset);
-    a_offset /= QUANT_K;
 
     y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
 

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl

@@ -65,9 +65,9 @@ void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) {
 
     a_offset =
 #ifdef MUL_MAT_ID
-            expert_id * p.batch_stride_a;
+            expert_id * (p.batch_stride_a / QUANT_K);
 #else
-            batch_idx_a * p.batch_stride_a;
+            batch_idx_a * (p.batch_stride_a / QUANT_K);
 #endif
     b_offset =
 #ifdef MUL_MAT_ID

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_m.comp

@@ -11,7 +11,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32,
                                const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
     // Compute starting index in matrix B for this superblock
     const uint y_idx = i * QUANT_K + 32 * ib32;
-    uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+    uint ibi = a_offset + first_row * num_blocks_per_row + i;
 
     // Precompute indices for quantization lookup tables
     const uint qh_base = 2 * ib32;

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp

@@ -17,7 +17,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32,
             const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]);
 
             // index for data_a
-            uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+            uint ibi = a_offset + first_row * num_blocks_per_row + i;
 
             [[unroll]] for (uint n = 0; n < num_rows; ++n) {
                 const float d = float(data_a[ibi].d);

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_s.comp

@@ -12,7 +12,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
     const uint nibble_shift = 4 * (itid & 1);
     const uint ib32 = itid / 2; // 0..7
 
-    uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+    uint ibi = a_offset + 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 scale = (data_a[ibi].scales[ib32] >> nibble_shift) & 0xF;

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_xs.comp

@@ -11,7 +11,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
     const uint y_idx = i * QUANT_K + 16 * itid;
     const uint nibble_shift = 4 * (itid & 1);
     const uint ib32 = itid / 2; // 0..7
-    uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+    uint ibi = a_offset + first_row * num_blocks_per_row + i;
     // Precompute db multiplication factors
     float db_vals[NUM_ROWS];
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
@@ -22,7 +22,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
         db_vals[n] = d * (0.125f + float(scale) * 0.25f);
         ibi += num_blocks_per_row;
     }
-    ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+    ibi = a_offset + first_row * num_blocks_per_row + i;
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
         // Preload grid and sign data for all l values
         vec4 grid0_vals[2], grid1_vals[2];

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_xxs.comp

@@ -11,7 +11,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
     const uint y_idx = i * QUANT_K + 16 * itid;
     const uint ib32 = itid / 2; // 0..7
 
-    uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+    uint ibi = a_offset + 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 signscale = pack32(u16vec2(

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq3_s.comp

@@ -10,7 +10,7 @@ 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;
+    uint ibi = a_offset + 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 scale = (data_a[ibi].scales[ib32/2] >> (4 * (ib32 & 1))) & 0xF;

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq3_xxs.comp

@@ -11,7 +11,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
     const uint y_idx = i * QUANT_K + 16 * itid;
     const uint ib32 = itid / 2; // 0..7
 
-    uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+    uint ibi = a_offset + 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 signscale = pack32(u16vec2(

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp

@@ -15,7 +15,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
     const uint y_idx = i * QUANT_K + y_offset;
 
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row;
         csel ^= 1;
 
         if (!all_threads) { // when we don't have enough blocks to use all threads

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp

@@ -14,7 +14,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint ix, co
     const uint y_idx = i * QUANT_K + y_offset;
 
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row;
         csel ^= 1;
 
         if (!all_threads) { // when we don't have enough blocks to use all threads

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp

@@ -13,7 +13,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint v_im,
     const uint y2_idx = y1_idx + 128;
 
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row;
         const FLOAT_TYPE_VEC2 dm = FLOAT_TYPE_VEC2(data_a[ib0 + i].dm);
 
         const uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp

@@ -13,7 +13,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint v_im,
     const uint y2_idx = y1_idx + 128;
 
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row;
         const FLOAT_TYPE_VEC2 dm = FLOAT_TYPE_VEC2(data_a[ib0 + i].dm);
 
         const uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im    ];

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp

@@ -15,7 +15,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
     const uint y_idx = i * QUANT_K + y_offset;
 
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row;
         csel ^= 1;
 
         if (!all_threads) { // when we don't have enough blocks to use all threads

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp

@@ -79,7 +79,7 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint tid = gl_LocalInvocationID.x;
 
     get_offsets(a_offset, b_offset, d_offset);
-    a_offset /= QUANT_K_Q8_1;
+    a_offset *= QUANT_K / QUANT_K_Q8_1;
     b_offset /= QUANT_K_Q8_1;
 
     FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp

@@ -234,13 +234,13 @@ void main() {
     const uint end_k = min(p.K, (ik + 1) * p.k_split);
 #endif
 
-    uint pos_a = (
+    uint pos_a =
 #ifdef MUL_MAT_ID
-        expert_idx * p.batch_stride_a +
+        expert_idx * (p.batch_stride_a / LOAD_VEC_A) +
 #else
-        batch_idx_a * p.batch_stride_a +
+        batch_idx_a * (p.batch_stride_a / LOAD_VEC_A) +
 #endif
-        ir * BM * p.stride_a + start_k) / LOAD_VEC_A;
+        (ir * BM * p.stride_a + start_k) / LOAD_VEC_A;
 #ifdef MUL_MAT_ID
     uint pos_b = 0;
 #else

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp

@@ -250,10 +250,10 @@ void main() {
 #endif
 
 #ifdef MUL_MAT_ID
-    uint pos_a = (expert_idx * p.batch_stride_a) / QUANT_K;
+    uint pos_a = expert_idx * (p.batch_stride_a / QUANT_K);
     uint pos_b = 0;
 #else
-    uint pos_a = (batch_idx_a * p.batch_stride_a) / QUANT_K;
+    uint pos_a = batch_idx_a * (p.batch_stride_a / QUANT_K);
     uint pos_b = batch_idx * p.batch_stride_b;
     uint pos_d = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
 #endif

ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp

@@ -189,13 +189,13 @@ void main() {
     const uint end_k = min(p.K, (ik + 1) * p.k_split);
 #endif
 
-    uint pos_a_ib = (
+    uint pos_a_ib =
 #ifdef MUL_MAT_ID
-        expert_idx * p.batch_stride_a +
+        expert_idx * (p.batch_stride_a / BK) +
 #else
-        batch_idx_a * p.batch_stride_a +
+        batch_idx_a * (p.batch_stride_a / BK) +
 #endif
-        ir * BM * p.stride_a + start_k) / BK;
+        (ir * BM * p.stride_a + start_k) / BK;
 #ifdef MUL_MAT_ID
     uint pos_b_ib = 0;
 #else

ggml/src/ggml-webgpu/ggml-webgpu.cpp

@@ -19,6 +19,7 @@
 
 #include <atomic>
 #include <condition_variable>
+#include <cstdint>
 #include <cstring>
 #include <iostream>
 #include <map>
@@ -1880,9 +1881,18 @@ static const char * ggml_backend_webgpu_device_get_description(ggml_backend_dev_
 
 static void ggml_backend_webgpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
     ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(dev->context);
-    // TODO: what do we actually want to return here? maxBufferSize might not be the full available memory.
-    *free                                    = ctx->webgpu_ctx->limits.maxBufferSize;
-    *total                                   = ctx->webgpu_ctx->limits.maxBufferSize;
+    // TODO: for now, return maxBufferSize as both free and total memory
+    // Track https://github.com/gpuweb/gpuweb/issues/5505 for updates.
+    uint64_t max_buffer_size = ctx->webgpu_ctx->limits.maxBufferSize;
+    // If we're on a 32-bit system, clamp to UINTPTR_MAX
+#if UINTPTR_MAX < UINT64_MAX
+    uint64_t max_ptr_size = static_cast<uint64_t>(UINTPTR_MAX);
+    if (max_buffer_size > max_ptr_size) {
+        max_buffer_size = max_ptr_size;
+    }
+#endif
+    *free  = static_cast<size_t>(max_buffer_size);
+    *total = static_cast<size_t>(max_buffer_size);
 }
 
 static enum ggml_backend_dev_type ggml_backend_webgpu_device_get_type(ggml_backend_dev_t dev) {

gguf-py/gguf/constants.py

@@ -276,12 +276,13 @@ class Keys:
         DATASETS    = "imatrix.datasets"
 
     class Clip:
-        PROJECTOR_TYPE      = "clip.projector_type"
-        HAS_VISION_ENCODER  = "clip.has_vision_encoder"
-        HAS_AUDIO_ENCODER   = "clip.has_audio_encoder"
-        HAS_LLAVA_PROJECTOR = "clip.has_llava_projector"
+        PROJECTOR_TYPE        = "clip.projector_type"
+        HAS_VISION_ENCODER    = "clip.has_vision_encoder"
+        HAS_AUDIO_ENCODER     = "clip.has_audio_encoder"
+        HAS_LLAVA_PROJECTOR   = "clip.has_llava_projector"
 
     class ClipVision:
+        PROJECTOR_TYPE      = "clip.vision.projector_type" # for mixed modality models
         IMAGE_SIZE          = "clip.vision.image_size"
         PREPROC_IMAGE_SIZE  = "clip.vision.preproc_image_size"
         PATCH_SIZE          = "clip.vision.patch_size"
@@ -307,6 +308,7 @@ class Keys:
             SCALE_FACTOR    = "clip.vision.projector.scale_factor"
 
     class ClipAudio:
+        PROJECTOR_TYPE      = "clip.audio.projector_type" # for mixed modality models
         NUM_MEL_BINS        = "clip.audio.num_mel_bins"
         EMBEDDING_LENGTH    = "clip.audio.embedding_length"
         FEED_FORWARD_LENGTH = "clip.audio.feed_forward_length"
@@ -465,6 +467,7 @@ class VISION_PROJECTOR_TYPE(IntEnum):
     RESAMPLER = auto()
     GLM_EDGE  = auto()
     MERGER    = auto()
+    GEMMA3N   = auto()
     GEMMA3    = auto()
     QWEN3VL   = auto()
     COGVLM    = auto()
@@ -675,6 +678,15 @@ class MODEL_TENSOR(IntEnum):
     V_MM_INP_NORM        = auto()
     V_MM_INP_PROJ        = auto() # gemma3
     V_MM_SOFT_EMB_NORM   = auto() # gemma3
+    V_MM_EMBEDDING       = auto() # gemma3n
+    V_MM_HARD_EMB_NORM   = auto() # gemma3n
+    V_ENC_CONV_STEM      = auto() # gemma3n
+    V_ENC_CONV_STEM_NORM = auto() # gemma3n
+    V_ENC_MSFA_EXP       = auto() # gemma3n
+    V_ENC_MSFA_EXP_NORM  = auto() # gemma3n
+    V_ENC_MSFA_PROJ      = auto() # gemma3n
+    V_ENC_MSFA_PROJ_NORM = auto() # gemma3n
+    V_ENC_MSFA_NORM      = auto() # gemma3n
     V_RESMPL_POS_EMBD_K  = auto() # minicpmv
     V_RESMPL_ATTN_Q      = auto() # minicpmv
     V_RESMPL_ATTN_K      = auto() # minicpmv
@@ -698,30 +710,41 @@ class MODEL_TENSOR(IntEnum):
     V_TOK_BOI            = auto() # cogvlm
     V_TOK_EOI            = auto() # cogvlm
     # audio (mtmd)
-    A_ENC_EMBD_POS       = auto()
-    A_ENC_EMBD_NORM      = auto()
-    A_ENC_EMBD_TO_LOGITS = auto()
-    A_ENC_CONV1D         = auto()
-    A_PRE_NORM           = auto()
-    A_POST_NORM          = auto()
-    A_ENC_ATTN_Q         = auto()
-    A_ENC_ATTN_K         = auto()
-    A_ENC_ATTN_V         = auto()
-    A_ENC_INPUT_NORM     = auto()
-    A_ENC_OUTPUT         = auto()
-    A_ENC_OUTPUT_NORM    = auto()
-    A_ENC_FFN_UP         = auto()
-    A_ENC_FFN_NORM       = auto()
-    A_ENC_FFN_GATE       = auto()
-    A_ENC_FFN_DOWN       = auto()
-    A_ENC_FFN_UP_1       = auto()
-    A_ENC_FFN_NORM_1     = auto()
-    A_ENC_FFN_GATE_1     = auto()
-    A_ENC_FFN_DOWN_1     = auto()
-    A_MMPROJ             = auto()
-    A_MMPROJ_FC          = auto()
-    A_MM_NORM_PRE        = auto()
-    A_MM_NORM_MID        = auto()
+    A_ENC_EMBD_POS        = auto()
+    A_ENC_EMBD_NORM       = auto()
+    A_ENC_EMBD_TO_LOGITS  = auto() # lfm2
+    A_ENC_CONV1D          = auto()
+    A_ENC_CONV1D_NORM     = auto() # gemma3n
+    A_PRE_NORM            = auto()
+    A_POST_NORM           = auto()
+    A_ENC_LAYER_PRE_NORM  = auto() # gemma3n
+    A_ENC_ATTN_Q          = auto()
+    A_ENC_ATTN_K          = auto()
+    A_ENC_ATTN_V          = auto()
+    A_ENC_PER_DIM_SCALE   = auto() # gemma3n
+    A_ENC_INPUT_NORM      = auto()
+    A_ENC_OUTPUT          = auto()
+    A_ENC_OUTPUT_NORM     = auto()
+    A_ENC_FFN_UP          = auto()
+    A_ENC_FFN_NORM        = auto()
+    A_ENC_FFN_POST_NORM   = auto() # gemma3n
+    A_ENC_FFN_SCALE       = auto() # gemma3n
+    A_ENC_FFN_GATE        = auto()
+    A_ENC_FFN_DOWN        = auto()
+    A_ENC_FFN_UP_1        = auto() # lfm2, gemma3n
+    A_ENC_FFN_NORM_1      = auto() # lfm2, gemma3n (pre-norm)
+    A_ENC_FFN_POST_NORM_1 = auto() # gemma3n
+    A_ENC_FFN_SCALE_1     = auto() # gemma3n
+    A_ENC_FFN_GATE_1      = auto() # lfm2, gemma3n
+    A_ENC_FFN_DOWN_1      = auto() # lfm2, gemma3n
+    A_MMPROJ              = auto()
+    A_MMPROJ_FC           = auto()
+    A_MM_NORM_PRE         = auto()
+    A_MM_NORM_MID         = auto()
+    A_MM_EMBEDDING        = auto() # gemma3n
+    A_MM_HARD_EMB_NORM    = auto() # gemma3n
+    A_MM_SOFT_EMB_NORM    = auto() # gemma3n
+    A_MM_INP_PROJ         = auto() # gemma3n
     # nextn/mtp
     NEXTN_EH_PROJ        = auto()
     NEXTN_EMBED_TOKENS   = auto()
@@ -1071,7 +1094,16 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.V_MM_POST_NORM:            "mm.post_norm",
     MODEL_TENSOR.V_MM_INP_PROJ:             "mm.input_projection",
     MODEL_TENSOR.V_MM_INP_NORM:             "mm.input_norm",
-    MODEL_TENSOR.V_MM_SOFT_EMB_NORM:        "mm.soft_emb_norm",
+    MODEL_TENSOR.V_MM_SOFT_EMB_NORM:        "mm.soft_emb_norm",         # gemma3n
+    MODEL_TENSOR.V_MM_EMBEDDING:            "mm.embedding",             # gemma3n
+    MODEL_TENSOR.V_MM_HARD_EMB_NORM:        "mm.hard_emb_norm",         # gemma3n
+    MODEL_TENSOR.V_ENC_CONV_STEM:           "v.conv_stem.conv",         # gemma3n
+    MODEL_TENSOR.V_ENC_CONV_STEM_NORM:      "v.conv_stem.bn",           # gemma3n
+    MODEL_TENSOR.V_ENC_MSFA_EXP:            "v.msfa.ffn.pw_exp.conv",   # gemma3n
+    MODEL_TENSOR.V_ENC_MSFA_EXP_NORM:       "v.msfa.ffn.pw_exp.bn",     # gemma3n
+    MODEL_TENSOR.V_ENC_MSFA_PROJ:           "v.msfa.ffn.pw_proj.conv",  # gemma3n
+    MODEL_TENSOR.V_ENC_MSFA_PROJ_NORM:      "v.msfa.ffn.pw_proj.bn",    # gemma3n
+    MODEL_TENSOR.V_ENC_MSFA_NORM:           "v.msfa.norm",              # gemma3n
     MODEL_TENSOR.V_RESMPL_POS_EMBD_K:       "resampler.pos_embd_k",
     MODEL_TENSOR.V_RESMPL_ATTN_Q:           "resampler.attn.q",
     MODEL_TENSOR.V_RESMPL_ATTN_K:           "resampler.attn.k",
@@ -1100,19 +1132,26 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.A_ENC_EMBD_NORM:           "a.position_embd_norm",
     MODEL_TENSOR.A_ENC_EMBD_TO_LOGITS:      "a.embd_to_logits",
     MODEL_TENSOR.A_ENC_CONV1D:              "a.conv1d.{bid}",
+    MODEL_TENSOR.A_ENC_CONV1D_NORM:         "a.conv1d.{bid}.norm",
     MODEL_TENSOR.A_PRE_NORM:                "a.pre_ln",
     MODEL_TENSOR.A_POST_NORM:               "a.post_ln",
+    MODEL_TENSOR.A_ENC_LAYER_PRE_NORM:      "a.blk.{bid}.layer_pre_norm",
     MODEL_TENSOR.A_ENC_ATTN_Q:              "a.blk.{bid}.attn_q",
     MODEL_TENSOR.A_ENC_ATTN_K:              "a.blk.{bid}.attn_k",
     MODEL_TENSOR.A_ENC_ATTN_V:              "a.blk.{bid}.attn_v",
+    MODEL_TENSOR.A_ENC_PER_DIM_SCALE:       "a.blk.{bid}.per_dim_scale",
     MODEL_TENSOR.A_ENC_INPUT_NORM:          "a.blk.{bid}.ln1",
     MODEL_TENSOR.A_ENC_OUTPUT:              "a.blk.{bid}.attn_out",
     MODEL_TENSOR.A_ENC_OUTPUT_NORM:         "a.blk.{bid}.ln2",
     MODEL_TENSOR.A_ENC_FFN_NORM:            "a.blk.{bid}.ffn_norm",
+    MODEL_TENSOR.A_ENC_FFN_POST_NORM:       "a.blk.{bid}.ffn_post_norm",
+    MODEL_TENSOR.A_ENC_FFN_SCALE:           "a.blk.{bid}.ffn_scale",
     MODEL_TENSOR.A_ENC_FFN_UP:              "a.blk.{bid}.ffn_up",
     MODEL_TENSOR.A_ENC_FFN_GATE:            "a.blk.{bid}.ffn_gate",
     MODEL_TENSOR.A_ENC_FFN_DOWN:            "a.blk.{bid}.ffn_down",
     MODEL_TENSOR.A_ENC_FFN_NORM_1:          "a.blk.{bid}.ffn_norm_1",
+    MODEL_TENSOR.A_ENC_FFN_POST_NORM_1:     "a.blk.{bid}.ffn_post_norm_1",
+    MODEL_TENSOR.A_ENC_FFN_SCALE_1:         "a.blk.{bid}.ffn_scale_1",
     MODEL_TENSOR.A_ENC_FFN_UP_1:            "a.blk.{bid}.ffn_up_1",
     MODEL_TENSOR.A_ENC_FFN_GATE_1:          "a.blk.{bid}.ffn_gate_1",
     MODEL_TENSOR.A_ENC_FFN_DOWN_1:          "a.blk.{bid}.ffn_down_1",
@@ -1120,6 +1159,10 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.A_MMPROJ_FC:               "mm.a.fc",
     MODEL_TENSOR.A_MM_NORM_PRE:             "mm.a.norm_pre",
     MODEL_TENSOR.A_MM_NORM_MID:             "mm.a.norm_mid",
+    MODEL_TENSOR.A_MM_INP_PROJ:             "mm.a.input_projection",      # gemma3n
+    MODEL_TENSOR.A_MM_SOFT_EMB_NORM:        "mm.a.soft_emb_norm",         # gemma3n
+    MODEL_TENSOR.A_MM_EMBEDDING:            "mm.a.embedding",             # gemma3n
+    MODEL_TENSOR.A_MM_HARD_EMB_NORM:        "mm.a.hard_emb_norm",         # gemma3n
     # lfm2 audio
     MODEL_TENSOR.A_ENC_NORM_CONV:           "a.blk.{bid}.norm_conv",
     MODEL_TENSOR.A_ENC_LINEAR_POS:          "a.blk.{bid}.linear_pos",
@@ -1170,6 +1213,15 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.V_MM_INP_PROJ,
         MODEL_TENSOR.V_MM_INP_NORM,
         MODEL_TENSOR.V_MM_SOFT_EMB_NORM,
+        MODEL_TENSOR.V_MM_EMBEDDING,
+        MODEL_TENSOR.V_MM_HARD_EMB_NORM,
+        MODEL_TENSOR.V_ENC_CONV_STEM,
+        MODEL_TENSOR.V_ENC_CONV_STEM_NORM,
+        MODEL_TENSOR.V_ENC_MSFA_EXP,
+        MODEL_TENSOR.V_ENC_MSFA_EXP_NORM,
+        MODEL_TENSOR.V_ENC_MSFA_PROJ,
+        MODEL_TENSOR.V_ENC_MSFA_PROJ_NORM,
+        MODEL_TENSOR.V_ENC_MSFA_NORM,
         MODEL_TENSOR.V_RESMPL_POS_EMBD_K,
         MODEL_TENSOR.V_RESMPL_ATTN_Q,
         MODEL_TENSOR.V_RESMPL_ATTN_K,
@@ -1197,19 +1249,26 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.A_ENC_EMBD_NORM,
         MODEL_TENSOR.A_ENC_EMBD_TO_LOGITS,
         MODEL_TENSOR.A_ENC_CONV1D,
+        MODEL_TENSOR.A_ENC_CONV1D_NORM,
         MODEL_TENSOR.A_PRE_NORM,
         MODEL_TENSOR.A_POST_NORM,
+        MODEL_TENSOR.A_ENC_LAYER_PRE_NORM,
         MODEL_TENSOR.A_ENC_ATTN_Q,
         MODEL_TENSOR.A_ENC_ATTN_K,
         MODEL_TENSOR.A_ENC_ATTN_V,
+        MODEL_TENSOR.A_ENC_PER_DIM_SCALE,
         MODEL_TENSOR.A_ENC_INPUT_NORM,
         MODEL_TENSOR.A_ENC_OUTPUT,
         MODEL_TENSOR.A_ENC_OUTPUT_NORM,
         MODEL_TENSOR.A_ENC_FFN_NORM,
+        MODEL_TENSOR.A_ENC_FFN_POST_NORM,
+        MODEL_TENSOR.A_ENC_FFN_SCALE,
         MODEL_TENSOR.A_ENC_FFN_UP,
         MODEL_TENSOR.A_ENC_FFN_GATE,
         MODEL_TENSOR.A_ENC_FFN_DOWN,
         MODEL_TENSOR.A_ENC_FFN_NORM_1,
+        MODEL_TENSOR.A_ENC_FFN_POST_NORM_1,
+        MODEL_TENSOR.A_ENC_FFN_SCALE_1,
         MODEL_TENSOR.A_ENC_FFN_UP_1,
         MODEL_TENSOR.A_ENC_FFN_GATE_1,
         MODEL_TENSOR.A_ENC_FFN_DOWN_1,
@@ -1226,6 +1285,10 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.A_ENC_CONV_NORM,
         MODEL_TENSOR.A_ENC_CONV_PW1,
         MODEL_TENSOR.A_ENC_CONV_PW2,
+        MODEL_TENSOR.A_MM_INP_PROJ,
+        MODEL_TENSOR.A_MM_SOFT_EMB_NORM,
+        MODEL_TENSOR.A_MM_EMBEDDING,
+        MODEL_TENSOR.A_MM_HARD_EMB_NORM,
     ],
     MODEL_ARCH.LLAMA: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -1675,6 +1738,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ATTN_OUT,
         MODEL_TENSOR.ATTN_POST_NORM,
         MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
         MODEL_TENSOR.FFN_GATE_INP,
         MODEL_TENSOR.FFN_GATE_INP_SHEXP,
         MODEL_TENSOR.FFN_UP_SHEXP,
@@ -3496,6 +3560,8 @@ class GGUFValueType(IntEnum):
 
 class VisionProjectorType:
     GEMMA3 = "gemma3"
+    GEMMA3NV = "gemma3nv"
+    GEMMA3NA = "gemma3na"
     IDEFICS3 = "idefics3"
     PIXTRAL = "pixtral"
     LLAMA4 = "llama4"

gguf-py/gguf/gguf_writer.py

@@ -1086,6 +1086,9 @@ class GGUFWriter:
     def add_clip_projector_type(self, value: str) -> None:
         self.add_string(Keys.Clip.PROJECTOR_TYPE, value)
 
+    def add_clip_vision_projector_type(self, value: str) -> None:
+        self.add_string(Keys.ClipVision.PROJECTOR_TYPE, value)
+
     def add_vision_projection_dim(self, value: int) -> None:
         self.add_uint32(Keys.ClipVision.PROJECTION_DIM, value)
 
@@ -1168,6 +1171,9 @@ class GGUFWriter:
 
     # audio models
 
+    def add_clip_audio_projector_type(self, value: str) -> None:
+        self.add_string(Keys.ClipAudio.PROJECTOR_TYPE, value)
+
     def add_audio_projection_dim(self, value: int) -> None:
         self.add_uint32(Keys.ClipAudio.PROJECTION_DIM, value)
 

gguf-py/gguf/tensor_mapping.py

@@ -123,6 +123,40 @@ class TensorNameMap:
         MODEL_TENSOR.CONV1D: (
             "backbone.embed", # roberta
         ),
+
+        MODEL_TENSOR.V_MM_EMBEDDING: (
+            "model.embed_vision.embedding", # gemma3n
+        ),
+        MODEL_TENSOR.V_MM_HARD_EMB_NORM: (
+            "model.embed_vision.hard_embedding_norm", # gemma3n
+        ),
+        MODEL_TENSOR.V_MM_INP_PROJ: (
+            "model.embed_vision.embedding_projection", # gemma3n
+        ),
+        MODEL_TENSOR.V_MM_SOFT_EMB_NORM: (
+            "model.embed_vision.soft_embedding_norm", # gemma3n
+        ),
+        MODEL_TENSOR.V_ENC_CONV_STEM: (
+            "model.vision_tower.timm_model.conv_stem.conv", # gemma3n
+        ),
+        MODEL_TENSOR.V_ENC_CONV_STEM_NORM: (
+            "model.vision_tower.timm_model.conv_stem.bn", # gemma3n
+        ),
+        MODEL_TENSOR.V_ENC_MSFA_EXP: (
+            "model.vision_tower.timm_model.msfa.ffn.pw_exp.conv", # gemma3n
+        ),
+        MODEL_TENSOR.V_ENC_MSFA_EXP_NORM: (
+            "model.vision_tower.timm_model.msfa.ffn.pw_exp.bn", # gemma3n
+        ),
+        MODEL_TENSOR.V_ENC_MSFA_PROJ: (
+            "model.vision_tower.timm_model.msfa.ffn.pw_proj.conv", # gemma3n
+        ),
+        MODEL_TENSOR.V_ENC_MSFA_PROJ_NORM: (
+            "model.vision_tower.timm_model.msfa.ffn.pw_proj.bn", # gemma3n
+        ),
+        MODEL_TENSOR.V_ENC_MSFA_NORM: (
+            "model.vision_tower.timm_model.msfa.norm", # gemma3n
+        ),
     }
 
     block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
@@ -1575,6 +1609,11 @@ class TensorNameMap:
         MODEL_TENSOR.A_ENC_CONV1D: (
             "audio_tower.conv{bid}", # ultravox
             "conformer.pre_encode.conv.{bid}", # lfm2
+            "model.audio_tower.subsample_conv_projection.conv_{bid}.conv", # gemma3n
+        ),
+
+        MODEL_TENSOR.A_ENC_CONV1D_NORM: (
+            "model.audio_tower.subsample_conv_projection.conv_{bid}.norm", # gemma3n
         ),
 
         MODEL_TENSOR.A_PRE_NORM: (),
@@ -1587,40 +1626,64 @@ class TensorNameMap:
         MODEL_TENSOR.A_ENC_ATTN_Q: (
             "audio_tower.layers.{bid}.self_attn.q_proj", # ultravox
             "conformer.layers.{bid}.self_attn.linear_q", # lfm2
+            "conformer.layers.{bid}.attention.attn.q_proj", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_ATTN_K: (
             "audio_tower.layers.{bid}.self_attn.k_proj", # ultravox
             "conformer.layers.{bid}.self_attn.linear_k", # lfm2
+            "conformer.layers.{bid}.attention.attn.k_proj", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_ATTN_V: (
             "audio_tower.layers.{bid}.self_attn.v_proj", # ultravox
             "conformer.layers.{bid}.self_attn.linear_v", # lfm2
+            "conformer.layers.{bid}.attention.attn.v_proj", # gemma3n
+        ),
+
+        MODEL_TENSOR.A_ENC_PER_DIM_SCALE: (
+            "conformer.layers.{bid}.attention.attn.per_dim_scale", # gemma3n
+        ),
+
+        MODEL_TENSOR.A_ENC_LAYER_PRE_NORM: (
+            "conformer.layers.{bid}.norm", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_INPUT_NORM: (
             "audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox
             "conformer.layers.{bid}.norm_self_att", # lfm2
+            "conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_OUTPUT: (
             "audio_tower.layers.{bid}.self_attn.out_proj", # ultravox
             "conformer.layers.{bid}.self_attn.linear_out", # lfm2
+            "conformer.layers.{bid}.attention.post", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_OUTPUT_NORM: (
             "audio_tower.layers.{bid}.final_layer_norm", # ultravox
             "conformer.layers.{bid}.norm_out", # lfm2
+            "conformer.layers.{bid}.attention.post_norm", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_FFN_NORM: (
             "conformer.layers.{bid}.norm_feed_forward1", # lfm2
+            "conformer.layers.{bid}.ffw_layer_start.pre_layer_norm", # gemma3n
+        ),
+
+        MODEL_TENSOR.A_ENC_FFN_POST_NORM: (
+            "conformer.layers.{bid}.ffw_layer_start.post_layer_norm", # gemma3n
+        ),
+
+        MODEL_TENSOR.A_ENC_FFN_SCALE: (
+            "conformer.layers.{bid}.ffw_layer_start.post_layer_scale", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_FFN_UP: (
             "audio_tower.layers.{bid}.fc1", # ultravox
             "conformer.layers.{bid}.feed_forward1.linear1", # lfm2
+            "conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_FFN_GATE: (),
@@ -1628,22 +1691,35 @@ class TensorNameMap:
         MODEL_TENSOR.A_ENC_FFN_DOWN: (
             "audio_tower.layers.{bid}.fc2", # ultravox
             "conformer.layers.{bid}.feed_forward1.linear2", # lfm2
+            "conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_FFN_UP_1: (
             "conformer.layers.{bid}.feed_forward2.linear1", # lfm2
+            "conformer.layers.{bid}.ffw_layer_end.ffw_layer_1", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_FFN_DOWN_1: (
             "conformer.layers.{bid}.feed_forward2.linear2", # lfm2
+            "conformer.layers.{bid}.ffw_layer_end.ffw_layer_2", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_FFN_NORM_1: (
             "conformer.layers.{bid}.norm_feed_forward2", # lfm2
+            "conformer.layers.{bid}.ffw_layer_end.pre_layer_norm", # gemma3n
+        ),
+
+        MODEL_TENSOR.A_ENC_FFN_POST_NORM_1: (
+            "conformer.layers.{bid}.ffw_layer_end.post_layer_norm", # gemma3n
+        ),
+
+        MODEL_TENSOR.A_ENC_FFN_SCALE_1: (
+            "conformer.layers.{bid}.ffw_layer_end.post_layer_scale", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_LINEAR_POS: (
             "conformer.layers.{bid}.self_attn.linear_pos", # lfm2
+            "conformer.layers.{bid}.attention.attn.relative_position_embedding.pos_proj", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_POS_BIAS_U: (
@@ -1656,6 +1732,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.A_ENC_OUT: (
             "conformer.pre_encode.out", # lfm2
+            "model.audio_tower.subsample_conv_projection.input_proj_linear", # gemma3n
         ),
 
         # note: some tensors below has "audio." pseudo-prefix, to prevent conflicts with vision tensors
@@ -1681,22 +1758,40 @@ class TensorNameMap:
 
         MODEL_TENSOR.A_ENC_CONV_DW: (
             "conformer.layers.{bid}.conv.depthwise_conv", # lfm2
+            "conformer.layers.{bid}.lconv1d.depthwise_conv1d", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_CONV_NORM: (
             "conformer.layers.{bid}.conv.batch_norm", # lfm2
+            "conformer.layers.{bid}.lconv1d.pre_layer_norm", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_CONV_PW1: (
             "conformer.layers.{bid}.conv.pointwise_conv1", # lfm2
+            "conformer.layers.{bid}.lconv1d.linear_start", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_CONV_PW2: (
             "conformer.layers.{bid}.conv.pointwise_conv2", # lfm2
+            "conformer.layers.{bid}.lconv1d.linear_end", # gemma3n
         ),
 
         MODEL_TENSOR.A_ENC_NORM_CONV: (
             "conformer.layers.{bid}.norm_conv", # lfm2
+            "conformer.layers.{bid}.lconv1d.conv_norm", # gemma3n
+        ),
+
+        MODEL_TENSOR.A_MM_EMBEDDING: (
+            "model.embed_audio.embedding", # gemma3n
+        ),
+        MODEL_TENSOR.A_MM_HARD_EMB_NORM: (
+            "model.embed_audio.hard_embedding_norm", # gemma3n
+        ),
+        MODEL_TENSOR.A_MM_INP_PROJ: (
+            "model.embed_audio.embedding_projection", # gemma3n
+        ),
+        MODEL_TENSOR.A_MM_SOFT_EMB_NORM: (
+            "model.embed_audio.soft_embedding_norm", # gemma3n
         ),
 
         # NextN/MTP tensors for GLM4_MOE

licenses/LICENSE-curl

@@ -1,9 +1,22 @@
-Copyright (c) 1996 - 2025, Daniel Stenberg, daniel@haxx.se, and many contributors, see the THANKS file.
+COPYRIGHT AND PERMISSION NOTICE
+
+Copyright (c) 1996 - 2026, Daniel Stenberg, <daniel@haxx.se>, and many
+contributors, see the THANKS file.
 
 All rights reserved.
 
-Permission to use, copy, modify, and distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.
+Permission to use, copy, modify, and distribute this software for any purpose
+with or without fee is hereby granted, provided that the above copyright
+notice and this permission notice appear in all copies.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN
+NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
+OR OTHER DEALINGS IN THE SOFTWARE.
 
-Except as contained in this notice, the name of a copyright holder shall not be used in advertising or otherwise to promote the sale, use or other dealings in this Software without prior written authorization of the copyright holder.
+Except as contained in this notice, the name of a copyright holder shall not
+be used in advertising or otherwise to promote the sale, use or other dealings
+in this Software without prior written authorization of the copyright holder.

scripts/pr2wt.sh

@@ -40,7 +40,7 @@ org_repo=${org_repo%.git}
 
 echo "org/repo: $org_repo"
 
-meta=$(curl -sSf -H "Accept: application/vnd.github+json" "https://api.github.com/repos/$org_repo/pulls/$PR")
+meta=$(curl -sSLf -H "Accept: application/vnd.github+json" "https://api.github.com/repos/$org_repo/pulls/$PR")
 
 url_remote=$(echo "$meta" | jq -r '.head.repo.clone_url')
 head_ref=$(echo "$meta" | jq -r '.head.ref')

scripts/sync_vendor.py

@@ -16,7 +16,8 @@ vendor = {
     # "https://github.com/mackron/miniaudio/raw/refs/tags/0.11.23/miniaudio.h": "vendor/miniaudio/miniaudio.h",
     "https://github.com/mackron/miniaudio/raw/669ed3e844524fcd883231b13095baee9f6de304/miniaudio.h": "vendor/miniaudio/miniaudio.h",
 
-    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.0/httplib.h": "vendor/cpp-httplib/httplib.h",
+    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.1/httplib.h": "vendor/cpp-httplib/httplib.h",
+    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.1/LICENSE":   "vendor/cpp-httplib/LICENSE",
 
     "https://raw.githubusercontent.com/sheredom/subprocess.h/b49c56e9fe214488493021017bf3954b91c7c1f5/subprocess.h": "vendor/sheredom/subprocess.h",
 }

src/llama-arch.cpp

@@ -950,6 +950,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_ATTN_K_NORM,
                 LLM_TENSOR_ATTN_V,
                 LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
                 LLM_TENSOR_FFN_NORM,
                 LLM_TENSOR_FFN_GATE_INP,
                 LLM_TENSOR_FFN_GATE_EXPS,

src/llama-model.cpp

@@ -6763,7 +6763,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         } else {
                             // Linear attention (gated delta net) specific tensors
                             // Create tensors with calculated dimensions
-                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, qkvz_dim }, 0);
+                            // note: ssm_in is used by legacy GGUF
+                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, qkvz_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
                             layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
                             layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
                             layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);

src/models/gemma3n-iswa.cpp

@@ -255,10 +255,20 @@ ggml_tensor * llm_build_gemma3n_iswa::get_per_layer_inputs() {
         inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, n_tokens);
         inp_per_layer = ggml_scale(ctx0, inp_per_layer, sqrtf((float) n_embd_altup));
         cb(inp_per_layer, "inp_per_layer_selected", -1);
+        res->add_input(std::move(inp));
     } else {
-        GGML_ABORT("TODO: support embd input");
+        // Vision embedding path: use padding token (ID=0) embedding
+        const int64_t embd_size = model.tok_embd_per_layer->ne[0];  // n_embd_altup * n_layer
+
+        // Extract and dequantize padding token embedding (column 0)
+        ggml_tensor * padding_q = ggml_view_1d(ctx0, model.tok_embd_per_layer, embd_size, 0);
+        ggml_tensor * padding_f32 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, embd_size);
+        inp_per_layer = ggml_cpy(ctx0, padding_q, padding_f32);
+
+        // Reshape to [n_embd_altup, n_layer, 1]
+        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, 1);
+        cb(inp_per_layer, "inp_per_layer_vision", -1);
     }
-    res->add_input(std::move(inp));
     return inp_per_layer;
 }
 
@@ -276,7 +286,7 @@ ggml_tensor * llm_build_gemma3n_iswa::project_per_layer_inputs(ggml_tensor * inp
                                               -1);  // [n_embd_altup, n_layer, n_tokens]
     cb(per_layer_proj, "per_layer_proj", -1);
 
-    inp_per_layer = ggml_add(ctx0, inp_per_layer, per_layer_proj);
+    inp_per_layer = ggml_add(ctx0, per_layer_proj, inp_per_layer);
     inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
     cb(inp_per_layer, "inp_per_layer", -1);
 

src/models/models.h

@@ -466,7 +466,8 @@ private:
                 ggml_tensor * cur,
                         int   il);
 
-    ggml_tensor * build_delta_net_chunking(
+    // returns pair of output and new state
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_chunking(
                 ggml_tensor * q,
                 ggml_tensor * k,
                 ggml_tensor * v,
@@ -478,7 +479,8 @@ private:
                 ggml_tensor * diag_mask,
                         int   il);
 
-    ggml_tensor * build_delta_net_autoregressive(
+    // returns pair of output and new state
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_autoregressive(
                 ggml_tensor * q,
                 ggml_tensor * k,
                 ggml_tensor * v,
@@ -493,6 +495,11 @@ private:
                 ggml_tensor * gate,
                         int   layer);
 
+    // returns pair of qkv, z
+    std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+                ggml_tensor * input,
+                        int   il);
+
     const llama_model & model;
 };
 

src/models/qwen3next.cpp

@@ -86,7 +86,15 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
     ggml_build_forward_expand(gf, cur);
 }
 
-ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
+// utility to get one slice from the third dimension
+// input dim:  [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chunking(
         ggml_tensor * q,
         ggml_tensor * k,
         ggml_tensor * v,
@@ -187,18 +195,16 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
     beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
 
     ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
 
-    cb(g_cumsum, "g_cumsum", il);
-
-    ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
     ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
 
     ggml_tensor * gcs_j_broadcast =
         ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
 
     ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-
-    cb(decay_mask, "decay_mask", il);
+    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
 
     decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
     decay_mask = ggml_exp(ctx0, decay_mask);
@@ -208,8 +214,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
 
     ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
     ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-
-    cb(attn, "attn_pre_solve", il);
+    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
 
     ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
     ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
@@ -217,8 +222,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
     ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
     attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
     attn                     = ggml_add(ctx0, attn, identity);
-
-    cb(attn, "attn_solved", il);
+    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
 
     v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
 
@@ -226,116 +230,126 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
     ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
 
     ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
-
-    cb(kbeta_gexp, "kbeta_gexp", il);
+    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
 
     ggml_tensor * k_cumdecay =
         ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
 
-    cb(k_cumdecay, "k_cumdecay", il);
+    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
 
+
+    // vectorized calculation of key_gdiff
+    // improved from the chunked version:
+    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+    //   key_gdiff = key * g_diff.unsqueeze(-1)
+    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+    // get last element in g_cumsum along chunk_size dimension (ne0)
+    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+    g_last = ggml_cont(ctx0, g_last);
+    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
+    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+
+    // state to be updated per chunk
+    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
     ggml_tensor * core_attn_out = nullptr;
-    ggml_tensor * new_state = ggml_dup(ctx0, state);
-
-    cb(new_state, "new_state", il);
 
     for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
-        auto chunkify = [=](ggml_tensor * t) {
-            return ggml_cont(ctx0, ggml_view_4d(ctx0, t, t->ne[0], chunk_size, 1, t->ne[3],
-                t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
-        };
+        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
 
-        auto chunkify_g = [=](ggml_tensor * t) {
-            return ggml_cont(ctx0, ggml_view_4d(ctx0, t, chunk_size, t->ne[1], 1, t->ne[3],
-                t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
-        };
+        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
 
-        ggml_tensor * k_chunk = chunkify(k);
-        ggml_tensor * q_chunk = chunkify(q);
-        ggml_tensor * v_chunk = chunkify(v);
+        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
 
-        ggml_tensor * g_cs_chunk = chunkify_g(g_cumsum);
-        ggml_tensor * g_cs_chunk_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cs_chunk));
-
-        ggml_tensor * decay_mask_chunk = chunkify(decay_mask);
-        ggml_tensor * k_cumdecay_chunk = chunkify(k_cumdecay);
-
-        ggml_tensor * gexp_chunk = ggml_exp(ctx0, g_cs_chunk_t);
+        // shape: (chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
 
         // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
-        attn = ggml_mul_mat(ctx0, k_chunk, q_chunk);
-        attn = ggml_mul(ctx0, attn, decay_mask_chunk);
-        attn = ggml_mul(ctx0, attn, diag_mask);
+        // replaced by precomputed attn_kq
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        cb(attn_chunk, "attn_chunk", il);
 
         ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
 
         // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
         ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
 
         // v_new = v_i - v_prime
         ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
         ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
 
         // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
         ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
         ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
 
         // core_attn_out[:, :, i] = attn_inter + attn @ v_new
-        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn);
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
 
         ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
 
-        core_attn_out = core_attn_out == nullptr ? core_attn_out_chunk : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 1);
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
 
-        // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
-        // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
-        // key_gdiff = key * g_diff.unsqueeze(-1)
         // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+        ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
+        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
+
         // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
-
-        ggml_tensor * g_cum_last =
-            ggml_cont(ctx0, ggml_view_4d(ctx0, g_cs_chunk_t, g_cs_chunk_t->ne[0], 1, g_cs_chunk_t->ne[2], g_cs_chunk_t->ne[3],
-                                        g_cs_chunk_t->nb[1], g_cs_chunk_t->nb[2], g_cs_chunk_t->nb[3],
-                                        g_cs_chunk_t->nb[0] * (g_cs_chunk_t->ne[1] - 1)));
-
-        ggml_tensor * gexp_last =
-            ggml_reshape_4d(ctx0, ggml_exp(ctx0, g_cum_last), 1, 1, g_cum_last->ne[0] * g_cum_last->ne[2], g_cum_last->ne[3]);
-
-        ggml_tensor * g_cum_last_3d =
-            ggml_reshape_3d(ctx0, g_cum_last, g_cum_last->ne[0], g_cum_last->ne[2], g_cum_last->ne[3]);
-
-        ggml_tensor * g_cumsum_3d = ggml_reshape_3d(ctx0, g_cs_chunk, g_cs_chunk->ne[0], g_cs_chunk->ne[2], g_cs_chunk->ne[3]);
-
-        ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum_3d, g_cum_last_3d));
-
-        ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
-
-        ggml_tensor * key_gdiff = ggml_mul(ctx0, k_chunk,
-                                        ggml_reshape_4d(ctx0, g_diff_exp, 1, g_diff_exp->ne[0], g_diff_exp->ne[1],
-                                                        g_diff_exp->ne[2] * g_diff_exp->ne[3]));
-
-        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)));
-
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
         new_state = ggml_add(ctx0,
-            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last, gexp_last->ne[0], gexp_last->ne[1], H_v, n_seqs)),
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
             ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
     }
 
-    core_attn_out = ggml_cont_4d(ctx0, core_attn_out, S_v, chunk_size * n_chunks, H_v, n_seqs);
-
-    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out, S_v, n_tokens, H_v, n_seqs, core_attn_out->nb[1], core_attn_out->nb[2], core_attn_out->nb[3], 0);
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
     cb(output_tokens, "output_tokens", il);
 
-    // flatten output
-    ggml_tensor * flat_output =
-        ggml_cont_1d(ctx0, ggml_permute(ctx0, output_tokens, 0, 2, 1, 3), S_v * H_v * n_tokens * n_seqs);
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
 
-    ggml_tensor * flat_state = ggml_cont_1d(ctx0, new_state, S_v * S_v * H_v * n_seqs);
-
-    return ggml_concat(ctx0, flat_output, flat_state, 0);
+    return {output_tokens, new_state};
 }
 
-ggml_tensor * llm_build_qwen3next::build_delta_net_autoregressive(
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
         ggml_tensor * q,
         ggml_tensor * k,
         ggml_tensor * v,
@@ -419,11 +433,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_autoregressive(
     cb(core_attn_out, "output_tokens", il);
     cb(state, "new_state", il);
 
-    // flatten output, no need to permute since n_tokens is 1 so [S_v, 1, H_v, n_seqs] and [S_v, H_v, 1, n_seqs] are equivalent memory-layout wise
-    ggml_tensor * flat_output = ggml_reshape_1d(ctx0, core_attn_out, S_v * H_v * n_tokens * n_seqs);
-    ggml_tensor * flat_state  = ggml_reshape_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
-
-    return ggml_concat(ctx0, flat_output, flat_state, 0);
+    return {core_attn_out, state};
 }
 
 ggml_tensor * llm_build_qwen3next::build_norm_gated(
@@ -523,6 +533,88 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn(
     return cur;
 }
 
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_qkvz(
+                ggml_tensor * input,
+                        int   il) {
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    if (model.layers[il].wqkv) {
+        // optimized path
+        ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+        qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+        cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+        ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+        cb(z, "z", il);
+
+        return { qkv_mixed, z };
+
+    } else {
+        // legacy (slower) path
+        ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
+        cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
+
+        int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
+        ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+
+        // Split mixed_qkvz into query, key, value, z
+        int64_t split_sizes_qkvz[4] = {
+            head_k_dim,                              // query size
+            head_k_dim,                              // key size
+            head_v_dim * num_v_heads / num_k_heads,  // value size
+            head_v_dim * num_v_heads / num_k_heads   // z size
+        };
+
+        ggml_tensor * query =
+            ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
+                        mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
+        cb(query, "q", il);
+
+        ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
+                                        mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                                        split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped));
+        cb(key, "k", il);
+
+        ggml_tensor * value =
+            ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
+                        mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                        (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * ggml_element_size(mixed_qkvz_reshaped));
+        cb(value, "v", il);
+
+        ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
+                                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                                    (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * ggml_element_size(mixed_qkvz_reshaped));
+        z = ggml_cont(ctx0, z);
+        cb(z, "z", il);
+
+        // After creating query, key, and value_reshaped, reshape each to flatten the head dimensions
+        // query: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
+        ggml_tensor * query_flat = ggml_cont_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
+        cb(query_flat, "query_flat", il);
+
+        // key: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
+        ggml_tensor * key_flat = ggml_cont_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
+        cb(key_flat, "key_flat", il);
+
+        // value_reshaped: [head_v_dim, num_v_heads, n_tokens, n_seqs] -> [head_v_dim * num_v_heads, n_tokens, n_seqs]
+        ggml_tensor * value_flat = ggml_cont_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+        cb(value_flat, "value_flat", il);
+
+        // Now concatenate along the feature dimension (dim 0) to get [conv_dim, n_tokens, n_seqs]
+        ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
+        qkv_mixed               = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
+        cb(qkv_mixed, "qkv_mixed", il);
+
+        return { qkv_mixed, z };
+    }
+}
+
 ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
         llm_graph_input_rs * inp,
         ggml_tensor *        cur,
@@ -547,15 +639,13 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
 
     // Input projections
-    ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, cur);
-    cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
+    auto qkvz = build_qkvz(cur, il);
+    ggml_tensor * qkv_mixed = qkvz.first;
+    ggml_tensor * z         = qkvz.second;
 
     ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
     cb(mixed_ba, "linear_attn_mixed_ba", il);
 
-    int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
-    ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
     // Reshape mixed_ba: [batch, seq_len, hidden_size] -> [batch, seq_len, num_k_heads, 2*num_v_heads/num_k_heads]
     int64_t       ba_new_dim        = 2 * num_v_heads / num_k_heads;
     ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
@@ -575,8 +665,9 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
                                    split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
     cb(a, "a", il);
 
-    // Reshape b and a to merge head dimensions: [batch, seq_len, num_k_heads, num_v_heads/num_k_heads] -> [batch, seq_len, num_v_heads]
-    ggml_tensor * beta  = ggml_cont_3d(ctx0, b, num_v_heads, n_seq_tokens, n_seqs);
+    ggml_tensor * beta  = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
+
+    // Reshape a to merge head dimensions: [batch, seq_len, num_k_heads, num_v_heads/num_k_heads] -> [batch, seq_len, num_v_heads]
     ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
 
     ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
@@ -585,48 +676,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);  // -A_log.exp() * softplus
     cb(gate, "gate", il);
 
-    // Split mixed_qkvz into query, key, value, z
-    int64_t split_sizes_qkvz[4] = {
-        head_k_dim,                              // query size
-        head_k_dim,                              // key size
-        head_v_dim * num_v_heads / num_k_heads,  // value size
-        head_v_dim * num_v_heads / num_k_heads   // z size
-    };
-
-    ggml_tensor * query =
-        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
-                     mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
-    cb(query, "q", il);
-
-    ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
-                                     mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                                     split_sizes_qkvz[0] * sizeof(float));
-    cb(key, "k", il);
-
-    ggml_tensor * value =
-        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
-                     mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                     (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * sizeof(float));
-    cb(value, "v", il);
-
-    ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
-                                   mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                                   (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * sizeof(float));
-    cb(z, "z", il);
-
-    // After creating query, key, and value_reshaped, reshape each to flatten the head dimensions
-    // query: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
-    ggml_tensor * query_flat = ggml_cont_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
-    cb(query_flat, "query_flat", il);
-
-    // key: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
-    ggml_tensor * key_flat = ggml_cont_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
-    cb(key_flat, "key_flat", il);
-
-    // value_reshaped: [head_v_dim, num_v_heads, n_tokens, n_seqs] -> [head_v_dim * num_v_heads, n_tokens, n_seqs]
-    ggml_tensor * value_flat = ggml_cont_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
-    cb(value_flat, "value_flat", il);
-
     // Get convolution states from cache
     ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
     ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
@@ -637,17 +686,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
     cb(conv_states, "conv_states", il);
 
-    // Now concatenate along the feature dimension (dim 0) to get [conv_dim, n_tokens, n_seqs]
-    ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
-    qkv_mixed               = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
-    cb(qkv_mixed, "qkv_mixed", il);
-
-    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
-    cb(qkv_mixed, "qkv_mixed_permuted", il);
-
-    // Calculate the total conv dimension
-    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
-
     // Calculate convolution kernel size
     ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
     const int64_t conv_kernel_size = conv_kernel->ne[0];
@@ -655,6 +693,9 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
     cb(conv_states, "conv_states_reshaped", il);
 
+    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+    cb(qkv_mixed, "qkv_mixed_permuted", il);
+
     ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
     cb(conv_input, "conv_input", il);
 
@@ -677,26 +718,25 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
     cb(conv_output_proper, "conv_output_raw", il);
 
-    conv_output_proper = ggml_cont(ctx0, ggml_transpose(ctx0, conv_output_proper));
-    cb(conv_output_proper, "conv_output_pre_silu", il);
-
     ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
     cb(conv_output_silu, "conv_output_silu", il);
 
-    ggml_tensor * conv_qkv_mix =
-        ggml_cont_2d(ctx0, ggml_transpose(ctx0, conv_output_silu), qkv_dim, n_seq_tokens * n_seqs);
-    cb(conv_qkv_mix, "conv_qkv_mix", il);
+    ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+    // Calculate the total conv dimension
+    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
 
     // Extract the convolved Q, K, V from conv_output
     ggml_tensor * q_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, conv_qkv_mix->nb[1], 0);
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
     cb(q_conv, "q_conv", il);
     ggml_tensor * k_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, conv_qkv_mix->nb[1],
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
                      head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
     cb(k_conv, "k_conv", il);
     ggml_tensor * v_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, conv_qkv_mix->nb[1],
+        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
                      2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
     cb(v_conv, "v_conv", il);
 
@@ -705,8 +745,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
     v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
 
-    beta = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
-
     ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
     state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
     cb(state, "state_predelta", il);
@@ -738,45 +776,29 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(v_conv, "v_conv_predelta", il);
 
     // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
-    ggml_tensor * attn_out;
+    std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
     if (n_seq_tokens == 1) {
         attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
     } else {
         attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
     }
-    cb(attn_out, "attn_out", il);
-
-    // The tensors were concatenated 1d, so we need to extract them 1d as well
-    const int64_t output_flat_size = head_v_dim * num_v_heads * n_seq_tokens * n_seqs;
-    ggml_tensor * attn_out_1d      = ggml_view_1d(ctx0, attn_out, output_flat_size, 0);
-    cb(attn_out_1d, "attn_out_1d", il);
-
-    ggml_tensor * attn_out_final = ggml_cont_4d(ctx0, attn_out_1d, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
-    cb(attn_out_final, "attn_out_reshaped", il);
-
-    // Extract the state part (second part of the concatenated tensor)
-    // State starts after n_tokens elements along dimension 1
-    const int64_t state_flat_size = head_v_dim * head_v_dim * num_v_heads * n_seqs;
-
-    ggml_tensor * state_1d =
-        ggml_view_1d(ctx0, attn_out, state_flat_size, output_flat_size * ggml_element_size(attn_out));
-    cb(state_1d, "state_1d", il);
+    ggml_tensor * output    = attn_out.first;
+    ggml_tensor * new_state = attn_out.second;
+    cb(output, "attn_output", il);
+    cb(new_state, "new_state", il);
 
     // Update the recurrent states
     ggml_build_forward_expand(gf,
-                              ggml_cpy(ctx0, state_1d,
+                              ggml_cpy(ctx0, new_state,
                                        ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
                                                     kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
 
-    GGML_ASSERT(ggml_nelements(attn_out_1d) + ggml_nelements(state_1d) == ggml_nelements(attn_out));
-
     // Reshape both attn_out_final and z to 2D tensors for normalization
     // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
-    ggml_tensor * attn_out_2d_final =
-        ggml_cont_2d(ctx0, attn_out_final, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
 
     // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
-    ggml_tensor * z_2d = ggml_cont_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
 
     // Apply gated normalization: self.norm(core_attn_out, z)
     ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
@@ -828,12 +850,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const int
             shared_gate = ggml_sigmoid(ctx0, shared_gate);
             cb(shared_gate, "shared_expert_gate_sigmoid", il);
 
-            // The gate needs to be broadcast to match the dimensions of ffn_shexp
-            // ffn_shexp is [n_embd, n_tokens, 1, 1] and shared_gate is [1, n_tokens, 1, 1]
-            // We need to repeat the gate along the feature dimension
-            shared_gate = ggml_repeat(ctx0, shared_gate, ffn_shexp);
-            cb(shared_gate, "shared_expert_gate_broadcast", il);
-
             // Apply the gate to the shared expert output
             ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
             cb(ffn_shexp, "ffn_shexp_gated", il);

tests/CMakeLists.txt

@@ -223,15 +223,6 @@ llama_build_and_test(test-model-load-cancel.cpp LABEL "model")
 llama_build_and_test(test-autorelease.cpp       LABEL "model")
 llama_build_and_test(test-backend-sampler.cpp   LABEL "model")
 
-llama_test(test-backend-sampler NAME test-backend-sampler-greedy       ARGS --test greedy)
-llama_test(test-backend-sampler NAME test-backend-sampler-temp         ARGS --test temp)
-llama_test(test-backend-sampler NAME test-backend-sampler-top_k        ARGS --test top_k)
-llama_test(test-backend-sampler NAME test-backend-sampler-dist         ARGS --test dist)
-llama_test(test-backend-sampler NAME test-backend-sampler-dist-and-cpu ARGS --test dist_and_cpu)
-llama_test(test-backend-sampler NAME test-backend-sampler-logit-bias   ARGS --test logit_bias)
-llama_test(test-backend-sampler NAME test-backend-sampler-mul_seq      ARGS --test multi_sequence)
-llama_test(test-backend-sampler NAME test-backend-sampler-set-sampler  ARGS --test set_sampler)
-
 # Test for state restore with fragmented KV cache
 # Requires a model, uses same args pattern as test-thread-safety
 if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")

tests/test-backend-ops.cpp

@@ -454,6 +454,28 @@ static bool ggml_is_view_op(enum ggml_op op) {
     return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
 }
 
+static bool backend_has_feature(ggml_backend_t backend, const char * feature_name) {
+    ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+    ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+
+    auto get_features = (ggml_backend_get_features_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_get_features");
+    if (!get_features) {
+        return false;
+    }
+
+    const ggml_backend_feature * features = get_features(reg);
+    if (!features) {
+        return false;
+    }
+
+    for (const ggml_backend_feature * f = features; f->name; ++f) {
+        if (strcmp(f->name, feature_name) == 0 && strcmp(f->value, "1") == 0) {
+            return true;
+        }
+    }
+    return false;
+}
+
 enum test_mode {
     MODE_TEST,
     MODE_PERF,
@@ -1101,6 +1123,11 @@ struct test_case {
         return 1e-7;
     }
 
+    virtual double max_nmse_err(ggml_backend_t backend) {
+        GGML_UNUSED(backend);
+        return max_nmse_err();
+    }
+
     virtual double max_maa_err() {
         return 1e-4;
     }
@@ -1109,6 +1136,10 @@ struct test_case {
         return max_nmse_err();
     }
 
+    virtual double max_err(ggml_backend_t backend) {
+        return max_nmse_err(backend);
+    }
+
     virtual double err(const float * a, const float * b, size_t n) {
         return nmse(a, b, n);
     }
@@ -1378,8 +1409,8 @@ struct test_case {
             }
 
             double err = ud->tc->err(f1.data(), f2.data(), f1.size());
-            if (err > ud->tc->max_err()) {
-                printf("[%s] ERR = %.9f > %.9f ", ggml_op_desc(t1), err, ud->tc->max_err());
+            if (err > ud->tc->max_err(ud->backend1)) {
+                printf("[%s] ERR = %.9f > %.9f ", ggml_op_desc(t1), err, ud->tc->max_err(ud->backend1));
                 //for (int i = 0; i < (int) f1.size(); i++) {
                 //    printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]);
                 //}
@@ -3686,6 +3717,14 @@ struct test_mul_mat : public test_case {
         return 5e-4;
     }
 
+    double max_nmse_err(ggml_backend_t backend) override {
+        // for blackwell we quantize activations to mxfp4 instead of q8_1 so we add higher tolerance
+        if (type_a == GGML_TYPE_MXFP4 && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
+            return 2e-2;
+        }
+        return max_nmse_err();
+    }
+
     int64_t grad_nmax() override {
         return 20000;
     }
@@ -3814,6 +3853,14 @@ struct test_mul_mat_id : public test_case {
         return 5e-4;
     }
 
+    double max_nmse_err(ggml_backend_t backend) override {
+        // for blackwell we quantize activations to mxfp4 instead of q8_1 so we add higher tolerance
+        if (type_a == GGML_TYPE_MXFP4 && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
+            return 2e-2;
+        }
+        return max_nmse_err();
+    }
+
     uint64_t op_flops(ggml_tensor * t) override {
         GGML_UNUSED(t);
         return 2 * m * k * n * n_used;
@@ -7513,6 +7560,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F16, 1700000, 96, 2592, {1, 1}, {1, 1}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F16, 1700000,  3, 2592, {1, 1}, {1, 1}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F16, 1700000,  1, 2592, {1, 1}, {1, 1}));
+
+    test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_Q8_0, GGML_TYPE_F32, 128, 128, false, 8192, 2, 5120)); // Llama-4-Maverick-17B-128E-PAB-Q8_0
+    test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_Q8_0, GGML_TYPE_F32, 128, 128, false, 8192, 1, 5120)); // Llama-4-Maverick-17B-128E-PAB-Q8_0
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 8192, 1, 5120, {128, 1}, {1, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 8192, 512, 5120, {128, 1}, {1, 1}));
 #endif
 
     for (ggml_type type_a : all_types) {

tests/test-backend-sampler.cpp

@@ -11,76 +11,78 @@
 #include <algorithm>
 #include <cstdlib>
 #include <cstring>
-#include <iostream>
 #include <fstream>
 #include <map>
 #include <string>
 #include <unordered_map>
 #include <vector>
 
-struct backend_cli_args {
-    const char * model = nullptr;
-    const char * test = nullptr;
-    const char * device = "cpu";
+struct test_args {
+    std::string model;
+    std::string test;
+    std::string device = "auto";
 };
 
-struct test_model_context {
-    llama_model_ptr   model;
+struct test_params {
+    llama_model_ptr model;
+};
+
+static llama_model_ptr load_model(const test_args & args) {
+    auto mparams = llama_model_default_params();
+
+    ggml_backend_dev_t devs[2] = { nullptr, nullptr };
+
+    if (args.device != "auto") {
+        if (args.device == "gpu") {
+            devs[0] = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
+
+            if (devs[0] == nullptr) {
+                fprintf(stderr, "Error: GPU requested but not available\n");
+                return nullptr;
+            }
+
+            mparams.n_gpu_layers = 999;
+        } else if (args.device == "cpu") {
+            devs[0] = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+
+            mparams.n_gpu_layers = 0;
+        } else {
+            fprintf(stderr, "Error: invalid device '%s'\n", args.device.c_str());
+            return nullptr;
+        }
+
+        mparams.devices = devs;
+
+        fprintf(stderr, "Using device: %s\n", ggml_backend_dev_name(devs[0]));
+    }
+
+    llama_model_ptr res;
+
+    res.reset(llama_model_load_from_file(args.model.c_str(), mparams));
+
+    if (!res) {
+        fprintf(stderr, "Warning: failed to load model '%s', skipping test\n", args.model.c_str());
+        return nullptr;
+    }
+
+    return res;
+}
+
+struct test_context {
     llama_context_ptr ctx;
-    int               n_vocab = 0;
+
+    int n_vocab = 0;
+
+    const llama_vocab * vocab = nullptr;
 
     std::unordered_map<llama_seq_id, int32_t> seq_positions;
     std::unordered_map<llama_seq_id, int32_t> last_batch_info;
 
-    bool load_model(const backend_cli_args & args) {
-        if (model) {
-            return true;
-        }
+    test_context(const test_params & params, std::vector<llama_sampler_seq_config> & configs, int32_t n_seq_max = -1) {
+        auto * model = params.model.get();
 
-        llama_backend_init();
-
-        auto mparams = llama_model_default_params();
-
-        ggml_backend_dev_t devs[2];
-        if (std::string_view(args.device) == "gpu") {
-            ggml_backend_dev_t gpu = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
-            if (gpu == nullptr) {
-                fprintf(stderr, "Error: GPU requested but not available\n");
-                return false;
-            }
-            devs[0] = gpu;
-            devs[1] = nullptr; // null terminator
-            mparams.devices = devs;
-            mparams.n_gpu_layers = 999;
-        } else if (std::string_view(args.device) == "cpu") {
-            ggml_backend_dev_t cpu = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-            devs[0] = cpu;
-            devs[1] = nullptr; // null terminator
-            mparams.devices = devs;
-        }
-
-        fprintf(stderr, "Using device: %s\n", ggml_backend_dev_name(devs[0]));
-
-        model.reset(llama_model_load_from_file(args.model, mparams));
-
-        if (!model) {
-            fprintf(stderr, "Warning: failed to load model '%s', skipping test\n", args.model);
-            return false;
-        }
-        n_vocab = llama_vocab_n_tokens(get_vocab());
-        fprintf(stderr, "Vocabulary size: %d\n", n_vocab);
-
-        return true;
-    }
-
-    bool setup(const backend_cli_args & args, std::vector<llama_sampler_seq_config> & configs, int32_t n_seq_max = -1) {
-        if (!model) {
-            load_model(args);
-        }
-
-        if (ctx) {
-            return true;
-        }
+        GGML_ASSERT(model);
+        GGML_ASSERT(!ctx);
 
         llama_context_params cparams = llama_context_default_params();
         cparams.n_ctx = 512;
@@ -99,26 +101,23 @@ struct test_model_context {
             cparams.n_seq_max = n_seq_max;
         }
 
-        ctx.reset(llama_init_from_model(model.get(), cparams));
+        ctx.reset(llama_init_from_model(model, cparams));
         if (!ctx) {
-            fprintf(stderr, "Warning: failed to create context, skipping test\n");
-            return false;
+            throw std::runtime_error("failed to create context");
         }
+
         llama_set_warmup(ctx.get(), false);
 
-        return true;
+        vocab = llama_model_get_vocab(model);
+        n_vocab = llama_vocab_n_tokens(vocab);
     }
 
     bool decode(const std::map<llama_seq_id, std::string> & prompts) {
-        if (!ctx) {
-            fprintf(stderr, "Error: context not initialized, call setup() first\n");
-            return false;
-        }
+        GGML_ASSERT(ctx);
 
         last_batch_info.clear();
         llama_batch batch = llama_batch_init(512, 0, prompts.size());
 
-        auto vocab = get_vocab();
         for (const auto & [seq_id, prompt] : prompts) {
             std::vector<llama_token> tokens;
             tokens.push_back(llama_vocab_bos(vocab));
@@ -199,10 +198,7 @@ struct test_model_context {
     }
 
     bool decode_token(llama_token token, llama_seq_id seq_id = 0) {
-        if (ctx == nullptr) {
-            fprintf(stderr, "Error: context not initialized, call setup() first\n");
-            return false;
-        }
+        GGML_ASSERT(ctx);
 
         llama_batch batch = llama_batch_init(1, 0, 1);
         int32_t pos = seq_positions[seq_id];
@@ -218,14 +214,12 @@ struct test_model_context {
 
         seq_positions[seq_id]++;
         llama_batch_free(batch);
+
         return true;
     }
 
     bool decode_tokens(const std::map<llama_seq_id, llama_token> & seq_tokens) {
-        if (ctx == nullptr) {
-            fprintf(stderr, "Error: context not initialized, call setup() first\n");
-            return false;
-        }
+        GGML_ASSERT(ctx);
 
         llama_batch batch = llama_batch_init(seq_tokens.size(), 0, seq_tokens.size());
 
@@ -247,40 +241,27 @@ struct test_model_context {
         update_batch_info(batch);
 
         llama_batch_free(batch);
+
         return true;
     }
 
-    std::string token_to_piece(llama_token token, bool special) {
+    std::string token_to_piece(llama_token token, bool special) const {
         std::string piece;
         piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
-        const int n_chars = llama_token_to_piece(get_vocab(), token, &piece[0], piece.size(), 0, special);
+        const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
         if (n_chars < 0) {
             piece.resize(-n_chars);
-            int check = llama_token_to_piece(get_vocab(), token, &piece[0], piece.size(), 0, special);
+            int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
             GGML_ASSERT(check == -n_chars);
-        }
-        else {
+        } else {
             piece.resize(n_chars);
         }
 
         return piece;
     }
-
-    void reset() {
-        ctx.reset();
-        seq_positions.clear();
-        last_batch_info.clear();
-    }
-
-    const llama_vocab * get_vocab() const {
-        return model ? llama_model_get_vocab(model.get()) : nullptr;
-    }
-
 };
 
-static void test_backend_greedy_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_greedy_sampling(const test_params & params) {
     const int seq_id = 0;
 
     struct llama_sampler_chain_params backend_sampler_params = llama_sampler_chain_default_params();
@@ -289,9 +270,7 @@ static void test_backend_greedy_sampling(const backend_cli_args & args) {
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_greedy());
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     if (!test_ctx.decode({{seq_id, "Some"}})) {
         GGML_ASSERT(false && "Failed to decode token");
@@ -317,9 +296,7 @@ static void test_backend_greedy_sampling(const backend_cli_args & args) {
     }
 }
 
-static void test_backend_top_k_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_top_k_sampling(const test_params & params) {
     const int seq_id = 0;
     const int32_t k = 8;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
@@ -327,9 +304,7 @@ static void test_backend_top_k_sampling(const backend_cli_args & args) {
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_top_k(k));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
         GGML_ASSERT(false && "Failed to decode token");
@@ -358,16 +333,12 @@ static void test_backend_top_k_sampling(const backend_cli_args & args) {
 
     llama_sampler_chain_add(chain.get(), llama_sampler_init_dist(18));
     llama_token token = llama_sampler_sample(chain.get(), test_ctx.ctx.get(), batch_idx);
-    const std::string token_str = test_ctx.token_to_piece(token, false);
     GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab);
 
     printf("backend top-k hybrid sampling test PASSED\n");
 }
 
-static void test_backend_temp_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
-
+static void test_backend_temp_sampling(const test_params & params) {
     {
         const float temp_0 = 0.8f;
         struct llama_sampler_chain_params backend_chain_params_0 = llama_sampler_chain_default_params();
@@ -384,9 +355,7 @@ static void test_backend_temp_sampling(const backend_cli_args & args) {
             { 1, backend_sampler_chain_1.get() }
         };
 
-        if (!test_ctx.setup(args, backend_sampler_configs)) {
-            return;
-        }
+        test_context test_ctx(params, backend_sampler_configs);
 
         if (!test_ctx.decode({{0, "Some where over the"}, {1, "Once upon a"}})) {
             GGML_ASSERT(false && "Failed to decode token");
@@ -430,8 +399,6 @@ static void test_backend_temp_sampling(const backend_cli_args & args) {
     auto test_argmax_temp = [&](float temp) {
         printf("\nTesting temperature = %.1f\n", temp);
 
-        test_ctx.reset();
-
         int seq_id = 0;
         struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
         llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
@@ -441,9 +408,7 @@ static void test_backend_temp_sampling(const backend_cli_args & args) {
             { seq_id, backend_sampler_chain.get() },
         };
 
-        if (!test_ctx.setup(args, backend_sampler_configs)) {
-            return;
-        }
+        test_context test_ctx(params, backend_sampler_configs);
 
         if (!test_ctx.decode({{seq_id, "Once"}})) {
             GGML_ASSERT(false && "Failed to decode token");
@@ -459,12 +424,9 @@ static void test_backend_temp_sampling(const backend_cli_args & args) {
     test_argmax_temp(-1.0f);
 
     printf("backend temp sampling test PASSED\n");
-
 }
 
-static void test_backend_temp_ext_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_temp_ext_sampling(const test_params & params) {
     {
         int seq_id = 0;
         const float temp = 0.8f;
@@ -478,9 +440,7 @@ static void test_backend_temp_ext_sampling(const backend_cli_args & args) {
             { seq_id, backend_sampler_chain.get() },
         };
 
-        if (!test_ctx.setup(args, backend_sampler_configs)) {
-            return;
-        }
+        test_context test_ctx(params, backend_sampler_configs);
 
         if (!test_ctx.decode({{seq_id, "Once upon a"}})) {
             GGML_ASSERT(false && "Failed to decode token");
@@ -494,14 +454,10 @@ static void test_backend_temp_ext_sampling(const backend_cli_args & args) {
         }
     }
 
-    test_ctx.reset();
-
     // lambda to testing non-positive temp/delta/exponent values.
     auto test_argmax_temp = [&](float temp, float delta, float exponent) {
         printf("\nTesting temperature = %.1f, delta = %1.f, exponent = %1.f\n", temp, delta, exponent);
 
-        test_ctx.reset();
-
         int seq_id = 0;
         struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
         llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
@@ -511,9 +467,7 @@ static void test_backend_temp_ext_sampling(const backend_cli_args & args) {
             { seq_id, backend_sampler_chain.get() },
         };
 
-        if (!test_ctx.setup(args, backend_sampler_configs)) {
-            return;
-        }
+        test_context test_ctx(params, backend_sampler_configs);
 
         if (!test_ctx.decode({{seq_id, "Once"}})) {
             GGML_ASSERT(false && "Failed to decode token");
@@ -535,12 +489,9 @@ static void test_backend_temp_ext_sampling(const backend_cli_args & args) {
     test_argmax_temp(0.8f,  0.0f, 2.0f); // Temperature scaling
 
     printf("backend temp_ext sampling test PASSED\n");
-
 }
 
-static void test_backend_min_p_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_min_p_sampling(const test_params & params) {
     const int seq_id = 0;
     const float p = 0.1;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
@@ -548,9 +499,7 @@ static void test_backend_min_p_sampling(const backend_cli_args & args) {
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_min_p(p, 0));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
         GGML_ASSERT(false && "Failed to decode token");
@@ -594,9 +543,7 @@ static void test_backend_min_p_sampling(const backend_cli_args & args) {
     printf("min-p sampling test PASSED\n");
 }
 
-static void test_backend_top_p_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_top_p_sampling(const test_params & params) {
     const int seq_id = 0;
     const float p = 0.9;
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
@@ -604,9 +551,7 @@ static void test_backend_top_p_sampling(const backend_cli_args & args) {
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_top_p(p, 0));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
         return;
@@ -648,9 +593,7 @@ static void test_backend_top_p_sampling(const backend_cli_args & args) {
     printf("top-p sampling test PASSED\n");
 }
 
-static void test_backend_multi_sequence_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_multi_sequence_sampling(const test_params & params) {
     struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
     llama_sampler_ptr sampler_chain_0(llama_sampler_chain_init(chain_params_0));
     llama_sampler_chain_add(sampler_chain_0.get(), llama_sampler_init_greedy());
@@ -665,9 +608,7 @@ static void test_backend_multi_sequence_sampling(const backend_cli_args & args)
         { 1, sampler_chain_1.get() }
     };
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     std::map<llama_seq_id, std::string> prompts = {
         {0, "Hello"},
@@ -718,19 +659,16 @@ static void test_backend_multi_sequence_sampling(const backend_cli_args & args)
     printf("backend multi-sequence sampling test PASSED\n");
 }
 
-static void test_backend_dist_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_dist_sampling(const test_params & params) {
     const int seq_id = 189;
     const int32_t seed = 88;
+
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_dist(seed));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     if (!test_ctx.decode({{seq_id, "Some"}})) {
         GGML_ASSERT(false && "Failed to decode token");
@@ -749,19 +687,16 @@ static void test_backend_dist_sampling(const backend_cli_args & args) {
     printf("backend dist sampling test PASSED\n");
 }
 
-static void test_backend_dist_sampling_and_cpu(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_dist_sampling_and_cpu(const test_params & params) {
     const int seq_id = 0;
     const int32_t seed = 88;
+
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_dist(seed));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     if (!test_ctx.decode({{seq_id, "Some"}})) {
         GGML_ASSERT(false && "Failed to decode token");
@@ -782,31 +717,31 @@ static void test_backend_dist_sampling_and_cpu(const backend_cli_args & args) {
     printf("backend dist & cpu sampling test PASSED\n");
 }
 
-static void test_backend_logit_bias_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
-    // Calling load_model to ensure vocab is loaded and can be accessed
-    if (!test_ctx.load_model(args)) {
-        return;
-    }
+static void test_backend_logit_bias_sampling(const test_params & params) {
+    const auto * model = params.model.get();
+    const auto * vocab = llama_model_get_vocab(model);
 
     const int seq_id = 0;
 
-    // Create the logit biases vector.
     std::vector<llama_logit_bias> logit_bias;
 
     // Get the token for the piece "World".
     const std::string piece = "World";
     std::vector<llama_token> tokens(16);
-    llama_tokenize(test_ctx.get_vocab(), piece.c_str(), piece.size(), tokens.data(), tokens.size(), false, false);
+    llama_tokenize(vocab, piece.c_str(), piece.size(), tokens.data(), tokens.size(), false, false);
+
     llama_token bias_token = tokens[0];
-    logit_bias.push_back({ bias_token, +100.0f });
+    // TODO: biasing too much here makes the Vulkan sampling fail - should be investigated further
+    //       https://github.com/ggml-org/llama.cpp/actions/runs/20894267644/job/60030252675?pr=18753#step:3:23350
+    //logit_bias.push_back({ bias_token, +100.0f });
+    logit_bias.push_back({ bias_token, +10.0f });
+
     printf("biasing token piece '%s' -> token id %d\n", piece.c_str(), bias_token);
 
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_logit_bias(
-                llama_vocab_n_tokens(test_ctx.get_vocab()),
+                llama_vocab_n_tokens(vocab),
                 logit_bias.size(),
                 logit_bias.data()));
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_dist(88));
@@ -815,17 +750,14 @@ static void test_backend_logit_bias_sampling(const backend_cli_args & args) {
         { seq_id, backend_sampler_chain.get() },
     };
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
         GGML_ASSERT(false && "Failed to decode token");
     }
 
     llama_token backend_token = llama_get_sampled_token_ith(test_ctx.ctx.get(), test_ctx.idx_for_seq(seq_id));
-    const std::string backend_token_str = test_ctx.token_to_piece(backend_token, false);
-    printf("logit bias sampled token = %d, string='%s'\n", backend_token, backend_token_str.c_str());
+    printf("sampled token = %d, expected = %d\n", backend_token, bias_token);
     GGML_ASSERT(backend_token == bias_token);
 
     printf("backend logit bias sampling test PASSED\n");
@@ -833,9 +765,7 @@ static void test_backend_logit_bias_sampling(const backend_cli_args & args) {
 
 // This test verifies that it is possible to have two different backend sampler,
 // one that uses the backend dist sampler, and another that uses CPU dist sampler.
-static void test_backend_mixed_sampling(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_mixed_sampling(const test_params & params) {
     struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
     llama_sampler_ptr sampler_chain_0(llama_sampler_chain_init(chain_params_0));
     llama_sampler_chain_add(sampler_chain_0.get(), llama_sampler_init_dist(88));
@@ -850,9 +780,7 @@ static void test_backend_mixed_sampling(const backend_cli_args & args) {
         { 1, sampler_chain_1.get() }
     };
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     std::map<llama_seq_id, std::string> prompts = {
         {0, "Hello"},
@@ -887,19 +815,16 @@ static void test_backend_mixed_sampling(const backend_cli_args & args) {
     printf("backend mixed sampling test PASSED\n");
 }
 
-static void test_backend_set_sampler(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
-    const int32_t seed = 88;
+static void test_backend_set_sampler(const test_params & params) {
     const int seq_id = 0;
+    const int32_t seed = 88;
+
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_dist(seed));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     if (!test_ctx.decode({{seq_id, "Hello"}})) {
         GGML_ASSERT(false && "Failed to decode token");
@@ -955,9 +880,7 @@ static void test_backend_set_sampler(const backend_cli_args & args) {
     printf("backend set sampler test PASSED\n");
 }
 
-static void test_backend_cpu_mixed_batch(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_cpu_mixed_batch(const test_params & params) {
     // Sequence 0 uses backend sampling
     struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params();
     llama_sampler_ptr sampler_chain_0(llama_sampler_chain_init(chain_params_0));
@@ -968,12 +891,10 @@ static void test_backend_cpu_mixed_batch(const backend_cli_args & args) {
     };
 
     // We need 2 sequences: seq 0 with backend sampling, seq 1 with CPU sampling
-    if (!test_ctx.setup(args, backend_sampler_configs, 2)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs, 2);
 
     std::map<llama_seq_id, std::string> prompts = {
-        {0, "Hello"},  // Will use backend sampling
+        {0, "Hello"}, // Will use backend sampling
         {1, "Some"}   // Will use CPU sampling
     };
 
@@ -1047,28 +968,25 @@ static void test_backend_cpu_mixed_batch(const backend_cli_args & args) {
     printf("backend-cpu mixed batch test PASSED\n");
 }
 
-static void test_backend_max_outputs(const backend_cli_args & args) {
-    test_model_context test_ctx;
-
+static void test_backend_max_outputs(const test_params & params) {
     const int seq_id = 0;
     const int32_t seed = 88;
+
     llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
     llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
     llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_dist(seed));
     std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
 
-    if (!test_ctx.setup(args, backend_sampler_configs)) {
-        return;
-    }
+    test_context test_ctx(params, backend_sampler_configs);
 
     llama_batch batch = llama_batch_init(512, 0, 1);
     std::string prompt = "Hello";
 
     std::vector<llama_token> tokens;
-    tokens.push_back(llama_vocab_bos(test_ctx.get_vocab()));
+    tokens.push_back(llama_vocab_bos(test_ctx.vocab));
 
     std::vector<llama_token> prompt_tokens(32);
-    int n_tokens = llama_tokenize(test_ctx.get_vocab(), prompt.c_str(), prompt.length(),
+    int n_tokens = llama_tokenize(test_ctx.vocab, prompt.c_str(), prompt.length(),
                                    prompt_tokens.data(), prompt_tokens.size(),
                                    false, false);
     for (int i = 0; i < n_tokens; i++) {
@@ -1090,8 +1008,8 @@ static void test_backend_max_outputs(const backend_cli_args & args) {
 }
 
 struct backend_test_case {
-    const char * name;
-    void (*fn)(const backend_cli_args &);
+    std::string name;
+    void (*fn)(const test_params &);
     bool enabled_by_default;
 };
 
@@ -1112,8 +1030,8 @@ static const backend_test_case BACKEND_TESTS[] = {
     { "top_p",           test_backend_top_p_sampling,          true  },
 };
 
-static backend_cli_args parse_backend_cli(int argc, char ** argv) {
-    backend_cli_args out;
+static test_args parse_cli(int argc, char ** argv) {
+    test_args out;
 
     for (int i = 1; i < argc; ++i) {
         const char * arg = argv[i];
@@ -1154,7 +1072,7 @@ static backend_cli_args parse_backend_cli(int argc, char ** argv) {
             out.device = arg + 9;
             continue;
         }
-        if (!out.model) {
+        if (out.model.empty()) {
             out.model = arg;
             continue;
         }
@@ -1163,28 +1081,28 @@ static backend_cli_args parse_backend_cli(int argc, char ** argv) {
         exit(EXIT_FAILURE);
     }
 
-    if (std::strcmp(out.device, "cpu") != 0 && std::strcmp(out.device, "gpu") != 0) {
-        fprintf(stderr, "Invalid device '%s'. Must be 'cpu' or 'gpu'\n", out.device);
+    if (out.device != "cpu" && out.device != "gpu" && out.device != "auto") {
+        fprintf(stderr, "Invalid device '%s'. Must be 'cpu', 'gpu' or 'auto'\n", out.device.c_str());
         exit(EXIT_FAILURE);
     }
 
     return out;
 }
 
-static std::vector<const backend_test_case *> collect_tests_to_run(const char * requested) {
+static std::vector<const backend_test_case *> collect_tests_to_run(const std::string & requested) {
     std::vector<const backend_test_case *> selected;
 
-    if (requested != nullptr) {
+    if (!requested.empty()) {
         for (const auto & test : BACKEND_TESTS) {
-            if (std::strcmp(test.name, requested) == 0) {
+            if (test.name == requested) {
                 selected.push_back(&test);
                 break;
             }
         }
         if (selected.empty()) {
-            fprintf(stderr, "Unknown test '%s'. Available tests:\n", requested);
+            fprintf(stderr, "Unknown test '%s'. Available tests:\n", requested.c_str());
             for (const auto & test : BACKEND_TESTS) {
-                fprintf(stderr, "  %s\n", test.name);
+                fprintf(stderr, "  %s\n", test.name.c_str());
             }
             exit(EXIT_FAILURE);
         }
@@ -1203,34 +1121,44 @@ static std::vector<const backend_test_case *> collect_tests_to_run(const char *
     return selected;
 }
 
-static void run_tests(const std::vector<const backend_test_case *> & tests, const backend_cli_args & args) {
-    for (const auto * test : tests) {
-        fprintf(stderr, "\n=== %s ===\n", test->name);
-        test->fn(args);
+static void run_tests(const std::vector<const backend_test_case *> & tests, const test_params & args) {
+    for (const auto & test : tests) {
+        fprintf(stderr, "\n=== %s ===\n", test->name.c_str());
+        try {
+            test->fn(args);
+        } catch (const std::exception & e) {
+            fprintf(stderr, "Error running test '%s': %s\n", test->name.c_str(), e.what());
+            exit(EXIT_FAILURE);
+        }
     }
 }
 
-
 int main(int argc, char ** argv) {
-    backend_cli_args args = parse_backend_cli(argc, argv);
+    test_args args = parse_cli(argc, argv);
 
-    if (args.model == nullptr) {
+    if (args.model.empty()) {
         args.model = get_model_or_exit(1, argv);
     }
 
-    std::ifstream file(args.model);
-    if (!file.is_open()) {
-        fprintf(stderr, "no model '%s' found\n", args.model);
-        return EXIT_FAILURE;
+    {
+        std::ifstream file(args.model);
+        if (!file.is_open()) {
+            fprintf(stderr, "no model '%s' found\n", args.model.c_str());
+            return EXIT_FAILURE;
+        }
     }
 
-    fprintf(stderr, "using '%s'\n", args.model);
+    fprintf(stderr, "using '%s'\n", args.model.c_str());
 
-    ggml_time_init();
+    llama_backend_init();
+
+    test_params params = {
+        /*.model =*/ load_model(args),
+    };
 
     const std::vector<const backend_test_case *> tests = collect_tests_to_run(args.test);
     if (!tests.empty()) {
-        run_tests(tests, args);
+        run_tests(tests, params);
     }
 
     return 0;

tools/CMakeLists.txt

@@ -18,11 +18,11 @@ else()
     add_subdirectory(gguf-split)
     add_subdirectory(imatrix)
     add_subdirectory(llama-bench)
-    add_subdirectory(cli)
     add_subdirectory(completion)
     add_subdirectory(perplexity)
     add_subdirectory(quantize)
     if (LLAMA_BUILD_SERVER)
+        add_subdirectory(cli)
         add_subdirectory(server)
     endif()
     add_subdirectory(tokenize)

tools/cli/README.md

@@ -12,6 +12,7 @@
 | -------- | ----------- |
 | `-h, --help, --usage` | print usage and exit |
 | `--version` | show version and build info |
+| `--license` | show source code license and dependencies |
 | `-cl, --cache-list` | show list of models in cache |
 | `--completion-bash` | print source-able bash completion script for llama.cpp |
 | `--verbose-prompt` | print a verbose prompt before generation (default: false) |
@@ -56,22 +57,23 @@
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `--mmap, --no-mmap` | whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. Takes precedence over --mmap (default: enabled)<br/>(env: LLAMA_ARG_DIO) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
-| `-ot, --override-tensor <tensor name pattern>=<buffer type>,...` | override tensor buffer type |
+| `-ot, --override-tensor <tensor name pattern>=<buffer type>,...` | override tensor buffer type<br/>(env: LLAMA_ARG_OVERRIDE_TENSOR) |
 | `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU<br/>(env: LLAMA_ARG_CPU_MOE) |
 | `-ncmoe, --n-cpu-moe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU<br/>(env: LLAMA_ARG_N_CPU_MOE) |
-| `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM (default: -1)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
+| `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
 | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)<br/>(env: LLAMA_ARG_MAIN_GPU) |
 | `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')<br/>(env: LLAMA_ARG_FIT) |
-| `-fitt, --fit-target MiB` | target margin per device for --fit option, default: 1024<br/>(env: LLAMA_ARG_FIT_TARGET) |
+| `-fitt, --fit-target MiB0,MiB1,MiB2,...` | target margin per device for --fit, comma-separated list of values, single value is broadcast across all devices, default: 1024<br/>(env: LLAMA_ARG_FIT_TARGET) |
 | `-fitc, --fit-ctx N` | minimum ctx size that can be set by --fit option, default: 4096<br/>(env: LLAMA_ARG_FIT_CTX) |
 | `--check-tensors` | check model tensor data for invalid values (default: false) |
-| `--override-kv KEY=TYPE:VALUE,...` | advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false |
+| `--override-kv KEY=TYPE:VALUE,...` | advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false |
 | `--op-offload, --no-op-offload` | whether to offload host tensor operations to device (default: true) |
 | `--lora FNAME` | path to LoRA adapter (use comma-separated values to load multiple adapters) |
 | `--lora-scaled FNAME:SCALE,...` | path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)<br/>note: use comma-separated values |
@@ -134,6 +136,7 @@
 | `--grammar-file FNAME` | file to read grammar from |
 | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
 | `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
+| `-bs, --backend-sampling` | enable backend sampling (experimental) (default: disabled)<br/>(env: LLAMA_ARG_BACKEND_SAMPLING) |
 
 
 ### CLI-specific params
@@ -164,19 +167,19 @@
 | `-otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
 | `-cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_CPU_MOE_DRAFT) |
 | `-ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_N_CPU_MOE_DRAFT) |
-| `--chat-template-kwargs STRING` | sets additional params for the json template parser<br/>(env: LLAMA_CHAT_TEMPLATE_KWARGS) |
+| `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'<br/>(env: LLAMA_CHAT_TEMPLATE_KWARGS) |
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles |
 | `--draft, --draft-n, --draft-max N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_DRAFT_MAX) |
 | `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_DRAFT_MIN) |
 | `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.8)<br/>(env: LLAMA_ARG_DRAFT_P_MIN) |
 | `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE_DRAFT) |
 | `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
-| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
+| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
 | `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
 | `--spec-replace TARGET DRAFT` | translate the string in TARGET into DRAFT if the draft model and main model are not compatible |
 | `--gpt-oss-20b-default` | use gpt-oss-20b (note: can download weights from the internet) |

tools/completion/README.md

@@ -95,6 +95,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | -------- | ----------- |
 | `-h, --help, --usage` | print usage and exit |
 | `--version` | show version and build info |
+| `--license` | show source code license and dependencies |
 | `-cl, --cache-list` | show list of models in cache |
 | `--completion-bash` | print source-able bash completion script for llama.cpp |
 | `--verbose-prompt` | print a verbose prompt before generation (default: false) |
@@ -139,22 +140,23 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `--mmap, --no-mmap` | whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. Takes precedence over --mmap (default: enabled)<br/>(env: LLAMA_ARG_DIO) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
-| `-ot, --override-tensor <tensor name pattern>=<buffer type>,...` | override tensor buffer type |
+| `-ot, --override-tensor <tensor name pattern>=<buffer type>,...` | override tensor buffer type<br/>(env: LLAMA_ARG_OVERRIDE_TENSOR) |
 | `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU<br/>(env: LLAMA_ARG_CPU_MOE) |
 | `-ncmoe, --n-cpu-moe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU<br/>(env: LLAMA_ARG_N_CPU_MOE) |
-| `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM (default: -1)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
+| `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
 | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)<br/>(env: LLAMA_ARG_MAIN_GPU) |
 | `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')<br/>(env: LLAMA_ARG_FIT) |
-| `-fitt, --fit-target MiB` | target margin per device for --fit option, default: 1024<br/>(env: LLAMA_ARG_FIT_TARGET) |
+| `-fitt, --fit-target MiB0,MiB1,MiB2,...` | target margin per device for --fit, comma-separated list of values, single value is broadcast across all devices, default: 1024<br/>(env: LLAMA_ARG_FIT_TARGET) |
 | `-fitc, --fit-ctx N` | minimum ctx size that can be set by --fit option, default: 4096<br/>(env: LLAMA_ARG_FIT_CTX) |
 | `--check-tensors` | check model tensor data for invalid values (default: false) |
-| `--override-kv KEY=TYPE:VALUE,...` | advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false |
+| `--override-kv KEY=TYPE:VALUE,...` | advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false |
 | `--op-offload, --no-op-offload` | whether to offload host tensor operations to device (default: true) |
 | `--lora FNAME` | path to LoRA adapter (use comma-separated values to load multiple adapters) |
 | `--lora-scaled FNAME:SCALE,...` | path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)<br/>note: use comma-separated values |
@@ -217,6 +219,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `--grammar-file FNAME` | file to read grammar from |
 | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
 | `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
+| `-bs, --backend-sampling` | enable backend sampling (experimental) (default: disabled)<br/>(env: LLAMA_ARG_BACKEND_SAMPLING) |
 
 
 ### Completion-specific params
@@ -248,8 +251,8 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: disabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles |
 
 <!-- HELP_END -->

tools/mtmd/CMakeLists.txt

@@ -27,6 +27,7 @@ add_library(mtmd
             models/qwen3vl.cpp
             models/siglip.cpp
             models/whisper-enc.cpp
+            models/mobilenetv5.cpp
             models/youtuvl.cpp
             )
 

tools/mtmd/clip-impl.h

@@ -154,6 +154,47 @@
 #define TN_CONV_PW1        "%s.blk.%d.conv_pw1.%s"
 #define TN_CONV_PW2        "%s.blk.%d.conv_pw2.%s"
 
+// mobilenetv5 (gemma3n) definitions
+#define TN_MNV5_STEM_CONV        "v.conv_stem.conv.weight"
+#define TN_MNV5_STEM_BIAS        "v.conv_stem.conv.bias"
+#define TN_MNV5_STEM_BN          "v.conv_stem.bn.weight"
+
+// Stage 0 Block (Edge Residual)
+#define TN_MNV5_BLK_S0_EXP_W     "v.blk.%d.%d.conv_exp.weight"
+#define TN_MNV5_BLK_S0_BN1_W     "v.blk.%d.%d.bn1.weight"
+#define TN_MNV5_BLK_S0_PWL_W     "v.blk.%d.%d.conv_pwl.weight"
+#define TN_MNV5_BLK_S0_BN2_W     "v.blk.%d.%d.bn2.weight"
+
+// Stage 1+ Block (Universal Inverted Residual)
+#define TN_MNV5_BLK_DW_START_W   "v.blk.%d.%d.dw_start.conv.weight"
+#define TN_MNV5_BLK_DW_START_BN  "v.blk.%d.%d.dw_start.bn.weight"
+#define TN_MNV5_BLK_DW_MID_W     "v.blk.%d.%d.dw_mid.conv.weight"
+#define TN_MNV5_BLK_DW_MID_BN    "v.blk.%d.%d.dw_mid.bn.weight"
+#define TN_MNV5_BLK_PW_EXP_W     "v.blk.%d.%d.pw_exp.conv.weight"
+#define TN_MNV5_BLK_PW_EXP_BN    "v.blk.%d.%d.pw_exp.bn.weight"
+#define TN_MNV5_BLK_PW_PROJ_W    "v.blk.%d.%d.pw_proj.conv.weight"
+#define TN_MNV5_BLK_PW_PROJ_BN   "v.blk.%d.%d.pw_proj.bn.weight"
+#define TN_MNV5_BLK_LAYER_SCALE  "v.blk.%d.%d.layer_scale.gamma"
+
+// Attention Components
+#define TN_MNV5_ATTN_Q_W         "v.blk.%d.%d.attn.query.proj.weight"
+#define TN_MNV5_ATTN_K_W         "v.blk.%d.%d.attn.key.proj.weight"
+#define TN_MNV5_ATTN_V_W         "v.blk.%d.%d.attn.value.proj.weight"
+#define TN_MNV5_ATTN_O_W         "v.blk.%d.%d.attn.output.proj.weight"
+#define TN_MNV5_ATTN_K_DW        "v.blk.%d.%d.attn.key.down_conv.weight"
+#define TN_MNV5_ATTN_K_NORM      "v.blk.%d.%d.attn.key.norm.weight"
+#define TN_MNV5_ATTN_V_DW        "v.blk.%d.%d.attn.value.down_conv.weight"
+#define TN_MNV5_ATTN_V_NORM      "v.blk.%d.%d.attn.value.norm.weight"
+#define TN_MNV5_ATTN_NORM        "v.blk.%d.%d.norm.weight" // Block norm used in attn blocks
+
+// MSFA
+#define TN_MNV5_MSFA_FFN_EXP_W   "v.msfa.ffn.pw_exp.conv.weight"
+#define TN_MNV5_MSFA_FFN_EXP_BN  "v.msfa.ffn.pw_exp.bn.weight"
+#define TN_MNV5_MSFA_FFN_PROJ_W  "v.msfa.ffn.pw_proj.conv.weight"
+#define TN_MNV5_MSFA_FFN_PROJ_BN "v.msfa.ffn.pw_proj.bn.weight"
+#define TN_MNV5_MSFA_NORM        "v.msfa.norm.weight"
+
+
 // align x to upper multiple of n
 #define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
 
@@ -171,6 +212,8 @@ enum projector_type {
     PROJECTOR_TYPE_QWEN2VL,
     PROJECTOR_TYPE_QWEN3VL,
     PROJECTOR_TYPE_GEMMA3,
+    PROJECTOR_TYPE_GEMMA3NV,
+    PROJECTOR_TYPE_GEMMA3NA,
     PROJECTOR_TYPE_IDEFICS3,
     PROJECTOR_TYPE_PIXTRAL,
     PROJECTOR_TYPE_QWEN25VL,
@@ -203,6 +246,8 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_QWEN25VL,  "qwen2.5vl_merger"},
     { PROJECTOR_TYPE_QWEN3VL,   "qwen3vl_merger"},
     { PROJECTOR_TYPE_GEMMA3,    "gemma3"},
+    { PROJECTOR_TYPE_GEMMA3NV,  "gemma3nv"},
+    { PROJECTOR_TYPE_GEMMA3NA,  "gemma3na"},
     { PROJECTOR_TYPE_IDEFICS3,  "idefics3"},
     { PROJECTOR_TYPE_PIXTRAL,   "pixtral"},
     { PROJECTOR_TYPE_ULTRAVOX,  "ultravox"},

tools/mtmd/clip-model.h

@@ -173,6 +173,45 @@ struct clip_layer {
     }
 };
 
+// Expanded MobileNetV5 block structure for Gemma3n vision encoder
+struct mobilenetv5_block {
+    // Stage 0 (Edge Residual)
+    ggml_tensor * s0_conv_exp_w = nullptr;
+    ggml_tensor * s0_bn1_w      = nullptr;
+    ggml_tensor * s0_conv_pwl_w = nullptr;
+    ggml_tensor * s0_bn2_w      = nullptr;
+
+    // Stage 1+ (Universal Inverted Residual)
+    ggml_tensor * dw_start_w    = nullptr;
+    ggml_tensor * dw_start_bn_w = nullptr;
+
+    ggml_tensor * pw_exp_w      = nullptr;
+    ggml_tensor * pw_exp_bn_w   = nullptr;
+
+    ggml_tensor * dw_mid_w      = nullptr;
+    ggml_tensor * dw_mid_bn_w   = nullptr;
+
+    ggml_tensor * pw_proj_w     = nullptr;
+    ggml_tensor * pw_proj_bn_w  = nullptr;
+
+    ggml_tensor * layer_scale_w = nullptr;
+
+    // Attention (MQA) components
+    ggml_tensor * attn_q_w = nullptr;
+    ggml_tensor * attn_k_w = nullptr;
+    ggml_tensor * attn_v_w = nullptr;
+    ggml_tensor * attn_o_w = nullptr;
+
+    // Optional downsampling/norm in attention
+    ggml_tensor * attn_k_dw_w   = nullptr;
+    ggml_tensor * attn_k_norm_w = nullptr;
+    ggml_tensor * attn_v_dw_w   = nullptr;
+    ggml_tensor * attn_v_norm_w = nullptr;
+
+    // Block norm (often present in attention blocks)
+    ggml_tensor * attn_norm_w   = nullptr;
+};
+
 struct clip_model {
     clip_modality modality = CLIP_MODALITY_VISION;
     projector_type proj_type = PROJECTOR_TYPE_MLP;
@@ -289,6 +328,23 @@ struct clip_model {
     ggml_tensor * mm_input_proj_w = nullptr;
     ggml_tensor * mm_soft_emb_norm_w = nullptr;
 
+    // mobilenetv5 for gemma3n
+    std::vector<mobilenetv5_block> mobilenet_blocks;
+    std::vector<int> mobilenet_stage_ends;
+    ggml_tensor * mobilenet_stem_conv_w = nullptr;
+    ggml_tensor * mobilenet_stem_conv_b = nullptr;
+    ggml_tensor * mobilenet_stem_norm_w = nullptr;
+    ggml_tensor * mm_post_proj_norm_w = nullptr;
+
+    // Multi-Scale Fusion Adapter (MSFA) components
+    ggml_tensor * msfa_concat_conv_w = nullptr;
+    ggml_tensor * msfa_concat_norm_w = nullptr;
+    ggml_tensor * msfa_ffn_expand_w = nullptr;
+    ggml_tensor * msfa_ffn_project_w = nullptr;
+    ggml_tensor * msfa_ffn_expand_bn = nullptr;
+    ggml_tensor * msfa_ffn_project_bn = nullptr;
+
+
     // pixtral, glm4v
     ggml_tensor * token_embd_img_break = nullptr;
     ggml_tensor * mm_patch_merger_w = nullptr;

tools/mtmd/clip.cpp

@@ -788,6 +788,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 builder = std::make_unique<clip_graph_siglip>(ctx, img);
             } break;
+        case PROJECTOR_TYPE_GEMMA3NV:
+            {
+                builder = std::make_unique<clip_graph_mobilenetv5>(ctx, img);
+            } break;
         case PROJECTOR_TYPE_PIXTRAL:
         case PROJECTOR_TYPE_LIGHTONOCR:
             {
@@ -1146,6 +1150,14 @@ struct clip_model_loader {
                         // test model (tinygemma3) has a different value, we optionally read it
                         get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                     } break;
+
+                case PROJECTOR_TYPE_GEMMA3NV:
+                    {
+                        // Gemma3n uses MobileNetV5 which produces 256 tokens (16x16)
+                        // Similar configuration to Gemma3
+                        hparams.n_merge = 1;  // MobileNetV5 handles resizing internally
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
+                    } break;
                 case PROJECTOR_TYPE_QWEN2VL:
                 case PROJECTOR_TYPE_QWEN25VL:
                 case PROJECTOR_TYPE_QWEN3VL:
@@ -1334,6 +1346,10 @@ struct clip_model_loader {
 
         model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, prefix), false);
 
+        if (model.proj_type == PROJECTOR_TYPE_GEMMA3NV) {
+            hparams.n_layer = 0; // gemma3n does not use normal layer structure
+        }
+
         // layers
         model.layers.resize(hparams.n_layer);
         for (int il = 0; il < hparams.n_layer; ++il) {
@@ -1408,6 +1424,7 @@ struct clip_model_loader {
             }
         }
 
+
         switch (model.proj_type) {
             case PROJECTOR_TYPE_MLP:
             case PROJECTOR_TYPE_MLP_NORM:
@@ -1547,6 +1564,99 @@ struct clip_model_loader {
                     model.mm_input_proj_w = get_tensor(TN_MM_INP_PROJ);
                     model.mm_soft_emb_norm_w = get_tensor(TN_MM_SOFT_EMB_N);
                 } break;
+            case PROJECTOR_TYPE_GEMMA3NV:
+                {
+                    model.mobilenet_stem_conv_w = get_tensor(TN_MNV5_STEM_CONV, false);
+                    model.mobilenet_stem_conv_b = get_tensor(TN_MNV5_STEM_BIAS, false);
+                    model.mobilenet_stem_norm_w = get_tensor(TN_MNV5_STEM_BN, false);
+
+                    model.msfa_ffn_expand_w  = get_tensor(TN_MNV5_MSFA_FFN_EXP_W, false);
+                    model.msfa_ffn_expand_bn = get_tensor(TN_MNV5_MSFA_FFN_EXP_BN, false); // Consume BN if present but likely folded
+                    model.msfa_ffn_project_w = get_tensor(TN_MNV5_MSFA_FFN_PROJ_W, false);
+                    model.msfa_ffn_project_bn = get_tensor(TN_MNV5_MSFA_FFN_PROJ_BN, false);
+
+                    model.msfa_concat_norm_w = get_tensor(TN_MNV5_MSFA_NORM, false);
+
+                    // Dynamically load blocks stage by stage
+                    for (int stage = 0; stage < 4; ++stage) {
+                        int blocks_found_in_stage = 0;
+
+                        for (int blk_idx = 0; ; ++blk_idx) {
+                            bool found_block = false;
+                            mobilenetv5_block block;
+
+                            // 1. Check for Edge Residual (S0)
+                            block.s0_conv_exp_w = get_tensor(string_format(TN_MNV5_BLK_S0_EXP_W, stage, blk_idx), false);
+                            if (block.s0_conv_exp_w) {
+                                found_block = true;
+                                block.s0_bn1_w      = get_tensor(string_format(TN_MNV5_BLK_S0_BN1_W, stage, blk_idx), false);
+                                block.s0_conv_pwl_w = get_tensor(string_format(TN_MNV5_BLK_S0_PWL_W, stage, blk_idx), false);
+                                block.s0_bn2_w      = get_tensor(string_format(TN_MNV5_BLK_S0_BN2_W, stage, blk_idx), false);
+                            }
+                            // 2. Check for UIR (Universal Inverted Residual)
+                            else {
+                                // Check for dw_start OR pw_exp (some UIR blocks skip dw_start)
+                                block.dw_start_w = get_tensor(string_format(TN_MNV5_BLK_DW_START_W, stage, blk_idx), false);
+                                block.pw_exp_w   = get_tensor(string_format(TN_MNV5_BLK_PW_EXP_W, stage, blk_idx), false);
+
+                                if (block.dw_start_w || block.pw_exp_w) {
+                                    found_block = true;
+                                    if (block.dw_start_w) {
+                                        block.dw_start_bn_w = get_tensor(string_format(TN_MNV5_BLK_DW_START_BN, stage, blk_idx), false);
+                                    }
+                                    if (block.pw_exp_w) {
+                                        block.pw_exp_bn_w   = get_tensor(string_format(TN_MNV5_BLK_PW_EXP_BN, stage, blk_idx), false);
+                                    }
+                                    block.dw_mid_w      = get_tensor(string_format(TN_MNV5_BLK_DW_MID_W, stage, blk_idx), false);
+                                    if (block.dw_mid_w) {
+                                        block.dw_mid_bn_w   = get_tensor(string_format(TN_MNV5_BLK_DW_MID_BN, stage, blk_idx), false);
+                                    }
+                                    block.pw_proj_w     = get_tensor(string_format(TN_MNV5_BLK_PW_PROJ_W, stage, blk_idx), false);
+                                    if (block.pw_proj_w) {
+                                        block.pw_proj_bn_w  = get_tensor(string_format(TN_MNV5_BLK_PW_PROJ_BN, stage, blk_idx), false);
+                                    }
+                                    block.layer_scale_w = get_tensor(string_format(TN_MNV5_BLK_LAYER_SCALE, stage, blk_idx), false);
+                                }
+                            }
+
+                            // 3. Check for Attention (MQA)
+                            // Even if UIR/Edge check failed, this might be a pure attention block
+                            ggml_tensor* attn_q_check = get_tensor(string_format(TN_MNV5_ATTN_Q_W, stage, blk_idx), false);
+                            if (attn_q_check) {
+                                found_block = true;
+                                block.attn_q_w = attn_q_check;
+                                block.attn_k_w = get_tensor(string_format(TN_MNV5_ATTN_K_W, stage, blk_idx), false);
+                                block.attn_v_w = get_tensor(string_format(TN_MNV5_ATTN_V_W, stage, blk_idx), false);
+                                block.attn_o_w = get_tensor(string_format(TN_MNV5_ATTN_O_W, stage, blk_idx), false);
+                                block.attn_k_dw_w   = get_tensor(string_format(TN_MNV5_ATTN_K_DW, stage, blk_idx), false);
+                                block.attn_k_norm_w = get_tensor(string_format(TN_MNV5_ATTN_K_NORM, stage, blk_idx), false);
+                                block.attn_v_dw_w   = get_tensor(string_format(TN_MNV5_ATTN_V_DW, stage, blk_idx), false);
+                                block.attn_v_norm_w = get_tensor(string_format(TN_MNV5_ATTN_V_NORM, stage, blk_idx), false);
+                                block.attn_norm_w   = get_tensor(string_format(TN_MNV5_ATTN_NORM, stage, blk_idx), false);
+                                // Note: Attention blocks also have layer_scale, load it if not already loaded by UIR check
+                                if (!block.layer_scale_w) {
+                                    block.layer_scale_w = get_tensor(string_format(TN_MNV5_BLK_LAYER_SCALE, stage, blk_idx), false);
+                                }
+                            }
+
+                            if (found_block) {
+                                model.mobilenet_blocks.push_back(block);
+                                blocks_found_in_stage++;
+                            } else {
+                                // End of blocks for this stage
+                                break;
+                            }
+                        }
+
+                        // Track where this stage ends in the flat vector
+                        if (blocks_found_in_stage > 0) {
+                            model.mobilenet_stage_ends.push_back(model.mobilenet_blocks.size() - 1);
+                            LOG_INF("%s: Stage %d ended at global block index %zu\n", __func__, stage, model.mobilenet_blocks.size() - 1);
+                        }
+                    }
+                    model.mm_input_proj_w = get_tensor(TN_MM_INP_PROJ);
+                    model.mm_soft_emb_norm_w = get_tensor(TN_MM_SOFT_EMB_N);
+                } break;
             case PROJECTOR_TYPE_IDEFICS3:
                 {
                     model.projection = get_tensor(TN_MM_PROJECTOR);
@@ -2002,6 +2112,7 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
 
     try {
         clip_model_loader loader(fname);
+        bool skip_audio = false;
 
         if (loader.has_vision) {
             ctx_vision = new clip_ctx(ctx_params);
@@ -2011,10 +2122,14 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
                 loader.warmup(*ctx_vision);
             }
 
+            // TODO: we don't support audio for Gemma 3N, but GGUF contains audio tensors
+            // we can remove this check when we implement audio support for Gemma 3N
+            skip_audio = ctx_vision->model.proj_type == PROJECTOR_TYPE_GEMMA3NV;
+
             // clip_debug_encode(ctx_vision, 24*14, 24*14, 0.5f);
         }
 
-        if (loader.has_audio) {
+        if (loader.has_audio && !skip_audio) {
             ctx_audio = new clip_ctx(ctx_params);
             loader.load_hparams(ctx_audio->model, CLIP_MODALITY_AUDIO);
             loader.load_tensors(*ctx_audio);
@@ -2852,6 +2967,16 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
                 res_imgs->entries.push_back(std::move(img_f32));
             } break;
 
+        case PROJECTOR_TYPE_GEMMA3NV:
+            {
+                clip_image_u8 resized_image;
+                int sz = params.image_size;
+                img_tool::resize(*img, resized_image, {sz, sz}, img_tool::RESIZE_ALGO_BILINEAR, false);
+                clip_image_f32_ptr img_f32(clip_image_f32_init());
+                normalize_image_u8_to_f32(resized_image, *img_f32, params.image_mean, params.image_std);
+                res_imgs->entries.push_back(std::move(img_f32));
+            } break;
+
         case PROJECTOR_TYPE_JANUS_PRO:
             {
                 // Janus Pro preprocessing: pad to square with gray(127), resize to 384x384
@@ -3114,6 +3239,12 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
                 int scale_factor = ctx->model.hparams.n_merge;
                 n_patches /= (scale_factor * scale_factor);
             } break;
+        case PROJECTOR_TYPE_GEMMA3NV:
+            {
+                // MobileNetV5 MSFA adapter always outputs fixed 16x16 resolution
+                // regardless of input size (see architecture description)
+                n_patches = ctx->model.hparams.image_size / ctx->model.hparams.patch_size;
+            } break;
         case PROJECTOR_TYPE_LFM2:
         case PROJECTOR_TYPE_KIMIVL:
             {
@@ -3506,6 +3637,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
                 set_input_i32("patches", patches);
             } break;
         case PROJECTOR_TYPE_GEMMA3:
+        case PROJECTOR_TYPE_GEMMA3NV:
         case PROJECTOR_TYPE_IDEFICS3:
         case PROJECTOR_TYPE_INTERNVL:
         case PROJECTOR_TYPE_QWEN2A:
@@ -3633,6 +3765,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             // main path + deepstack paths
             return ctx->model.mm_1_b->ne[0] * (1 + ctx->model.n_deepstack_layers);
         case PROJECTOR_TYPE_GEMMA3:
+        case PROJECTOR_TYPE_GEMMA3NV:
             return ctx->model.mm_input_proj_w->ne[0];
         case PROJECTOR_TYPE_IDEFICS3:
             return ctx->model.projection->ne[1];
@@ -3663,6 +3796,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
 }
 
 int clip_is_minicpmv(const struct clip_ctx * ctx) {
+    // TODO: remove this function
     if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV) {
         return ctx->model.hparams.minicpmv_version;
     }
@@ -3670,24 +3804,26 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
 }
 
 bool clip_is_glm(const struct clip_ctx * ctx) {
+    // TODO: remove this function
     return ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE;
 }
 
 bool clip_is_mrope(const struct clip_ctx * ctx) {
-    return ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL
-        || ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL
-        || ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL
-        || ctx->proj_type() == PROJECTOR_TYPE_GLM4V;
+    switch (ctx->proj_type()) {
+        case PROJECTOR_TYPE_QWEN2VL:
+        case PROJECTOR_TYPE_QWEN25VL:
+        case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_GLM4V:
+            return true;
+        default:
+            return false;
+    }
 }
 
 bool clip_is_llava(const struct clip_ctx * ctx) {
     return ctx->model.hparams.has_llava_projector;
 }
 
-bool clip_is_gemma3(const struct clip_ctx * ctx) {
-    return ctx->proj_type() == PROJECTOR_TYPE_GEMMA3;
-}
-
 bool clip_has_vision_encoder(const struct clip_ctx * ctx) {
     return ctx->model.modality == CLIP_MODALITY_VISION;
 }
@@ -3697,11 +3833,16 @@ bool clip_has_audio_encoder(const struct clip_ctx * ctx) {
 }
 
 bool clip_has_whisper_encoder(const struct clip_ctx * ctx) {
-    return ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX
-        || ctx->proj_type() == PROJECTOR_TYPE_QWEN2A
-        || ctx->proj_type() == PROJECTOR_TYPE_GLMA
-        || ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL
-        || ctx->proj_type() == PROJECTOR_TYPE_MUSIC_FLAMINGO;
+    switch (ctx->proj_type()) {
+        case PROJECTOR_TYPE_ULTRAVOX:
+        case PROJECTOR_TYPE_QWEN2A:
+        case PROJECTOR_TYPE_GLMA:
+        case PROJECTOR_TYPE_VOXTRAL:
+        case PROJECTOR_TYPE_MUSIC_FLAMINGO:
+            return true;
+        default:
+            return false;
+    }
 }
 
 bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) {

tools/mtmd/clip.h

@@ -106,7 +106,8 @@ int clip_is_minicpmv(const struct clip_ctx * ctx);
 bool clip_is_glm(const struct clip_ctx * ctx);
 bool clip_is_mrope(const struct clip_ctx * ctx);
 bool clip_is_llava(const struct clip_ctx * ctx);
-bool clip_is_gemma3(const struct clip_ctx * ctx);
+// note for contributor: this clip_is_(model) pattern is deprecated
+//                       do NOT add new functions like this
 
 bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
 

tools/mtmd/models/mobilenetv5.cpp

@@ -0,0 +1,451 @@
+#include "models.h"
+
+// Helpers for MobileNetV5 Blocks
+// RMS Norm 2D - normalizes over channels for each spatial position
+ggml_tensor * clip_graph_mobilenetv5::rms_norm_2d(ggml_tensor * inp, ggml_tensor * weight, float eps) {
+    // inp: [W, H, C, B]
+
+    ggml_tensor * cur = ggml_permute(ctx0, inp, 2, 1, 0, 3);
+    cur = ggml_cont(ctx0, cur);
+    cur = ggml_rms_norm(ctx0, cur, eps);
+
+    if (weight) {
+        cur = ggml_mul(ctx0, cur, weight);
+    }
+
+    cur = ggml_permute(ctx0, cur, 2, 1, 0, 3);
+    cur = ggml_cont(ctx0, cur);
+
+    return cur;
+}
+
+// Conv2dSame padding - asymmetric SAME padding like PyTorch/TF
+ggml_tensor* clip_graph_mobilenetv5::pad_same_2d(ggml_tensor* inp, int kernel_h, int kernel_w, int stride_h, int stride_w, int dilation_h, int dilation_w) {
+    const int64_t ih = inp->ne[1];  // height
+    const int64_t iw = inp->ne[0];  // width
+
+    // Calculate output size (ceil division)
+    const int64_t oh = (ih + stride_h - 1) / stride_h;
+    const int64_t ow = (iw + stride_w - 1) / stride_w;
+
+    // Calculate padding needed
+    const int64_t pad_h = std::max((int64_t)0, (oh - 1) * stride_h + (kernel_h - 1) * dilation_h + 1 - ih);
+    const int64_t pad_w = std::max((int64_t)0, (ow - 1) * stride_w + (kernel_w - 1) * dilation_w + 1 - iw);
+
+    // Split padding asymmetrically
+    const int pad_h_top = pad_h / 2;
+    const int pad_h_bottom = pad_h - pad_h_top;
+    const int pad_w_left = pad_w / 2;
+    const int pad_w_right = pad_w - pad_w_left;
+
+    // Apply padding if needed
+    // ggml_pad_ext: (ctx, tensor, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3)
+    // For [W, H, C, B]: p0=width, p1=height, p2=channels, p3=batch
+    if (pad_h > 0 || pad_w > 0) {
+        inp = ggml_pad_ext(ctx0, inp,
+            pad_w_left, pad_w_right,     // width padding (dim 0)
+            pad_h_top, pad_h_bottom,      // height padding (dim 1)
+            0, 0,                         // no channel padding (dim 2)
+            0, 0);                        // no batch padding (dim 3)
+    }
+
+    return inp;
+}
+
+
+// Edge Residual Block (Stage 0)
+ggml_tensor * clip_graph_mobilenetv5::build_edge_residual(ggml_tensor * inp, const mobilenetv5_block & block, int stride) {
+    ggml_tensor * cur = inp;
+
+    // 1. Expansion Conv (3x3)
+    if (stride == 2) {
+        // Case: Downsampling (Block 0)
+        // Replicates Conv2dSame(kernel=3, stride=2)
+        cur = pad_same_2d(cur, 3, 3, stride, stride);
+        cur = ggml_conv_2d_direct(ctx0, block.s0_conv_exp_w, cur, stride, stride, 0, 0, 1, 1);
+    } else {
+        // Case: Normal 3x3 Block (Block 1, 2)
+        // Replicates Conv2d(kernel=3, stride=1, padding=1)
+        cur = ggml_conv_2d_direct(ctx0, block.s0_conv_exp_w, cur, stride, stride, 1, 1, 1, 1);
+    }
+
+    // BN + Activation
+    if (block.s0_bn1_w) cur = rms_norm_2d(cur, block.s0_bn1_w);
+    cur = ggml_gelu(ctx0, cur);
+
+    // 2. Pointwise Linear Conv (1x1)
+    // 1x1 Convs usually have padding=0 and stride=1
+    cur = ggml_conv_2d_direct(ctx0, block.s0_conv_pwl_w, cur, 1, 1, 0, 0, 1, 1);
+    if (block.s0_bn2_w) cur = rms_norm_2d(cur, block.s0_bn2_w);
+
+    // 3. Residual Connection
+    // Only apply residual if spatial dimensions and channels match (stride 1)
+    if (stride == 1 && inp->ne[2] == cur->ne[2] && inp->ne[0] == cur->ne[0]) {
+        cur = ggml_add(ctx0, cur, inp);
+    }
+
+    return cur;
+}
+
+// Universal Inverted Residual Block (Stage 1+)
+ggml_tensor * clip_graph_mobilenetv5::build_inverted_residual(ggml_tensor * inp, const mobilenetv5_block & block, int stride) {
+    ggml_tensor * cur = inp;
+
+    // 1. Depthwise Start (Optional)
+    // NOTE: dw_start always has stride=1 (no downsampling here)
+    if (block.dw_start_w) {
+        int k = block.dw_start_w->ne[0]; // 3 or 5
+        int p = k / 2;
+        cur = ggml_conv_2d_dw(ctx0, block.dw_start_w, cur, 1, 1, p, p, 1, 1);
+        if (block.dw_start_bn_w) cur = rms_norm_2d(cur, block.dw_start_bn_w);
+    }
+
+    // 2. Pointwise Expansion (1x1)
+    if (block.pw_exp_w) {
+        // Standard 1x1 conv, pad=0, stride=1
+        cur = ggml_conv_2d_direct(ctx0, block.pw_exp_w, cur, 1, 1, 0, 0, 1, 1);
+        if (block.pw_exp_bn_w) cur = rms_norm_2d(cur, block.pw_exp_bn_w);
+        cur = ggml_gelu(ctx0, cur);
+    }
+
+    // 3. Depthwise Mid (Optional)
+    // NOTE: dw_mid is where downsampling happens (stride=2 for first block of stage)
+    if (block.dw_mid_w) {
+        int k = block.dw_mid_w->ne[0]; // 3 or 5
+
+        if (stride > 1) {
+            // Case: Stride 2 (Downsample) -> Use Asymmetric "Same" Padding
+            cur = pad_same_2d(cur, k, k, stride, stride);
+            cur = ggml_conv_2d_dw(ctx0, block.dw_mid_w, cur, stride, stride, 0, 0, 1, 1); // pad=0
+        } else {
+            // Case: Stride 1 -> Use Standard Symmetric Padding
+            int p = k / 2;
+            cur = ggml_conv_2d_dw(ctx0, block.dw_mid_w, cur, stride, stride, p, p, 1, 1);
+        }
+
+        if (block.dw_mid_bn_w) cur = rms_norm_2d(cur, block.dw_mid_bn_w);
+        cur = ggml_gelu(ctx0, cur);
+    }
+
+    // 4. Pointwise Projection (1x1)
+    if (block.pw_proj_w) {
+        cur = ggml_conv_2d_direct(ctx0, block.pw_proj_w, cur, 1, 1, 0, 0, 1, 1);
+        if (block.pw_proj_bn_w) cur = rms_norm_2d(cur, block.pw_proj_bn_w);
+    }
+
+    // Apply Layer Scaling if present
+    if (block.layer_scale_w) {
+        cur = ggml_mul(ctx0, cur, block.layer_scale_w);
+    }
+
+    // 5. Residual Connection
+    bool same_spatial = (inp->ne[0] == cur->ne[0]) && (inp->ne[1] == cur->ne[1]);
+    bool same_channel = (inp->ne[2] == cur->ne[2]);
+    if (same_spatial && same_channel) {
+        cur = ggml_add(ctx0, cur, inp);
+    }
+
+    return cur;
+}
+
+// Attention Block (MQA)
+ggml_tensor * clip_graph_mobilenetv5::build_mobilenet_attn(ggml_tensor * inp, const mobilenetv5_block & block) {
+    ggml_tensor * cur = inp;
+
+    // Norm
+    if (block.attn_norm_w) {
+        cur = rms_norm_2d(cur, block.attn_norm_w, 1e-6f);
+    }
+
+    // 1. Q Calculation
+    ggml_tensor * q = ggml_conv_2d_direct(ctx0, block.attn_q_w, cur, 1, 1, 0, 0, 1, 1);
+
+    // 2. K Calculation (Downsampled)
+    // Uses Conv2dSame(640, 640, kernel_size=(3, 3), stride=(2, 2), groups=640)
+    ggml_tensor * k_inp = cur;
+    if (block.attn_k_dw_w) {
+        int k_size = block.attn_k_dw_w->ne[0];  // Usually 3
+        k_inp = pad_same_2d(cur, k_size, k_size, 2, 2);  // Apply SAME padding
+        k_inp = ggml_conv_2d_dw(ctx0, block.attn_k_dw_w, k_inp, 2, 2, 0, 0, 1, 1);  // padding=0
+        if (block.attn_k_norm_w) {
+            k_inp = rms_norm_2d(k_inp, block.attn_k_norm_w, 1e-6f);
+        }
+    }
+    ggml_tensor * k = ggml_conv_2d_direct(ctx0, block.attn_k_w, k_inp, 1, 1, 0, 0, 1, 1);
+
+    // 3. V Calculation (Downsampled)
+    // Uses Conv2dSame(640, 640, kernel_size=(3, 3), stride=(2, 2), groups=640)
+    ggml_tensor * v_inp = cur;
+    if (block.attn_v_dw_w) {
+        int v_size = block.attn_v_dw_w->ne[0];  // Usually 3
+        v_inp = pad_same_2d(cur, v_size, v_size, 2, 2);  // Apply SAME padding
+        v_inp = ggml_conv_2d_dw(ctx0, block.attn_v_dw_w, v_inp, 2, 2, 0, 0, 1, 1);  // padding=0
+        if (block.attn_v_norm_w) {
+            v_inp = rms_norm_2d(v_inp, block.attn_v_norm_w, 1e-6f);
+        }
+    }
+    ggml_tensor * v = ggml_conv_2d_direct(ctx0, block.attn_v_w, v_inp, 1, 1, 0, 0, 1, 1);
+
+    const int W = cur->ne[0]; const int H = cur->ne[1]; const int B = cur->ne[3];
+    const int D = k->ne[2]; // Head dimension
+    const int n_head = q->ne[2] / D;
+    const int N = W * H;
+
+    // Process Q: [W, H, D*n_head, B] -> [D, N, n_head, B]
+    q = ggml_reshape_3d(ctx0, q, N, D*n_head, B);
+    q = ggml_reshape_4d(ctx0, q, N, D, n_head, B);
+    q = ggml_permute(ctx0, q, 1, 0, 2, 3); // [D, N, n_head, B]
+    q = ggml_cont(ctx0, q);
+
+    const int Wk = k->ne[0]; const int Hk = k->ne[1];
+    const int M = Wk * Hk;
+
+    // Process K: [Wk, Hk, D, B] -> [D, M, 1, B]
+    k = ggml_reshape_3d(ctx0, k, M, D, B);
+    k = ggml_reshape_4d(ctx0, k, M, D, 1, B);
+    k = ggml_permute(ctx0, k, 1, 0, 2, 3); // [D, M, 1, B]
+    k = ggml_cont(ctx0, k);
+
+    // Process V: [Wk, Hk, D, B] -> [M, D, 1, B]
+    v = ggml_reshape_3d(ctx0, v, M, D, B);
+    v = ggml_reshape_4d(ctx0, v, M, D, 1, B);
+    v = ggml_cont(ctx0, v); // [M, D, 1, B]
+
+    // Multi-Query Attention
+    float scale = 1.0f / sqrtf((float)D);
+
+    // Step 1: Compute Q @ K.T
+    ggml_tensor * scores = ggml_mul_mat(ctx0, k, q);
+
+    scores = ggml_scale(ctx0, scores, scale);
+
+    scores = ggml_soft_max(ctx0, scores);
+
+    ggml_tensor * kqv = ggml_mul_mat(ctx0, v, scores);
+
+    kqv = ggml_permute(ctx0, kqv, 1, 0, 2, 3);
+    kqv = ggml_cont(ctx0, kqv);
+
+
+    kqv = ggml_reshape_3d(ctx0, kqv, N, D * n_head, B);
+    kqv = ggml_reshape_4d(ctx0, kqv, W, H, D * n_head, B);
+    kqv = ggml_cont(ctx0, kqv);
+
+    // Output projection
+    cur = ggml_conv_2d_direct(ctx0, block.attn_o_w, kqv, 1, 1, 0, 0, 1, 1);
+
+    // Residual & Layer Scale
+    if (inp->ne[0] == cur->ne[0] && inp->ne[2] == cur->ne[2]) {
+        if (block.layer_scale_w) {
+            cur = ggml_mul(ctx0, cur, block.layer_scale_w);
+        }
+        cur = ggml_add(ctx0, cur, inp);
+    }
+
+    return cur;
+}
+
+ggml_cgraph * clip_graph_mobilenetv5::build() {
+    ggml_tensor * inp = build_inp_raw();
+
+    // 1. Stem - Conv2dSame(3, 64, kernel_size=(3, 3), stride=(2, 2))
+    ggml_tensor * cur = pad_same_2d(inp, 3, 3, 2, 2);  // Apply SAME padding
+
+    cur = ggml_conv_2d_direct(ctx0, model.mobilenet_stem_conv_w, cur, 2, 2, 0, 0, 1, 1);  // padding=0
+    if (model.mobilenet_stem_conv_b) {
+        cur = ggml_add(ctx0, cur, model.mobilenet_stem_conv_b);
+    }
+    if (model.mobilenet_stem_norm_w) cur = rms_norm_2d(cur, model.mobilenet_stem_norm_w);
+    cur = ggml_gelu(ctx0, cur);
+
+
+    // 2. Blocks
+    std::vector<ggml_tensor*> intermediate_features;
+    const int total_blocks = model.mobilenet_blocks.size();
+
+    auto is_stage_start = [&](int i) {
+        if (i == 0) return true;
+        for (int end_idx : model.mobilenet_stage_ends) {
+            if (i == end_idx + 1) return true;
+        }
+        return false;
+    };
+
+    auto is_fusion_point = [&](int i) {
+        if (model.mobilenet_stage_ends.size() >= 4) {
+                if (i == model.mobilenet_stage_ends[2]) return true; // End of Stage 2
+                if (i == model.mobilenet_stage_ends[3]) return true; // End of Stage 3
+        } else {
+            if (i == total_blocks - 1) return true;
+        }
+        return false;
+    };
+
+    for (int i = 0; i < total_blocks; i++) {
+        const auto & block = model.mobilenet_blocks[i];
+        int stride = is_stage_start(i) ? 2 : 1;
+
+        if (block.s0_conv_exp_w)      cur = build_edge_residual(cur, block, stride);
+        else if (block.attn_q_w)      cur = build_mobilenet_attn(cur, block);
+        else                          cur = build_inverted_residual(cur, block, stride);
+
+        if (is_fusion_point(i)) {
+
+            intermediate_features.push_back(cur);
+        }
+    }
+
+    // 3. Multi-Scale Fusion Adapter (MSFA)
+    if (!intermediate_features.empty()) {
+
+        // A. Reference Resolution: PyTorch implementation uses inputs[0]
+        // We assume intermediate_features[0] is the "High Resolution" target.
+        // In MobileNet designs, this is typically the feature map with the smallest stride (e.g. 32x32).
+        ggml_tensor* target_feat = intermediate_features[0];
+        int high_res_w = target_feat->ne[0];
+        int high_res_h = target_feat->ne[1];
+
+        std::vector<ggml_tensor*> resized_feats;
+
+        // B. Resize inputs to match inputs[0] (High Resolution)
+        for (auto feat : intermediate_features) {
+            int feat_w = feat->ne[0];
+            int feat_h = feat->ne[1];
+
+            // PyTorch: if feat_size < high_resolution: interpolate
+            if (feat_w < high_res_w || feat_h < high_res_h) {
+                // Calculate scale factor.
+                // Note: PyTorch 'nearest' works on arbitrary float scales.
+                // ggml_upscale generally takes integer factors or target sizes depending on helper.
+                // Assuming standard power-of-2 scaling (e.g. 16 -> 32 means scale=2).
+                int scale_w = high_res_w / feat_w;
+                // int scale_h = high_res_h / feat_h;
+
+                // Safety check for non-integer scaling if strictly replicating
+                GGML_ASSERT(high_res_w % feat_w == 0);
+
+                // Upsample (Nearest Neighbor)
+                // 2 is the scale factor
+                feat = ggml_upscale(ctx0, feat, scale_w, ggml_scale_mode::GGML_SCALE_MODE_NEAREST);
+            }
+            resized_feats.push_back(feat);
+        }
+
+        // C. Concatenate at High Resolution (Channel Dim = 2 in ggml)
+        cur = resized_feats[0];
+        for (size_t k = 1; k < resized_feats.size(); ++k) {
+            cur = ggml_concat(ctx0, cur, resized_feats[k], 2);
+        }
+
+        // D. FFN (UniversalInvertedResidual)
+        // Structure: Expand Conv -> Norm -> GELU -> Project Conv -> Norm
+
+        // 1. Expansion
+        if (model.msfa_ffn_expand_w) {
+            // 1x1 Conv
+            cur = ggml_conv_2d_direct(ctx0, model.msfa_ffn_expand_w, cur, 1, 1, 0, 0, 1, 1);
+
+            if (model.msfa_ffn_expand_bn) {
+                cur = rms_norm_2d(cur, model.msfa_ffn_expand_bn);
+            }
+
+            cur = ggml_gelu(ctx0, cur);
+
+        }
+
+        // 2. Projection (No DW because kernel_size=0)
+        if (model.msfa_ffn_project_w) {
+            // 1x1 Conv
+            cur = ggml_conv_2d_direct(ctx0, model.msfa_ffn_project_w, cur, 1, 1, 0, 0, 1, 1);
+
+            // UniversalInvertedResidual typically has a norm after projection
+            if (model.msfa_ffn_project_bn) {
+                cur = rms_norm_2d(cur, model.msfa_ffn_project_bn);
+            }
+
+        }
+
+        // E. Final Downsample to Target Resolution (Output Resolution)
+        // PyTorch: matches self.output_resolution (e.g. 16x16)
+        const int target_out_res = 16;
+        int current_w = cur->ne[0];
+
+        if (current_w > target_out_res) {
+            int s = current_w / target_out_res;
+
+            GGML_ASSERT(current_w % target_out_res == 0);
+
+            // Avg Pool: Kernel=s, Stride=s
+            cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, s, s, s, s, 0, 0);
+
+        }
+
+        // F. Final Norm
+        if (model.msfa_concat_norm_w) {
+            cur = rms_norm_2d(cur, model.msfa_concat_norm_w);
+
+        }
+    }
+
+    // 4. Gemma 3n Multimodal Projection (Embedder)
+    // Input: 'cur' is [Width, Height, Channels, Batch]
+    int W = cur->ne[0];
+    int H = cur->ne[1];
+    int C = cur->ne[2];
+    int B = cur->ne[3];
+
+    GGML_ASSERT(C == hparams.n_embd);
+
+    // 1. Permute and Flatten to [Channels, Tokens, Batch]
+    // PyTorch expects (Batch, Seq, Hidden), GGML usually processes (Hidden, Seq, Batch)
+    cur = ggml_permute(ctx0, cur, 2, 1, 0, 3); // -> [C, H, W, B]
+    cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); // -> [C, W, H, B]
+    cur = ggml_cont(ctx0, cur);
+    cur = ggml_reshape_3d(ctx0, cur, C, W*H, B);
+    cur = ggml_cont(ctx0, cur);
+
+
+    // 2. FEATURE SCALING
+    // PyTorch: vision_outputs *= self.config.vision_config.hidden_size**0.5
+    const float scale_factor = sqrtf((float)C);
+    cur = ggml_scale(ctx0, cur, scale_factor);
+
+
+    // 3. SOFT EMBEDDING NORM
+    // PyTorch: self._norm(x) * self.weight
+    // We must normalize regardless, then multiply if weight exists.
+    {
+        const float eps = 1e-6f; // Gemma3n uses 1e-6
+        cur = ggml_rms_norm(ctx0, cur, eps);
+
+        if (model.mm_soft_emb_norm_w) {
+            // Weight shape is (2048,) -> Element-wise broadcast multiply
+            cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w);
+        }
+
+    }
+
+    // 4. PROJECTION
+    // PyTorch: embedding_projection = nn.Linear(vision_hidden, text_hidden, bias=False)
+    // Weight stored as [out_features, in_features] = [text_hidden_size, vision_hidden_size]
+    if (model.mm_input_proj_w) {
+        cur = ggml_mul_mat(ctx0, model.mm_input_proj_w, cur);
+    }
+
+    // 5. POST PROJECTION NORM
+    // PyTorch: embedding_post_projection_norm = Gemma3nRMSNorm(..., with_scale=False)
+    // with_scale=False means weight is registered as buffer with value 1.0
+    // So output = rms_norm(x) * 1.0 = rms_norm(x), magnitude ~1
+    {
+        const float eps = 1e-6f;
+        cur = ggml_rms_norm(ctx0, cur, eps);
+
+        if (model.mm_post_proj_norm_w) {
+            // If weight is loaded, multiply (should be ~1.0 anyway)
+            cur = ggml_mul(ctx0, cur, model.mm_post_proj_norm_w);
+        }
+    }
+
+    ggml_build_forward_expand(gf, cur);
+    return gf;
+}

tools/mtmd/models/models.h

@@ -76,3 +76,36 @@ struct clip_graph_glm4v : clip_graph {
     clip_graph_glm4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
     ggml_cgraph * build() override;
 };
+
+struct clip_graph_mobilenetv5 : clip_graph {
+    clip_graph_mobilenetv5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+
+    ggml_tensor * rms_norm_2d(
+        ggml_tensor * inp,
+        ggml_tensor * weight,
+        float eps = 1e-6f);
+
+    ggml_tensor* pad_same_2d(
+        ggml_tensor* inp,
+        int kernel_h,
+        int kernel_w,
+        int stride_h,
+        int stride_w,
+        int dilation_h = 1,
+        int dilation_w = 1);
+
+    ggml_tensor * build_edge_residual(
+        ggml_tensor * inp,
+        const mobilenetv5_block & block,
+        int stride);
+
+    ggml_tensor * build_inverted_residual(
+        ggml_tensor * inp,
+        const mobilenetv5_block & block,
+        int stride);
+
+    ggml_tensor * build_mobilenet_attn(
+        ggml_tensor * inp,
+        const mobilenetv5_block & block);
+};

tools/mtmd/mtmd.cpp

@@ -266,7 +266,7 @@ struct mtmd_context {
         }
 
         // set boi/eoi
-        if (proj == PROJECTOR_TYPE_GEMMA3) {
+        if (proj == PROJECTOR_TYPE_GEMMA3 || proj == PROJECTOR_TYPE_GEMMA3NV) {
             // <start_of_image> ... (image embeddings) ... <end_of_image>
             img_beg = "<start_of_image>";
             img_end = "<end_of_image>";
@@ -862,10 +862,15 @@ float * mtmd_get_output_embd(mtmd_context * ctx) {
 }
 
 bool mtmd_decode_use_non_causal(mtmd_context * ctx) {
-    if (ctx->ctx_v && clip_get_projector_type(ctx->ctx_v) == PROJECTOR_TYPE_GEMMA3) {
-        return true;
+    switch (ctx->proj_type_v()) {
+        case PROJECTOR_TYPE_QWEN2VL:
+        case PROJECTOR_TYPE_QWEN25VL:
+        case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_YOUTUVL:
+            return true;
+        default:
+            return false;
     }
-    return false;
 }
 
 bool mtmd_decode_use_mrope(mtmd_context * ctx) {

tools/server/README.md

@@ -33,6 +33,7 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | -------- | ----------- |
 | `-h, --help, --usage` | print usage and exit |
 | `--version` | show version and build info |
+| `--license` | show source code license and dependencies |
 | `-cl, --cache-list` | show list of models in cache |
 | `--completion-bash` | print source-able bash completion script for llama.cpp |
 | `--verbose-prompt` | print a verbose prompt before generation (default: false) |
@@ -73,22 +74,23 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `--mmap, --no-mmap` | whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. Takes precedence over --mmap (default: enabled)<br/>(env: LLAMA_ARG_DIO) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
-| `-ot, --override-tensor <tensor name pattern>=<buffer type>,...` | override tensor buffer type |
+| `-ot, --override-tensor <tensor name pattern>=<buffer type>,...` | override tensor buffer type<br/>(env: LLAMA_ARG_OVERRIDE_TENSOR) |
 | `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU<br/>(env: LLAMA_ARG_CPU_MOE) |
 | `-ncmoe, --n-cpu-moe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU<br/>(env: LLAMA_ARG_N_CPU_MOE) |
-| `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM (default: -1)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
+| `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
 | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)<br/>(env: LLAMA_ARG_MAIN_GPU) |
 | `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')<br/>(env: LLAMA_ARG_FIT) |
-| `-fitt, --fit-target MiB` | target margin per device for --fit option, default: 1024<br/>(env: LLAMA_ARG_FIT_TARGET) |
+| `-fitt, --fit-target MiB0,MiB1,MiB2,...` | target margin per device for --fit, comma-separated list of values, single value is broadcast across all devices, default: 1024<br/>(env: LLAMA_ARG_FIT_TARGET) |
 | `-fitc, --fit-ctx N` | minimum ctx size that can be set by --fit option, default: 4096<br/>(env: LLAMA_ARG_FIT_CTX) |
 | `--check-tensors` | check model tensor data for invalid values (default: false) |
-| `--override-kv KEY=TYPE:VALUE,...` | advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false |
+| `--override-kv KEY=TYPE:VALUE,...` | advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false |
 | `--op-offload, --no-op-offload` | whether to offload host tensor operations to device (default: true) |
 | `--lora FNAME` | path to LoRA adapter (use comma-separated values to load multiple adapters) |
 | `--lora-scaled FNAME:SCALE,...` | path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)<br/>note: use comma-separated values |
@@ -151,6 +153,7 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--grammar-file FNAME` | file to read grammar from |
 | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
 | `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
+| `-bs, --backend-sampling` | enable backend sampling (experimental) (default: disabled)<br/>(env: LLAMA_ARG_BACKEND_SAMPLING) |
 
 
 ### Server-specific params
@@ -187,11 +190,11 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--webui, --no-webui` | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_WEBUI) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--rerank, --reranking` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
-| `--api-key KEY` | API key to use for authentication (default: none)<br/>(env: LLAMA_API_KEY) |
+| `--api-key KEY` | API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)<br/>(env: LLAMA_API_KEY) |
 | `--api-key-file FNAME` | path to file containing API keys (default: none) |
 | `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key<br/>(env: LLAMA_ARG_SSL_KEY_FILE) |
 | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate<br/>(env: LLAMA_ARG_SSL_CERT_FILE) |
-| `--chat-template-kwargs STRING` | sets additional params for the json template parser<br/>(env: LLAMA_CHAT_TEMPLATE_KWARGS) |
+| `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'<br/>(env: LLAMA_CHAT_TEMPLATE_KWARGS) |
 | `-to, --timeout N` | server read/write timeout in seconds (default: 600)<br/>(env: LLAMA_ARG_TIMEOUT) |
 | `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
 | `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)<br/>[(card)](https://ggml.ai/f0.png)<br/>(env: LLAMA_ARG_CACHE_REUSE) |
@@ -207,8 +210,8 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled<br/><br/>(env: LLAMA_ARG_PREFILL_ASSISTANT) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled) |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
@@ -220,7 +223,7 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.8)<br/>(env: LLAMA_ARG_DRAFT_P_MIN) |
 | `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE_DRAFT) |
 | `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
-| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
+| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
 | `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
 | `--spec-replace TARGET DRAFT` | translate the string in TARGET into DRAFT if the draft model and main model are not compatible |
 | `-mv, --model-vocoder FNAME` | vocoder model for audio generation (default: unused) |
@@ -779,7 +782,8 @@ By default, it is read-only. To make POST request to change global properties, y
   "modalities": {
     "vision": false
   },
-  "build_info": "b(build number)-(build commit hash)"
+  "build_info": "b(build number)-(build commit hash)",
+  "is_sleeping": false
 }
 ```
 
@@ -788,6 +792,7 @@ By default, it is read-only. To make POST request to change global properties, y
 - `model_path` - the path to model file (same with `-m` argument)
 - `chat_template` - the model's original Jinja2 prompt template
 - `modalities` - the list of supported modalities
+- `is_sleeping` - sleeping status, see [Sleeping on idle](#sleeping-on-idle)
 
 ### POST `/props`: Change server global properties.
 
@@ -1630,9 +1635,12 @@ The server supports an automatic sleep mode that activates after a specified per
 
 When the server enters sleep mode, the model and its associated memory (including the KV cache) are unloaded from RAM to conserve resources. Any new incoming task will automatically trigger the model to reload.
 
+The sleeping status can be retrieved from the `GET /props` endpoint (or `/props?model=(model_name)` in router mode).
+
 Note that the following endpoints are exempt from being considered as incoming tasks. They do not trigger model reloading and do not reset the idle timer:
 - `GET /health`
 - `GET /props`
+- `GET /models`
 
 ## More examples
 

tools/server/server-context.cpp

@@ -79,6 +79,8 @@ struct server_slot {
 
     common_speculative * spec = nullptr;
 
+    // TODO: move members that belong to the task (such as `generated_text`, `has_new_line`) to task_results_state
+    //       see https://github.com/ggml-org/llama.cpp/pull/18283#issuecomment-3710175837
     std::unique_ptr<const server_task> task;
     std::unique_ptr<const server_task> task_prev; // used for debugging
 
@@ -153,7 +155,7 @@ struct server_slot {
 
     common_sampler_ptr smpl;
 
-    llama_token sampled; // in speculative mode, this is the last accepted token
+    llama_token  sampled; // in speculative mode, this is the last accepted token
     llama_tokens drafted;
 
     // stats
@@ -201,12 +203,46 @@ struct server_slot {
         alora_invocation_start = -1;
     }
 
+    // remove cached prompt + tokens
+    void clear(bool allow_processing) {
+        if (!allow_processing) {
+            GGML_ASSERT(!is_processing());
+        }
+
+        SLT_INF(*this, "clearing slot with %zu tokens\n", prompt.tokens.size());
+
+        llama_memory_seq_rm(llama_get_memory(ctx), id, -1, -1);
+        prompt.tokens.clear();
+    }
+
+    void init_sampler() const {
+        const int64_t t_start = ggml_time_us();
+
+        common_sampler_reset(smpl.get());
+
+        int n_text = 0;
+
+        for (int i = 0; i < (int) prompt.tokens.size(); i++) {
+            const llama_token id = prompt.tokens[i];
+
+            if (id != LLAMA_TOKEN_NULL) {
+                common_sampler_accept(smpl.get(), id, false);
+                n_text++;
+            }
+        }
+
+        SLT_INF(*this, "init sampler, took %0.2f ms, tokens: text = %d, total = %d\n",
+                (ggml_time_us() - t_start) / 1000.0, n_text, (int) prompt.tokens.size());
+    }
+
+    // TODO: move to server_task
     bool need_embd() const {
         GGML_ASSERT(task);
 
         return server_task_type_need_embd(task->type);
     }
 
+    // TODO: move to server_task
     bool need_logits() const {
         GGML_ASSERT(task);
 
@@ -258,10 +294,13 @@ struct server_slot {
             SLT_WRN(*this, "%s", "slot is not processing\n");
             return;
         }
+
         generated_token_probs.push_back(token);
     }
 
     int get_n_draft_max() const {
+        GGML_ASSERT(task);
+
         if (!can_speculate()) {
             return 0;
         }
@@ -287,12 +326,14 @@ struct server_slot {
     }
 
     // note: a slot can also be either a parent or a child
+    // TODO: move to server_task
     bool is_parent() const {
-        return is_processing() && task->n_children > 0;
+        return task->n_children > 0;
     }
 
+    // TODO: move to server_task
     bool is_child() const {
-        return is_processing() && task->id_parent >= 0;
+        return task->id_parent >= 0;
     }
 
     void release() {
@@ -301,10 +342,16 @@ struct server_slot {
 
             SLT_INF(*this, "stop processing: n_tokens = %d, truncated = %d\n", prompt.n_tokens(), truncated);
 
-            t_last_used = ggml_time_us();
+            t_last_used        =  ggml_time_us();
             t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
+
             state = SLOT_STATE_IDLE;
 
+            // do not keep context of the child slots - the parent's context is enough
+            if (is_child()) {
+                clear(false);
+            }
+
             task_prev = std::move(task);
             task.reset();
 
@@ -425,14 +472,22 @@ struct server_slot {
     }
 
     void copy_state_to(server_slot & other) const {
-        llama_memory_seq_rm(llama_get_memory(ctx), other.id, 0, -1);
-        llama_memory_seq_cp(llama_get_memory(ctx), id, other.id, 0, -1);
+        GGML_ASSERT(state == SLOT_STATE_DONE_PROMPT);
+
+        llama_memory_seq_rm(llama_get_memory(ctx), other.id,     -1, -1);
+        llama_memory_seq_cp(llama_get_memory(ctx), id, other.id, -1, -1);
+
         other.n_decoded   = n_decoded;
         other.n_remaining = n_remaining;
         other.i_batch     = i_batch;
+
+        other.t_start_process_prompt    = t_start_process_prompt;
+        other.t_prompt_processing       = t_prompt_processing;
         other.n_prompt_tokens_cache     = n_prompt_tokens_cache;
         other.n_prompt_tokens_processed = n_prompt_tokens_processed;
+
         other.prompt = prompt.clone();
+        other.init_sampler();
     }
 };
 
@@ -745,6 +800,7 @@ private:
         }
 
         slots.clear();
+
         for (int i = 0; i < params_base.n_parallel; i++) {
             server_slot slot;
 
@@ -993,7 +1049,7 @@ private:
                 ret->prompt_save(*prompt_cache);
 
                 if (!ret->prompt_load(*prompt_cache, task.tokens)) {
-                    clear_slot(*ret);
+                    ret->clear(false);
                 }
 
                 prompt_cache->update();
@@ -1005,17 +1061,6 @@ private:
         return ret;
     }
 
-    void clear_slot(server_slot & slot, bool allow_processing = false) const {
-        if (!allow_processing) {
-            GGML_ASSERT(!slot.is_processing());
-        }
-
-        SLT_WRN(slot, "clearing slot with %zu tokens\n", slot.prompt.tokens.size());
-
-        llama_memory_seq_rm(llama_get_memory(ctx), slot.id, -1, -1);
-        slot.prompt.tokens.clear();
-    }
-
     // return true if at least one slot has been cleared
     // TODO: improve logic
     //       - smarter decision which slot to clear (LRU or longest prompt?)
@@ -1036,7 +1081,7 @@ private:
             if (slot.prompt.n_tokens() > 0) {
                 SRV_WRN("purging slot %d with %zu tokens\n", slot.id, slot.prompt.tokens.size());
 
-                clear_slot(slot);
+                slot.clear(false);
 
                 res = true;
 
@@ -1182,7 +1227,7 @@ private:
             ? SLOT_STATE_WAIT_OTHER // wait for the parent to process prompt
             : SLOT_STATE_STARTED;
 
-        SLT_INF(slot, "%s", "processing task\n");
+        SLT_INF(slot, "processing task, is_child = %d\n", slot.is_child());
 
         return true;
     }
@@ -1819,7 +1864,7 @@ private:
                     // Erase token cache
                     const size_t n_erased = slot->prompt.tokens.size();
 
-                    clear_slot(*slot);
+                    slot->clear(false);
 
                     auto res = std::make_unique<server_task_result_slot_erase>();
                     res->id       = task.id;
@@ -2053,8 +2098,29 @@ private:
                     continue;
                 }
 
+                // check if this is a child slot
+                if (slot.state == SLOT_STATE_WAIT_OTHER) {
+                    SLT_DBG(slot, "%s", "waiting for parent slot to complete\n");
+                    continue;
+                }
+
                 // this slot still has a prompt to be processed
                 if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) {
+                    // wait for all children to be launched
+                    if (slot.is_parent()) {
+                        int n_launched = 0;
+                        for (auto & other : slots) {
+                            if (other.is_processing() && other.is_child() && other.task->id_parent == slot.task->id) {
+                                ++n_launched;
+                            }
+                        }
+
+                        if (n_launched < slot.task->n_children) {
+                            SLT_DBG(slot, "waiting for children to be launched, n_children = %d, n_launched = %d\n", slot.task->n_children, n_launched);
+                            continue;
+                        }
+                    }
+
                     const auto & input_tokens = slot.task->tokens;
 
                     // TODO: maybe move branch to outside of this loop in the future
@@ -2355,7 +2421,7 @@ private:
                     if (!llama_memory_seq_rm(llama_get_memory(ctx), slot.id, p0, -1)) {
                         SLT_WRN(slot, "failed to truncate tokens with position >= %d - clearing the memory\n", p0);
 
-                        clear_slot(slot, /*allow_processing=*/true);
+                        slot.clear(true);
 
                         // there is no common part left
                         slot.n_prompt_tokens_cache = 0;
@@ -2455,16 +2521,6 @@ private:
 
                         GGML_ASSERT(batch.n_tokens > 0);
 
-                        common_sampler_reset(slot.smpl.get());
-
-                        // Process all prompt tokens through sampler system
-                        for (int i = 0; i < slot.task->n_tokens(); ++i) {
-                            llama_token id = input_tokens[i];
-                            if (id != LLAMA_TOKEN_NULL) {
-                                common_sampler_accept(slot.smpl.get(), id, false);
-                            }
-                        }
-
                         // extract the logits only for the last token
                         batch.logits[batch.n_tokens - 1] = true;
 
@@ -2473,6 +2529,8 @@ private:
 
                         SLT_INF(slot, "prompt done, n_tokens = %d, batch.n_tokens = %d\n", slot.prompt.n_tokens(), batch.n_tokens);
 
+                        slot.init_sampler();
+
                         const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
                         const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id);
 
@@ -2519,11 +2577,6 @@ private:
             }
         }
 
-        if (batch.n_tokens == 0) {
-            SRV_WRN("%s", "no tokens to decode\n");
-            return;
-        }
-
         SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens);
 
         if (slot_batched) {
@@ -2540,6 +2593,10 @@ private:
             llama_set_embeddings(ctx, slot_batched->need_embd());
         }
 
+        if (batch.n_tokens == 0) {
+            SRV_WRN("%s", "no tokens to decode\n");
+        }
+
         int32_t i_next = 0;
 
         // process the created batch of tokens
@@ -2591,7 +2648,7 @@ private:
 
                                 // note: it's complicated to keep track of how much of the current batch has been
                                 //       processed before the error occurred, so we simply clear the entire context
-                                clear_slot(slot);
+                                slot.clear(false);
                             }
                         }
 
@@ -2615,27 +2672,34 @@ private:
             // on successful decode, restore the original batch size
             n_batch = llama_n_batch(ctx);
 
+            // handle `n_cmpl > 1` tasks - when the main prompt is processed, activate all child tasks too
             for (auto & slot : slots) {
-                // may need to copy state to other slots
                 if (slot.state == SLOT_STATE_DONE_PROMPT && slot.is_parent()) {
-                    std::vector<server_slot *> child_slots;
+                    SLT_INF(slot, "parent task prompt done, n_children = %d\n", slot.task->n_children);
+
+                    std::vector<server_slot *> children;
                     for (auto & other : slots) {
                         if (other.state == SLOT_STATE_WAIT_OTHER && slot.task->id == other.task->id_parent) {
-                            child_slots.push_back(&other);
+                            children.push_back(&other);
                         }
                     }
 
                     // we can only proceed if all child slots are having the correct tasks
-                    if (child_slots.size() == slot.task->n_children) {
+                    if (slot.task->n_children == (int) children.size()) {
                         // copy state to the child slots
-                        for (auto & child : child_slots) {
-                            SLT_INF(slot, "copying state to child %d\n", child->id);
+                        for (auto & child : children) {
+                            SLT_INF(slot, " - copying state to child %d\n", child->id);
+
+                            GGML_ASSERT(child->state == SLOT_STATE_WAIT_OTHER);
+
                             slot.copy_state_to(*child);
                             child->state = SLOT_STATE_DONE_PROMPT;
                         }
                     }
                 }
+            }
 
+            for (auto & slot : slots) {
                 // optionally send prompt processing progress
                 if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_DONE_PROMPT) {
                     if (slot.task->params.stream && slot.task->params.return_progress) {
@@ -2720,7 +2784,7 @@ private:
                     continue;
                 }
 
-                size_t n_draft = slot.drafted.size();
+                const size_t n_draft = slot.drafted.size();
 
                 // the accepted tokens from the speculation
                 const auto ids = common_sampler_sample_and_accept_n(slot.smpl.get(), ctx, slot.i_batch_dft, slot.drafted);
@@ -2923,9 +2987,11 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
             task.params.oaicompat_cmpl_id = completion_id;
             task.params.oaicompat_model   = meta->model_name;
 
+            // prepare child tasks
             if (task.params.n_cmpl > 1) {
                 task.n_children = task.params.n_cmpl - 1;
-                for (size_t j = 0; j < task.n_children; j++) {
+
+                for (int j = 0; j < task.n_children; j++) {
                     server_task child = task.create_child(task.id, rd.get_new_id());
 
                     // use different sampling seed for each child
@@ -2938,7 +3004,8 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
                 }
             }
 
-            tasks.push_back(std::move(task));
+            // note: the parent task always launches first
+            tasks.insert(tasks.begin(), std::move(task));
         }
 
         rd.post_tasks(std::move(tasks));

tools/server/server-task.cpp

@@ -160,6 +160,7 @@ task_params server_task::params_from_json_cmpl(
     defaults.n_keep        = params_base.n_keep;
     defaults.n_predict     = params_base.n_predict;
     defaults.n_cache_reuse = params_base.n_cache_reuse;
+    defaults.cache_prompt  = params_base.cache_prompt;
     defaults.antiprompt    = params_base.antiprompt;
 
     // enabling this will output extra debug information in the HTTP responses from the server
@@ -169,7 +170,7 @@ task_params server_task::params_from_json_cmpl(
     params.stream           = json_value(data,       "stream",             false);
     auto stream_opt         = json_value(data,       "stream_options",     json::object());
     params.include_usage    = json_value(stream_opt, "include_usage",      false);
-    params.cache_prompt     = json_value(data,       "cache_prompt",       true);
+    params.cache_prompt     = json_value(data,       "cache_prompt",       defaults.cache_prompt);
     params.return_tokens    = json_value(data,       "return_tokens",      false);
     params.return_progress  = json_value(data,       "return_progress",    false);
     params.n_predict        = json_value(data,       "n_predict",          json_value(data, "max_tokens", defaults.n_predict));

tools/server/server-task.h

@@ -121,8 +121,8 @@ struct server_task {
     int id_slot   = -1;
 
     // used by parallel sampling (multiple completions from same prompt)
-    size_t n_children =  0; // number of tasks reusing this prompt
-    int    id_parent  = -1;
+    int n_children =  0; // number of tasks reusing this prompt
+    int id_parent  = -1;
 
     // used by SERVER_TASK_TYPE_INFERENCE
     task_params   params;
@@ -173,11 +173,13 @@ struct server_task {
 
     server_task create_child(int id_parent, int id_child) const {
         server_task copy;
+
         copy.id        = id_child;
         copy.id_parent = id_parent;
         copy.params    = params;
         copy.type      = type;
         copy.tokens    = tokens.clone();
+
         return copy;
     }
 

tools/server/tests/unit/test_completion.py

@@ -393,12 +393,12 @@ def test_completion_unified(n_ctx, n_slots, n_predict_vals, expected_success):
     for res, n_predict, expect_ok in zip(results, n_predict_vals, expected_success):
         if expect_ok:
             assert res.status_code == 200
+
+        # note: https://github.com/ggml-org/llama.cpp/pull/18700#issuecomment-3728695581
+        if res.status_code == 200:
             assert "content" in res.body
             if "timings" in res.body:
                 assert res.body["timings"]["predicted_n"] == n_predict
-        else:
-            assert res.status_code == 500
-            assert "content" not in res.body
 
 
 @pytest.mark.parametrize(

tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte

@@ -10,21 +10,11 @@
 	import { INPUT_CLASSES } from '$lib/constants/input-classes';
 	import { SETTING_CONFIG_DEFAULT } from '$lib/constants/settings-config';
 	import { config } from '$lib/stores/settings.svelte';
-	import { modelsStore, modelOptions, selectedModelId } from '$lib/stores/models.svelte';
+	import { modelOptions, selectedModelId } from '$lib/stores/models.svelte';
 	import { isRouterMode } from '$lib/stores/server.svelte';
 	import { chatStore } from '$lib/stores/chat.svelte';
 	import { activeMessages } from '$lib/stores/conversations.svelte';
-	import {
-		FileTypeCategory,
-		MimeTypeApplication,
-		FileExtensionAudio,
-		FileExtensionImage,
-		FileExtensionPdf,
-		FileExtensionText,
-		MimeTypeAudio,
-		MimeTypeImage,
-		MimeTypeText
-	} from '$lib/enums';
+	import { MimeTypeText } from '$lib/enums';
 	import { isIMEComposing, parseClipboardContent } from '$lib/utils';
 	import {
 		AudioRecorder,
@@ -61,7 +51,6 @@
 	let audioRecorder: AudioRecorder | undefined;
 	let chatFormActionsRef: ChatFormActions | undefined = $state(undefined);
 	let currentConfig = $derived(config());
-	let fileAcceptString = $state<string | undefined>(undefined);
 	let fileInputRef: ChatFormFileInputInvisible | undefined = $state(undefined);
 	let isRecording = $state(false);
 	let message = $state('');
@@ -104,40 +93,6 @@
 		return null;
 	});
 
-	// State for model props reactivity
-	let modelPropsVersion = $state(0);
-
-	// Fetch model props when active model changes (works for both MODEL and ROUTER mode)
-	$effect(() => {
-		if (activeModelId) {
-			const cached = modelsStore.getModelProps(activeModelId);
-			if (!cached) {
-				modelsStore.fetchModelProps(activeModelId).then(() => {
-					modelPropsVersion++;
-				});
-			}
-		}
-	});
-
-	// Derive modalities from active model (works for both MODEL and ROUTER mode)
-	let hasAudioModality = $derived.by(() => {
-		if (activeModelId) {
-			void modelPropsVersion; // Trigger reactivity on props fetch
-			return modelsStore.modelSupportsAudio(activeModelId);
-		}
-
-		return false;
-	});
-
-	let hasVisionModality = $derived.by(() => {
-		if (activeModelId) {
-			void modelPropsVersion; // Trigger reactivity on props fetch
-			return modelsStore.modelSupportsVision(activeModelId);
-		}
-
-		return false;
-	});
-
 	function checkModelSelected(): boolean {
 		if (!hasModelSelected) {
 			// Open the model selector
@@ -148,42 +103,12 @@
 		return true;
 	}
 
-	function getAcceptStringForFileType(fileType: FileTypeCategory): string {
-		switch (fileType) {
-			case FileTypeCategory.IMAGE:
-				return [...Object.values(FileExtensionImage), ...Object.values(MimeTypeImage)].join(',');
-
-			case FileTypeCategory.AUDIO:
-				return [...Object.values(FileExtensionAudio), ...Object.values(MimeTypeAudio)].join(',');
-
-			case FileTypeCategory.PDF:
-				return [...Object.values(FileExtensionPdf), ...Object.values(MimeTypeApplication)].join(
-					','
-				);
-
-			case FileTypeCategory.TEXT:
-				return [...Object.values(FileExtensionText), MimeTypeText.PLAIN].join(',');
-
-			default:
-				return '';
-		}
-	}
-
 	function handleFileSelect(files: File[]) {
 		onFileUpload?.(files);
 	}
 
-	function handleFileUpload(fileType?: FileTypeCategory) {
-		if (fileType) {
-			fileAcceptString = getAcceptStringForFileType(fileType);
-		} else {
-			fileAcceptString = undefined;
-		}
-
-		// Use setTimeout to ensure the accept attribute is applied before opening dialog
-		setTimeout(() => {
-			fileInputRef?.click();
-		}, 10);
+	function handleFileUpload() {
+		fileInputRef?.click();
 	}
 
 	async function handleKeydown(event: KeyboardEvent) {
@@ -343,13 +268,7 @@
 	});
 </script>
 
-<ChatFormFileInputInvisible
-	bind:this={fileInputRef}
-	bind:accept={fileAcceptString}
-	{hasAudioModality}
-	{hasVisionModality}
-	onFileSelect={handleFileSelect}
-/>
+<ChatFormFileInputInvisible bind:this={fileInputRef} onFileSelect={handleFileSelect} />
 
 <form
 	onsubmit={handleSubmit}

tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte

@@ -4,14 +4,13 @@
 	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { FILE_TYPE_ICONS } from '$lib/constants/icons';
-	import { FileTypeCategory } from '$lib/enums';
 
 	interface Props {
 		class?: string;
 		disabled?: boolean;
 		hasAudioModality?: boolean;
 		hasVisionModality?: boolean;
-		onFileUpload?: (fileType?: FileTypeCategory) => void;
+		onFileUpload?: () => void;
 	}
 
 	let {
@@ -27,10 +26,6 @@
 			? 'Text files and PDFs supported. Images, audio, and video require vision models.'
 			: 'Attach files';
 	});
-
-	function handleFileUpload(fileType?: FileTypeCategory) {
-		onFileUpload?.(fileType);
-	}
 </script>
 
 <div class="flex items-center gap-1 {className}">
@@ -61,7 +56,7 @@
 					<DropdownMenu.Item
 						class="images-button flex cursor-pointer items-center gap-2"
 						disabled={!hasVisionModality}
-						onclick={() => handleFileUpload(FileTypeCategory.IMAGE)}
+						onclick={() => onFileUpload?.()}
 					>
 						<FILE_TYPE_ICONS.image class="h-4 w-4" />
 
@@ -81,7 +76,7 @@
 					<DropdownMenu.Item
 						class="audio-button flex cursor-pointer items-center gap-2"
 						disabled={!hasAudioModality}
-						onclick={() => handleFileUpload(FileTypeCategory.AUDIO)}
+						onclick={() => onFileUpload?.()}
 					>
 						<FILE_TYPE_ICONS.audio class="h-4 w-4" />
 
@@ -98,7 +93,7 @@
 
 			<DropdownMenu.Item
 				class="flex cursor-pointer items-center gap-2"
-				onclick={() => handleFileUpload(FileTypeCategory.TEXT)}
+				onclick={() => onFileUpload?.()}
 			>
 				<FILE_TYPE_ICONS.text class="h-4 w-4" />
 
@@ -109,7 +104,7 @@
 				<Tooltip.Trigger class="w-full">
 					<DropdownMenu.Item
 						class="flex cursor-pointer items-center gap-2"
-						onclick={() => handleFileUpload(FileTypeCategory.PDF)}
+						onclick={() => onFileUpload?.()}
 					>
 						<FILE_TYPE_ICONS.pdf class="h-4 w-4" />
 

tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte

@@ -24,7 +24,7 @@
 		isRecording?: boolean;
 		hasText?: boolean;
 		uploadedFiles?: ChatUploadedFile[];
-		onFileUpload?: (fileType?: FileTypeCategory) => void;
+		onFileUpload?: () => void;
 		onMicClick?: () => void;
 		onStop?: () => void;
 	}

tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormFileInputInvisible.svelte

@@ -1,35 +1,14 @@
 <script lang="ts">
-	import { generateModalityAwareAcceptString } from '$lib/utils';
-
 	interface Props {
-		accept?: string;
 		class?: string;
-		hasAudioModality?: boolean;
-		hasVisionModality?: boolean;
 		multiple?: boolean;
 		onFileSelect?: (files: File[]) => void;
 	}
 
-	let {
-		accept = $bindable(),
-		class: className = '',
-		hasAudioModality = false,
-		hasVisionModality = false,
-		multiple = true,
-		onFileSelect
-	}: Props = $props();
+	let { class: className = '', multiple = true, onFileSelect }: Props = $props();
 
 	let fileInputElement: HTMLInputElement | undefined;
 
-	// Use modality-aware accept string by default, but allow override
-	let finalAccept = $derived(
-		accept ??
-			generateModalityAwareAcceptString({
-				hasVision: hasVisionModality,
-				hasAudio: hasAudioModality
-			})
-	);
-
 	export function click() {
 		fileInputElement?.click();
 	}
@@ -46,7 +25,6 @@
 	bind:this={fileInputElement}
 	type="file"
 	{multiple}
-	accept={finalAccept}
 	onchange={handleFileSelect}
 	class="hidden {className}"
 />

tools/server/webui/src/lib/utils/file-type.ts

@@ -195,9 +195,28 @@ export function getFileTypeByExtension(filename: string): string | null {
 }
 
 export function isFileTypeSupported(filename: string, mimeType?: string): boolean {
-	if (mimeType && getFileTypeCategory(mimeType)) {
+	// Images are detected and handled separately for vision models
+	if (mimeType) {
+		const category = getFileTypeCategory(mimeType);
+		if (
+			category === FileTypeCategory.IMAGE ||
+			category === FileTypeCategory.AUDIO ||
+			category === FileTypeCategory.PDF
+		) {
+			return true;
+		}
+	}
+
+	// Check extension for known types (especially images without MIME)
+	const extCategory = getFileTypeCategoryByExtension(filename);
+	if (
+		extCategory === FileTypeCategory.IMAGE ||
+		extCategory === FileTypeCategory.AUDIO ||
+		extCategory === FileTypeCategory.PDF
+	) {
 		return true;
 	}
 
-	return getFileTypeByExtension(filename) !== null;
+	// Fallback: treat everything else as text (inclusive by default)
+	return true;
 }

tools/server/webui/src/lib/utils/index.ts

@@ -76,7 +76,6 @@ export {
 	isFileTypeSupportedByModel,
 	filterFilesByModalities,
 	generateModalityErrorMessage,
-	generateModalityAwareAcceptString,
 	type ModalityCapabilities
 } from './modality-file-validation';
 

tools/server/webui/src/lib/utils/modality-file-validation.ts

@@ -4,17 +4,7 @@
  */
 
 import { getFileTypeCategory } from '$lib/utils';
-import {
-	FileExtensionAudio,
-	FileExtensionImage,
-	FileExtensionPdf,
-	FileExtensionText,
-	MimeTypeAudio,
-	MimeTypeImage,
-	MimeTypeApplication,
-	MimeTypeText,
-	FileTypeCategory
-} from '$lib/enums';
+import { FileTypeCategory } from '$lib/enums';
 
 /** Modality capabilities for file validation */
 export interface ModalityCapabilities {
@@ -170,29 +160,3 @@ export function generateModalityErrorMessage(
  * @param capabilities - The modality capabilities to check against
  * @returns Accept string for HTML file input element
  */
-export function generateModalityAwareAcceptString(capabilities: ModalityCapabilities): string {
-	const { hasVision, hasAudio } = capabilities;
-
-	const acceptedExtensions: string[] = [];
-	const acceptedMimeTypes: string[] = [];
-
-	// Always include text files and PDFs
-	acceptedExtensions.push(...Object.values(FileExtensionText));
-	acceptedMimeTypes.push(...Object.values(MimeTypeText));
-	acceptedExtensions.push(...Object.values(FileExtensionPdf));
-	acceptedMimeTypes.push(...Object.values(MimeTypeApplication));
-
-	// Include images only if vision is supported
-	if (hasVision) {
-		acceptedExtensions.push(...Object.values(FileExtensionImage));
-		acceptedMimeTypes.push(...Object.values(MimeTypeImage));
-	}
-
-	// Include audio only if audio is supported
-	if (hasAudio) {
-		acceptedExtensions.push(...Object.values(FileExtensionAudio));
-		acceptedMimeTypes.push(...Object.values(MimeTypeAudio));
-	}
-
-	return [...acceptedExtensions, ...acceptedMimeTypes].join(',');
-}

tools/server/webui/src/lib/utils/process-uploaded-files.ts

@@ -1,5 +1,4 @@
 import { isSvgMimeType, svgBase64UrlToPngDataURL } from './svg-to-png';
-import { isTextFileByName } from './text-files';
 import { isWebpMimeType, webpBase64UrlToPngDataURL } from './webp-to-png';
 import { FileTypeCategory } from '$lib/enums';
 import { modelsStore } from '$lib/stores/models.svelte';
@@ -84,17 +83,6 @@ export async function processFilesToChatUploaded(
 				}
 
 				results.push({ ...base, preview });
-			} else if (
-				getFileTypeCategory(file.type) === FileTypeCategory.TEXT ||
-				isTextFileByName(file.name)
-			) {
-				try {
-					const textContent = await readFileAsUTF8(file);
-					results.push({ ...base, textContent });
-				} catch (err) {
-					console.warn('Failed to read text file, adding without content:', err);
-					results.push(base);
-				}
 			} else if (getFileTypeCategory(file.type) === FileTypeCategory.PDF) {
 				// Extract text content from PDF for preview
 				try {
@@ -129,8 +117,14 @@ export async function processFilesToChatUploaded(
 				const preview = await readFileAsDataURL(file);
 				results.push({ ...base, preview });
 			} else {
-				// Other files: add as-is
-				results.push(base);
+				// Fallback: treat unknown files as text
+				try {
+					const textContent = await readFileAsUTF8(file);
+					results.push({ ...base, textContent });
+				} catch (err) {
+					console.warn('Failed to read file as text, adding without content:', err);
+					results.push(base);
+				}
 			}
 		} catch (error) {
 			console.error('Error processing file', file.name, error);

tools/server/webui/tests/stories/ChatForm.stories.svelte

@@ -65,10 +65,7 @@
 		await expect(textarea).toHaveValue(text);
 
 		const fileInput = document.querySelector('input[type="file"]');
-		const acceptAttr = fileInput?.getAttribute('accept');
-		await expect(fileInput).toHaveAttribute('accept');
-		await expect(acceptAttr).not.toContain('image/');
-		await expect(acceptAttr).not.toContain('audio/');
+		await expect(fileInput).not.toHaveAttribute('accept');
 
 		// Open file attachments dropdown
 		const fileUploadButton = canvas.getByText('Attach files');

vendor/cpp-httplib/CMakeLists.txt

@@ -1,4 +1,5 @@
 set(TARGET cpp-httplib)
+license_add_file("cpp-httplib" "LICENSE")
 
 find_package(Threads REQUIRED)
 
@@ -8,7 +9,7 @@ if (NOT MSVC)
     target_compile_options(${TARGET} PRIVATE -w)
 endif()
 
-target_link_libraries  (${TARGET} PRIVATE Threads::Threads)
+target_link_libraries(${TARGET} PRIVATE Threads::Threads)
 
 if (WIN32 AND NOT MSVC)
     target_link_libraries(${TARGET} PRIVATE ws2_32)
@@ -67,6 +68,8 @@ if (LLAMA_BUILD_BORINGSSL)
     set(BUILD_SHARED_LIBS ${SAVED_BUILD_SHARED_LIBS})
     set(BUILD_TESTING ${SAVED_BUILD_TESTING})
 
+    license_add_file("BoringSSL" "${boringssl_SOURCE_DIR}/LICENSE")
+
     set(CPPHTTPLIB_OPENSSL_SUPPORT TRUE)
     target_link_libraries(${TARGET} PUBLIC ssl crypto)
 
@@ -108,6 +111,8 @@ elseif (LLAMA_BUILD_LIBRESSL)
     set(BUILD_SHARED_LIBS ${SAVED_BUILD_SHARED_LIBS})
     set(BUILD_TESTING ${SAVED_BUILD_TESTING})
 
+    license_add_file("LibreSSL" "${libressl_SOURCE_DIR}/COPYING")
+
     set(CPPHTTPLIB_OPENSSL_SUPPORT TRUE)
     target_link_libraries(${TARGET} PUBLIC ssl crypto)
 

vendor/cpp-httplib/LICENSE

@@ -19,3 +19,4 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
+

vendor/cpp-httplib/httplib.cpp

@@ -1138,6 +1138,7 @@ int getaddrinfo_with_timeout(const char *node, const char *service,
 
   return ret;
 #elif TARGET_OS_MAC
+  if (!node) { return EAI_NONAME; }
   // macOS implementation using CFHost API for asynchronous DNS resolution
   CFStringRef hostname_ref = CFStringCreateWithCString(
       kCFAllocatorDefault, node, kCFStringEncodingUTF8);
@@ -5569,14 +5570,11 @@ bool Server::read_content(Stream &strm, Request &req, Response &res) {
           strm, req, res,
           // Regular
           [&](const char *buf, size_t n) {
-            // Prevent arithmetic overflow when checking sizes.
-            // Avoid computing (req.body.size() + n) directly because
-            // adding two unsigned `size_t` values can wrap around and
-            // produce a small result instead of indicating overflow.
-            // Instead, check using subtraction: ensure `n` does not
-            // exceed the remaining capacity `max_size() - size()`.
-            if (req.body.size() >= req.body.max_size() ||
-                n > req.body.max_size() - req.body.size()) {
+            // Limit decompressed body size to payload_max_length_ to protect
+            // against "zip bomb" attacks where a small compressed payload
+            // decompresses to a massive size.
+            if (req.body.size() + n > payload_max_length_ ||
+                req.body.size() + n > req.body.max_size()) {
               return false;
             }
             req.body.append(buf, n);

vendor/cpp-httplib/httplib.h

@@ -8,8 +8,8 @@
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.30.0"
-#define CPPHTTPLIB_VERSION_NUM "0x001E00"
+#define CPPHTTPLIB_VERSION "0.30.1"
+#define CPPHTTPLIB_VERSION_NUM "0x001E01"
 
 /*
  * Platform compatibility check
@@ -205,7 +205,10 @@
 
 #pragma comment(lib, "ws2_32.lib")
 
+#ifndef _SSIZE_T_DEFINED
 using ssize_t = __int64;
+#define _SSIZE_T_DEFINED
+#endif
 #endif // _MSC_VER
 
 #ifndef S_ISREG
@@ -2443,16 +2446,20 @@ namespace detail {
 
 #if defined(_WIN32)
 inline std::wstring u8string_to_wstring(const char *s) {
-  std::wstring ws;
+  if (!s) { return std::wstring(); }
+
   auto len = static_cast<int>(strlen(s));
+  if (!len) { return std::wstring(); }
+
   auto wlen = ::MultiByteToWideChar(CP_UTF8, 0, s, len, nullptr, 0);
-  if (wlen > 0) {
-    ws.resize(wlen);
-    wlen = ::MultiByteToWideChar(
-        CP_UTF8, 0, s, len,
-        const_cast<LPWSTR>(reinterpret_cast<LPCWSTR>(ws.data())), wlen);
-    if (wlen != static_cast<int>(ws.size())) { ws.clear(); }
-  }
+  if (!wlen) { return std::wstring(); }
+
+  std::wstring ws;
+  ws.resize(wlen);
+  wlen = ::MultiByteToWideChar(
+      CP_UTF8, 0, s, len,
+      const_cast<LPWSTR>(reinterpret_cast<LPCWSTR>(ws.data())), wlen);
+  if (wlen != static_cast<int>(ws.size())) { ws.clear(); }
   return ws;
 }
 #endif