model: try to improve Qwen3 Next (#18683 )

* qwen3next: simplify qkvz projection * use ggml_swiglu_split * revert swiglu_split, but remove redundant repeat() * fix missing reshape * rm 2 redundant transposes * move mul_mat(k,q) to outside of chunking * rm redundant cont * improve g_cs_chunk * add comments about no cont * use std::pair instead of ggml_concat * vectorize key_gdiff calculation * rm unused tensor * avoid ggml_concat inside loop * bring back ggml_concat as it may not work on other backend * nits
readme : update UIs (#18751 )
2026-05-07 09:34:07 +00:00 · 2026-01-11 12:53:33 +01:00 · 2026-01-11 13:46:50 +02:00 · 2026-01-11 12:23:36 +01:00 · 2026-01-10 21:57:44 -08:00 · 2026-01-11 01:12:57 +08:00
61 changed files with 12769 additions and 4486 deletions
--- a/CMakeLists.txt
+++ b/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
 #
--- a/README.md
+++ b/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)
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -1,12 +1,48 @@
 # 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/*` (note: Web UI is not covered)
+
+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 +91,3 @@ 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
--- a/cmake/license.cmake
+++ b/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()
--- a/common/CMakeLists.txt
+++ b/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()
--- a/common/arg.cpp
+++ b/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",
--- a/common/download.cpp
+++ b/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;

--- a/common/download.h
+++ b/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;
--- a/common/preset.cpp
+++ b/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()
+                ));
            }
        }

--- a/convert_hf_to_gguf.py
+++ b/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")
--- a/docs/ops.md
+++ b/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 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
--- a/docs/ops/BLAS.csv
+++ b/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"
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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"
+"BLAS","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=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"
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 "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"
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-"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"
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+"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"
--- a/docs/ops/zDNN.csv
+++ b/docs/ops/zDNN.csv
--- a/docs/preset.md
+++ b/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.`
--- a/ggml/src/ggml-blas/CMakeLists.txt
+++ b/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()
--- a/ggml/src/ggml-blas/ggml-blas.cpp
+++ b/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";
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/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;
    }

--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/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
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/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;
--- a/ggml/src/ggml-opencl/kernels/expm1.cl
+++ b/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;
+        }
+    }
+}
--- a/ggml/src/ggml-opencl/kernels/softplus.cl
+++ b/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)));
+        }
+    }
+}
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/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) {
--- a/gguf-py/gguf/constants.py
+++ b/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"
--- a/gguf-py/gguf/gguf_writer.py
+++ b/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)

--- a/gguf-py/gguf/tensor_mapping.py
+++ b/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
--- a/include/llama.h
+++ b/include/llama.h
@@ -1292,7 +1292,9 @@ extern "C" {
    // available samplers:

    LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void);
-    LLAMA_API struct llama_sampler * llama_sampler_init_dist  (uint32_t seed);
+
+    /// seed == LLAMA_DEFAULT_SEED to use a random seed.
+    LLAMA_API struct llama_sampler * llama_sampler_init_dist(uint32_t seed);

    /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
    /// Setting k <= 0 makes this a noop
--- a/licenses/LICENSE-curl
+++ b/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.
--- a/scripts/pr2wt.sh
+++ b/scripts/pr2wt.sh
@@ -4,12 +4,13 @@
 #
 # - creates a new remote using the fork's clone URL
 # - creates a local branch tracking the remote branch
-# - creates a new worktree in a parent folder, suffixed with "-pr-${PR}"
+# - creates a new worktree in a parent folder, suffixed with "-pr-$PR"
 #
 # sample usage:
 #   ./scripts/pr2wt.sh 12345
 #   ./scripts/pr2wt.sh 12345 opencode
 #   ./scripts/pr2wt.sh 12345 "cmake -B build && cmake --build build"
+#   ./scripts/pr2wt.sh 12345 "bash -l"

 function usage() {
    echo "usage: $0 <pr_number> [cmd]"
@@ -39,7 +40,7 @@ org_repo=${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')
@@ -47,21 +48,32 @@ head_ref=$(echo "$meta" | jq -r '.head.ref')
 echo "url:      $url_remote"
 echo "head_ref: $head_ref"

-git remote rm  pr/${PR} 2> /dev/null
-git remote add pr/${PR} $url_remote
-git fetch      pr/${PR} $head_ref
+url_remote_cur=$(git config --get "remote.pr/$PR.url" 2>/dev/null || true)
+
+if [[ "$url_remote_cur" != "$url_remote" ]]; then
+    git remote rm  pr/$PR 2> /dev/null
+    git remote add pr/$PR "$url_remote"
+fi
+
+git fetch "pr/$PR" "$head_ref"

 dir=$(basename $(pwd))

 git branch -D pr/$PR 2> /dev/null
-git worktree add -b pr/$PR ../$dir-pr-$PR pr/$PR/${head_ref} 2> /dev/null
+git worktree add -b pr/$PR ../$dir-pr-$PR pr/$PR/$head_ref 2> /dev/null

 wt_path=$(cd ../$dir-pr-$PR && pwd)

 echo "git worktree created in $wt_path"

-# if a command was provided, execute it
+cd $wt_path
+git branch --set-upstream-to=pr/$PR/$head_ref
+git pull   --ff-only || {
+    echo "error: failed to pull pr/$PR"
+    exit 1
+}
+
 if [[ $# -eq 2 ]]; then
-    cd ../$dir-pr-$PR
+    echo "executing: $2"
    eval "$2"
 fi
--- a/scripts/sync_vendor.py
+++ b/scripts/sync_vendor.py
@@ -17,6 +17,7 @@ vendor = {
    "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/v0.30.0/LICENSE":   "vendor/cpp-httplib/LICENSE",

    "https://raw.githubusercontent.com/sheredom/subprocess.h/b49c56e9fe214488493021017bf3954b91c7c1f5/subprocess.h": "vendor/sheredom/subprocess.h",
 }
--- a/src/llama-arch.cpp
+++ b/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,
--- a/src/llama-model.cpp
+++ b/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);
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -2142,7 +2142,7 @@ struct llama_sampler_xtc {
    const uint32_t seed;
    uint32_t       seed_cur;

-    std::mt19937    rng;
+    std::mt19937   rng;
 };

 static const char * llama_sampler_xtc_name(const struct llama_sampler * /*smpl*/) {
--- a/src/models/gemma3n-iswa.cpp
+++ b/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);

--- a/src/models/models.h
+++ b/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;
 };

--- a/src/models/qwen3next.cpp
+++ b/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 +533,87 @@ 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));
+        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 +638,13 @@ 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 +664,9 @@ 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 +675,6 @@ 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 +685,6 @@ 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 +692,9 @@ 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 +717,25 @@ 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 +744,6 @@ 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 +775,29 @@ 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 +849,6 @@ 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);
--- a/tests/test-backend-ops.cpp
+++ b/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;
--- a/tools/CMakeLists.txt
+++ b/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)
--- a/tools/mtmd/CMakeLists.txt
+++ b/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
            )

--- a/tools/mtmd/clip-impl.h
+++ b/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"},
--- a/tools/mtmd/clip-model.h
+++ b/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;
--- a/tools/mtmd/clip.cpp
+++ b/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) {
--- a/tools/mtmd/clip.h
+++ b/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);

--- a/tools/mtmd/models/mobilenetv5.cpp
+++ b/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;
+}
--- a/tools/mtmd/models/models.h
+++ b/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);
+};
--- a/tools/mtmd/mtmd.cpp
+++ b/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) {
--- a/tools/server/public/index.html.gz
+++ b/tools/server/public/index.html.gz
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -4,7 +4,6 @@
 #include "server-task.h"
 #include "server-queue.h"

-#include "arg.h"
 #include "common.h"
 #include "llama.h"
 #include "log.h"
@@ -16,7 +15,6 @@
 #include <cstddef>
 #include <cinttypes>
 #include <memory>
-#include <unordered_set>
 #include <filesystem>

 // fix problem with std::min and std::max
@@ -81,6 +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

@@ -155,7 +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
@@ -203,12 +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);

@@ -260,10 +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;
        }
@@ -289,12 +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() {
@@ -303,10 +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();

@@ -427,14 +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();
    }
 };

@@ -747,6 +800,7 @@ private:
        }

        slots.clear();
+
        for (int i = 0; i < params_base.n_parallel; i++) {
            server_slot slot;

@@ -995,7 +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();
@@ -1007,17 +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?)
@@ -1038,7 +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;

@@ -1184,7 +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;
    }
@@ -1821,7 +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;
@@ -2055,8 +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
@@ -2357,7 +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;
@@ -2457,16 +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;

@@ -2475,6 +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);

@@ -2521,11 +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) {
@@ -2542,6 +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
@@ -2593,7 +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);
                            }
                        }

@@ -2617,31 +2672,34 @@ private:
            // on successful decode, restore the original batch size
            n_batch = llama_n_batch(ctx);

-            // technically, measuring the time here excludes the sampling time for the last batch
-            // but on the other hand, we don't want to do too many system calls to measure the time, so it's ok
-            const int64_t t_current = ggml_time_us();
-
+            // 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) {
@@ -2687,6 +2745,9 @@ private:

                common_sampler_accept(slot.smpl.get(), id, true);

+                // here we have synchronized the llama_context (due to the sampling above), so we can do time measurement
+                const int64_t t_current = ggml_time_us();
+
                slot.n_decoded += 1;

                if (slot.n_decoded == 1) {
@@ -2723,13 +2784,15 @@ 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);
                slot.i_batch_dft.clear();
                slot.drafted.clear();

+                const int64_t t_current = ggml_time_us();
+
                slot.n_decoded += ids.size();

                slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;
@@ -2924,17 +2987,25 @@ 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++) {
-                    server_task child = task.create_child(
-                        task.id,
-                        rd.get_new_id());
+
+                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
+                    // note: https://github.com/ggml-org/llama.cpp/pull/18700#discussion_r2675115723
+                    if (child.params.sampling.seed != LLAMA_DEFAULT_SEED) {
+                        child.params.sampling.seed += j + 1;
+                    }
+
                    tasks.push_back(std::move(child));
                }
            }

-            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));
--- a/tools/server/server-task.h
+++ b/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;
    }

--- a/tools/server/tests/unit/test_chat_completion.py
+++ b/tools/server/tests/unit/test_chat_completion.py
@@ -503,5 +503,4 @@ def test_chat_completions_multiple_choices():
    assert len(res.body["choices"]) == 2
    for choice in res.body["choices"]:
        assert "assistant" == choice["message"]["role"]
-        assert match_regex("Suddenly", choice["message"]["content"])
        assert choice["finish_reason"] == "length"
--- a/tools/server/tests/unit/test_completion.py
+++ b/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(
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
+++ b/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}
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte
+++ b/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" />

--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte
+++ b/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;
 	}
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormFileInputInvisible.svelte
+++ b/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}"
 />
--- a/tools/server/webui/src/lib/utils/file-type.ts
+++ b/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;
 }
--- a/tools/server/webui/src/lib/utils/index.ts
+++ b/tools/server/webui/src/lib/utils/index.ts
@@ -76,7 +76,6 @@ export {
 	isFileTypeSupportedByModel,
 	filterFilesByModalities,
 	generateModalityErrorMessage,
-	generateModalityAwareAcceptString,
 	type ModalityCapabilities
 } from './modality-file-validation';

--- a/tools/server/webui/src/lib/utils/modality-file-validation.ts
+++ b/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(',');
-}
--- a/tools/server/webui/src/lib/utils/process-uploaded-files.ts
+++ b/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);
--- a/tools/server/webui/tests/stories/ChatForm.stories.svelte
+++ b/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');
--- a/vendor/cpp-httplib/CMakeLists.txt
+++ b/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)

--- a/vendor/cpp-httplib/LICENSE
+++ b/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.
+
Author	SHA1	Message	Date
Xuan-Son Nguyen	506bb6e010	model: try to improve Qwen3 Next (#18683 ) * qwen3next: simplify qkvz projection * use ggml_swiglu_split * revert swiglu_split, but remove redundant repeat() * fix missing reshape * rm 2 redundant transposes * move mul_mat(k,q) to outside of chunking * rm redundant cont * improve g_cs_chunk * add comments about no cont * use std::pair instead of ggml_concat * vectorize key_gdiff calculation * rm unused tensor * avoid ggml_concat inside loop * bring back ggml_concat as it may not work on other backend * nits	2026-01-11 12:53:33 +01:00
thom-dev-fr	79456a690a	readme : update UIs (#18751 )	2026-01-11 13:46:50 +02:00
Xuan-Son Nguyen	28068af789	security: narrow down the scope of what we consider a vulnerability (#18752 ) * security: narrow down the scope of what we consider a vulnerability * fix typo	2026-01-11 12:23:36 +01:00
shaofeiqi	707cbafcaa	opencl: add SOFTPLUS op support (#18726 )	2026-01-10 21:57:44 -08:00
Aman Gupta	b137718878	test-backend-ops: fix mxfp4 tests on blackwell (#18736 )	2026-01-11 01:12:57 +08:00
Johannes Gäßler	d2ff4e23ac	HIP: adjust RDNA3.5 MMQ kernel selction logic (#18666 )	2026-01-10 17:19:01 +01:00
Perry Naseck	657a2e644b	cmake : update blas logic (#18205 )	2026-01-10 18:00:54 +02:00
Georgi Gerganov	f307926482	server : adjust unified KV cache tests (#18716 )	2026-01-10 17:51:56 +02:00
Sigbjørn Skjæret	7fdc8c893d	scripts : follow api redirects in pr2wt.sh (#18739 )	2026-01-10 16:04:05 +01:00
Xuan-Son Nguyen	23f82f2420	preset: allow named remote preset (#18728 ) * preset: allow named remote preset * nits: fix docs * cont docs	2026-01-10 15:12:29 +01:00
Aaron Teo	2656c0d265	docs(ggml): update backend ops (#18734 ) Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>	2026-01-10 18:48:17 +08:00
Michael Wand	600a366478	Corrected: changed s13 = src1->nb[3] instead of nb[2] (#18724 )	2026-01-10 10:16:07 +01:00
Adrien Gallouët	ea23c15990	common : add --license to display embedded licenses (#18696 ) This commit introduces a mechanism to embed all licenses directly into the compiled binaries. This eliminates the need to distribute separate LICENSE files alongside the executable, making the binaries self-contained and simplifying deployment.	2026-01-10 09:46:24 +01:00
Xuan-Son Nguyen	9ac2693a30	server: fix n_cmpl not skipping processing prompt (#18663 ) * server: fix n_cmpl not skipping processing * fix infinite loop on empty batch * cont : init child samplers + modify child logic * cont : cleanup * cont : improve n_cmpl logic - launch the parent task first so it finds the slot with best cache - parent task waits for child tasks to be launched - when a child task finishes - remove its cache * cont : remove redundant function * cont : reduce parent checks * fix : nullptr task dereference --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2026-01-10 00:00:41 +01:00
Simranjeet Singh	a61c8bc3bf	mtmd: Add Gemma3n multimodal support with MobileNetV5 vision encoder (#18256 ) * Add Gemma3nVisionModel - MobileNetV5 vision encoder convertor to convert_hf_to_gguf.py. Add gemma3n to vision projectors in gguf-py/gguf/constants.py. * Add mobilenetv5 impl * Fix comments, remove unused vars * Fix permute and remove transpose of projection weights * Fix comments, remove debugging prints from hf_to_gguf * 1. Hard-code image_mean = 0 and image_std = 1 2. Use available tensor mapping logic 3. Remove redundant chat template replacement of soft tokens placeholder with media placeholder * 1. Move mobilenetv5 helpers declarations to `clip_graph_mobilenetv5` struct and definitions to mobilenetv5.cpp 2.Remove unused `clip_is_gemma3n` func declarations and definitions 3. Remove redundant `rescale_image_u8_to_f32` func and use `normalize_image_u8_to_f32` with zero mean and unit std 4. Calculate n_patches using image_size / patch_size * Remove obsolete comments * - convert_hf_to_gguf.py & constants.py & tensor_mapping.py: Use explicit mapping: Custom map for double indexed blocks and tensor_mapping.py for rest - convert_hf_to_gguf.py: Unsqueeze Stem Bias and Layer scale tensors to correct shape while converting to gguf - mobilenetv5.cpp: Remove explicit reshaping of Stem Bias and Layer scale which are now handled while converting to gguf, replace fprintf with LOG_* - clip.cpp: Remove unused embedding and hard_emb_norm tensor loading * - Rename tensors to v.conv..., v.blk..., v.msfa... to better align with already existing terminology * Fix stem conv bias name * Remove explicit handling of bias term for stem conv * - Change order of addition in "project_per_layer_inputs" to support broadcasting of vision inp_per_layer - Simplify the vision embeddings path of "get_per_layer_inputs" to output [n_embd_altup, n_layer, 1], broadcastable * clean up conversion script * fix code style * also preserve audio tensors * trailing space * split arch A and V * rm unused gemma3 func * fix alignment --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>	2026-01-09 23:42:38 +01:00
shaofeiqi	593da7fa49	opencl: add EXPM1 op (#18704 )	2026-01-09 10:13:13 -08:00
Reese Levine	9e41884dce	Updates to webgpu get_memory (#18707 )	2026-01-09 08:17:18 -08:00
Pascal	ec8fd7876b	Webui/file upload (#18694 ) * webui: fix restrictive file type validation * webui: simplify file processing logic * chore: update webui build output * webui: remove file picker extension whitelist (1/2) * webui: remove file picker extension whitelist (2/2) * chore: update webui build output * refactor: Cleanup * chore: update webui build output * fix: update ChatForm storybook test after removing accept attribute * chore: update webui build output * refactor: more cleanup * chore: update webui build output	2026-01-09 16:45:32 +01:00
Asbjørn Olling	a180ba78c7	cmake: only build cli when server is enabled (#18670 )	2026-01-09 16:43:26 +01:00
Georgi Gerganov	53eb9435da	server : fix timing of prompt/generation (#18713 )	2026-01-09 12:59:50 +02:00
Georgi Gerganov	d3435efc8a	scripts : pr2wt.sh reset to remote head (#18695 ) * scripts : pr2wt.sh reset to remote head * cont : cleaner * cont : restore --set-upstream-to	2026-01-09 12:16:40 +02:00
Georgi Gerganov	f5f8812f7c	server : use different seeds for child completions (#18700 ) * server : use different seeds for child completions * cont : handle default seed * cont : note	2026-01-09 09:33:50 +02:00