llama : use LLM_KV for quantization_version & file_type (#24802)

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

.devops/s390x.Dockerfile

@@ -4,20 +4,6 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
-ARG NODE_VERSION=24
-
-FROM docker.io/node:$NODE_VERSION AS web
-
-ARG APP_VERSION
-
-WORKDIR /app/tools/ui
-
-COPY tools/ui/package.json tools/ui/package-lock.json ./
-RUN npm ci
-
-COPY tools/ui/ ./
-RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
-
 ### Build Llama.cpp stage
 FROM docker.io/gcc:${GCC_VERSION} AS build
 
@@ -34,8 +20,6 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
 WORKDIR /app
 COPY . .
 
-COPY --from=web /app/tools/ui/dist tools/ui/dist
-
 RUN --mount=type=cache,target=/root/.ccache \
     --mount=type=cache,target=/app/build \
     cmake -S . -B build -G Ninja \

.dockerignore

@@ -11,7 +11,6 @@
 build*/
 
 tools/ui/node_modules/
-tools/ui/dist/
 
 models/*
 

.github/workflows/docker.yml

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

.github/workflows/release.yml

@@ -1627,6 +1627,7 @@ jobs:
             **Windows:**
             - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip)
             - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
+            - [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-opencl-adreno-arm64.zip)
             - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
             - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.3-x64.zip)
             - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)

common/arg.cpp

@@ -924,8 +924,8 @@ static utf8_argv make_utf8_argv() {
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
 #ifdef _WIN32
     auto utf8 = make_utf8_argv();
-    if (!utf8.ptrs.empty()) {
-        argc = static_cast<int>(utf8.buf.size());
+    // repair argv only when it matches the process command line
+    if (static_cast<int>(utf8.buf.size()) == argc) {
         argv = utf8.ptrs.data();
     }
 #endif
@@ -2897,7 +2897,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.server_tools = parse_csv_row(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
-        add_opt(common_arg(
+    add_opt(common_arg(
         {"-ag", "--agent"},
         {"-no-ag", "--no-agent"},
         "whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)",

conversion/bailingmoe.py

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

conversion/base.py

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

conversion/chatglm.py

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

conversion/deci.py

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

conversion/exaone.py

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

conversion/gemma.py

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

conversion/glm.py

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

conversion/llama.py

@@ -289,7 +289,7 @@ class LlamaModel(TextModel):
                 factor = rope_params.get("factor", 8.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor

conversion/mimo.py

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

conversion/minicpm.py

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

conversion/nemotron.py

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

conversion/phi.py

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

conversion/qwen.py

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

conversion/stablelm.py

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

conversion/step3.py

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

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

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

src/llama-quant.cpp

@@ -932,8 +932,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
 
     // copy the KV pairs from the input file
     gguf_set_kv     (ctx_out.get(), ml.metadata);
-    gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
-    gguf_set_val_u32(ctx_out.get(), "general.file_type", ftype); // TODO: use LLM_KV
+    gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION).c_str(), GGML_QNT_VERSION);
+    gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_GENERAL_FILE_TYPE).c_str(), ftype);
 
     // Remove split metadata
     gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str());

src/models/glm-dsa.cpp

@@ -101,11 +101,11 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
         layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
 
         // DSA indexer
-        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags);
-        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags);
-        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags);
-        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags);
-        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
+        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
         if (i < (int) hparams.n_layer_dense_lead) {
             layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);
             layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);

tests/test-arg-parser.cpp

@@ -10,7 +10,7 @@
 #undef NDEBUG
 #include <cassert>
 
-int main(void) {
+static void test(void) {
     common_params params;
 
     printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
@@ -210,3 +210,13 @@ int main(void) {
 
     printf("test-arg-parser: all tests OK\n\n");
 }
+
+int main(void) {
+    try {
+        test();
+    } catch (std::exception & e) {
+        fprintf(stderr, "test-arg-parser: exception: %s\n", e.what());
+        return 1;
+    }
+    return 0;
+}

tools/server/server-cors-proxy.h

@@ -7,9 +7,18 @@
 #include <unordered_set>
 #include <list>
 #include <map>
+#include <algorithm>
+#include <cctype>
 
 #include "server-http.h"
 
+static std::string proxy_header_to_lower(std::string header) {
+    std::transform(header.begin(), header.end(), header.begin(), [](unsigned char c) {
+        return std::tolower(c);
+    });
+    return header;
+}
+
 static server_http_res_ptr proxy_request(const server_http_req & req, std::string method) {
     std::string target_url = req.get_param("url");
     common_http_url parsed_url = common_http_parse_url(target_url);
@@ -33,11 +42,18 @@ static server_http_res_ptr proxy_request(const server_http_req & req, std::strin
     SRV_INF("proxying %s request to %s://%s:%i%s\n", method.c_str(), parsed_url.scheme.c_str(), parsed_url.host.c_str(), parsed_url.port, parsed_url.path.c_str());
 
     std::map<std::string, std::string> headers;
+    const std::string proxy_header_prefix = "x-llama-server-proxy-header-";
     for (auto [key, value] : req.headers) {
-        auto new_key = key;
-        if (string_starts_with(new_key, "x-proxy-header-")) {
-            string_replace_all(new_key, "x-proxy-header-", "");
+        const std::string lowered_key = proxy_header_to_lower(key);
+        if (!string_starts_with(lowered_key, proxy_header_prefix)) {
+            continue;
         }
+
+        auto new_key = key.substr(proxy_header_prefix.size());
+        if (new_key.empty()) {
+            continue;
+        }
+
         headers[new_key] = value;
     }
 

tools/server/tests/unit/test_security.py

@@ -1,6 +1,8 @@
 import pytest
 from openai import OpenAI
 from utils import *
+import threading
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 
 server = ServerPreset.tinyllama2()
 
@@ -105,6 +107,49 @@ def test_cors_options(origin: str, cors_header: str, cors_header_value: str):
     assert res.headers[cors_header] == cors_header_value
 
 
+def test_cors_proxy_only_forwards_explicit_proxy_headers():
+    class CaptureHeadersHandler(BaseHTTPRequestHandler):
+        def do_GET(self):
+            self.server.captured_headers = dict(self.headers)
+            self.send_response(200)
+            self.end_headers()
+            self.wfile.write(b"ok")
+
+        def log_message(self, format, *args):
+            pass
+
+    target = ThreadingHTTPServer(("127.0.0.1", 0), CaptureHeadersHandler)
+    target.captured_headers = {}
+    target_thread = threading.Thread(target=target.serve_forever, daemon=True)
+    target_thread.start()
+
+    try:
+        server = ServerPreset.tinyllama2()
+        server.api_key = TEST_API_KEY
+        server.ui_mcp_proxy = True
+        server.start()
+
+        res = server.make_request("GET", f"/cors-proxy?url=http://127.0.0.1:{target.server_port}/capture", headers={
+            "Authorization": f"Bearer {TEST_API_KEY}",
+            "Proxy-Authorization": "Basic secret",
+            "X-Api-Key": TEST_API_KEY,
+            "Cookie": "session=secret",
+            "x-llama-server-proxy-header-accept": "application/json",
+            "x-llama-server-proxy-header-authorization": "Bearer explicit",
+        })
+
+        assert res.status_code == 200
+        captured = {key.lower(): value for key, value in target.captured_headers.items()}
+        assert captured["accept"] == "application/json"
+        assert captured["authorization"] == "Bearer explicit"
+        assert "proxy-authorization" not in captured
+        assert "x-api-key" not in captured
+        assert "cookie" not in captured
+    finally:
+        target.shutdown()
+        target.server_close()
+
+
 @pytest.mark.parametrize(
     "media_path, image_url, success",
     [

tools/ui/src/lib/constants/mcp.ts

@@ -51,6 +51,9 @@ export const EXPECTED_THEMED_ICON_PAIR_COUNT = 2;
 /** CORS proxy URL query parameter name */
 export const CORS_PROXY_URL_PARAM = 'url';
 
+/** Header prefix for headers that should be forwarded by the CORS proxy */
+export const CORS_PROXY_HEADER_PREFIX = 'x-llama-server-proxy-header-';
+
 /** Number of trailing characters to keep visible when partially redacting mcp-session-id */
 export const MCP_SESSION_ID_VISIBLE_CHARS = 5;
 

tools/ui/src/lib/services/mcp.service.ts

@@ -16,6 +16,7 @@ import {
 	DEFAULT_MCP_CONFIG,
 	DEFAULT_CLIENT_VERSION,
 	DEFAULT_IMAGE_MIME_TYPE,
+	CORS_PROXY_HEADER_PREFIX,
 	MCP_PARTIAL_REDACT_HEADERS,
 	CORS_PROXY_ENDPOINT
 } from '$lib/constants';
@@ -133,6 +134,20 @@ export class MCPService {
 		return details;
 	}
 
+	private static addRequestHeaders(
+		requestHeaders: Headers,
+		headers: HeadersInit,
+		useProxy: boolean
+	) {
+		for (const [key, value] of new Headers(headers).entries()) {
+			const proxiedKey =
+				useProxy && !key.toLowerCase().startsWith(CORS_PROXY_HEADER_PREFIX)
+					? `${CORS_PROXY_HEADER_PREFIX}${key}`
+					: key;
+			requestHeaders.set(proxiedKey, value);
+		}
+	}
+
 	private static summarizeError(error: unknown): Record<string, unknown> {
 		if (error instanceof Error) {
 			return {
@@ -271,15 +286,11 @@ export class MCPService {
 				const requestHeaders = new Headers(baseInit.headers);
 
 				if (typeof Request !== 'undefined' && input instanceof Request) {
-					for (const [key, value] of input.headers.entries()) {
-						requestHeaders.set(key, value);
-					}
+					this.addRequestHeaders(requestHeaders, input.headers, useProxy);
 				}
 
 				if (init?.headers) {
-					for (const [key, value] of new Headers(init.headers).entries()) {
-						requestHeaders.set(key, value);
-					}
+					this.addRequestHeaders(requestHeaders, init.headers, useProxy);
 				}
 
 				const request = this.createDiagnosticRequestDetails(

tools/ui/src/lib/utils/api-headers.ts

@@ -1,5 +1,5 @@
 import { config } from '$lib/stores/settings.svelte';
-import { REDACTED_HEADERS } from '$lib/constants';
+import { CORS_PROXY_HEADER_PREFIX, REDACTED_HEADERS } from '$lib/constants';
 import { redactValue } from './redact';
 
 /**
@@ -52,11 +52,20 @@ export function sanitizeHeaders(
 
 	for (const [key, value] of normalized.entries()) {
 		const normalizedKey = key.toLowerCase();
-		const partialChars = partialRedactHeaders?.get(normalizedKey);
+		const unproxiedKey = normalizedKey.startsWith(CORS_PROXY_HEADER_PREFIX)
+			? normalizedKey.slice(CORS_PROXY_HEADER_PREFIX.length)
+			: normalizedKey;
+		const partialChars =
+			partialRedactHeaders?.get(normalizedKey) ?? partialRedactHeaders?.get(unproxiedKey);
 
 		if (partialChars !== undefined) {
 			sanitized[key] = redactValue(value, partialChars);
-		} else if (REDACTED_HEADERS.has(normalizedKey) || redactedHeaders.has(normalizedKey)) {
+		} else if (
+			REDACTED_HEADERS.has(normalizedKey) ||
+			REDACTED_HEADERS.has(unproxiedKey) ||
+			redactedHeaders.has(normalizedKey) ||
+			redactedHeaders.has(unproxiedKey)
+		) {
 			sanitized[key] = redactValue(value);
 		} else {
 			sanitized[key] = value;

tools/ui/src/lib/utils/cors-proxy.ts

@@ -3,7 +3,11 @@
  */
 
 import { base } from '$app/paths';
-import { CORS_PROXY_ENDPOINT, CORS_PROXY_URL_PARAM } from '$lib/constants';
+import {
+	CORS_PROXY_ENDPOINT,
+	CORS_PROXY_HEADER_PREFIX,
+	CORS_PROXY_URL_PARAM
+} from '$lib/constants';
 
 /**
  * Build a proxied URL that routes through llama-server's CORS proxy.
@@ -28,7 +32,7 @@ export function buildProxiedHeaders(headers: Record<string, string>): Record<str
 	const proxiedHeaders: Record<string, string> = {};
 
 	for (const [key, value] of Object.entries(headers)) {
-		proxiedHeaders[`x-proxy-header-${key}`] = value;
+		proxiedHeaders[`${CORS_PROXY_HEADER_PREFIX}${key}`] = value;
 	}
 
 	return proxiedHeaders;

tools/ui/tests/e2e/pwa.e2e.ts

@@ -39,8 +39,8 @@ test.describe('PWA Service Worker', () => {
 		const swContent = await swResponse.text();
 
 		// Precache contains SvelteKit content-hashed bundle paths
-		expect(swContent).toMatch(/"_app\/immutable\/bundle\.[a-zA-Z0-9-]+\.js"/);
-		expect(swContent).toMatch(/"_app\/immutable\/assets\/bundle\.[a-zA-Z0-9-]+\.css"/);
+		expect(swContent).toMatch(/"_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"/);
+		expect(swContent).toMatch(/"_app\/immutable\/assets\/bundle\.[a-zA-Z0-9_-]+\.css"/);
 		expect(swContent).toMatch(/"manifest\.webmanifest"/);
 		expect(swContent).toMatch(/"_app\/version\.json"/);
 		expect(swContent).toMatch(/NavigationRoute/);
@@ -99,8 +99,8 @@ test.describe('PWA Service Worker', () => {
 		const html = await response.text();
 
 		// SvelteKit outputs content-hashed bundle names in _app/immutable/
-		expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9-]+\.js"/);
-		expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/assets\/bundle\.[a-zA-Z0-9-]+\.css"/);
-		expect(html).toMatch(/import\("(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9-]+\.js"\)/);
+		expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"/);
+		expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/assets\/bundle\.[a-zA-Z0-9_-]+\.css"/);
+		expect(html).toMatch(/import\("(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"\)/);
 	});
 });

tools/ui/tests/unit/mcp-service.test.ts

@@ -3,6 +3,7 @@ import { Client } from '@modelcontextprotocol/sdk/client';
 import { MCPService } from '$lib/services/mcp.service';
 import { MCPConnectionPhase, MCPTransportType } from '$lib/enums';
 import type { MCPConnectionLog, MCPServerConfig } from '$lib/types';
+import { CORS_PROXY_HEADER_PREFIX } from '$lib/constants';
 
 type DiagnosticFetchFactory = (
 	serverName: string,
@@ -16,11 +17,12 @@ type DiagnosticFetchFactory = (
 const createDiagnosticFetch = (
 	config: MCPServerConfig,
 	onLog?: (log: MCPConnectionLog) => void,
-	baseInit: RequestInit = {}
+	baseInit: RequestInit = {},
+	useProxy = false
 ) =>
 	(
 		MCPService as unknown as { createDiagnosticFetch: DiagnosticFetchFactory }
-	).createDiagnosticFetch('test-server', config, baseInit, new URL(config.url), false, onLog);
+	).createDiagnosticFetch('test-server', config, baseInit, new URL(config.url), useProxy, onLog);
 
 describe('MCPService', () => {
 	afterEach(() => {
@@ -94,6 +96,64 @@ describe('MCPService', () => {
 		});
 	});
 
+	it('wraps dynamic request headers when using the CORS proxy', async () => {
+		const logs: MCPConnectionLog[] = [];
+		const proxiedAuthToken = `${CORS_PROXY_HEADER_PREFIX}x-auth-token`;
+		const proxiedContentType = `${CORS_PROXY_HEADER_PREFIX}content-type`;
+		const proxiedSessionId = `${CORS_PROXY_HEADER_PREFIX}mcp-session-id`;
+		const response = new Response('{}', {
+			status: 200,
+			headers: { 'content-type': 'application/json' }
+		});
+		const fetchMock = vi.fn().mockResolvedValue(response);
+
+		vi.stubGlobal('fetch', fetchMock);
+
+		const config: MCPServerConfig = {
+			url: 'https://example.com/mcp',
+			transport: MCPTransportType.STREAMABLE_HTTP,
+			useProxy: true
+		};
+
+		const controller = createDiagnosticFetch(
+			config,
+			(log) => logs.push(log),
+			{
+				headers: {
+					authorization: 'Bearer llama-server-key',
+					[proxiedAuthToken]: 'target-token'
+				}
+			},
+			true
+		);
+
+		await controller.fetch('http://localhost:8080/cors-proxy?url=https%3A%2F%2Fexample.com%2Fmcp', {
+			method: 'POST',
+			headers: {
+				'content-type': 'application/json',
+				'mcp-session-id': 'session-request-12345'
+			},
+			body: '{}'
+		});
+
+		const sentHeaders = fetchMock.mock.calls[0]?.[1]?.headers as Headers;
+		expect(sentHeaders.get('authorization')).toBe('Bearer llama-server-key');
+		expect(sentHeaders.get(proxiedAuthToken)).toBe('target-token');
+		expect(sentHeaders.get(proxiedContentType)).toBe('application/json');
+		expect(sentHeaders.get(proxiedSessionId)).toBe('session-request-12345');
+		expect(sentHeaders.has('content-type')).toBe(false);
+		expect(sentHeaders.has('mcp-session-id')).toBe(false);
+		expect(logs[0].details).toMatchObject({
+			request: {
+				headers: {
+					authorization: '[redacted]',
+					[proxiedAuthToken]: '[redacted]',
+					[proxiedSessionId]: '....12345'
+				}
+			}
+		});
+	});
+
 	it('partially redacts mcp-session-id in diagnostic request and response logs', async () => {
 		const logs: MCPConnectionLog[] = [];
 		const response = new Response('{}', {

tools/ui/tests/unit/sanitize-headers.test.ts

@@ -1,5 +1,6 @@
 import { describe, expect, it } from 'vitest';
 import { sanitizeHeaders } from '$lib/utils/api-headers';
+import { CORS_PROXY_HEADER_PREFIX } from '$lib/constants';
 
 describe('sanitizeHeaders', () => {
 	it('returns empty object for undefined input', () => {
@@ -52,4 +53,21 @@ describe('sanitizeHeaders', () => {
 		const result = sanitizeHeaders(headers, ['X-CUSTOM-TOKEN']);
 		expect(result['x-custom-token']).toBe('[redacted]');
 	});
+
+	it('redacts proxied sensitive and custom target headers', () => {
+		const proxiedAuthorization = `${CORS_PROXY_HEADER_PREFIX}authorization`;
+		const proxiedSessionId = `${CORS_PROXY_HEADER_PREFIX}mcp-session-id`;
+		const proxiedVendorKey = `${CORS_PROXY_HEADER_PREFIX}x-vendor-key`;
+		const headers = new Headers({
+			[proxiedAuthorization]: 'Bearer secret',
+			[proxiedSessionId]: 'session-12345',
+			[proxiedVendorKey]: 'vendor-secret'
+		});
+		const partial = new Map([['mcp-session-id', 5]]);
+		const result = sanitizeHeaders(headers, ['x-vendor-key'], partial);
+
+		expect(result[proxiedAuthorization]).toBe('[redacted]');
+		expect(result[proxiedSessionId]).toBe('....12345');
+		expect(result[proxiedVendorKey]).toBe('[redacted]');
+	});
 });