Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	.github/workflows/build.yml
#	ci/run.sh
#	examples/model-conversion/Makefile
#	examples/model-conversion/README.md
#	examples/model-conversion/logits.cpp
#	examples/model-conversion/requirements.txt
#	examples/model-conversion/scripts/embedding/convert-model.sh
#	examples/model-conversion/scripts/embedding/run-converted-model.sh
#	examples/model-conversion/scripts/embedding/run-original-model.py
#	examples/model-conversion/scripts/utils/semantic_check.py
#	ggml/src/ggml-cann/common.h
#	ggml/src/ggml-cann/ggml-cann.cpp
#	ggml/src/ggml-cpu/kleidiai/kernels.cpp
#	ggml/src/ggml-cpu/kleidiai/kernels.h
#	ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
#	ggml/src/ggml-sycl/common.hpp
#	ggml/src/ggml-sycl/dpct/helper.hpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-sycl/softmax.cpp
#	ggml/src/ggml-sycl/softmax.hpp
#	requirements/requirements-all.txt
#	tests/test-chat-parser.cpp
#	tools/server/README.md

common/arg.cpp

@@ -3455,7 +3455,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--reasoning-format"}, "FORMAT",
         "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
         "- none: leaves thoughts unparsed in `message.content`\n"
-        "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
+        "- deepseek: puts thoughts in `message.reasoning_content`\n"
+        "- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`\n"
         "(default: auto)",
         [](common_params & params, const std::string & value) {
             params.reasoning_format = common_reasoning_format_from_name(value);

common/chat-parser.cpp

@@ -3,9 +3,12 @@
 #include "log.h"
 #include "regex-partial.h"
 
+#include <algorithm>
+#include <cctype>
 #include <optional>
 #include <stdexcept>
 #include <string>
+#include <string_view>
 #include <vector>
 
 using json = nlohmann::ordered_json;
@@ -166,6 +169,27 @@ void common_chat_msg_parser::consume_literal(const std::string & literal) {
 }
 
 bool common_chat_msg_parser::try_parse_reasoning(const std::string & start_think, const std::string & end_think) {
+    std::string pending_reasoning_prefix;
+
+    if (syntax_.reasoning_format == COMMON_REASONING_FORMAT_NONE) {
+        return false;
+    }
+
+    auto set_reasoning_prefix = [&](size_t prefix_pos) {
+        if (!syntax_.thinking_forced_open || syntax_.reasoning_in_content) {
+            return;
+        }
+        if (prefix_pos + start_think.size() > input_.size()) {
+            pending_reasoning_prefix.clear();
+            return;
+        }
+        // Capture the exact literal that opened the reasoning section so we can
+        // surface it back to callers. This ensures formats that force the
+        // reasoning tag open (e.g. DeepSeek R1) retain their original prefix
+        // instead of dropping it during parsing.
+        pending_reasoning_prefix = input_.substr(prefix_pos, start_think.size());
+    };
+
     auto handle_reasoning = [&](const std::string & reasoning, bool closed) {
         auto stripped_reasoning = string_strip(reasoning);
         if (stripped_reasoning.empty()) {
@@ -178,28 +202,116 @@ bool common_chat_msg_parser::try_parse_reasoning(const std::string & start_think
                 add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "</think>" : end_think);
             }
         } else {
+            if (!pending_reasoning_prefix.empty()) {
+                add_reasoning_content(pending_reasoning_prefix);
+                pending_reasoning_prefix.clear();
+            }
             add_reasoning_content(stripped_reasoning);
         }
     };
-    if (syntax_.reasoning_format != COMMON_REASONING_FORMAT_NONE) {
-        if (syntax_.thinking_forced_open || try_consume_literal(start_think)) {
-            if (auto res = try_find_literal(end_think)) {
-                handle_reasoning(res->prelude, /* closed */ true);
-                consume_spaces();
-                return true;
-            }
-            auto rest = consume_rest();
+
+    const size_t saved_pos = pos_;
+    const size_t saved_content_size = result_.content.size();
+    const size_t saved_reasoning_size = result_.reasoning_content.size();
+
+    auto restore_state = [&]() {
+        move_to(saved_pos);
+        result_.content.resize(saved_content_size);
+        result_.reasoning_content.resize(saved_reasoning_size);
+    };
+
+    // Allow leading whitespace to be preserved as content when reasoning is present at the start
+    size_t cursor = pos_;
+    size_t whitespace_end = cursor;
+    while (whitespace_end < input_.size() && std::isspace(static_cast<unsigned char>(input_[whitespace_end]))) {
+        ++whitespace_end;
+    }
+
+    if (whitespace_end >= input_.size()) {
+        restore_state();
+        if (syntax_.thinking_forced_open) {
+            auto rest = input_.substr(saved_pos);
             if (!rest.empty()) {
                 handle_reasoning(rest, /* closed */ !is_partial());
             }
-            // Allow unclosed thinking tags, for now (https://github.com/ggml-org/llama.cpp/issues/13812, https://github.com/ggml-org/llama.cpp/issues/13877)
-            // if (!syntax_.thinking_forced_open) {
-            //     throw common_chat_msg_partial_exception(end_think);
-            // }
+            move_to(input_.size());
             return true;
         }
+        return false;
+    }
+
+    cursor = whitespace_end;
+    const size_t remaining = input_.size() - cursor;
+    const size_t start_prefix = std::min(start_think.size(), remaining);
+    const bool has_start_tag = input_.compare(cursor, start_prefix, start_think, 0, start_prefix) == 0;
+
+    if (has_start_tag && start_prefix < start_think.size()) {
+        move_to(input_.size());
+        return true;
+    }
+
+    if (has_start_tag) {
+        if (whitespace_end > pos_) {
+            add_content(input_.substr(pos_, whitespace_end - pos_));
+        }
+        set_reasoning_prefix(cursor);
+        cursor += start_think.size();
+    } else if (syntax_.thinking_forced_open) {
+        cursor = whitespace_end;
+    } else {
+        restore_state();
+        return false;
+    }
+    while (true) {
+        if (cursor >= input_.size()) {
+            move_to(input_.size());
+            return true;
+        }
+
+        size_t end_pos = input_.find(end_think, cursor);
+        if (end_pos == std::string::npos) {
+            std::string_view remaining_view(input_.data() + cursor, input_.size() - cursor);
+            size_t partial_off = string_find_partial_stop(remaining_view, end_think);
+            size_t reasoning_end = partial_off == std::string::npos ? input_.size() : cursor + partial_off;
+            if (reasoning_end > cursor) {
+                handle_reasoning(input_.substr(cursor, reasoning_end - cursor), /* closed */ partial_off == std::string::npos && !is_partial());
+            }
+            move_to(input_.size());
+            return true;
+        }
+
+        if (end_pos > cursor) {
+            handle_reasoning(input_.substr(cursor, end_pos - cursor), /* closed */ true);
+        } else {
+            handle_reasoning("", /* closed */ true);
+        }
+
+        cursor = end_pos + end_think.size();
+
+        while (cursor < input_.size() && std::isspace(static_cast<unsigned char>(input_[cursor]))) {
+            ++cursor;
+        }
+
+        const size_t next_remaining = input_.size() - cursor;
+        if (next_remaining == 0) {
+            move_to(cursor);
+            return true;
+        }
+
+        const size_t next_prefix = std::min(start_think.size(), next_remaining);
+        if (input_.compare(cursor, next_prefix, start_think, 0, next_prefix) == 0) {
+            if (next_prefix < start_think.size()) {
+                move_to(input_.size());
+                return true;
+            }
+            set_reasoning_prefix(cursor);
+            cursor += start_think.size();
+            continue;
+        }
+
+        move_to(cursor);
+        return true;
     }
-    return false;
 }
 
 std::string common_chat_msg_parser::consume_rest() {

common/chat.cpp

@@ -1408,6 +1408,8 @@ static common_chat_params common_chat_params_init_apertus(const common_chat_temp
     return data;
 }
 static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool with_builtin_tools = false) {
+    builder.try_parse_reasoning("<think>", "</think>");
+
     if (!builder.syntax().parse_tool_calls) {
         builder.add_content(builder.consume_rest());
         return;
@@ -2862,6 +2864,7 @@ common_chat_params common_chat_templates_apply(
 }
 
 static void common_chat_parse_content_only(common_chat_msg_parser & builder) {
+    builder.try_parse_reasoning("<think>", "</think>");
     builder.add_content(builder.consume_rest());
 }
 

common/common.h

@@ -429,7 +429,7 @@ struct common_params {
     std::string chat_template = "";                                                                         // NOLINT
     bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
-    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
     int reasoning_budget = -1;
     bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
 

convert_hf_to_gguf.py

@@ -96,13 +96,15 @@ class ModelBase:
     # Mistral format specifics
     is_mistral_format: bool = False
     disable_mistral_community_chat_template: bool = False
+    sentence_transformers_dense_modules: bool = False
 
     def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False,
                  use_temp_file: bool = False, eager: bool = False,
                  metadata_override: Path | None = None, model_name: str | None = None,
                  split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
                  small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
-                 disable_mistral_community_chat_template: bool = False):
+                 disable_mistral_community_chat_template: bool = False,
+                 sentence_transformers_dense_modules: bool = False):
         if type(self) is ModelBase or \
                 type(self) is TextModel or \
                 type(self) is MmprojModel:
@@ -117,6 +119,7 @@ class ModelBase:
         self.lazy = not eager or (remote_hf_model_id is not None)
         self.dry_run = dry_run
         self.remote_hf_model_id = remote_hf_model_id
+        self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
         if remote_hf_model_id is not None:
             self.is_safetensors = True
 
@@ -5274,6 +5277,53 @@ class Gemma3Model(TextModel):
 @ModelBase.register("Gemma3TextModel")
 class EmbeddingGemma(Gemma3Model):
     model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
+    module_paths = []
+    dense_features_dims = {}
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.sentence_transformers_dense_modules:
+            # read modules.json to determine if model has Dense layers
+            modules_file = self.dir_model / "modules.json"
+            if modules_file.is_file():
+                with open(modules_file, encoding="utf-8") as modules_json_file:
+                    mods = json.load(modules_json_file)
+                for mod in mods:
+                    if mod["type"] == "sentence_transformers.models.Dense":
+                        mod_path = mod["path"]
+                        # check if model.safetensors file for Dense layer exists
+                        model_tensors_file = self.dir_model / mod_path / "model.safetensors"
+                        if model_tensors_file.is_file():
+                            self.module_paths.append(mod_path)
+                            # read config.json of the Dense layer to get in/out features
+                            mod_conf_file = self.dir_model / mod_path / "config.json"
+                            if mod_conf_file.is_file():
+                                with open(mod_conf_file, encoding="utf-8") as mod_conf_json_file:
+                                    mod_conf = json.load(mod_conf_json_file)
+                                    # hparams dense_2_feat_out and dense_3_feat_in are required when loading model's dense weights
+                                    prefix = self._get_dense_prefix(mod_path)
+                                    if mod_conf["in_features"] is not None and mod_conf["out_features"] is not None:
+                                        self.dense_features_dims[prefix] = (mod_conf["in_features"], mod_conf["out_features"])
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        from safetensors.torch import load_file
+        module_paths = list(self.module_paths)
+        for i, module_path in enumerate(module_paths):
+            tensors_file = self.dir_model / module_path / "model.safetensors"
+            local_tensors = load_file(tensors_file)
+            tensor_name = self._get_dense_prefix(module_path)
+            for name, local_tensor in local_tensors.items():
+                if not name.endswith(".weight"):
+                    continue
+                orig_name = name.replace("linear", tensor_name)
+                name = self.map_tensor_name(orig_name)
+                yield name, local_tensor.clone()
+
+    @staticmethod
+    def _get_dense_prefix(module_path) -> str:
+        """Get the tensor name prefix for the Dense layer from module path."""
+        tensor_name = "dense_2" if module_path == "2_Dense" else "dense_3"
+        return tensor_name
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
@@ -5290,6 +5340,10 @@ class EmbeddingGemma(Gemma3Model):
             logger.info(f"Using original sliding_window from config: {orig_sliding_window} "
                         f"instead of {self.hparams['sliding_window']}")
             self.gguf_writer.add_sliding_window(orig_sliding_window)
+        if self.sentence_transformers_dense_modules:
+            for dense, dims in self.dense_features_dims.items():
+                logger.info(f"Setting dense layer {dense} in/out features to {dims}")
+                self.gguf_writer.add_dense_features_dims(dense, dims[0], dims[1])
 
         self._try_set_pooling_type()
 
@@ -9340,6 +9394,13 @@ def parse_args() -> argparse.Namespace:
         )
     )
 
+    parser.add_argument(
+        "--sentence-transformers-dense-modules", action="store_true",
+        help=("Whether to include sentence-transformers dense modules."
+              "It can be used for sentence-transformers models, like google/embeddinggemma-300m"
+              "Default these modules are not included.")
+    )
+
     args = parser.parse_args()
     if not args.print_supported_models and args.model is None:
         parser.error("the following arguments are required: model")
@@ -9402,9 +9463,13 @@ def main() -> None:
     if args.remote:
         hf_repo_id = args.model
         from huggingface_hub import snapshot_download
+        allowed_patterns = ["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"]
+        if args.sentence_transformers_dense_modules:
+            # include sentence-transformers dense modules safetensors files
+            allowed_patterns.append("*.safetensors")
         local_dir = snapshot_download(
             repo_id=hf_repo_id,
-            allow_patterns=["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"])
+            allow_patterns=allowed_patterns)
         dir_model = Path(local_dir)
         logger.info(f"Downloaded config and tokenizer to {local_dir}")
     else:
@@ -9472,7 +9537,8 @@ def main() -> None:
                                      split_max_tensors=args.split_max_tensors,
                                      split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
                                      small_first_shard=args.no_tensor_first_split,
-                                     remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template
+                                     remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template,
+                                     sentence_transformers_dense_modules=args.sentence_transformers_dense_modules
                                      )
 
         if args.vocab_only:

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

@@ -234,7 +234,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
 
         info.default_tensor_split[id] = total_vram;
         total_vram += prop.totalGlobalMem;
-        info.devices[id].integrated = prop.integrated;
+        info.devices[id].integrated = false; // Temporarily disabled due to issues with corrupted output (e.g. #15034)
         info.devices[id].nsm        = prop.multiProcessorCount;
         info.devices[id].smpb       = prop.sharedMemPerBlock;
         info.devices[id].warp_size  = prop.warpSize;

gguf-py/gguf/constants.py

@@ -128,6 +128,8 @@ class Keys:
         ALTUP_ACTIVE_IDX                  = "{arch}.altup.active_idx"
         ALTUP_NUM_INPUTS                  = "{arch}.altup.num_inputs"
         EMBD_LENGTH_PER_LAYER_INP         = "{arch}.embedding_length_per_layer_input"
+        DENSE_FEAT_IN_SIZE                = "{arch}.{dense}_feat_in"
+        DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
 
     class Attention:
         HEAD_COUNT                   = "{arch}.attention.head_count"
@@ -433,6 +435,8 @@ class MODEL_TENSOR(IntEnum):
     TOKEN_TYPES          = auto()
     POS_EMBD             = auto()
     OUTPUT               = auto()
+    DENSE_2_OUT          = auto() # embeddinggemma 2_Dense
+    DENSE_3_OUT          = auto() # embeddinggemma 3_Dense
     OUTPUT_NORM          = auto()
     ROPE_FREQS           = auto()
     ROPE_FACTORS_LONG    = auto()
@@ -777,6 +781,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.POS_EMBD:                  "position_embd",
     MODEL_TENSOR.OUTPUT_NORM:               "output_norm",
     MODEL_TENSOR.OUTPUT:                    "output",
+    MODEL_TENSOR.DENSE_2_OUT:                "dense_2", # embeddinggemma 2_Dense
+    MODEL_TENSOR.DENSE_3_OUT:                "dense_3", # embeddinggemma 2_Dense
     MODEL_TENSOR.ROPE_FREQS:                "rope_freqs",
     MODEL_TENSOR.ROPE_FACTORS_LONG:         "rope_factors_long",
     MODEL_TENSOR.ROPE_FACTORS_SHORT:        "rope_factors_short",
@@ -1759,6 +1765,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
     MODEL_ARCH.GEMMA_EMBEDDING: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.DENSE_2_OUT,
+        MODEL_TENSOR.DENSE_3_OUT,
         MODEL_TENSOR.OUTPUT_NORM,
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_Q_NORM,

gguf-py/gguf/gguf_writer.py

@@ -730,6 +730,10 @@ class GGUFWriter:
     def add_sliding_window_pattern(self, value: Sequence[bool]) -> None:
         self.add_array(Keys.Attention.SLIDING_WINDOW_PATTERN.format(arch=self.arch), value)
 
+    def add_dense_features_dims(self, dense:str, in_f:int, out_f:int) -> None:
+        self.add_uint32(Keys.LLM.DENSE_FEAT_IN_SIZE.format(arch=self.arch, dense=dense), in_f)
+        self.add_uint32(Keys.LLM.DENSE_FEAT_OUT_SIZE.format(arch=self.arch, dense=dense), out_f)
+
     def add_logit_scale(self, value: float) -> None:
         self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value)
 

gguf-py/gguf/tensor_mapping.py

@@ -76,7 +76,12 @@ class TensorNameMap:
             "lm_head",                   # llama4
             "model.transformer.ff_out",  # llada
         ),
-
+        MODEL_TENSOR.DENSE_2_OUT: (
+            "dense_2_out",  # embeddinggemma
+        ),
+        MODEL_TENSOR.DENSE_3_OUT: (
+            "dense_3_out",  # embeddinggemma
+        ),
         # Output norm
         MODEL_TENSOR.OUTPUT_NORM: (
             "gpt_neox.final_layer_norm",               # gptneox

src/llama-arch.cpp

@@ -219,6 +219,11 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
 
     { LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
+    // sentence-transformers dense modules feature dims
+    { LLM_KV_DENSE_2_FEAT_IN,        "%s.dense_2_feat_in"  },
+    { LLM_KV_DENSE_2_FEAT_OUT,       "%s.dense_2_feat_out"  },
+    { LLM_KV_DENSE_3_FEAT_IN,        "%s.dense_3_feat_in"   },
+    { LLM_KV_DENSE_3_FEAT_OUT,       "%s.dense_3_feat_out"  },
 
     { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
     { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
@@ -1071,6 +1076,8 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
             { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
             { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_DENSE_2_OUT,     "dense_2" },
+            { LLM_TENSOR_DENSE_3_OUT,     "dense_3" },
             { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
             { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
             { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
@@ -2281,6 +2288,8 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_OUTPUT,                     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS,                        {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS_OUT,                    {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DENSE_2_OUT,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
+    {LLM_TENSOR_DENSE_3_OUT,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
     {LLM_TENSOR_OUTPUT_NORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_DEC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_ENC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},

src/llama-arch.h

@@ -271,6 +271,12 @@ enum llm_kv {
     LLM_KV_TOKENIZER_PREFIX_ID,
     LLM_KV_TOKENIZER_SUFFIX_ID,
     LLM_KV_TOKENIZER_MIDDLE_ID,
+
+    // sentence-transformers dense layers in and out features
+    LLM_KV_DENSE_2_FEAT_IN,
+    LLM_KV_DENSE_2_FEAT_OUT,
+    LLM_KV_DENSE_3_FEAT_IN,
+    LLM_KV_DENSE_3_FEAT_OUT,
 };
 
 enum llm_tensor {
@@ -278,6 +284,8 @@ enum llm_tensor {
     LLM_TENSOR_TOKEN_EMBD_NORM,
     LLM_TENSOR_TOKEN_TYPES,
     LLM_TENSOR_POS_EMBD,
+    LLM_TENSOR_DENSE_2_OUT,
+    LLM_TENSOR_DENSE_3_OUT,
     LLM_TENSOR_OUTPUT,
     LLM_TENSOR_OUTPUT_NORM,
     LLM_TENSOR_ROPE_FREQS,

src/llama-context.cpp

@@ -2346,6 +2346,12 @@ llama_context * llama_init_from_model(
         return nullptr;
     }
 
+    if (params.pooling_type != model->hparams.pooling_type) {
+        //user-specified pooling-type is different from the model default
+        LLAMA_LOG_WARN("%s: model default pooling_type is [%d], but [%d] was specified\n", __func__,
+                       model->hparams.pooling_type, params.pooling_type);
+    }
+
     try {
         auto * ctx = new llama_context(*model, params);
         return ctx;

src/llama-graph.cpp

@@ -1853,6 +1853,23 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
     return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
 }
 
+void llm_graph_context::build_dense_out(
+    ggml_tensor * dense_2,
+    ggml_tensor * dense_3) const {
+    if (!cparams.embeddings || dense_2 == nullptr || dense_3 == nullptr) {
+        return;
+    }
+    ggml_tensor * cur = res->t_embd_pooled != nullptr ? res->t_embd_pooled : res->t_embd;
+    GGML_ASSERT(cur != nullptr && "missing t_embd_pooled/t_embd");
+
+    cur = ggml_mul_mat(ctx0, dense_2, cur);
+    cur = ggml_mul_mat(ctx0, dense_3, cur);
+    cb(cur, "result_embd_pooled", -1);
+    res->t_embd_pooled = cur;
+    ggml_build_forward_expand(gf, cur);
+}
+
+
 void llm_graph_context::build_pooling(
         ggml_tensor * cls,
         ggml_tensor * cls_b,

src/llama-graph.h

@@ -814,6 +814,14 @@ struct llm_graph_context {
             ggml_tensor * cls_b,
             ggml_tensor * cls_out,
             ggml_tensor * cls_out_b) const;
+
+    //
+    // dense (out)
+    //
+
+    void build_dense_out(
+            ggml_tensor * dense_2,
+            ggml_tensor * dense_3) const;
 };
 
 // TODO: better name

src/llama-hparams.h

@@ -169,6 +169,12 @@ struct llama_hparams {
     uint32_t laurel_rank  = 64;
     uint32_t n_embd_altup = 256;
 
+    // needed for sentence-transformers dense layers
+    uint32_t dense_2_feat_in  = 0;  // in_features of the 2_Dense
+    uint32_t dense_2_feat_out = 0;  // out_features of the 2_Dense
+    uint32_t dense_3_feat_in  = 0;  // in_features of the 3_Dense
+    uint32_t dense_3_feat_out = 0;  // out_features of the 3_Dense
+
     // xIELU
     std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_n;
     std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_p;

src/llama-model.cpp

@@ -1223,12 +1223,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 hparams.set_swa_pattern(6);
 
                 hparams.causal_attn = false; // embeddings do not use causal attention
-                hparams.rope_freq_base_train_swa  = 10000.0f;
+                hparams.rope_freq_base_train_swa = 10000.0f;
                 hparams.rope_freq_scale_train_swa = 1.0f;
 
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_POOLING_TYPE,                hparams.pooling_type);
+                ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
+
+                //applied only if model converted with --sentence-transformers-dense-modules
+                ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
+                ml.get_key(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out, false);
+                ml.get_key(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in, false);
+                ml.get_key(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out, false);
+
+                GGML_ASSERT((hparams.dense_2_feat_in == 0 || hparams.dense_2_feat_in == hparams.n_embd) && "dense_2_feat_in must be equal to n_embd");
+                GGML_ASSERT((hparams.dense_3_feat_out == 0 || hparams.dense_3_feat_out == hparams.n_embd) && "dense_3_feat_out must be equal to n_embd");
 
                 switch (hparams.n_layer) {
                     case 24: type = LLM_TYPE_0_3B; break;
@@ -3744,6 +3753,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,   "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
                     }
 
+                    // Dense linear weights
+                    dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);
+                    dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);
+
+
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];
 
@@ -19955,6 +19969,12 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
     // add on pooling layer
     llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
 
+    // if the gguf model was converted with --sentence-transformers-dense-modules
+    // there will be two additional dense projection layers
+    // dense linear projections are applied after pooling
+    // TODO: move reranking logic here and generalize
+    llm->build_dense_out(dense_2_out_layers, dense_3_out_layers);
+
     return llm->res->get_gf();
 }
 

src/llama-model.h

@@ -438,6 +438,12 @@ struct llama_model {
 
     std::vector<llama_layer> layers;
 
+    //Dense linear projections for SentenceTransformers models like embeddinggemma
+    // For Sentence Transformers models structure see
+    // https://sbert.net/docs/sentence_transformer/usage/custom_models.html#structure-of-sentence-transformer-models
+    struct ggml_tensor * dense_2_out_layers = nullptr;
+    struct ggml_tensor * dense_3_out_layers = nullptr;
+
     llama_model_params params;
 
     // gguf metadata

tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte

@@ -1,7 +1,6 @@
 <script lang="ts">
 	import { getDeletionInfo } from '$lib/stores/chat.svelte';
 	import { copyToClipboard } from '$lib/utils/copy';
-	import { parseThinkingContent } from '$lib/utils/thinking';
 	import ChatMessageAssistant from './ChatMessageAssistant.svelte';
 	import ChatMessageUser from './ChatMessageUser.svelte';
 
@@ -47,26 +46,13 @@
 
 	let thinkingContent = $derived.by(() => {
 		if (message.role === 'assistant') {
-			if (message.thinking) {
-				return message.thinking;
-			}
+			const trimmedThinking = message.thinking?.trim();
 
-			const parsed = parseThinkingContent(message.content);
-
-			return parsed.thinking;
+			return trimmedThinking ? trimmedThinking : null;
 		}
 		return null;
 	});
 
-	let messageContent = $derived.by(() => {
-		if (message.role === 'assistant') {
-			const parsed = parseThinkingContent(message.content);
-			return parsed.cleanContent?.replace('<|channel|>analysis', '');
-		}
-
-		return message.content?.replace('<|channel|>analysis', '');
-	});
-
 	function handleCancelEdit() {
 		isEditing = false;
 		editedContent = message.content;
@@ -165,7 +151,7 @@
 		{editedContent}
 		{isEditing}
 		{message}
-		{messageContent}
+		messageContent={message.content}
 		onCancelEdit={handleCancelEdit}
 		onConfirmDelete={handleConfirmDelete}
 		onCopy={handleCopy}

tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte

@@ -131,7 +131,11 @@
 			</div>
 		</div>
 	{:else if message.role === 'assistant'}
-		<MarkdownContent content={messageContent || ''} />
+		{#if config().disableReasoningFormat}
+			<pre class="raw-output">{messageContent || ''}</pre>
+		{:else}
+			<MarkdownContent content={messageContent || ''} />
+		{/if}
 	{:else}
 		<div class="text-sm whitespace-pre-wrap">
 			{messageContent}
@@ -203,4 +207,21 @@
 			background-position: -200% 0;
 		}
 	}
+
+	.raw-output {
+		width: 100%;
+		max-width: 48rem;
+		margin-top: 1.5rem;
+		padding: 1rem 1.25rem;
+		border-radius: 1rem;
+		background: hsl(var(--muted) / 0.3);
+		color: var(--foreground);
+		font-family:
+			ui-monospace, SFMono-Regular, 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas,
+			'Liberation Mono', Menlo, monospace;
+		font-size: 0.875rem;
+		line-height: 1.6;
+		white-space: pre-wrap;
+		word-break: break-word;
+	}
 </style>

tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageThinkingBlock.svelte

@@ -4,7 +4,6 @@
 	import * as Collapsible from '$lib/components/ui/collapsible/index.js';
 	import { buttonVariants } from '$lib/components/ui/button/index.js';
 	import { Card } from '$lib/components/ui/card';
-	import { MarkdownContent } from '$lib/components/app';
 	import { config } from '$lib/stores/settings.svelte';
 
 	interface Props {
@@ -59,7 +58,9 @@
 		<Collapsible.Content>
 			<div class="border-t border-muted px-3 pb-3">
 				<div class="pt-3">
-					<MarkdownContent content={reasoningContent || ''} class="text-xs leading-relaxed" />
+					<div class="text-xs leading-relaxed break-words whitespace-pre-wrap">
+						{reasoningContent ?? ''}
+					</div>
 				</div>
 			</div>
 		</Collapsible.Content>

tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte

@@ -148,6 +148,12 @@
 					key: 'showThoughtInProgress',
 					label: 'Show thought in progress',
 					type: 'checkbox'
+				},
+				{
+					key: 'disableReasoningFormat',
+					label:
+						'Show raw LLM output without backend parsing and frontend Markdown rendering to inspect streaming across different models.',
+					type: 'checkbox'
 				}
 			]
 		},

tools/server/webui/src/lib/constants/settings-config.ts

@@ -6,6 +6,7 @@ export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean> =
 	theme: 'system',
 	showTokensPerSecond: false,
 	showThoughtInProgress: false,
+	disableReasoningFormat: false,
 	keepStatsVisible: false,
 	askForTitleConfirmation: false,
 	pasteLongTextToFileLen: 2500,
@@ -76,6 +77,8 @@ export const SETTING_CONFIG_INFO: Record<string, string> = {
 	custom: 'Custom JSON parameters to send to the API. Must be valid JSON format.',
 	showTokensPerSecond: 'Display generation speed in tokens per second during streaming.',
 	showThoughtInProgress: 'Expand thought process by default when generating messages.',
+	disableReasoningFormat:
+		'Show raw LLM output without backend parsing and frontend Markdown rendering to inspect streaming across different models.',
 	keepStatsVisible: 'Keep processing statistics visible after generation finishes.',
 	askForTitleConfirmation:
 		'Ask for confirmation before automatically changing conversation title when editing the first message.',

tools/server/webui/src/lib/services/chat.ts

@@ -78,6 +78,8 @@ export class ChatService {
 			timings_per_token
 		} = options;
 
+		const currentConfig = config();
+
 		// Cancel any ongoing request and create a new abort controller
 		this.abort();
 		this.abortController = new AbortController();
@@ -117,7 +119,7 @@ export class ChatService {
 			stream
 		};
 
-		requestBody.reasoning_format = 'auto';
+		requestBody.reasoning_format = currentConfig.disableReasoningFormat ? 'none' : 'auto';
 
 		if (temperature !== undefined) requestBody.temperature = temperature;
 		// Set max_tokens to -1 (infinite) if not provided or empty
@@ -161,7 +163,6 @@ export class ChatService {
 		}
 
 		try {
-			const currentConfig = config();
 			const apiKey = currentConfig.apiKey?.toString().trim();
 
 			const response = await fetch(`./v1/chat/completions`, {
@@ -256,10 +257,8 @@ export class ChatService {
 		}
 
 		const decoder = new TextDecoder();
-		let fullResponse = '';
+		let aggregatedContent = '';
 		let fullReasoningContent = '';
-		let regularContent = '';
-		let insideThinkTag = false;
 		let hasReceivedData = false;
 		let lastTimings: ChatMessageTimings | undefined;
 
@@ -277,7 +276,7 @@ export class ChatService {
 					if (line.startsWith('data: ')) {
 						const data = line.slice(6);
 						if (data === '[DONE]') {
-							if (!hasReceivedData && fullResponse.length === 0) {
+							if (!hasReceivedData && aggregatedContent.length === 0) {
 								const contextError = new Error(
 									'The request exceeds the available context size. Try increasing the context size or enable context shift.'
 								);
@@ -286,7 +285,7 @@ export class ChatService {
 								return;
 							}
 
-							onComplete?.(regularContent, fullReasoningContent || undefined, lastTimings);
+							onComplete?.(aggregatedContent, fullReasoningContent || undefined, lastTimings);
 
 							return;
 						}
@@ -310,27 +309,8 @@ export class ChatService {
 
 							if (content) {
 								hasReceivedData = true;
-								fullResponse += content;
-
-								// Track the regular content before processing this chunk
-								const regularContentBefore = regularContent;
-
-								// Process content character by character to handle think tags
-								insideThinkTag = this.processContentForThinkTags(
-									content,
-									insideThinkTag,
-									() => {
-										// Think content is ignored - we don't include it in API requests
-									},
-									(regularChunk) => {
-										regularContent += regularChunk;
-									}
-								);
-
-								const newRegularContent = regularContent.slice(regularContentBefore.length);
-								if (newRegularContent) {
-									onChunk?.(newRegularContent);
-								}
+								aggregatedContent += content;
+								onChunk?.(content);
 							}
 
 							if (reasoningContent) {
@@ -345,7 +325,7 @@ export class ChatService {
 				}
 			}
 
-			if (!hasReceivedData && fullResponse.length === 0) {
+			if (!hasReceivedData && aggregatedContent.length === 0) {
 				const contextError = new Error(
 					'The request exceeds the available context size. Try increasing the context size or enable context shift.'
 				);
@@ -552,51 +532,6 @@ export class ChatService {
 		}
 	}
 
-	/**
-	 * Processes content to separate thinking tags from regular content.
-	 * Parses <think> and </think> tags to route content to appropriate handlers.
-	 *
-	 * @param content - The content string to process
-	 * @param currentInsideThinkTag - Current state of whether we're inside a think tag
-	 * @param addThinkContent - Callback to handle content inside think tags
-	 * @param addRegularContent - Callback to handle regular content outside think tags
-	 * @returns Boolean indicating if we're still inside a think tag after processing
-	 * @private
-	 */
-	private processContentForThinkTags(
-		content: string,
-		currentInsideThinkTag: boolean,
-		addThinkContent: (chunk: string) => void,
-		addRegularContent: (chunk: string) => void
-	): boolean {
-		let i = 0;
-		let insideThinkTag = currentInsideThinkTag;
-
-		while (i < content.length) {
-			if (!insideThinkTag && content.substring(i, i + 7) === '<think>') {
-				insideThinkTag = true;
-				i += 7; // Skip the <think> tag
-				continue;
-			}
-
-			if (insideThinkTag && content.substring(i, i + 8) === '</think>') {
-				insideThinkTag = false;
-				i += 8; // Skip the </think> tag
-				continue;
-			}
-
-			if (insideThinkTag) {
-				addThinkContent(content[i]);
-			} else {
-				addRegularContent(content[i]);
-			}
-
-			i++;
-		}
-
-		return insideThinkTag;
-	}
-
 	/**
 	 * Aborts any ongoing chat completion request.
 	 * Cancels the current request and cleans up the abort controller.

tools/server/webui/src/lib/stores/chat.svelte.ts

@@ -5,7 +5,6 @@ import { config } from '$lib/stores/settings.svelte';
 import { filterByLeafNodeId, findLeafNode, findDescendantMessages } from '$lib/utils/branching';
 import { browser } from '$app/environment';
 import { goto } from '$app/navigation';
-import { extractPartialThinking } from '$lib/utils/thinking';
 import { toast } from 'svelte-sonner';
 import type { ExportedConversations } from '$lib/types/database';
 
@@ -344,11 +343,9 @@ class ChatStore {
 				this.currentResponse = streamedContent;
 
 				captureModelIfNeeded();
-
-				const partialThinking = extractPartialThinking(streamedContent);
 				const messageIndex = this.findMessageIndex(assistantMessage.id);
 				this.updateMessageAtIndex(messageIndex, {
-					content: partialThinking.remainingContent || streamedContent
+					content: streamedContent
 				});
 			},
 
@@ -696,18 +693,16 @@ class ChatStore {
 
 		if (lastMessage && lastMessage.role === 'assistant') {
 			try {
-				const partialThinking = extractPartialThinking(this.currentResponse);
-
 				const updateData: {
 					content: string;
 					thinking?: string;
 					timings?: ChatMessageTimings;
 				} = {
-					content: partialThinking.remainingContent || this.currentResponse
+					content: this.currentResponse
 				};
 
-				if (partialThinking.thinking) {
-					updateData.thinking = partialThinking.thinking;
+				if (lastMessage.thinking?.trim()) {
+					updateData.thinking = lastMessage.thinking;
 				}
 
 				const lastKnownState = await slotsService.getCurrentState();
@@ -727,7 +722,10 @@ class ChatStore {
 
 				await DatabaseStore.updateMessage(lastMessage.id, updateData);
 
-				lastMessage.content = partialThinking.remainingContent || this.currentResponse;
+				lastMessage.content = this.currentResponse;
+				if (updateData.thinking !== undefined) {
+					lastMessage.thinking = updateData.thinking;
+				}
 				if (updateData.timings) {
 					lastMessage.timings = updateData.timings;
 				}

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

@@ -1,143 +0,0 @@
-/**
- * Parses thinking content from a message that may contain <think> tags or [THINK] tags
- * Returns an object with thinking content and cleaned message content
- * Handles both complete blocks and incomplete blocks (streaming)
- * Supports formats: <think>...</think> and [THINK]...[/THINK]
- * @param content - The message content to parse
- * @returns An object containing the extracted thinking content and the cleaned message content
- */
-export function parseThinkingContent(content: string): {
-	thinking: string | null;
-	cleanContent: string;
-} {
-	const incompleteThinkMatch = content.includes('<think>') && !content.includes('</think>');
-	const incompleteThinkBracketMatch = content.includes('[THINK]') && !content.includes('[/THINK]');
-
-	if (incompleteThinkMatch) {
-		const cleanContent = content.split('</think>')?.[1]?.trim();
-		const thinkingContent = content.split('<think>')?.[1]?.trim();
-
-		return {
-			cleanContent,
-			thinking: thinkingContent
-		};
-	}
-
-	if (incompleteThinkBracketMatch) {
-		const cleanContent = content.split('[/THINK]')?.[1]?.trim();
-		const thinkingContent = content.split('[THINK]')?.[1]?.trim();
-
-		return {
-			cleanContent,
-			thinking: thinkingContent
-		};
-	}
-
-	const completeThinkMatch = content.match(/<think>([\s\S]*?)<\/think>/);
-	const completeThinkBracketMatch = content.match(/\[THINK\]([\s\S]*?)\[\/THINK\]/);
-
-	if (completeThinkMatch) {
-		const thinkingContent = completeThinkMatch[1]?.trim() ?? '';
-		const cleanContent = `${content.slice(0, completeThinkMatch.index ?? 0)}${content.slice(
-			(completeThinkMatch.index ?? 0) + completeThinkMatch[0].length
-		)}`.trim();
-
-		return {
-			thinking: thinkingContent,
-			cleanContent
-		};
-	}
-
-	if (completeThinkBracketMatch) {
-		const thinkingContent = completeThinkBracketMatch[1]?.trim() ?? '';
-		const cleanContent = `${content.slice(0, completeThinkBracketMatch.index ?? 0)}${content.slice(
-			(completeThinkBracketMatch.index ?? 0) + completeThinkBracketMatch[0].length
-		)}`.trim();
-
-		return {
-			thinking: thinkingContent,
-			cleanContent
-		};
-	}
-
-	return {
-		thinking: null,
-		cleanContent: content
-	};
-}
-
-/**
- * Checks if content contains an opening thinking tag (for streaming)
- * Supports both <think> and [THINK] formats
- * @param content - The message content to check
- * @returns True if the content contains an opening thinking tag
- */
-export function hasThinkingStart(content: string): boolean {
-	return (
-		content.includes('<think>') ||
-		content.includes('[THINK]') ||
-		content.includes('<|channel|>analysis')
-	);
-}
-
-/**
- * Checks if content contains a closing thinking tag (for streaming)
- * Supports both </think> and [/THINK] formats
- * @param content - The message content to check
- * @returns True if the content contains a closing thinking tag
- */
-export function hasThinkingEnd(content: string): boolean {
-	return content.includes('</think>') || content.includes('[/THINK]');
-}
-
-/**
- * Extracts partial thinking content during streaming
- * Supports both <think> and [THINK] formats
- * Used when we have opening tag but not yet closing tag
- * @param content - The message content to extract partial thinking from
- * @returns An object containing the extracted partial thinking content and the remaining content
- */
-export function extractPartialThinking(content: string): {
-	thinking: string | null;
-	remainingContent: string;
-} {
-	const thinkStartIndex = content.indexOf('<think>');
-	const thinkEndIndex = content.indexOf('</think>');
-
-	const bracketStartIndex = content.indexOf('[THINK]');
-	const bracketEndIndex = content.indexOf('[/THINK]');
-
-	const useThinkFormat =
-		thinkStartIndex !== -1 && (bracketStartIndex === -1 || thinkStartIndex < bracketStartIndex);
-	const useBracketFormat =
-		bracketStartIndex !== -1 && (thinkStartIndex === -1 || bracketStartIndex < thinkStartIndex);
-
-	if (useThinkFormat) {
-		if (thinkEndIndex === -1) {
-			const thinkingStart = thinkStartIndex + '<think>'.length;
-
-			return {
-				thinking: content.substring(thinkingStart),
-				remainingContent: content.substring(0, thinkStartIndex)
-			};
-		}
-	} else if (useBracketFormat) {
-		if (bracketEndIndex === -1) {
-			const thinkingStart = bracketStartIndex + '[THINK]'.length;
-
-			return {
-				thinking: content.substring(thinkingStart),
-				remainingContent: content.substring(0, bracketStartIndex)
-			};
-		}
-	} else {
-		return { thinking: null, remainingContent: content };
-	}
-
-	const parsed = parseThinkingContent(content);
-
-	return {
-		thinking: parsed.thinking,
-		remainingContent: parsed.cleanContent
-	};
-}

tools/server/webui/src/stories/ChatMessage.stories.svelte

@@ -36,6 +36,31 @@
 		children: []
 	};
 
+	const assistantWithReasoning: DatabaseMessage = {
+		id: '3',
+		convId: 'conv-1',
+		type: 'message',
+		timestamp: Date.now() - 1000 * 60 * 2,
+		role: 'assistant',
+		content: "Here's the concise answer, now that I've thought it through carefully for you.",
+		parent: '1',
+		thinking:
+			"Let's consider the user's question step by step:\\n\\n1. Identify the core problem\\n2. Evaluate relevant information\\n3. Formulate a clear answer\\n\\nFollowing this process ensures the final response stays focused and accurate.",
+		children: []
+	};
+	const rawOutputMessage: DatabaseMessage = {
+		id: '6',
+		convId: 'conv-1',
+		type: 'message',
+		timestamp: Date.now() - 1000 * 60,
+		role: 'assistant',
+		content:
+			'<|channel|>analysis<|message|>User greeted me. Initiating overcomplicated analysis: Is this a trap? No, just a normal hello. Respond calmly, act like a helpful assistant, and do not start explaining quantum physics again. Confidence 0.73. Engaging socially acceptable greeting protocol...<|end|>Hello there! How can I help you today?',
+		parent: '1',
+		thinking: '',
+		children: []
+	};
+
 	let processingMessage = $state({
 		id: '4',
 		convId: 'conv-1',
@@ -59,60 +84,6 @@
 		thinking: '',
 		children: []
 	});
-
-	// Message with <think> format thinking content
-	const thinkTagMessage: DatabaseMessage = {
-		id: '6',
-		convId: 'conv-1',
-		type: 'message',
-		timestamp: Date.now() - 1000 * 60 * 2,
-		role: 'assistant',
-		content:
-			"<think>\nLet me analyze this step by step:\n\n1. The user is asking about thinking formats\n2. I need to demonstrate the &lt;think&gt; tag format\n3. This content should be displayed in the thinking section\n4. The main response should be separate\n\nThis is a good example of reasoning content.\n</think>\n\nHere's my response after thinking through the problem. The thinking content above should be displayed separately from this main response content.",
-		parent: '1',
-		thinking: '',
-		children: []
-	};
-
-	// Message with [THINK] format thinking content
-	const thinkBracketMessage: DatabaseMessage = {
-		id: '7',
-		convId: 'conv-1',
-		type: 'message',
-		timestamp: Date.now() - 1000 * 60 * 1,
-		role: 'assistant',
-		content:
-			'[THINK]\nThis is the DeepSeek-style thinking format:\n\n- Using square brackets instead of angle brackets\n- Should work identically to the &lt;think&gt; format\n- Content parsing should extract this reasoning\n- Display should be the same as &lt;think&gt; format\n\nBoth formats should be supported seamlessly.\n[/THINK]\n\nThis is the main response content that comes after the [THINK] block. The reasoning above should be parsed and displayed in the thinking section.',
-		parent: '1',
-		thinking: '',
-		children: []
-	};
-
-	// Streaming message for <think> format
-	let streamingThinkMessage = $state({
-		id: '8',
-		convId: 'conv-1',
-		type: 'message',
-		timestamp: 0, // No timestamp = streaming
-		role: 'assistant',
-		content: '',
-		parent: '1',
-		thinking: '',
-		children: []
-	});
-
-	// Streaming message for [THINK] format
-	let streamingBracketMessage = $state({
-		id: '9',
-		convId: 'conv-1',
-		type: 'message',
-		timestamp: 0, // No timestamp = streaming
-		role: 'assistant',
-		content: '',
-		parent: '1',
-		thinking: '',
-		children: []
-	});
 </script>
 
 <Story
@@ -120,6 +91,10 @@
 	args={{
 		message: userMessage
 	}}
+	play={async () => {
+		const { updateConfig } = await import('$lib/stores/settings.svelte');
+		updateConfig('disableReasoningFormat', false);
+	}}
 />
 
 <Story
@@ -128,15 +103,45 @@
 		class: 'max-w-[56rem] w-[calc(100vw-2rem)]',
 		message: assistantMessage
 	}}
+	play={async () => {
+		const { updateConfig } = await import('$lib/stores/settings.svelte');
+		updateConfig('disableReasoningFormat', false);
+	}}
 />
 
 <Story
-	name="WithThinkingBlock"
+	name="AssistantWithReasoning"
+	args={{
+		class: 'max-w-[56rem] w-[calc(100vw-2rem)]',
+		message: assistantWithReasoning
+	}}
+	play={async () => {
+		const { updateConfig } = await import('$lib/stores/settings.svelte');
+		updateConfig('disableReasoningFormat', false);
+	}}
+/>
+
+<Story
+	name="RawLlmOutput"
+	args={{
+		class: 'max-w-[56rem] w-[calc(100vw-2rem)]',
+		message: rawOutputMessage
+	}}
+	play={async () => {
+		const { updateConfig } = await import('$lib/stores/settings.svelte');
+		updateConfig('disableReasoningFormat', true);
+	}}
+/>
+
+<Story
+	name="WithReasoningContent"
 	args={{
 		message: streamingMessage
 	}}
 	asChild
 	play={async () => {
+		const { updateConfig } = await import('$lib/stores/settings.svelte');
+		updateConfig('disableReasoningFormat', false);
 		// Phase 1: Stream reasoning content in chunks
 		let reasoningText =
 			'I need to think about this carefully. Let me break down the problem:\n\n1. The user is asking for help with something complex\n2. I should provide a thorough and helpful response\n3. I need to consider multiple approaches\n4. The best solution would be to explain step by step\n\nThis approach will ensure clarity and understanding.';
@@ -187,126 +192,16 @@
 		message: processingMessage
 	}}
 	play={async () => {
+		const { updateConfig } = await import('$lib/stores/settings.svelte');
+		updateConfig('disableReasoningFormat', false);
 		// Import the chat store to simulate loading state
 		const { chatStore } = await import('$lib/stores/chat.svelte');
-		
+
 		// Set loading state to true to trigger the processing UI
 		chatStore.isLoading = true;
-		
+
 		// Simulate the processing state hook behavior
 		// This will show the "Generating..." text and parameter details
-		await new Promise(resolve => setTimeout(resolve, 100));
+		await new Promise((resolve) => setTimeout(resolve, 100));
 	}}
 />
-
-<Story
-	name="ThinkTagFormat"
-	args={{
-		class: 'max-w-[56rem] w-[calc(100vw-2rem)]',
-		message: thinkTagMessage
-	}}
-/>
-
-<Story
-	name="ThinkBracketFormat"
-	args={{
-		class: 'max-w-[56rem] w-[calc(100vw-2rem)]',
-		message: thinkBracketMessage
-	}}
-/>
-
-<Story
-	name="StreamingThinkTag"
-	args={{
-		message: streamingThinkMessage
-	}}
-	parameters={{
-		test: {
-			timeout: 30000
-		}
-	}}
-	asChild
-	play={async () => {
-		// Phase 1: Stream <think> reasoning content
-		const thinkingContent =
-			'Let me work through this problem systematically:\n\n1. First, I need to understand what the user is asking\n2. Then I should consider different approaches\n3. I need to evaluate the pros and cons\n4. Finally, I should provide a clear recommendation\n\nThis step-by-step approach will ensure accuracy.';
-
-		let currentContent = '<think>\n';
-		streamingThinkMessage.content = currentContent;
-
-		for (let i = 0; i < thinkingContent.length; i++) {
-			currentContent += thinkingContent[i];
-			streamingThinkMessage.content = currentContent;
-			await new Promise((resolve) => setTimeout(resolve, 5));
-		}
-
-		// Close the thinking block
-		currentContent += '\n</think>\n\n';
-		streamingThinkMessage.content = currentContent;
-		await new Promise((resolve) => setTimeout(resolve, 200));
-
-		// Phase 2: Stream main response content
-		const responseContent =
-			"Based on my analysis above, here's the solution:\n\n**Key Points:**\n- The approach should be systematic\n- We need to consider all factors\n- Implementation should be step-by-step\n\nThis ensures the best possible outcome.";
-
-		for (let i = 0; i < responseContent.length; i++) {
-			currentContent += responseContent[i];
-			streamingThinkMessage.content = currentContent;
-			await new Promise((resolve) => setTimeout(resolve, 10));
-		}
-
-		streamingThinkMessage.timestamp = Date.now();
-	}}
->
-	<div class="w-[56rem]">
-		<ChatMessage message={streamingThinkMessage} />
-	</div>
-</Story>
-
-<Story
-	name="StreamingThinkBracket"
-	args={{
-		message: streamingBracketMessage
-	}}
-	parameters={{
-		test: {
-			timeout: 30000
-		}
-	}}
-	asChild
-	play={async () => {
-		// Phase 1: Stream [THINK] reasoning content
-		const thinkingContent =
-			'Using the DeepSeek format now:\n\n- This demonstrates the &#91;THINK&#93; bracket format\n- Should parse identically to &lt;think&gt; tags\n- The UI should display this in the thinking section\n- Main content should be separate\n\nBoth formats provide the same functionality.';
-
-		let currentContent = '[THINK]\n';
-		streamingBracketMessage.content = currentContent;
-
-		for (let i = 0; i < thinkingContent.length; i++) {
-			currentContent += thinkingContent[i];
-			streamingBracketMessage.content = currentContent;
-			await new Promise((resolve) => setTimeout(resolve, 5));
-		}
-
-		// Close the thinking block
-		currentContent += '\n[/THINK]\n\n';
-		streamingBracketMessage.content = currentContent;
-		await new Promise((resolve) => setTimeout(resolve, 200));
-
-		// Phase 2: Stream main response content
-		const responseContent =
-			"Here's my response after using the &#91;THINK&#93; format:\n\n**Observations:**\n- Both &lt;think&gt; and &#91;THINK&#93; formats work seamlessly\n- The parsing logic handles both cases\n- UI display is consistent across formats\n\nThis demonstrates the enhanced thinking content support.";
-
-		for (let i = 0; i < responseContent.length; i++) {
-			currentContent += responseContent[i];
-			streamingBracketMessage.content = currentContent;
-			await new Promise((resolve) => setTimeout(resolve, 10));
-		}
-
-		streamingBracketMessage.timestamp = Date.now();
-	}}
->
-	<div class="w-[56rem]">
-		<ChatMessage message={streamingBracketMessage} />
-	</div>
-</Story>