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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,168 @@
+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-Coder-30B-A3B-Instruct
+---
+
+# Qwen3-Coder-30B-A3B-Instruct
+<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## Highlights
+
+**Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements:  
+
+- **Significant Performance** among open models on **Agentic Coding**, **Agentic Browser-Use**, and other foundational coding tasks.
+- **Long-context Capabilities** with native support for **256K** tokens, extendable up to **1M** tokens using Yarn, optimized for repository-scale understanding.
+- **Agentic Coding** supporting for most platform such as **Qwen Code**, **CLINE**, featuring a specially designed function call format.
+
+![image/jpeg](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Coder/qwen3-coder-30a3-main.jpg)
+
+## Model Overview
+
+**Qwen3-Coder-30B-A3B-Instruct** has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training
+- Number of Parameters: 30.5B in total and 3.3B activated
+- Number of Layers: 48
+- Number of Attention Heads (GQA): 32 for Q and 4 for KV
+- Number of Experts: 128
+- Number of Activated Experts: 8
+- Context Length: **262,144 natively**. 
+
+**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
+
+For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-coder/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+
+
+## Quickstart
+
+We advise you to use the latest version of `transformers`.
+
+With `transformers<4.51.0`, you will encounter the following error:
+```
+KeyError: 'qwen3_moe'
+```
+
+The following contains a code snippet illustrating how to use the model generate content based on given inputs. 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "Write a quick sort algorithm."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=65536
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("content:", content)
+```
+
+**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
+
+For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
+
+## Agentic Coding
+
+Qwen3-Coder excels in tool calling capabilities. 
+
+You can simply define or use any tools as following example.
+```python
+# Your tool implementation
+def square_the_number(num: float) -> dict:
+    return num ** 2
+
+# Define Tools
+tools=[
+    {
+        "type":"function",
+        "function":{
+            "name": "square_the_number",
+            "description": "output the square of the number.",
+            "parameters": {
+                "type": "object",
+                "required": ["input_num"],
+                "properties": {
+                    'input_num': {
+                        'type': 'number', 
+                        'description': 'input_num is a number that will be squared'
+                        }
+                },
+            }
+        }
+    }
+]
+
+import OpenAI
+# Define LLM
+client = OpenAI(
+    # Use a custom endpoint compatible with OpenAI API
+    base_url='http://localhost:8000/v1',  # api_base
+    api_key="EMPTY"
+)
+ 
+messages = [{'role': 'user', 'content': 'square the number 1024'}]
+
+completion = client.chat.completions.create(
+    messages=messages,
+    model="Qwen3-Coder-30B-A3B-Instruct",
+    max_tokens=65536,
+    tools=tools,
+)
+
+print(completion.choice[0])
+```
+
+## Best Practices
+
+To achieve optimal performance, we recommend the following settings:
+
+1. **Sampling Parameters**:
+   - We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`.
+
+2. **Adequate Output Length**: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models.
+
+
+### Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{qwen3technicalreport,
+      title={Qwen3 Technical Report}, 
+      author={Qwen Team},
+      year={2025},
+      eprint={2505.09388},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.09388}, 
+}
+```

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,131 @@
+{% macro render_item_list(item_list, tag_name='required') %}
+    {%- if item_list is defined and item_list is iterable and item_list | length > 0 %}
+        {%- if tag_name %}{{- '\n<' ~ tag_name ~ '>' -}}{% endif %}
+            {{- '[' }}
+                {%- for item in item_list -%}
+                    {%- if loop.index > 1 %}{{- ", "}}{% endif -%}
+                    {%- if item is string -%}
+                        {{ "`" ~ item ~ "`" }}
+                    {%- else -%}
+                        {{ item }}
+                    {%- endif -%}
+                {%- endfor -%}
+            {{- ']' }}
+        {%- if tag_name %}{{- '</' ~ tag_name ~ '>' -}}{% endif %}
+    {%- endif %}
+{% endmacro %}
+
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+
+{%- if not tools is defined %}
+    {%- set tools = [] %}
+{%- endif %}
+
+{%- if system_message is defined %}
+    {{- "<|im_start|>system\n" + system_message }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- "<|im_start|>system\nYou are Qwen, a helpful AI assistant that can interact with a computer to solve tasks." }}
+    {%- endif %}
+{%- endif %}
+{%- if tools is iterable and tools | length > 0 %}
+    {{- "\n\nYou have access to the following functions:\n\n" }}
+    {{- "<tools>" }}
+    {%- for tool in tools %}
+        {%- if tool.function is defined %}
+            {%- set tool = tool.function %}
+        {%- endif %}
+        {{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }}
+        {{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
+        {{- '\n<parameters>' }}
+        {%- for param_name, param_fields in tool.parameters.properties|items %}
+            {{- '\n<parameter>' }}
+            {{- '\n<name>' ~ param_name ~ '</name>' }}
+            {%- if param_fields.type is defined %}
+                {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
+            {%- endif %}
+            {%- if param_fields.description is defined %}
+                {{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}
+            {%- endif %}
+            {{- render_item_list(param_fields.enum, 'enum') }}
+            {%- set handled_keys = ['type', 'description', 'enum', 'required'] %}
+            {%- for json_key in param_fields.keys() | reject("in", handled_keys) %}
+                {%- set normed_json_key = json_key | replace("-", "_") | replace(" ", "_") | replace("$", "") %}
+                {%- if param_fields[json_key] is mapping %}
+                    {{- '\n<' ~ normed_json_key ~ '>' ~ (param_fields[json_key] | tojson | safe) ~ '</' ~ normed_json_key ~ '>' }}
+                {%- else %}
+                    {{-'\n<' ~ normed_json_key ~ '>' ~ (param_fields[json_key] | string) ~ '</' ~ normed_json_key ~ '>' }}
+                {%- endif %}
+            {%- endfor %}
+            {{- render_item_list(param_fields.required, 'required') }}
+            {{- '\n</parameter>' }}
+        {%- endfor %}
+        {{- render_item_list(tool.parameters.required, 'required') }}
+        {{- '\n</parameters>' }}
+        {%- if tool.return is defined %}
+            {%- if tool.return is mapping %}
+                {{- '\n<return>' ~ (tool.return | tojson | safe) ~ '</return>' }}
+            {%- else %}
+                {{- '\n<return>' ~ (tool.return | string) ~ '</return>' }}
+            {%- endif %}
+        {%- endif %}
+        {{- '\n</function>' }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+{%- endif %}
+{%- if system_message is defined %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in loop_messages %}
+    {%- if message.role == "assistant" and message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
+            {{- '\n' + message.content | trim + '\n' }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+            {%- if tool_call.arguments is defined %}
+                {%- for args_name, args_value in tool_call.arguments|items %}
+                    {{- '<parameter=' + args_name + '>\n' }}
+                    {%- set args_value = args_value if args_value is string else args_value | string %}
+                    {{- args_value }}
+                    {{- '\n</parameter>\n' }}
+                {%- endfor %}
+            {%- endif %}
+            {{- '</function>\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "user" or message.role == "system" or message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user\n' }}
+        {%- endif %}
+        {{- '<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>\n' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,119 @@
+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5472,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 28,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "qkv_bias": false,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 128,
+          "num_bits": 4,
+          "observer": "minmax",
+          "observer_kwargs": {},
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int"
+        }
+      }
+    },
+    "format": "pack-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "model.layers.0.mlp.gate",
+      "model.layers.1.mlp.gate",
+      "model.layers.2.mlp.gate",
+      "model.layers.3.mlp.gate",
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+      "model.layers.43.mlp.gate",
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+      "model.layers.45.mlp.gate",
+      "model.layers.46.mlp.gate",
+      "model.layers.47.mlp.gate",
+      "lm_head"
+    ],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed"
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.0,
+  "shared_expert_intermediate_size": 0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0.dev0",
+  "use_cache": true,
+  "use_qk_norm": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.55.0.dev0"
+}

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qwen3coder_tool_parser.py

@@ -0,0 +1,675 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import re
+import uuid
+from collections.abc import Sequence
+from typing import Union, Optional, Any, List, Dict
+from enum import Enum
+
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionRequest,
+    ChatCompletionToolsParam,
+    DeltaMessage,
+    DeltaToolCall,
+    DeltaFunctionCall,
+    ExtractedToolCallInformation,
+    FunctionCall,
+    ToolCall,
+)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser,
+    ToolParserManager,
+)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module("qwen3_xml")
+class Qwen3XMLToolParser(ToolParser):
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[str] = []
+
+        # Sentinel tokens for streaming mode
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+        self.tool_call_prefix: str = "<function="
+        self.function_end_token: str = "</function>"
+        self.parameter_prefix: str = "<parameter="
+        self.parameter_end_token: str = "</parameter>"
+        self.is_tool_call_started: bool = False
+        self.failed_count: int = 0
+
+        # Enhanced streaming state - reset for each new message
+        self._reset_streaming_state()
+
+        # Regex patterns
+        self.tool_call_complete_regex = re.compile(
+            r"<tool_call>(.*?)</tool_call>", re.DOTALL
+        )
+        self.tool_call_regex = re.compile(
+            r"<tool_call>(.*?)</tool_call>|<tool_call>(.*?)$", re.DOTALL
+        )
+        self.tool_call_function_regex = re.compile(
+            r"<function=(.*?)</function>|<function=(.*)$", re.DOTALL
+        )
+        self.tool_call_parameter_regex = re.compile(
+            r"<parameter=(.*?)</parameter>|<parameter=(.*?)$", re.DOTALL
+        )
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ToolParser "
+                "constructor during construction."
+            )
+
+        self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
+        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
+
+        if self.tool_call_start_token_id is None or self.tool_call_end_token_id is None:
+            raise RuntimeError(
+                "Qwen3 XML Tool parser could not locate tool call start/end "
+                "tokens in the tokenizer!"
+            )
+
+        logger.info(f"vLLM Successfully import tool parser {self.__class__.__name__} !")
+
+    def _generate_tool_call_id(self) -> str:
+        """Generate a unique tool call ID."""
+        return f"call_{uuid.uuid4().hex[:24]}"
+
+    def _reset_streaming_state(self):
+        """Reset all streaming state."""
+        self.current_tool_index = 0
+        self.is_tool_call_started = False
+        self.header_sent = False
+        self.current_tool_id = None
+        self.current_function_name = None
+        self.current_param_name = None
+        self.current_param_value = ""
+        self.param_count = 0
+        self.in_param = False
+        self.in_function = False
+        self.accumulated_text = ""
+        self.json_started = False
+        self.json_closed = False
+
+    def _parse_xml_function_call(
+        self, function_call_str: str, tools: Optional[list[ChatCompletionToolsParam]]
+    ) -> Optional[ToolCall]:
+        def get_arguments_config(func_name: str) -> dict:
+            if tools is None:
+                return {}
+            for config in tools:
+                if not hasattr(config, "type") or not (
+                    hasattr(config, "function") and hasattr(config.function, "name")
+                ):
+                    continue
+                if config.type == "function" and config.function.name == func_name:
+                    if not hasattr(config.function, "parameters"):
+                        return {}
+                    params = config.function.parameters
+                    if isinstance(params, dict) and "properties" in params:
+                        return params["properties"]
+                    elif isinstance(params, dict):
+                        return params
+                    else:
+                        return {}
+            logger.warning(f"Tool '{func_name}' is not defined in the tools list.")
+            return {}
+
+        def convert_param_value(
+            param_value: str, param_name: str, param_config: dict, func_name: str
+        ) -> Any:
+            # Handle null value for any type
+            if param_value.lower() == "null":
+                return None
+
+            if param_name not in param_config:
+                if param_config != {}:
+                    logger.warning(
+                        f"Parsed parameter '{param_name}' is not defined in the tool "
+                        f"parameters for tool '{func_name}', directly returning the string value."
+                    )
+                return param_value
+
+            if (
+                isinstance(param_config[param_name], dict)
+                and "type" in param_config[param_name]
+            ):
+                param_type = str(param_config[param_name]["type"]).strip().lower()
+            else:
+                param_type = "string"
+            if param_type in ["string", "str", "text", "varchar", "char", "enum"]:
+                return param_value
+            elif (
+                param_type.startswith("int")
+                or param_type.startswith("uint")
+                or param_type.startswith("long")
+                or param_type.startswith("short")
+                or param_type.startswith("unsigned")
+            ):
+                try:
+                    param_value = int(param_value)
+                except:
+                    logger.warning(
+                        f"Parsed value '{param_value}' of parameter '{param_name}' is not an integer in tool "
+                        f"'{func_name}', degenerating to string."
+                    )
+                return param_value
+            elif param_type.startswith("num") or param_type.startswith("float"):
+                try:
+                    float_param_value = float(param_value)
+                    param_value = float_param_value if float_param_value - int(float_param_value) != 0 else int(float_param_value)
+                except:
+                    logger.warning(
+                        f"Parsed value '{param_value}' of parameter '{param_name}' is not a float in tool "
+                        f"'{func_name}', degenerating to string."
+                    )
+                return param_value
+            elif param_type in ["boolean", "bool", "binary"]:
+                param_value = param_value.lower()
+                if param_value not in ["true", "false"]:
+                    logger.warning(
+                        f"Parsed value '{param_value}' of parameter '{param_name}' is not a boolean (`true` of `false`) in tool '{func_name}', degenerating to false."
+                    )
+                return param_value == "true"
+            else:
+                if param_type == "object" or param_type.startswith("dict"):
+                    try:
+                        param_value = json.loads(param_value)
+                        return param_value
+                    except:
+                        logger.warning(
+                            f"Parsed value '{param_value}' of parameter '{param_name}' is not a valid JSON object in tool "
+                            f"'{func_name}', will try other methods to parse it."
+                        )
+                try:
+                    param_value = eval(param_value)
+                except:
+                    logger.warning(
+                        f"Parsed value '{param_value}' of parameter '{param_name}' cannot be converted via Python `eval()` in tool '{func_name}', degenerating to string."
+                    )
+                return param_value
+
+        # Extract function name
+        end_index = function_call_str.index(">")
+        function_name = function_call_str[:end_index]
+        param_config = get_arguments_config(function_name)
+        parameters = function_call_str[end_index + 1 :]
+        param_dict = {}
+        for match in self.tool_call_parameter_regex.findall(parameters):
+            match_text = match[0] if match[0] else match[1]
+            idx = match_text.index(">")
+            param_name = match_text[:idx]
+            param_value = str(match_text[idx + 1 :])
+            # Remove prefix and trailing \n
+            if param_value.startswith("\n"):
+                param_value = param_value[1:]
+            if param_value.endswith("\n"):
+                param_value = param_value[:-1]
+
+            param_dict[param_name] = convert_param_value(
+                param_value, param_name, param_config, function_name
+            )
+        return ToolCall(
+            type="function",
+            function=FunctionCall(
+                name=function_name, arguments=json.dumps(param_dict, ensure_ascii=False)
+            ),
+        )
+
+    def _get_function_calls(self, model_output: str) -> List[str]:
+        # Find all tool calls
+        matched_ranges = self.tool_call_regex.findall(model_output)
+        raw_tool_calls = [
+            match[0] if match[0] else match[1] for match in matched_ranges
+        ]
+
+        # Back-off strategy if no tool_call tags found
+        if len(raw_tool_calls) == 0:
+            raw_tool_calls = [model_output]
+
+        raw_function_calls = []
+        for tool_call in raw_tool_calls:
+            raw_function_calls.extend(self.tool_call_function_regex.findall(tool_call))
+
+        function_calls = [
+            match[0] if match[0] else match[1] for match in raw_function_calls
+        ]
+        return function_calls
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        # Quick check to avoid unnecessary processing
+        if self.tool_call_prefix not in model_output:
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+        try:
+            function_calls = self._get_function_calls(model_output)
+            if len(function_calls) == 0:
+                return ExtractedToolCallInformation(
+                    tools_called=False, tool_calls=[], content=model_output
+                )
+
+            tool_calls = [
+                self._parse_xml_function_call(function_call_str, request.tools)
+                for function_call_str in function_calls
+            ]
+
+            # Populate prev_tool_call_arr for serving layer to set finish_reason
+            self.prev_tool_call_arr.clear()  # Clear previous calls
+            for tool_call in tool_calls:
+                if tool_call:
+                    self.prev_tool_call_arr.append(
+                        {
+                            "name": tool_call.function.name,
+                            "arguments": tool_call.function.arguments,
+                        }
+                    )
+
+            # Extract content before tool calls
+            content_index = model_output.find(self.tool_call_start_token)
+            content_index = (
+                content_index
+                if content_index >= 0
+                else model_output.find(self.tool_call_prefix)
+            )
+            content = model_output[:content_index]  # .rstrip()
+
+            return ExtractedToolCallInformation(
+                tools_called=(len(tool_calls) > 0),
+                tool_calls=tool_calls,
+                content=content if content else None,
+            )
+
+        except Exception:
+            logger.exception("Error in extracting tool call from response.")
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+        # If no delta text, return None unless it's an EOS token after tool calls
+        if not delta_text:
+            # Check if this is an EOS token after all tool calls are complete
+            # We check for tool calls in the text even if is_tool_call_started is False
+            # because it might have been reset after processing all tools
+            if delta_token_ids and self.tool_call_end_token_id not in delta_token_ids:
+                # Count complete tool calls
+                complete_calls = len(
+                    self.tool_call_complete_regex.findall(current_text)
+                )
+
+                # If we have completed tool calls and populated prev_tool_call_arr
+                if complete_calls > 0 and len(self.prev_tool_call_arr) > 0:
+                    # Check if all tool calls are closed
+                    open_calls = current_text.count(
+                        self.tool_call_start_token
+                    ) - current_text.count(self.tool_call_end_token)
+                    if open_calls == 0:
+                        # Return empty delta message to allow finish_reason processing
+                        return DeltaMessage(content="")
+                elif not self.is_tool_call_started and current_text:
+                    # This is a regular content response that's now complete
+                    return DeltaMessage(content="")
+            return None
+
+        # Check if this is the first call (reset state if needed)
+        if not previous_text:
+            self._reset_streaming_state()
+
+        # Update accumulated text
+        self.accumulated_text = current_text
+
+        # Check if we need to advance to next tool
+        if self.json_closed and not self.in_function:
+            # Check if this tool call has ended
+            tool_ends = current_text.count(self.tool_call_end_token)
+            if tool_ends > self.current_tool_index:
+                # This tool has ended, advance to next
+                self.current_tool_index += 1
+                self.header_sent = False
+                self.param_count = 0
+                self.json_started = False
+                self.json_closed = False
+
+                # Check if there are more tool calls
+                tool_starts = current_text.count(self.tool_call_start_token)
+                if self.current_tool_index >= tool_starts:
+                    # No more tool calls
+                    self.is_tool_call_started = False
+                # Continue processing next tool
+                return None
+
+        # Handle normal content before tool calls
+        if not self.is_tool_call_started:
+            # Check if tool call is starting
+            if (
+                self.tool_call_start_token_id in delta_token_ids
+                or self.tool_call_start_token in delta_text
+            ):
+                self.is_tool_call_started = True
+                # Return any content before the tool call
+                if self.tool_call_start_token in delta_text:
+                    content_before = delta_text[
+                        : delta_text.index(self.tool_call_start_token)
+                    ]
+                    if content_before:
+                        return DeltaMessage(content=content_before)
+                return None
+            else:
+                # Check if we're between tool calls - skip whitespace
+                if current_text.rstrip().endswith(self.tool_call_end_token):
+                    # We just ended a tool call, skip whitespace
+                    if delta_text.strip() == "":
+                        return None
+                # Normal content, no tool call
+                return DeltaMessage(content=delta_text)
+
+        # Check if we're between tool calls (waiting for next one)
+        # Count tool calls we've seen vs processed
+        tool_starts_count = current_text.count(self.tool_call_start_token)
+        if self.current_tool_index >= tool_starts_count:
+            # We're past all tool calls, shouldn't be here
+            return None
+
+        # We're in a tool call, find the current tool call portion
+        # Need to find the correct tool call based on current_tool_index
+        tool_starts = []
+        idx = 0
+        while True:
+            idx = current_text.find(self.tool_call_start_token, idx)
+            if idx == -1:
+                break
+            tool_starts.append(idx)
+            idx += len(self.tool_call_start_token)
+
+        if self.current_tool_index >= len(tool_starts):
+            # No more tool calls to process yet
+            return None
+
+        tool_start_idx = tool_starts[self.current_tool_index]
+        # Find where this tool call ends (or current position if not ended yet)
+        tool_end_idx = current_text.find(self.tool_call_end_token, tool_start_idx)
+        if tool_end_idx == -1:
+            tool_text = current_text[tool_start_idx:]
+        else:
+            tool_text = current_text[
+                tool_start_idx : tool_end_idx + len(self.tool_call_end_token)
+            ]
+
+        # Looking for function header
+        if not self.header_sent:
+            if self.tool_call_prefix in tool_text:
+                func_start = tool_text.find(self.tool_call_prefix) + len(
+                    self.tool_call_prefix
+                )
+                func_end = tool_text.find(">", func_start)
+
+                if func_end != -1:
+                    # Found complete function name
+                    self.current_function_name = tool_text[func_start:func_end]
+                    self.current_tool_id = self._generate_tool_call_id()
+                    self.header_sent = True
+                    self.in_function = True
+
+                    # IMPORTANT: Add to prev_tool_call_arr immediately when we detect a tool call
+                    # This ensures finish_reason="tool_calls" even if parsing isn't complete
+                    already_added = any(
+                        tool.get("name") == self.current_function_name
+                        for tool in self.prev_tool_call_arr
+                    )
+                    if not already_added:
+                        self.prev_tool_call_arr.append(
+                            {
+                                "name": self.current_function_name,
+                                "arguments": "{}",  # Placeholder, will be updated later
+                            }
+                        )
+
+                    # Send header with function info
+                    return DeltaMessage(
+                        tool_calls=[
+                            DeltaToolCall(
+                                index=self.current_tool_index,
+                                id=self.current_tool_id,
+                                function=DeltaFunctionCall(
+                                    name=self.current_function_name, arguments=""
+                                ),
+                                type="function",
+                            )
+                        ]
+                    )
+            return None
+
+        # We've sent header, now handle function body
+        if self.in_function:
+            # Send opening brace if not sent yet
+            if not self.json_started and not self.parameter_prefix in delta_text:
+                self.json_started = True
+                return DeltaMessage(
+                    tool_calls=[
+                        DeltaToolCall(
+                            index=self.current_tool_index,
+                            function=DeltaFunctionCall(arguments="{"),
+                        )
+                    ]
+                )
+
+            # Make sure json_started is set if we're processing parameters
+            if not self.json_started:
+                self.json_started = True
+
+            # Check for function end in accumulated text
+            if not self.json_closed and self.function_end_token in tool_text:
+                # Close JSON
+                self.json_closed = True
+
+                # Extract the complete tool call to update prev_tool_call_arr with final arguments
+                # Find the function content
+                func_start = tool_text.find(self.tool_call_prefix) + len(
+                    self.tool_call_prefix
+                )
+                func_content_end = tool_text.find(self.function_end_token, func_start)
+                if func_content_end != -1:
+                    func_content = tool_text[func_start:func_content_end]
+                    # Parse to get the complete arguments
+                    try:
+                        parsed_tool = self._parse_xml_function_call(
+                            func_content, request.tools if request else None
+                        )
+                        if parsed_tool:
+                            # Update existing entry in prev_tool_call_arr with complete arguments
+                            for i, tool in enumerate(self.prev_tool_call_arr):
+                                if tool.get("name") == parsed_tool.function.name:
+                                    self.prev_tool_call_arr[i]["arguments"] = (
+                                        parsed_tool.function.arguments
+                                    )
+                                    break
+                    except Exception:
+                        pass  # Ignore parsing errors during streaming
+
+                result = DeltaMessage(
+                    tool_calls=[
+                        DeltaToolCall(
+                            index=self.current_tool_index,
+                            function=DeltaFunctionCall(arguments="}"),
+                        )
+                    ]
+                )
+
+                # Reset state for next tool
+                self.in_function = False
+                self.json_closed = True
+
+                return result
+
+            # Look for parameters
+            # Count how many complete parameters we have processed
+            complete_params = tool_text.count(self.parameter_end_token)
+
+            # Check if we should start a new parameter
+            if not self.in_param and self.param_count < complete_params:
+                # Find the unprocessed parameter
+                # Count parameter starts
+                param_starts = []
+                idx = 0
+                while True:
+                    idx = tool_text.find(self.parameter_prefix, idx)
+                    if idx == -1:
+                        break
+                    param_starts.append(idx)
+                    idx += len(self.parameter_prefix)
+
+                if len(param_starts) > self.param_count:
+                    # Process the next parameter
+                    param_idx = param_starts[self.param_count]
+                    param_start = param_idx + len(self.parameter_prefix)
+                    remaining = tool_text[param_start:]
+
+                    if ">" in remaining:
+                        # We have the complete parameter name
+                        name_end = remaining.find(">")
+                        self.current_param_name = remaining[:name_end]
+
+                        # Find the parameter value
+                        value_start = param_start + name_end + 1
+                        value_text = tool_text[value_start:]
+                        if value_text.startswith("\n"):
+                            value_text = value_text[1:]
+
+                        # Find where this parameter ends
+                        param_end_idx = value_text.find(self.parameter_end_token)
+                        if param_end_idx != -1:
+                            # Complete parameter found
+                            param_value = value_text[:param_end_idx]
+                            if param_value.endswith("\n"):
+                                param_value = param_value[:-1]
+
+                            # Build complete JSON fragment for this parameter
+                            if self.param_count == 0:
+                                json_fragment = (
+                                    '"'
+                                    + self.current_param_name
+                                    + '": "'
+                                    + json.dumps(param_value)[1:-1]
+                                    + '"'
+                                )
+                            else:
+                                json_fragment = (
+                                    ', "'
+                                    + self.current_param_name
+                                    + '": "'
+                                    + json.dumps(param_value)[1:-1]
+                                    + '"'
+                                )
+
+                            self.param_count += 1
+
+                            return DeltaMessage(
+                                tool_calls=[
+                                    DeltaToolCall(
+                                        index=self.current_tool_index,
+                                        function=DeltaFunctionCall(
+                                            arguments=json_fragment
+                                        ),
+                                    )
+                                ]
+                            )
+
+            # Continue parameter value
+            if self.in_param:
+                if self.parameter_end_token in delta_text:
+                    # End of parameter
+                    end_idx = delta_text.find(self.parameter_end_token)
+                    value_chunk = delta_text[:end_idx]
+
+                    # Skip past > if at start
+                    if not self.current_param_value and ">" in value_chunk:
+                        gt_idx = value_chunk.find(">")
+                        value_chunk = value_chunk[gt_idx + 1 :]
+
+                    if not self.current_param_value and value_chunk.startswith("\n"):
+                        value_chunk = value_chunk[1:]
+
+                    # Calculate incremental JSON
+                    full_value = self.current_param_value + value_chunk
+                    prev_escaped = (
+                        json.dumps(self.current_param_value)[1:-1]
+                        if self.current_param_value
+                        else ""
+                    )
+                    full_escaped = json.dumps(full_value)[1:-1]
+                    delta_escaped = full_escaped[len(prev_escaped) :]
+
+                    self.in_param = False
+                    self.current_param_value = ""
+
+                    return DeltaMessage(
+                        tool_calls=[
+                            DeltaToolCall(
+                                index=self.current_tool_index,
+                                function=DeltaFunctionCall(
+                                    arguments=delta_escaped + '"'
+                                ),
+                            )
+                        ]
+                    )
+                else:
+                    # Continue accumulating value
+                    value_chunk = delta_text
+
+                    # Handle first chunk after param name
+                    if not self.current_param_value and ">" in value_chunk:
+                        gt_idx = value_chunk.find(">")
+                        value_chunk = value_chunk[gt_idx + 1 :]
+
+                    if not self.current_param_value and value_chunk.startswith("\n"):
+                        value_chunk = value_chunk[1:]
+
+                    if value_chunk:
+                        # Stream the escaped delta
+                        prev_escaped = (
+                            json.dumps(self.current_param_value)[1:-1]
+                            if self.current_param_value
+                            else ""
+                        )
+                        self.current_param_value += value_chunk
+                        full_escaped = json.dumps(self.current_param_value)[1:-1]
+                        delta_escaped = full_escaped[len(prev_escaped) :]
+
+                        if delta_escaped:
+                            return DeltaMessage(
+                                tool_calls=[
+                                    DeltaToolCall(
+                                        index=self.current_tool_index,
+                                        function=DeltaFunctionCall(
+                                            arguments=delta_escaped
+                                        ),
+                                    )
+                                ]
+                            )
+
+        return None

recipe.yaml

@@ -0,0 +1,10 @@
+default_stage:
+  default_modifiers:
+    GPTQModifier:
+      targets: [Linear]
+      ignore: [lm_head, 're:.*mlp.gate$', 're:.*mlp.shared_expert_gate$']
+      scheme: W4A16
+      sequential_update: true
+      block_size: 128
+      dampening_frac: 0.01
+      offload_hessians: false

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

@@ -0,0 +1,239 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+}