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README.md

@@ -1,33 +1,56 @@
 ---
+library_name: transformers
 license: apache-2.0
-base_model:
-- Qwen/Qwen3-Coder-30B-A3B-Instruct
+license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
+pipeline_tag: text-generation
+---
 
+# 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 Details
+## Model Overview
 
-This model is an int4 model with group_size 128 and symmetric quantization of [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm.
-Please follow the license of the original model.
+**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**. 
 
-## How To Use
+**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/).
 
-**vLLM usage**
 
-~~~bash
-vllm serve Intel/Qwen3-Coder-30B-A3B-Instruct-int4-AutoRound --tensor-parallel-size 4  --max-model-len 65536
-~~~
+## Quickstart
 
-**INT4 Inference on CPU/Intel GPU/CUDA**
+We advise you to use the latest version of `transformers`.
 
-~~~python
+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 = "Intel/Qwen3-Coder-30B-A3B-Instruct-int4-AutoRound"
+model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
 
 # load the tokenizer and the model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -59,39 +82,85 @@ output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
 content = tokenizer.decode(output_ids, skip_special_tokens=True)
 
 print("content:", content)
-"""
-....will update later...
-"""
-~~~
-
-### Generate the model
-
-Here is the sample command to reproduce the model
-
-~~~bash
-auto-round --model Qwen/Qwen3-Coder-30B-A3B-Instruct --output_dir "./tmp_autoround" --enable_torch_compile  --nsamples 512 --fp_layers mlp.gate
-~~~
-
-## Ethical Considerations and Limitations
-
-The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+```
+
+**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'
+                        }
+                },
+            }
+        }
+    }
+]
 
-Therefore, before deploying any applications of the model, developers should perform safety testing.
+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,
+)
 
-## Caveats and Recommendations
+print(completion.choice[0])
+```
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+## Best Practices
 
-Here are a couple of useful links to learn more about Intel's AI software:
+To achieve optimal performance, we recommend the following settings:
 
-- Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
+1. **Sampling Parameters**:
+   - We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`.
 
-## Disclaimer
+2. **Adequate Output Length**: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models.
 
-The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
 
-## Cite
+### Citation
 
-@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
+If you find our work helpful, feel free to give us a cite.
 
-[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
+```
+@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}, 
+}
+```

config.json

@@ -0,0 +1,39 @@
+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "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,
+  "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": "4.52.3",
+  "use_cache": true,
+  "use_qk_norm": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20
+}

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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

tokenizer_config.json

@@ -0,0 +1,239 @@
+{
+    "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
+        },
+        "151649": {
+            "content": "<|box_end|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151650": {
+            "content": "<|quad_start|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151651": {
+            "content": "<|quad_end|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151652": {
+            "content": "<|vision_start|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151653": {
+            "content": "<|vision_end|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151654": {
+            "content": "<|vision_pad|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151655": {
+            "content": "<|image_pad|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151656": {
+            "content": "<|video_pad|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151657": {
+            "content": "<tool_call>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151658": {
+            "content": "</tool_call>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151659": {
+            "content": "<|fim_prefix|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151660": {
+            "content": "<|fim_middle|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151661": {
+            "content": "<|fim_suffix|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151662": {
+            "content": "<|fim_pad|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151663": {
+            "content": "<|repo_name|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151664": {
+            "content": "<|file_sep|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151665": {
+            "content": "<tool_response>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151666": {
+            "content": "</tool_response>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151667": {
+            "content": "<think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151668": {
+            "content": "</think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        }
+    },
+    "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|>"
+    ],
+    "bos_token": null,
+    "chat_template": "{% macro render_item_list(item_list, tag_name='required') %}\n    {%- if item_list is defined and item_list is iterable and item_list | length > 0 %}\n        {%- if tag_name %}{{- '\\n<' ~ tag_name ~ '>' -}}{% endif %}\n            {{- '[' }}\n                {%- for item in item_list -%}\n                    {%- if loop.index > 1 %}{{- \", \"}}{% endif -%}\n                    {%- if item is string -%}\n                        {{ \"`\" ~ item ~ \"`\" }}\n                    {%- else -%}\n                        {{ item }}\n                    {%- endif -%}\n                {%- endfor -%}\n            {{- ']' }}\n        {%- if tag_name %}{{- '</' ~ tag_name ~ '>' -}}{% endif %}\n    {%- endif %}\n{% endmacro %}\n\n{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n\n{%- if not tools is defined %}\n    {%- set tools = [] %}\n{%- endif %}\n\n{%- if system_message is defined %}\n    {{- \"<|im_start|>system\\n\" + system_message }}\n{%- else %}\n    {%- if tools is iterable and tools | length > 0 %}\n        {{- \"<|im_start|>system\\nYou are Qwen, a helpful AI assistant that can interact with a computer to solve tasks.\" }}\n    {%- endif %}\n{%- endif %}\n{%- if tools is iterable and tools | length > 0 %}\n    {{- \"\\n\\nYou have access to the following functions:\\n\\n\" }}\n    {{- \"<tools>\" }}\n    {%- for tool in tools %}\n        {%- if tool.function is defined %}\n            {%- set tool = tool.function %}\n        {%- endif %}\n        {{- \"\\n<function>\\n<name>\" ~ tool.name ~ \"</name>\" }}\n        {{- '\\n<description>' ~ (tool.description | trim) ~ '</description>' }}\n        {{- '\\n<parameters>' }}\n        {%- for param_name, param_fields in tool.parameters.properties|items %}\n            {{- '\\n<parameter>' }}\n            {{- '\\n<name>' ~ param_name ~ '</name>' }}\n            {%- if param_fields.type is defined %}\n                {{- '\\n<type>' ~ (param_fields.type | string) ~ '</type>' }}\n            {%- endif %}\n            {%- if param_fields.description is defined %}\n                {{- '\\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}\n            {%- endif %}\n            {{- render_item_list(param_fields.enum, 'enum') }}\n            {%- set handled_keys = ['type', 'description', 'enum', 'required'] %}\n            {%- for json_key in param_fields.keys() | reject(\"in\", handled_keys) %}\n                {%- set normed_json_key = json_key | replace(\"-\", \"_\") | replace(\" \", \"_\") | replace(\"$\", \"\") %}\n                {%- if param_fields[json_key] is mapping %}\n                    {{- '\\n<' ~ normed_json_key ~ '>' ~ (param_fields[json_key] | tojson | safe) ~ '</' ~ normed_json_key ~ '>' }}\n                {%- else %}\n                    {{-'\\n<' ~ normed_json_key ~ '>' ~ (param_fields[json_key] | string) ~ '</' ~ normed_json_key ~ '>' }}\n                {%- endif %}\n            {%- endfor %}\n            {{- render_item_list(param_fields.required, 'required') }}\n            {{- '\\n</parameter>' }}\n        {%- endfor %}\n        {{- render_item_list(tool.parameters.required, 'required') }}\n        {{- '\\n</parameters>' }}\n        {%- if tool.return is defined %}\n            {%- if tool.return is mapping %}\n                {{- '\\n<return>' ~ (tool.return | tojson | safe) ~ '</return>' }}\n            {%- else %}\n                {{- '\\n<return>' ~ (tool.return | string) ~ '</return>' }}\n            {%- endif %}\n        {%- endif %}\n        {{- '\\n</function>' }}\n    {%- endfor %}\n    {{- \"\\n</tools>\" }}\n    {{- '\\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>' }}\n{%- endif %}\n{%- if system_message is defined %}\n    {{- '<|im_end|>\\n' }}\n{%- else %}\n    {%- if tools is iterable and tools | length > 0 %}\n        {{- '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in loop_messages %}\n    {%- if message.role == \"assistant\" and message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}\n            {{- '\\n' + message.content | trim + '\\n' }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n            {%- if tool_call.arguments is defined %}\n                {%- for args_name, args_value in tool_call.arguments|items %}\n                    {{- '<parameter=' + args_name + '>\\n' }}\n                    {%- set args_value = args_value if args_value is string else args_value | string %}\n                    {{- args_value }}\n                    {{- '\\n</parameter>\\n' }}\n                {%- endfor %}\n            {%- endif %}\n            {{- '</function>\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"user\" or message.role == \"system\" or message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n            {{- '<|im_start|>user\\n' }}\n        {%- endif %}\n        {{- '<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>\\n' }}\n        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n            {{- '<|im_end|>\\n' }}\n        {%- elif loop.last %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- else %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+    "clean_up_tokenization_spaces": false,
+    "eos_token": "<|im_end|>",
+    "errors": "replace",
+    "model_max_length": 1048576,
+    "pad_token": "<|endoftext|>",
+    "split_special_tokens": false,
+    "tokenizer_class": "Qwen2Tokenizer",
+    "unk_token": null,
+    "add_bos_token": false
+}