Instructions to use YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound")
model = AutoModelForCausalLM.from_pretrained("YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound

SGLang

How to use YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound with Docker Model Runner:
```
docker model run hf.co/YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound
```

YCWTG commited on Mar 11

Commit

ca3ea92

verified ·

1 Parent(s): a071b48

Delete qwen3coder_tool_parser_vllm.py

Browse files

Files changed (1) hide show

qwen3coder_tool_parser_vllm.py +0 -690

qwen3coder_tool_parser_vllm.py DELETED Viewed

@@ -1,690 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import ast
-import json
-import uuid
-from collections.abc import Sequence
-from typing import Any, List, Optional, Union
-import regex as re
-from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
-                                              ChatCompletionToolsParam,
-                                              DeltaFunctionCall, DeltaMessage,
-                                              DeltaToolCall,
-                                              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_coder")
-class Qwen3CoderToolParser(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=)|(?=</function>)|$)",
-            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
-        # Store accumulated parameters for type conversion
-        self.accumulated_params = {}
-        self.streaming_request = None
-    def _get_arguments_config(
-            self, func_name: str,
-            tools: Optional[list[ChatCompletionToolsParam]]) -> dict:
-        """Extract argument configuration for a function."""
-        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(self, param_value: str, param_name: str,
-                             param_config: dict, func_name: str) -> Any:
-        """Convert parameter value based on its type in the schema."""
-        # 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:
-                maybe_convert = False if "." in param_value or "e" in param_value.lower() else True
-                param_value: float = float(param_value)
-                if maybe_convert and param_value.is_integer():
-                    param_value = int(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 in ["object", "array", "arr"
-                              ] or param_type.startswith(
-                                  "dict") or param_type.startswith("list"):
-                try:
-                    param_value = json.loads(param_value)
-                    return param_value
-                except:
-                    logger.warning(
-                        f"Parsed value '{param_value}' of parameter '{param_name}' cannot be parsed with json.loads in tool "
-                        f"'{func_name}', will try other methods to parse it.")
-            try:
-                param_value = ast.literal_eval(param_value)  # safer
-            except:
-                logger.warning(
-                    f"Parsed value '{param_value}' of parameter '{param_name}' cannot be converted via Python `ast.literal_eval()` in tool '{func_name}', degenerating to string."
-                )
-            return param_value
-    def _parse_xml_function_call(
-            self, function_call_str: str,
-            tools: Optional[list[ChatCompletionToolsParam]]
-    ) -> Optional[ToolCall]:
-        # Extract function name
-        end_index = function_call_str.index(">")
-        function_name = function_call_str[:end_index]
-        param_config = self._get_arguments_config(function_name, tools)
-        parameters = function_call_str[end_index + 1:]
-        param_dict = {}
-        for match_text in self.tool_call_parameter_regex.findall(parameters):
-            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] = self._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]:
-        # Store request for type conversion
-        if not previous_text:
-            self._reset_streaming_state()
-            self.streaming_request = request
-        # 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
-        # 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
-                self.accumulated_params = {}
-                # 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 self.parameter_prefix not 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, self.streaming_request.tools
-                            if self.streaming_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
-                self.accumulated_params = {}
-                return result
-            # Look for parameters
-            # Find all 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)
-            # Check if we should start a new parameter
-            if not self.in_param and self.param_count < len(param_starts):
-                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:
-                            # No closing tag, look for next parameter or function end
-                            next_param_idx = value_text.find(
-                                self.parameter_prefix)
-                            func_end_idx = value_text.find(
-                                self.function_end_token)
-                            if next_param_idx != -1 and (func_end_idx == -1
-                                                         or next_param_idx
-                                                         < func_end_idx):
-                                param_end_idx = next_param_idx
-                            elif func_end_idx != -1:
-                                param_end_idx = func_end_idx
-                            else:
-                                # Neither found, check if tool call is complete
-                                if self.tool_call_end_token in tool_text:
-                                    # Tool call is complete, so parameter must be complete too
-                                    # Use all remaining text before function end as value
-                                    param_end_idx = len(value_text)
-                                else:
-                                    # Still streaming, wait for more content
-                                    return None
-                        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]
-                            # Store raw value for later processing
-                            self.accumulated_params[
-                                self.current_param_name] = param_value
-                            # Get parameter configuration for type conversion
-                            param_config = self._get_arguments_config(
-                                self.current_function_name,
-                                self.streaming_request.tools
-                                if self.streaming_request else None)
-                            # Convert the parameter value to the appropriate type
-                            converted_value = self._convert_param_value(
-                                param_value, self.current_param_name,
-                                param_config, self.current_function_name)
-                            # Build JSON fragment based on the converted type
-                            # Use json.dumps to properly serialize the value
-                            serialized_value = json.dumps(converted_value,
-                                                          ensure_ascii=False)
-                            if self.param_count == 0:
-                                json_fragment = f'"{self.current_param_name}": {serialized_value}'
-                            else:
-                                json_fragment = f', "{self.current_param_name}": {serialized_value}'
-                            self.param_count += 1
-                            return DeltaMessage(tool_calls=[
-                                DeltaToolCall(
-                                    index=self.current_tool_index,
-                                    function=DeltaFunctionCall(
-                                        arguments=json_fragment),
-                                )
-                            ])
-            # Continue parameter value - Not used in the current implementation
-            # since we process complete parameters above
-            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:]
-                    # Store complete value
-                    full_value = self.current_param_value + value_chunk
-                    self.accumulated_params[
-                        self.current_param_name] = full_value
-                    # Get parameter configuration for type conversion
-                    param_config = self._get_arguments_config(
-                        self.current_function_name,
-                        self.streaming_request.tools
-                        if self.streaming_request else None)
-                    # Convert the parameter value to the appropriate type
-                    converted_value = self._convert_param_value(
-                        full_value, self.current_param_name, param_config,
-                        self.current_function_name)
-                    # Serialize the converted value
-                    serialized_value = json.dumps(converted_value,
-                                                  ensure_ascii=False)
-                    # Since we've been streaming the quoted version, we need to close it properly
-                    # This is complex - for now just complete the value
-                    self.in_param = False
-                    self.current_param_value = ""
-                    # Just close the current parameter string
-                    return DeltaMessage(tool_calls=[
-                        DeltaToolCall(
-                            index=self.current_tool_index,
-                            function=DeltaFunctionCall(
-                                arguments='"'),  # Close the string quote
-                        )
-                    ])
-                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, ensure_ascii=False
-                        )[1:-1] if self.current_param_value else ""
-                        self.current_param_value += value_chunk
-                        full_escaped = json.dumps(self.current_param_value,
-                                                  ensure_ascii=False)[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