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---
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
---
# 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},
}
```

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{% macro render_extra_keys(json_dict, handled_keys) %}
{%- if json_dict is mapping %}
{%- for json_key in json_dict if json_key not in handled_keys %}
{%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %}
{{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}
{%- else %}
{{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}
{%- endif %}
{%- endfor %}
{%- 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\n# Tools\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>" }}
{%- if tool.description is defined %}
{{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
{%- endif %}
{{- '\n<parameters>' }}
{%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
{%- 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 %}
{%- set handled_keys = ['name', 'type', 'description'] %}
{{- render_extra_keys(param_fields, handled_keys) }}
{{- '\n</parameter>' }}
{%- endfor %}
{%- endif %}
{% set handled_keys = ['type', 'properties'] %}
{{- render_extra_keys(tool.parameters, handled_keys) }}
{{- '\n</parameters>' }}
{%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
{{- render_extra_keys(tool, handled_keys) }}
{{- '\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 | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not 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 %}

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{
"architectures": [
"Qwen3MoeForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"decoder_sparse_step": 1,
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 6144,
"max_position_embeddings": 262144,
"max_window_layers": 48,
"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,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000000,
"router_aux_loss_coef": 0.001,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.52.3",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"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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# 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:
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 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

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