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---
license: apache-2.0
base_model:
- Qwen/Qwen3-32B
library_name: transformers
---
<style>
.no-border-table table, .no-border-table th, .no-border-table td {
border: none !important;
}
</style>
<div class="no-border-table">
| | |
|-|-|
| [![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/inclusionAI/AWorld/tree/main/train) | [![arXiv](http://img.shields.io/badge/cs.AI-arXiv%3A2508.20404-B31B1B.svg?logo=arxiv&logoColor=red)](https://arxiv.org/abs/2508.20404) |
</div>
# Qwen3-32B-AWorld
## Model Description
**Qwen3-32B-AWorld** is a large language model fine-tuned from `Qwen3-32B`, specializing in agent capabilities and proficient tool usage. The model excels at complex agent-based tasks through precise integration with external tools, achieving a pass@1 score on the GAIA benchmark that surpasses GPT-4o and is comparable to DeepSeek-V3.
<img src="qwen3-32b-aworld-performance.jpg" style="width:100%;">
## Quick Start
This guide provides instructions for quickly deploying and running inference with `Qwen3-32B-AWorld` using vLLM.
### Deployment with vLLM
To deploy the model, use the following `vllm serve` command:
```bash
vllm serve inclusionAI/Qwen3-32B-AWorld \
--rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' \
--max-model-len 131072 \
--gpu-memory-utilization 0.85 \
--dtype bfloat16 \
--tensor-parallel-size 8 \
--enable-auto-tool-choice \
--tool-call-parser hermes
```
**Key Configuration:**
* **Deployment Recommendation:** We recommend deploying the model on **8 GPUs** to enhance concurrency. The `tensor-parallel-size` argument should be set to the number of GPUs you are using (e.g., `8` in the command above).
* **Tool Usage Flags:** To enable the model's tool-calling capabilities, it is crucial to include the `--enable-auto-tool-choice` and `--tool-call-parser hermes` flags. These ensure that the model can correctly process tool calls and parse the results.
### Making Inference Calls
When making an inference request, you must include the `tools` you want the model to use. The format should follow the official OpenAI API specification.
Here is a complete Python example for making an API call to the deployed model using the requests library. This example demonstrates how to query the model with a specific tool.
```python
import requests
import json
# Define the tools available for the model to use
tools = [
{
"type": "function",
"function": {
"name": "mcp__google-search__search",
"description": "Perform a web search query",
"parameters": {
"type": "object",
"properties": {
"query": {
"description": "Search query",
"type": "string"
},
"num": {
"description": "Number of results (1-10)",
"type": "number"
}
},
"required": [
"query"
]
}
}
}
]
# Define the user's prompt
messages = [
{
"role": "user",
"content": "Search for hangzhou's weather today."
}
]
# Set generation parameters
temperature = 0.6
top_p = 0.95
top_k = 20
min_p = 0
# Prepare the request payload
data = {
"messages": messages,
"tools": tools,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k,
"min_p": min_p,
}
# The endpoint for the vLLM OpenAI-compatible server
# Replace {your_ip} and {your_port} with the actual IP address and port of your server.
url = "http://{your_ip}:{your_port}/v1/chat/completions"
# Send the POST request
response = requests.post(
url,
headers={"Content-Type": "application/json"},
data=json.dumps(data)
)
# Print the response from the server
print("Status Code:", response.status_code)
print("Response Body:", response.text)
```
**Note:**
* Remember to replace `{your_ip}` and `{your_port}` in the `url` variable with the actual IP address and port where your vLLM server is running. The default port is typically `8000`.

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"rms_norm_eps": 1e-06,
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"rope_type": "yarn",
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"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.51.1",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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