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# 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.

## 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`.