Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3-32B
+---
+
+<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%3A2506.07982-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="" 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`.
+
+