Instructions to use Bingguang/FunReason-MT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bingguang/FunReason-MT with Transformers:

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

pipe = pipeline("text-generation", model="Bingguang/FunReason-MT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bingguang/FunReason-MT")
model = AutoModelForCausalLM.from_pretrained("Bingguang/FunReason-MT")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Bingguang/FunReason-MT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Bingguang/FunReason-MT

SGLang

How to use Bingguang/FunReason-MT 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 "Bingguang/FunReason-MT" \
    --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": "Bingguang/FunReason-MT",
		"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 "Bingguang/FunReason-MT" \
        --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": "Bingguang/FunReason-MT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bingguang/FunReason-MT with Docker Model Runner:
```
docker model run hf.co/Bingguang/FunReason-MT
```

Bingguang commited on Nov 18, 2025

Commit

785b1c8

verified ·

1 Parent(s): 289fcc9

Update README.md

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README.md +4 -4

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ pipeline_tag: text-generation
 library_name: transformers
 ---
-# FunReason-MT Technical Report: Overcoming the Complexity Barrier in Multi-Turn Function Calling
 [![arXiv](https://img.shields.io/badge/arXiv-2510.24645-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2510.24645) [![Paper](https://img.shields.io/badge/Hugging%20Face-Paper-yellow?logo=huggingface)](https://huggingface.co/papers/2510.24645) [![Model](https://img.shields.io/badge/Hugging%20Face-Model-yellow?logo=huggingface)](https://huggingface.co/Bingguang/FunReason-MT) [![Dataset](https://img.shields.io/badge/Hugging%20Face-Dataset-yellow?logo=huggingface)](https://huggingface.co/datasets/Bingguang/FunReason-MT) [![GitHub](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/inclusionAI/AWorld-RL) [![Project Page](https://img.shields.io/badge/Project-AWorld-green)](https://github.com/inclusionAI/AWorld)
@@ -42,7 +42,7 @@ The model was rigorously evaluated on the Berkeley Function-Calling Leaderboard
 | Model (4B - 235B)                      |             Multi-Turn (Overall)             |            Single-Turn (Overall)            |
 | :------------------------------------- | :------------------------------------------: | :------------------------------------------: |
 | Qwen3-4B-Instruct (Base)               |        15.75         |        78.19         |
-| **Qwen3-4B + FunReason-MT (RL)** | **56.50**  | **85.02**  |
 | Claude-Sonnet-4-20250514               |        54.75         |        84.72         |
 | DeepSeek-R1-0528                       |        44.50         |        78.22         |
 | GPT-4o-2024-11-20                      |        42.50         |        77.21         |
@@ -233,8 +233,8 @@ If you use FunReason-MT in your research, please cite the technical report:
 ```
 @article{xu2025funreason,
-  title={FunReason-MT Technical Report: Overcoming the Complexity Barrier in Multi-Turn Function Calling},
-  author={Zengzhuang Xu and Bingguang Hao and Zechuan Wang and Yuntao Wen and Maolin Wang and Yang Liu and Long Chen and Dong Wang and Yicheng Chen and Cunyin Peng and Chenyi Zhuang and Jinjie Gu and Xiangyu Zhao and Shi Gu},
   journal={arXiv preprint arXiv:2510.24645},
   year={2025}
 }

 library_name: transformers
 ---
+# FunReason-MT Technical Report: Advanced Data Synthesis Solution for Real-world Multi-Turn Tool-use
 [![arXiv](https://img.shields.io/badge/arXiv-2510.24645-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2510.24645) [![Paper](https://img.shields.io/badge/Hugging%20Face-Paper-yellow?logo=huggingface)](https://huggingface.co/papers/2510.24645) [![Model](https://img.shields.io/badge/Hugging%20Face-Model-yellow?logo=huggingface)](https://huggingface.co/Bingguang/FunReason-MT) [![Dataset](https://img.shields.io/badge/Hugging%20Face-Dataset-yellow?logo=huggingface)](https://huggingface.co/datasets/Bingguang/FunReason-MT) [![GitHub](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/inclusionAI/AWorld-RL) [![Project Page](https://img.shields.io/badge/Project-AWorld-green)](https://github.com/inclusionAI/AWorld)
 | Model (4B - 235B)                      |             Multi-Turn (Overall)             |            Single-Turn (Overall)            |
 | :------------------------------------- | :------------------------------------------: | :------------------------------------------: |
 | Qwen3-4B-Instruct (Base)               |        15.75         |        78.19         |
+| **Qwen3-4B + FunReason-MT (RL)** | **57.75**  | **85.47**  |
 | Claude-Sonnet-4-20250514               |        54.75         |        84.72         |
 | DeepSeek-R1-0528                       |        44.50         |        78.22         |
 | GPT-4o-2024-11-20                      |        42.50         |        77.21         |
 ```
 @article{xu2025funreason,
+  title={FunReason-MT Technical Report: Advanced Data Synthesis Solution for Real-world Multi-Turn Tool-use},
+  author={Zengzhuang Xu, Bingguang Hao, Zechuan Wang, Yuntao Wen, Xinyi Xu, Yang Liu, Long Chen, Dong Wang, Maolin Wang, Tong Zhao, Yicheng Chen, Cunyin Peng, Jinjie Gu, Leilei Gan, Xiangyu Zhao, Chenyi Zhuang, Shi Gu},
   journal={arXiv preprint arXiv:2510.24645},
   year={2025}
 }