Instructions to use GuoxinChen/ReForm-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GuoxinChen/ReForm-32B with Transformers:

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

pipe = pipeline("text-generation", model="GuoxinChen/ReForm-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GuoxinChen/ReForm-32B")
model = AutoModelForCausalLM.from_pretrained("GuoxinChen/ReForm-32B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GuoxinChen/ReForm-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GuoxinChen/ReForm-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GuoxinChen/ReForm-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GuoxinChen/ReForm-32B

SGLang

How to use GuoxinChen/ReForm-32B 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 "GuoxinChen/ReForm-32B" \
    --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": "GuoxinChen/ReForm-32B",
		"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 "GuoxinChen/ReForm-32B" \
        --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": "GuoxinChen/ReForm-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GuoxinChen/ReForm-32B with Docker Model Runner:
```
docker model run hf.co/GuoxinChen/ReForm-32B
```

GuoxinChen commited on Oct 29, 2025

Commit

464c7c0

verified ·

1 Parent(s): d0f4968

Update README.md

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README.md +9 -6

README.md CHANGED Viewed

@@ -9,7 +9,7 @@ base_model:
 # 🧩 ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization
-<a href="https://arxiv.org/pdf/2502.06205"><img src="https://img.shields.io/badge/Paper-arXiv-d63031?logo=arxiv&logoColor=white"></a>
 <a href="https://huggingface.co/collections/GuoxinChen/reform"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-0984e3"></a>
 <a href="https://github.com/Chen-GX/ReForm"><img src="https://img.shields.io/badge/GitHub-ReForm-black?logo=github"></a>
@@ -65,10 +65,13 @@ More Details please refer to our [Github Repo](https://github.com/Chen-GX/ReForm
 If you find ReForm useful for your research, please cite:
 ```bibtex
-@article{chen2025reform,
-  title={ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization},
-  author={Chen, Guoxin and Wu, Jing and Chen, Xinjie and Zhao, Wayne Xin and Song, Ruihua and Li, Chengxi and Fan, Kai and Liu, Dayiheng and Liao, Minpeng},
-  journal={arXiv preprint arXiv:2502.06205},
-  year={2025}
 }
 ```

 # 🧩 ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization
+<a href="https://arxiv.org/pdf/2510.24592"><img src="https://img.shields.io/badge/Paper-arXiv-d63031?logo=arxiv&logoColor=white"></a>
 <a href="https://huggingface.co/collections/GuoxinChen/reform"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-0984e3"></a>
 <a href="https://github.com/Chen-GX/ReForm"><img src="https://img.shields.io/badge/GitHub-ReForm-black?logo=github"></a>
 If you find ReForm useful for your research, please cite:
 ```bibtex
+@misc{chen2025reform,
+      title={ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization},
+      author={Guoxin Chen and Jing Wu and Xinjie Chen and Wayne Xin Zhao and Ruihua Song and Chengxi Li and Kai Fan and Dayiheng Liu and Minpeng Liao},
+      year={2025},
+      eprint={2510.24592},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2510.24592},
 }
 ```