Instructions to use GuoxinChen/ReForm-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GuoxinChen/ReForm-8B with Transformers:

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

pipe = pipeline("text-generation", model="GuoxinChen/ReForm-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GuoxinChen/ReForm-8B")
model = AutoModelForCausalLM.from_pretrained("GuoxinChen/ReForm-8B")
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-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GuoxinChen/ReForm-8B"
# 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-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GuoxinChen/ReForm-8B

SGLang

How to use GuoxinChen/ReForm-8B 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-8B" \
    --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-8B",
		"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-8B" \
        --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-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GuoxinChen/ReForm-8B with Docker Model Runner:
```
docker model run hf.co/GuoxinChen/ReForm-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧩 ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization

ReForm is a reflective Autoformalization framework that enables large language models to generate → verify → refine formal mathematical statements in an integrated self-corrective loop. It introduces Prospective Bounded Sequence Optimization (PBSO) — a novel reinforcement learning algorithm designed for heterogeneous rewards at different sequence positions — enabling stable, reflective training and substantial gains in semantic fidelity.

🚀 Highlights

🪞 Reflective Autoformalization Paradigm
Turns single-pass translation into an iterative “generate–validate–refine” cycle, allowing the model to autonomously detect and correct semantic errors.
⚖️ Prospective Bounded Sequence Optimization (PBSO)
A reinforcement learning algorithm with position-specific rewards for both task accuracy and critique quality, ensuring stable and interpretable optimization.
📈 State-of-the-art Semantic Consistency
ReForm achieves an average +22.6pp improvement over the strongest baseline across four formalization benchmarks (miniF2F, ProofNet, Putnam, and AIME 2025).

_{Figure: ReForm consistently outperforms Goedel-V2 and Kimina-Autoformalizer on all benchmarks.}

💡 Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "GuoxinChen/ReForm-8B"  # or "GuoxinChen/ReForm-32B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

prompt = "Think step by step to translate the mathematical problem in natural language to Lean 4, and verify the consistency.\nLet $a_1, a_2,\\cdots, a_n$ be real constants, $x$ a real variable, and $f(x)=\\cos(a_1+x)+\\frac{1}{2}\\cos(a_2+x)+\\frac{1}{4}\\cos(a_3+x)+\\cdots+\\frac{1}{2^{n-1}}\\cos(a_n+x).$ Given that $f(x_1)=f(x_2)=0,$ prove that $x_2-x_1=m\\pi$ for some integer $m.$"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=32768)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

More Details please refer to our Github Repo.

📚 Citation

If you find ReForm useful for your research, please cite:

@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}, 
}