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openbmb/
MathForm-8B

Name: MathForm-8B
Availability: InStock

text-generation-inference

endpoints_compatible

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MathForm: Scaling Mathematical Autoformalization with Knowledge Retrieval and Verification-Guided Refinement

Paper

Code

FormalVerse Dataset

MathForm-8B is an autoformalization model that translates natural-language mathematical statements into Lean 4. It is released with the paper MathForm: Scaling Mathematical Autoformalization with Knowledge Retrieval and Verification-Guided Refinement.

The model is trained on FormalVerse through supervised fine-tuning followed by reinforcement learning using Lean compilation and semantic-consistency feedback.

Figure 1: Overview of the MathForm data construction and training pipeline. The system combines Mathlib knowledge retrieval, compilation and semantic verification, and iterative refinement to generate reliable formal data, followed by trajectory reconstruction and training of MathForm-8B.

Results

Figure 2: Pass@8 pass rates (%) under Syntax Check (SC) and Consistency Check (CC) for specialized autoformalizers on six benchmarks. AVG is the equally weighted macro-average across all six benchmarks. For each column, the best result is shown in bold and the second best is underlined.

Usage

Transformers

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "openbmb/MathForm-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

prompt = (
    "Please convert the following informal math problem to a formal one in Lean 4 with a header. "
    "Use the following theorem names: my_favorite_theorem.\n\n"
    "Show that for every real number x, x^2 is non-negative."
)

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs, max_new_tokens=16384, temperature=0.6, top_p=0.95
)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

vLLM

bash

vllm serve openbmb/MathForm-8B \
  --served-model-name MathForm-8B \
  --dtype bfloat16 \
  --max-model-len 16384

SGLang

bash

python -m sglang.launch_server \
  --model-path openbmb/MathForm-8B \
  --served-model-name MathForm-8B \
  --dtype bfloat16 \
  --context-length 16384

Both servers expose an OpenAI-compatible API at http://localhost:8000/v1/chat/completions.

Recommended Settings

Setting	Value
`temperature`	0.6
`top_p`	0.95
`max_new_tokens`	16384

Evaluation

The evaluation pipeline, benchmark files, and Pass@k scripts are available in the MathForm repository. Compilation checks require a running Kimina Lean Server. The experiments use Lean 4.21.0.

License

This project is licensed under the Apache License 2.0.

Citation

bibtex

@misc{pu2026mathformscalingmathematicalautoformalization,
      title={MathForm: Scaling Mathematical Autoformalization with Knowledge Retrieval and Verification-Guided Refinement}, 
      author={Lushi Pu and Weiming Zhang and Xinheng Xie and Zixuan Fu and Bingxiang He and Hengyu Zhao and Hongya Lyu and Xin Li and Jie Zhou and Yudong Wang},
      year={2026},
      eprint={2608.14221},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2608.14221}, 
}