QuantFactory/BFS-Prover-GGUF

license: apache-2.0 datasets:

• —internlm/Lean-Workbook
• —internlm/Lean-Github
• —AI-MO/NuminaMath-CoT language:
• —en base_model:
• —Qwen/Qwen2.5-Math-7B pipelinetag: text-generation libraryname: transformers tags:
• —lean4
• —theorem-proving
• —formal-mathematics


QuantFactory/BFS-Prover-GGUF

This is quantized version of bytedance-research/BFS-Prover created using llama.cpp

Original Model Card

<div align="center"> <h1 style="font-size: 2.0em;">🚀 BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving</h1> <div style="display: flex; justify-content: center; gap: 8px; flex-wrap: wrap;"> <a href="https://arxiv.org/abs/2502.03438"><img src="https://img.shields.io/badge/arXiv-2502.03438-b31b1b.svg" alt="arXiv"></a> <a href="https://choosealicense.com/licenses/apache-2.0/"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License: Apache 2.0"></a> <a href="https://github.com/leanprover-community/mathlib4"><img src="https://img.shields.io/badge/Lean-4-orange" alt="Lean 4"></a> </div> <h2>State-of-the-art tactic generation model in Lean4</h2> </div>

This repository contains the latest tactic generator model checkpoint from BFS-Prover, a state-of-the-art theorem proving system in Lean4. While the full BFS-Prover system integrates multiple components for scalable theorem proving, we are releasing the core tactic generation model here. Given a proof state in Lean4, the model generates a tactic that transforms the current proof state into a new state, progressively working towards completing the proof.

📄 Paper: [BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving](https://arxiv.org/abs/2502.03438)

✨ Model Details

• —Base Model: Qwen2.5-Math-7B
• —Training Approach:
• —Supervised Fine-Tuning (SFT) on state-tactic pairs
• —Direct Preference Optimization (DPO) using compiler feedback
• —Training Data Sources:
• —Mathlib (via LeanDojo)
• —Lean-Github repositories
• —Lean-Workbook
• —Autoformalized NuminaMath-CoT dataset

📈 Performance

BFS-Prover achieves state-of-the-art performance on the MiniF2F test benchmark. Here's a detailed comparison:

🔍 MiniF2F Test Benchmark Results

🔑 Key Advantages

• —✅ Achieves better performance without requiring a critic model (value function)
• —✅ Combined with simpler search method (BFS) rather than MCTS

⚙️ Usage

• —The model expects Lean4 tactic states in the format "{state}:::"
• —::: serves as a special indicator to signal the model to generate a tactic for the given state.
• —The model will echo back the input state followed by the generated tactic.

# Example code for loading and using the tactic generator model
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("bytedance-research/BFS-Prover")
tokenizer = AutoTokenizer.from_pretrained("bytedance-research/BFS-Prover")
state = "h : x = y + 2 ⊢ x - 1 = y + 1" 
sep = ":::"
prompt = state + sep  # Creates "h : x = y + 2 ⊢ x - 1 = y + 1:::"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
tactic = tokenizer.decode(outputs[0], skip_special_tokens=True).split(sep)[1]
print(tactic)

# Complete example:
# Input state:  "h : x = y + 2 ⊢ x - 1 = y + 1"
# Full prompt:  "h : x = y + 2 ⊢ x - 1 = y + 1:::"
# Model output: "h : x = y + 2 ⊢ x - 1 = y + 1:::simp [h]"
# Final tactic: "simp [h]"

📚 Citation

If you use this model in your research, please cite our paper:

@article{xin2025bfs,
  title={BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving},
  author={Xin, Ran and Xi, Chenguang and Yang, Jie and Chen, Feng and Wu, Hang and Xiao, Xia and Sun, Yifan and Zheng, Shen and Shen, Kai},
  journal={arXiv preprint arXiv:2502.03438},
  year={2025}
}

📄 License

https://choosealicense.com/licenses/apache-2.0/

📧 Contact

For questions and feedback about the tactic generator model, please contact:

• —Ran Xin (ran.xin@bytedance.com)
• —Kai Shen (shen.kai@bytedance.com)