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Through the fusion of formal knowledge and informal-to-formal reasoning capability, StepFun-Formalizer achieves strong performance on autoformalization tasks. Evaluated..."},"evidence_pages":[{"url":"https://huggingface.co/stepfun-ai/StepFun-Formalizer-7B/raw/main/README.md","final_url":"https://huggingface.co/stepfun-ai/StepFun-Formalizer-7B/raw/main/README.md","title":"stepfun-ai/StepFun-Formalizer-7B model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:50:37.283142+00:00","bytes":4407,"raw_path":"b67b7da18283e47646dac1a3914580b062f6119c8037a0a676623b0b9d12687f.md","content_hash":"c863993187169d618e130adbfbd220e7f15bbbc55697b701d6f0b11fb9e33d9f","excerpt_chars":1200,"truncated":true,"excerpt":"--- language: - en base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B tags: - chat library_name: transformers license: apache-2.0 datasets: - stepfun-ai/StepFun-Formalizer-Training --- <p align=\"center\"> <img src=\"assets/logo.png\" width=\"250px\"><br> </p> StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion <div align=\"center\"> <a href=\"https://www.arxiv.org/abs/2508.04440\"><img src=\"https://img.shields.io/static/v1?label=Paper&message=Arxiv&color=red\"></a> &ensp; <a href=\"https://huggingface.co/stepfun-ai/StepFun-Formalizer-7B\"><img src=\"https://img.shields.io/static/v1?label=Model&message=HuggingFace&color=yellow\"></a> &ensp; <a href=\"https://github.com/stepfun-ai/StepFun-Formalizer\"><img src=\"https://img.shields.io/static/v1?label=Code&message=Github&color=blue\"></a> &ensp; </div> <br> Introduction We introduce StepFun-Formalizer, a family of large language models designed to translate natural-language mathematical problems into formal statements in Lean 4. Through the fusion of formal knowledge and informal-to-formal reasoning capability, StepFun-Formalizer achieves strong performance on autoformalization tasks. 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