{"schema_version":"onlylabs.public_signal.v1","title":"StepFun Model: stepfun-ai/StepFun-Prover-Preview-7B","description":"StepFun model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/cb9aede2-4f2c-44f1-a371-286e63a1a0ef","json_url":"https://onlylabs.fyi/signals/cb9aede2-4f2c-44f1-a371-286e63a1a0ef/signal.json","generated_at":"2026-06-11T02:50:37.57456+00:00","org":{"slug":"stepfun","name":"StepFun","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/stepfun","dossier_json_url":"https://onlylabs.fyi/labs/stepfun/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/cb9aede2-4f2c-44f1-a371-286e63a1a0ef","signal_json":"https://onlylabs.fyi/signals/cb9aede2-4f2c-44f1-a371-286e63a1a0ef/signal.json","source":"https://huggingface.co/stepfun-ai/StepFun-Prover-Preview-7B","lab_dossier":"https://onlylabs.fyi/labs/stepfun","lab_dossier_json":"https://onlylabs.fyi/labs/stepfun/dossier.json","analysis":"https://onlylabs.fyi/analysis/stepfun","analysis_json":"https://onlylabs.fyi/analysis/stepfun/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/stepfun/evidence.json","category":"https://onlylabs.fyi/neolabs","category_json":"https://onlylabs.fyi/neolabs.json","category_feed":"https://onlylabs.fyi/neolabs/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neolab","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neolab","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neolab","data_business":null},"answer_pack":{"answer":"StepFun published stepfun-ai/StepFun-Prover-Preview-7B. 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It can iteratively refine the proof sketch via interacting with Lean4, and achieve 66.0% accuracy with Pass@1 on MiniF2F-test. Advanced usage examples can be seen in [github](https://github.com/stepfun-ai/StepFun-Prover-Preview). Quick Start with vLLM ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_name = \"Stepfun/Stepfun-Prover-Preview-7B\" model = LLM( model=model_name, tensor_parallel_size=4, ) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) formal_problem = \"\"\" import Mathlib theorem test_theorem (x y z : ℝ) (hx : 0 < x) (hy : 0 < y) (hz : 0 < z) : (x^2 - z^2) / (y + z) + (y^2 - x^2) / (z + x) + (z^2 - y^2) / (x + y) ≥ 0 := by \"\"\".strip() system_prompt = \"You will be given an unsolved Lean 4 problem. Think carefully and work towards a solution. 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It can iteratively refine the proof sketch via interacting with Lean4, and achieve 66.0% accuracy with Pass@1 on MiniF2F-test. Advanced usage examples can be seen in [github](https://github.com/stepfun-ai/StepFun-Prover-Preview). Quick Start with vLLM ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_name = \"Stepfun/Stepfun-Prover-Preview-7B\" model = LLM( model=model_name, tensor_parallel_size=4, ) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) formal_problem = \"\"\" import Mathlib theorem test_theorem (x y z : ℝ) (hx : 0 < x) (hy : 0 < y) (hz : 0 < z) : (x^2 - z^2) / (y + z) + (y^2 - x^2) / (z + x) + (z^2 - y^2) / (x + y) ≥ 0 := by \"\"\".strip() system_prompt = \"You will be given an unsolved Lean 4 problem. Think carefully and work towards a solution. 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