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Model: EPFL-LARA/lean-standard-prover-qwen3-8b-grpo
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-8B-Base
+base_model_relation: finetune
+language:
+- en
+tags:
+- lean
+- lean4
+- theorem-proving
+- qwen3
+- standard-prover
+- grpo
+datasets:
+- EPFL-LARA/lean-world-model-cpt
+- EPFL-LARA/lean-world-model-sft
+- Goedel-LM/RL_dataset_V2
+---
+
+# Lean Standard Prover — Qwen3-8B
+
+**Paper model:** S8 · **Stage:** After GRPO
+
+Generates complete Lean proofs from theorem statements using the standard prover response format.
+
+## Hugging Face Transformers
+
+Load the published weights from the Hub and generate on your GPU:
+
+```bash
+pip install 'transformers==4.57.3' accelerate torch
+hf download EPFL-LARA/lean-standard-prover-qwen3-8b-grpo example_transformers.py inference.py prompt_utils.py Example.lean --local-dir lean-standard-prover-qwen3-8b-grpo
+cd lean-standard-prover-qwen3-8b-grpo
+python example_transformers.py
+```
+
+The included `Example.lean` is the input; replace it with your own imports,
+context, and theorem ending in `by sorry`:
+
+```lean4
+import Mathlib
+
+theorem demo (n : ℕ) : n + 0 = n := by sorry
+```
+
+The complete Transformers example is:
+
+```python
+from pathlib import Path
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+
+from inference import build_prompt
+
+MODEL_ID = 'EPFL-LARA/lean-standard-prover-qwen3-8b-grpo'
+
+source = Path("Example.lean").read_text(encoding="utf-8")
+prompt = build_prompt(source)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID, dtype=torch.bfloat16, device_map="auto", attn_implementation="sdpa",
+).eval()
+inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
+prompt_length = inputs["input_ids"].shape[1]
+budget = min(2048, 32768 - prompt_length)
+if budget <= 0:
+    raise ValueError("The prompt must leave room for a proof in the 32,768-token context.")
+
+set_seed(0)
+with torch.inference_mode():
+    output = model.generate(
+        **inputs, max_new_tokens=budget, do_sample=True,
+        temperature=0.3, top_p=0.95, top_k=0, min_p=0.0,
+        repetition_penalty=1.0, num_beams=1,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+response = tokenizer.decode(output[0, prompt_length:], skip_special_tokens=True)
+print(response, end="")
+```
+
+`build_prompt` supplies this checkpoint's exact raw completion prompt.
+Tokenization uses `add_special_tokens=False`; the context budget is 32,768
+tokens. This compact example allows up to 2,048 generated tokens and prints
+only the generated response.
+
+## Example output
+
+Recorded with the script above on an H100 in BF16, using seed 0,
+temperature 0.3, and top-p 0.95. This is the extracted final proof, checked
+with Lean 4.15 and Mathlib:
+
+```lean4
+import Mathlib
+
+
+theorem demo (n : ℕ) : n + 0 = n := by 
+  
+  induction n with
+  
+  | zero => rfl
+  
+  | succ n ih => simp_all
+```
+
+<details>
+<summary>Full generated response</summary>
+
+````text
+The complete typechecked Lean 4 proof is:
+
+```lean4
+import Mathlib
+
+
+theorem demo (n : ℕ) : n + 0 = n := by 
+  
+  induction n with
+  
+  | zero => rfl
+  
+  | succ n ih => simp_all
+```
+````
+
+</details>
+
+[Full response](https://huggingface.co/EPFL-LARA/lean-standard-prover-qwen3-8b-grpo/blob/main/example_response.txt) ·
+[Input/output JSON](https://huggingface.co/EPFL-LARA/lean-standard-prover-qwen3-8b-grpo/blob/main/example_output.json) ·
+[Lean proof](https://huggingface.co/EPFL-LARA/lean-standard-prover-qwen3-8b-grpo/blob/main/example_proof.lean)
+
+## vLLM
+
+For multiple samples and longer output budgets:
+
+```bash
+pip install 'vllm==0.12.0'
+hf download EPFL-LARA/lean-standard-prover-qwen3-8b-grpo inference.py prompt_utils.py Example.lean --local-dir lean-standard-prover-qwen3-8b-grpo
+python lean-standard-prover-qwen3-8b-grpo/inference.py lean-standard-prover-qwen3-8b-grpo/Example.lean --samples 1
+```
+
+The vLLM runner uses the same raw prompt. Its default temperature is 0.3
+for one sample and 0.7 for 32 samples; top-p is 0.95. Output tokens fit
+within the context remaining after the prompt. Use `--print-prompt` to
+inspect the exact input without loading weights.
+
+## Prompt template
+
+The standard template follows `evaluate_prover_model_run.py`.
+Replace the placeholder with the imports, context, and theorem ending in
+`by sorry`, and keep the final newline:
+
+````text
+Solve the following problem with Lean 4 code and explanatory comments:
+
+```lean4
+{formal_statement_with_imports}
+```
+
+Replace every sorry statement with an appropriate proof. Provide a complete solution in the lean4 code block.
+````
+
+## Expected response
+
+The response contains a complete proof in a `lean4` code fence, together with explanatory text or comments.
+
+Use the **last complete Lean code block** as the final proof candidate for Lean verification. The vLLM runner returns the full `response`, extracted `lean_code`, and `finish_reason` as JSONL.
+
+[Formal Theorem Proving with Lean World Models](https://huggingface.co/collections/EPFL-LARA/formal-theorem-proving-with-lean-world-models-6ad2b4aa4400b3e2933f380d)