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

inference.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+"""Generate Lean proofs with the paper's prompt and last-complete-block parser."""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from prompt_utils import (
+    LEAN4_DEFAULT_HEADER, DEFAULT_GENERATION_TOP_P,
+    build_prover_prompt_for_example, default_generation_temperature,
+    default_generation_max_tokens, default_lean_stop_sequences,
+    parse_generated_lean, split_context_and_command,
+)
+
+DEFAULT_MODEL = 'EPFL-LARA/lean-standard-prover-qwen3-8b-grpo'
+FEEDBACK = False
+REASONING = False
+
+
+def build_prompt(source: str) -> str:
+    context, declaration, found = split_context_and_command(source)
+    if not found:
+        raise ValueError("Supply a Lean theorem, lemma, or example ending in 'by sorry'.")
+    example = {"formal_statement": declaration, "header": context or LEAN4_DEFAULT_HEADER}
+    _, prompt = build_prover_prompt_for_example(
+        example, use_informal_statement=False, is_deepseek=False,
+        tgat_prompt=FEEDBACK, reasoning=REASONING,
+    )
+    return prompt
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("files", nargs="+", type=Path, help="Lean problem files, including imports and a theorem to complete")
+    parser.add_argument("--model", default=DEFAULT_MODEL)
+    parser.add_argument("--samples", type=int, default=1)
+    parser.add_argument("--temperature", type=float)
+    parser.add_argument("--top-p", type=float, default=DEFAULT_GENERATION_TOP_P)
+    parser.add_argument("--max-new-tokens", type=int, default=default_generation_max_tokens(False))
+    parser.add_argument("--tensor-parallel-size", type=int, default=1)
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--print-prompt", action="store_true", help="Show the exact raw prompt without loading the model")
+    args = parser.parse_args()
+    if args.samples < 1 or args.max_new_tokens < 1:
+        parser.error("--samples and --max-new-tokens must be positive")
+    prompts = [build_prompt(path.read_text(encoding="utf-8")) for path in args.files]
+    if args.print_prompt:
+        for prompt in prompts:
+            print(prompt, end="")
+        return
+
+    from vllm import LLM, SamplingParams
+
+    llm = LLM(model=args.model, tokenizer=args.model,
+              tensor_parallel_size=args.tensor_parallel_size, dtype="auto",
+              max_model_len=32768, trust_remote_code=False, seed=args.seed,
+              gpu_memory_utilization=0.90)
+    tokenizer = llm.get_tokenizer()
+    tokenized = [tokenizer.encode(prompt, add_special_tokens=False) for prompt in prompts]
+    if any(len(ids) >= 32768 for ids in tokenized):
+        raise ValueError("The problem prompt must leave space in the 32,768-token context for a proof.")
+    temperature = args.temperature if args.temperature is not None else default_generation_temperature(args.samples)
+    params = [SamplingParams(n=args.samples, temperature=temperature,
+                             top_p=args.top_p,
+                             max_tokens=min(args.max_new_tokens, 32768-len(ids)),
+                             stop=default_lean_stop_sequences(False),
+                             include_stop_str_in_output=True, seed=args.seed)
+              for ids in tokenized]
+    outputs = llm.generate([{"prompt_token_ids": ids} for ids in tokenized], params)
+    for path, output in zip(args.files, outputs, strict=True):
+        for i, completion in enumerate(output.outputs):
+            print(json.dumps({
+                "source": str(path), "sample_id": i,
+                "response": completion.text,
+                "lean_code": parse_generated_lean(completion.text, is_deepseek=False),
+                "finish_reason": completion.finish_reason,
+            }, ensure_ascii=False), flush=True)
+
+
+if __name__ == "__main__":
+    main()