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

example_transformers.py

@@ -0,0 +1,31 @@
+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-reasoning-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="")