README.md

---
license: apache-2.0
base_model:
- Qwen/Qwen3-8B
---
This is the formalizer for translating infromal math problem into formal statement in Lean 4. In Goedel-Prover-V2 project we use this formalizer in generating lean4 statements.
Different from all previous open-source formalizers, this formalizer has the ability to think before generating the lean4 statements, which significantly increases the acurracy of the translation.
In our internal evaluation, we test the performance of formalizers on a eval set containing 300 informal statements from Omni-MATH. Kimina-formalizer-8B successfully translates 161/300 statements, while Goedel-Formalizer-V2-32B success in 228 statements. 

Here is an example code to use this formalizer:

```
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import re
torch.manual_seed(30)

model_id = "Goedel-LM/Goedel-Formalizer-V2-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)


problem_name = "test_problem"
informal_statement_content = "Prove that 3 cannot be written as the sum of two cubes."


# Construct the prompt for the model
user_prompt_content = (
    f"Please autoformalize the following natural language problem statement in Lean 4. "
    f"Use the following theorem name: {problem_name}\n"
    f"The natural language statement is: \n"
    f"{informal_statement_content}"
    f"Think before you provide the lean statement."
)



chat = [
  {"role": "user", "content": user_prompt_content},
]

inputs = tokenizer.apply_chat_template(chat, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

import time
start = time.time()
outputs = model.generate(inputs, max_new_tokens=16384, temperature = 0.9, do_sample = True, top_k=20, top_p=0.95)


model_output_text = tokenizer.batch_decode(outputs)[0]

def extract_code(text_input):
    """Extracts the last Lean 4 code block from the model's output."""
    try:
        matches = re.findall(r'```lean4\n(.*?)\n```', text_input, re.DOTALL)
        return matches[-1].strip() if matches else "No Lean 4 code block found."
    except Exception:
        return "Error during code extraction."


extracted_code = extract_code(model_output_text)

print(time.time() - start)
print("output:\n", model_output_text)
print("lean4 statement:\n", extracted_code)


```