Create README.md

README.md

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+---
+license: llama3
+language:
+- en
+---
+# SubgoalXL - Subgoal-Based Expert Learning for Theorem Proving
+
+## Model Description
+
+SubgoalXL is an advanced model for formal theorem proving, leveraging subgoal-based expert learning to enhance LLMs' formal reasoning capabilities. It addresses the challenges of scarce theorem-proving data and multi-step reasoning by optimizing data efficiency and employing subgoal-level supervision. SubgoalXL iteratively refines formal statement, proof, and subgoal generators, allowing it to extract richer information from limited proofs and generate more precise formal proofs.
+
+### Example Usage
+
+Below is an example code for using SubgoalXL with its custom prompt template for generating formal proofs:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("xl-zhao/formal_proof_generator_v1_iter3")
+model = AutoModelForCausalLM.from_pretrained("xl-zhao/formal_proof_generator_v1_iter3")
+
+informal_statement = ""  # Your informal statement here
+formal_statement = ""    # The formal statement generated by the model will be inserted here
+
+# Define the prompt
+prompt = f"### Problem:\n{informal_statement}\n\n### Proof:\n{formal_statement}"
+
+# Tokenize and generate the formal proof
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.8)
+
+# Decode the output
+formal_proof = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(formal_proof)
+```
+
+## Intended Use
+
+This model is intended for automated theorem proving, especially in environments that require high levels of mathematical rigor, such as Isabelle. By employing subgoal-based proof generation and expert learning, SubgoalXL is optimized for solving complex reasoning tasks in formal mathematics.
+
+## Performance
+
+SubgoalXL sets a new state-of-the-art benchmark in formal theorem proving within the Isabelle environment, achieving an accuracy of 56.1% on the miniF2F dataset, an absolute improvement of 4.9% over previous approaches. The model has successfully solved the following problems:
+
+- 41 AMC12 problems
+- 9 AIME problems
+- 3 IMO problems
+
+For more information on the model's design and performance, please refer to the paper [SubgoalXL: Subgoal-Based Expert Learning for Theorem Proving](https://arxiv.org/abs/2408.11172).
+
+## Limitations
+
+While SubgoalXL excels in formal theorem proving, its usage is tailored to this specific domain and may not generalize well to other tasks beyond formal logic and proof generation.
+
+## Citation
+
+If you use SubgoalXL in your research, please cite our paper:
+
+```
+@article{zhao2024subgoalxl,
+      title = {SubgoalXL: Subgoal-based Expert Learning for Theorem Proving},
+      author = {Zhao, Xueliang and Zheng, Lin and Bo, Haige and Hu, Changran and Thakker, Urmish and Kong, Lingpeng},
+      journal={arXiv preprint arXiv:2408.11172},
+      url={https://arxiv.org/abs/2408.11172}, 
+      year = {2024},
+}
+```
+
+## Contact
+
+For more information, please visit the project's [GitHub repository](https://github.com/zhaoxlpku/SubgoalXL) or reach out via email at [email protected].