README.md

---
license: mit
---
## [miniCTX: Neural Theorem Proving with (Long-)Contexts]()
File-tuned context model from [miniCTX: Neural Theorem Proving with
(Long-)Contexts](). 

- Base language model: `deepseek-ai/deepseek-coder-1.3b-base`
- Data: [ntp-mathlib-instruct-context](https://huggingface.co./datasets/l3lab/ntp-mathlib-instruct-context)

It is specifically finetuned for Lean 4 tactic prediction given proof states and optional file contexts. 

#### Performance

Please see our paper.

#### Example input
```
/- You are proving a theorem in Lean 4.
You are given the following information:
- The file contents up to the current tactic, inside [CTX]...[/CTX]
- The current proof state, inside [STATE]...[/STATE]

Your task is to generate the next tactic in the proof.
Put the next tactic inside [TAC]...[/TAC]
-/
[CTX]
import Mathlib.Data.Nat.Prime

theorem test_thm (m n : Nat) (h : m.Coprime n) : m.gcd n = 1 := by
  
[/CTX]
[STATE]
m n : ℕ
h : Nat.Coprime m n
⊢ Nat.gcd m n = 1
[/STATE]
[TAC]
```

#### Example output
```
rw [Nat.Coprime] at h
[/TAC]
```

#### Citation

Please cite:
```
@misc{hu2024minictx,
  title={miniCTX: Neural Theorem Proving with (Long-)Contexts}, 
  author={Jiewen Hu and Thomas Zhu and Sean Welleck},
  year={2024},
  eprint={2408.03350},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2408.03350}, 
}
```