l3lab/ntp-mathlib-context-deepseek-coder-1.3b

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

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}, 
}