初始化项目，由ModelHub XC社区提供模型

Model: l3lab/ntp-mathlib-context-deepseek-coder-1.3b
Source: Original Platform

README.md

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+---
+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}, 
+}
+```