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

.gitattributes

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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}, 
+}
+```

config.json

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+{
+  "_name_or_path": "deepseek-ai/deepseek-coder-1.3b-base",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 32013,
+  "eos_token_id": 32014,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5504,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 16,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "factor": 4.0,
+    "type": "linear"
+  },
+  "rope_theta": 100000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 32256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 32013,
+  "eos_token_id": 32014,
+  "transformers_version": "4.38.2"
+}

pytorch_model.bin

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special_tokens_map.json

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