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README.md

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+# aiXcoder-colt
+
+This repository contains the implementation of CoLT in our papers, a novel fine-tuning approach for enhancing large language models' ability to utilize information within long contexts for code completion tasks.
+
+## Resources
+
+### Dataset
+- **CoLT-132K dataset**: A large-scale repo-level code completion dataset comprising 132,000 samples across four programming languages.
+- **Download**: [https://zenodo.org/records/15019938](https://zenodo.org/records/15019938)
+
+### Models
+- **Trained Models**: This repository includes our models (aiXcoder-7B-v2, DS-Coder and Code Llama) trained with the CoLT approach.
+- **Download**: [https://zenodo.org/records/15021246](https://zenodo.org/records/15021246)
+
+## Experiment Reproduction
+
+We use the latest TRL framework(https://github.com/huggingface/trl) code for our experiments. To reproduce our results:
+
+### 1. Prompt Construction Files
+- `aiXcoder-colt/prompt/prompt_aixcoder_colt.py`
+- `aiXcoder-colt/prompt/prompt_codellama.py`
+- `aiXcoder-colt/prompt/prompt_codeqwen.py`
+- `aiXcoder-colt/prompt/prompt_deepseekcoder.py`
+
+### 2. Training Scripts
+- **SFT (Supervised Fine-Tuning)**: See scripts in `aiXcoder-colt/commands/sft/`
+- **DPO (Direct Preference Optimization)**: See scripts in `aiXcoder-colt/commands/po/`
+
+### 3. Reject Sampling for DPO
+
+The `aiXcoder-colt/Reject_Sample/` directory contains implementation and evaluation scripts for our reject sampling approach used in Direct Preference Optimization:
+
+- **Model-specific implementations**:
+  - `aixcoder/`:  Reject sampling for aiXcoder model
+  - `codellama/`: Reject sampling for Code Llama model
+  - `deepseek/`: Reject sampling for DeepSeek-Coder model
+
+- **Evaluation scripts**:
+  - `eval_api.py`: API-based evaluation script
+  - `eval_line.py`: Line-level evaluation script
+  - `eval_span.py`: Span-level evaluation script
+  - `inference.py`: Model inference script for generating completions
+
+## Dependencies
+
+In our experiments, we utilized two Docker environments for TRL training and vLLM (reject sampling). Below are the key dependencies for each environment, excluding redundant packages:
+
+### TRL Training Environment:
+- transformers==4.46.0.dev0
+- torch==2.4.0a0+07cecf4168.nv24.5
+- accelerate==1.0.0
+- deepspeed==0.15.2
+- peft==0.13.1
+- flash-attn==2.4.2
+- datasets==3.0.1
+- wandb==0.15.0
+
+### vLLM Inference Environment:
+- vllm==0.6.0+cu124
+- torch==2.4.0
+- transformers==4.44.2
+- vllm-flash-attn==2.6.1
+- xformers==0.0.27.post2
+- flashinfer==0.1.6+cu121torch2.4
+- fastapi==0.114.1
+- uvicorn==0.30.6
+
+Complete dependency lists can be found in the `dependency` directory.

config.json

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+{
+  "_name_or_path": "/nfs100/zhuhao/models/aixcoder/0124_model/checkpoint-892",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
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+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 14464,
+  "max_position_embeddings": 65536,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 256000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "use_cache": false,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.48.3"
+}

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special_tokens_map.json

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tokenizer_config.json

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