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MODEL_LICENSE

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+The aiXcoder Model License
+
+1. Definitions
+
+“Licensor” means the aiXcoder Model Team that distributes its Software.
+
+“Software” means the aiXcoder model parameters made available under this license.
+
+2. License Grant
+
+Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license to use the Software solely for your non-commercial research purposes.
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+3. Restriction
+
+You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any commercial, military, or illegal purposes.
+
+You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings.
+
+4. Disclaimer
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+5. Limitation of Liability
+
+EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+6. Dispute Resolution
+
+This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing.
+
+Note that the license is subject to update to a more comprehensive version. For any questions related to the license and copyright, please contact us at [email protected].

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/aiXcoder-7b-base-weights-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "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": "bfloat16",
+  "transformers_version": "4.46.0.dev0",
+  "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": "4.46.0.dev0"
+}

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