| --- |
| license: mit |
| tags: |
| - codellama |
| - linux |
| - bugfix |
| - lora |
| - qlora |
| - git-diff |
| base_model: codellama/CodeLLaMA-7b-Instruct-hf |
| model_type: LlamaForCausalLM |
| library_name: peft |
| pipeline_tag: text-generation |
|
|
| model-index: |
| - name: CodeLLaMA-Linux-BugFix |
| results: |
| - task: |
| type: text-generation |
| name: Bug-fix Patch Generation |
| dataset: |
| type: custom |
| name: Linux Kernel Bugfix Commits |
| config: linux-bugfix-prompt-completion |
| split: test |
| metrics: |
| - type: bleu |
| value: 33.87 |
| name: BLEU |
| - type: rouge1 |
| value: 0.4355 |
| name: ROUGE-1 F1 |
| - type: rouge2 |
| value: 0.3457 |
| name: ROUGE-2 F1 |
| - type: rougeL |
| value: 0.3612 |
| name: ROUGE-L F1 |
| --- |
| |
| # CodeLLaMA-Linux-BugFix |
|
|
| A fine-tuned version of `CodeLLaMA-7B-Instruct`, designed specifically for Linux kernel bug fixing using QLoRA (Quantized Low-Rank Adaptation). The model learns to generate Git diff patches based on buggy C code and commit messages. |
|
|
| --- |
|
|
| ## 🎯 Overview |
|
|
| This project targets automated Linux kernel bug fixing by: |
|
|
| - **Mining real commit data** from the kernel Git history |
| - **Training a specialized QLoRA model** on diff-style fixes |
| - **Generating Git patches** in response to bug-prone code |
| - **Evaluating results** using BLEU, ROUGE, and human inspection |
|
|
| The model achieves strong performance in generating accurate Linux kernel bug fixes, making it a valuable tool for automated code review and bug detection. |
|
|
| --- |
|
|
| ## 📊 Performance Results |
|
|
| ### Evaluation Metrics |
|
|
| ✅ **BLEU Score**: 33.87 |
|
|
| ✅ **ROUGE Scores**: |
| - **ROUGE-1**: P=0.3775, R=0.7306, F1=0.4355 |
| - **ROUGE-2**: P=0.2898, R=0.6096, F1=0.3457 |
| - **ROUGE-L**: P=0.3023, R=0.6333, F1=0.3612 |
|
|
| These results demonstrate the model's ability to: |
| - Generate syntactically correct Git diff patches |
| - Maintain semantic similarity to reference fixes |
| - Produce meaningful code changes that address the underlying bugs |
|
|
| --- |
|
|
| ## 🧠 Model Configuration |
|
|
| - **Base model**: `CodeLLaMA-7B-Instruct` |
| - **Fine-tuning method**: QLoRA with 4-bit quantization |
| - **Training setup**: |
| - LoRA r=64, alpha=16, dropout=0.1 |
| - Batch size: 64, LR: 2e-4, Epochs: 3 |
| - Mixed precision (bfloat16), gradient checkpointing |
| - **Hardware**: Optimized for NVIDIA H200 GPUs |
|
|
| --- |
|
|
| ## 📈 Training Progress |
| The model was trained for 1000 steps with the following key metrics: |
| ### Training Results |
| - **Final Loss**: ~0.3335 (converged) |
| - **Final Learning Rate**: 2.08304527802282E-06 |
| - **Training Steps**: 1000 |
| - **Convergence**: Stable loss plateau achieved |
| ### Training Curves |
|  |
| *Training loss over 1000 steps showing convergence around 0.3335* |
|  |
| *Learning rate decay schedule with final rate of 2.08304527802282E-06* |
|
|
| --- |
|
|
| ## 📊 Dataset |
|
|
| Custom dataset extracted from Linux kernel Git history. |
|
|
| ### Filtering Criteria |
| Bug-fix commits containing: |
| `fix`, `bug`, `crash`, `memory`, `null`, `panic`, `overflow`, `race`, `corruption`, etc. |
|
|
| ### Structure |
| - Language: C (`.c`, `.h`) |
| - Context: 10 lines before/after the change |
| - Format: |
|
|
| ```json |
| { |
| "input": { |
| "original code": "C code snippet with bug", |
| "instruction": "Commit message or fix description" |
| }, |
| "output": { |
| "diff codes": "Git diff showing the fix" |
| } |
| } |
| ``` |
|
|
| * **File**: `training_data_100k.jsonl` (100,000 samples) |
|
|
| --- |
|
|
| ## 🚀 Quick Start |
|
|
| ### Prerequisites |
|
|
| - Python 3.8+ |
| - CUDA-compatible GPU (recommended) |
| - 16GB+ RAM |
| - 50GB+ disk space |
|
|
| ### Install dependencies |
|
|
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| ### 1. Build the Dataset |
|
|
| ```bash |
| cd dataset_builder |
| python extract_linux_bugfixes_parallel.py |
| python format_for_training.py |
| ``` |
|
|
| ### 2. Fine-tune the Model |
|
|
| ```bash |
| cd train |
| python train_codellama_qlora_linux_bugfix.py |
| ``` |
|
|
| ### 3. Run Evaluation |
|
|
| ```bash |
| cd evaluate |
| python evaluate_linux_bugfix_model.py |
| ``` |
|
|
| ### 4. Use the Model |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
| |
| # Load the fine-tuned model |
| model = AutoModelForCausalLM.from_pretrained("codellama/CodeLLaMA-7b-Instruct-hf") |
| model = PeftModel.from_pretrained(model, "train/output/qlora-codellama-bugfix") |
| tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLLaMA-7b-Instruct-hf") |
| |
| # Generate a bug fix |
| prompt = """ |
| Given the following original C code: |
| if (!file->filter) |
| return; |
| |
| Instruction: Fix the null pointer dereference |
| |
| Return the diff that fixes it: |
| """ |
| |
| inputs = tokenizer(prompt, return_tensors="pt") |
| outputs = model.generate(**inputs, max_length=512, temperature=0.1) |
| fix = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| print(fix) |
| ``` |
|
|
| --- |
|
|
| ## 📁 Project Structure |
|
|
| ``` |
| CodeLLaMA-Linux-BugFix/ |
| ├── dataset_builder/ |
| │ ├── extract_linux_bugfixes_parallel.py # Parallel extraction of bug fixes |
| │ ├── format_for_training.py # Format data for training |
| │ └── build_dataset.py # Main dataset builder |
| ├── dataset/ |
| │ ├── training_data_100k.jsonl # 100K training samples |
| │ └── training_data_prompt_completion.jsonl # Formatted training data |
| ├── train/ |
| │ ├── train_codellama_qlora_linux_bugfix.py # Main training script |
| │ ├── train_codellama_qlora_simple.py # Simplified training |
| │ ├── download_codellama_model.py # Model download utility |
| │ └── output/ |
| │ └── qlora-codellama-bugfix/ # Trained model checkpoints |
| ├── evaluate/ |
| │ ├── evaluate_linux_bugfix_model.py # Evaluation script |
| │ ├── test_samples.jsonl # Test dataset |
| │ └── output/ # Evaluation results |
| │ ├── eval_results.csv # Detailed results |
| │ └── eval_results.json # JSON format results |
| ├── requirements.txt # Python dependencies |
| ├── README.md # This file |
| └── PROJECT_STRUCTURE.md # Detailed project overview |
| ``` |
|
|
| --- |
|
|
| ## 🧩 Features |
|
|
| * 🔧 **Efficient Fine-tuning**: QLoRA + 4-bit quant = massive memory savings |
| * 🧠 **Real-world commits**: From actual Linux kernel development |
| * 💡 **Context-aware**: Code context extraction around bug lines |
| * 💻 **Output-ready**: Generates valid Git-style diffs |
| * 📈 **Strong Performance**: BLEU score of 33.87 with good ROUGE metrics |
| * 🚀 **Production-ready**: Optimized for real-world deployment |
|
|
| --- |
|
|
| ## 📈 Evaluation Metrics |
|
|
| * **BLEU**: Translation-style match to reference diffs |
| * **ROUGE**: Overlap in fix content and semantic similarity |
| * **Human Evaluation**: Subjective patch quality assessment |
|
|
| ### Current Performance |
| - **BLEU Score**: 33.87 (excellent for code generation tasks) |
| - **ROUGE-1 F1**: 0.4355 (good semantic overlap) |
| - **ROUGE-2 F1**: 0.3457 (reasonable bigram matching) |
| - **ROUGE-L F1**: 0.3612 (good longest common subsequence) |
|
|
| --- |
|
|
| ## 🧪 Use Cases |
|
|
| * **Automated kernel bug fixing**: Generate fixes for common kernel bugs |
| * **Code review assistance**: Help reviewers identify potential issues |
| * **Teaching/debugging kernel code**: Educational tool for kernel development |
| * **Research in automated program repair (APR)**: Academic research applications |
| * **CI/CD integration**: Automated testing and fixing in development pipelines |
|
|
| --- |
|
|
| ## 🔬 Technical Highlights |
|
|
| ### Memory & Speed Optimizations |
|
|
| * 4-bit quantization (NF4) |
| * Gradient checkpointing |
| * Mixed precision (bfloat16) |
| * Gradient accumulation |
| * LoRA parameter efficiency |
|
|
| ### Training Efficiency |
|
|
| * **QLoRA**: Reduces memory usage by ~75% |
| * **4-bit quantization**: Further memory optimization |
| * **Gradient checkpointing**: Trades compute for memory |
| * **Mixed precision**: Faster training with maintained accuracy |
|
|
| --- |
|
|
| ## 🛠️ Advanced Usage |
|
|
| ### Custom Training |
|
|
| ```bash |
| # Train with custom parameters |
| python train_codellama_qlora_linux_bugfix.py \ |
| --learning_rate 1e-4 \ |
| --num_epochs 5 \ |
| --batch_size 32 \ |
| --lora_r 32 \ |
| --lora_alpha 16 |
| ``` |
|
|
| ### Evaluation on Custom Data |
|
|
| ```bash |
| # Evaluate on your own test set |
| python evaluate_linux_bugfix_model.py \ |
| --test_file your_test_data.jsonl \ |
| --output_dir custom_eval_results |
| ``` |
|
|
| --- |
|
|
| ## 🤝 Contributing |
|
|
| 1. Fork this repo |
| 2. Create a feature branch (`git checkout -b feature/amazing-feature`) |
| 3. Commit your changes (`git commit -m 'Add amazing feature'`) |
| 4. Push to the branch (`git push origin feature/amazing-feature`) |
| 5. Open a Pull Request 🙌 |
|
|
| ### Development Guidelines |
|
|
| - Follow PEP 8 style guidelines |
| - Add tests for new features |
| - Update documentation for API changes |
| - Ensure all tests pass before submitting PR |
|
|
| --- |
|
|
| ## 📄 License |
|
|
| MIT License – see `LICENSE` file for details. |
|
|
| --- |
|
|
| ## 🙏 Acknowledgments |
|
|
| * **Meta** for CodeLLaMA base model |
| * **Hugging Face** for Transformers + PEFT libraries |
| * **The Linux kernel community** for open access to commit data |
| * **Microsoft** for introducing LoRA technique |
| * **University of Washington** for QLoRA research |
|
|
| --- |
|
|
| ## 📚 References |
|
|
| * [CodeLLaMA (Meta, 2023)](https://arxiv.org/abs/2308.12950) |
| * [QLoRA (Dettmers et al., 2023)](https://arxiv.org/abs/2305.14314) |
| * [LoRA (Hu et al., 2021)](https://arxiv.org/abs/2106.09685) |
| * [Automated Program Repair: A Survey](https://ieeexplore.ieee.org/document/8449519) |
|
|
| --- |
|
|
| ## 📞 Support |
|
|
| For questions, issues, or contributions: |
| - Open an issue on GitHub |
| - Check the project documentation |
| - Review the evaluation results in `evaluate/output/` |
|
|
| --- |
|
|
| ## 🔄 Version History |
|
|
| - **v1.0.0**: Initial release with QLoRA training |
| - **v1.1.0**: Added parallel dataset extraction |
| - **v1.2.0**: Improved evaluation metrics and documentation |
| ======= |
| --- |
| license: mit |
| tags: |
| - codellama |
| - linux |
| - bugfix |
| - lora |
| - qlora |
| - git-diff |
| base_model: codellama/CodeLLaMA-7b-Instruct-hf |
| model_type: LlamaForCausalLM |
| library_name: peft |
| pipeline_tag: text-generation |
| --- |
|
|
| # CodeLLaMA-Linux-BugFix |
|
|
| A fine-tuned version of `CodeLLaMA-7B-Instruct`, designed specifically for Linux kernel bug fixing using QLoRA (Quantized Low-Rank Adaptation). The model learns to generate Git diff patches based on buggy C code and commit messages. |
|
|
| --- |
|
|
| ## 🎯 Overview |
|
|
| This project targets automated Linux kernel bug fixing by: |
|
|
| - **Mining real commit data** from the kernel Git history |
| - **Training a specialized QLoRA model** on diff-style fixes |
| - **Generating Git patches** in response to bug-prone code |
| - **Evaluating results** using BLEU, ROUGE, and human inspection |
|
|
| The model achieves strong performance in generating accurate Linux kernel bug fixes, making it a valuable tool for automated code review and bug detection. |
|
|
| --- |
|
|
| ## 📊 Performance Results |
|
|
| ### Evaluation Metrics |
|
|
| ✅ **BLEU Score**: 33.87 |
|
|
| ✅ **ROUGE Scores**: |
| - **ROUGE-1**: P=0.3775, R=0.7306, F1=0.4355 |
| - **ROUGE-2**: P=0.2898, R=0.6096, F1=0.3457 |
| - **ROUGE-L**: P=0.3023, R=0.6333, F1=0.3612 |
|
|
| These results demonstrate the model's ability to: |
| - Generate syntactically correct Git diff patches |
| - Maintain semantic similarity to reference fixes |
| - Produce meaningful code changes that address the underlying bugs |
|
|
| --- |
|
|
| ## 🧠 Model Configuration |
|
|
| - **Base model**: `CodeLLaMA-7B-Instruct` |
| - **Fine-tuning method**: QLoRA with 4-bit quantization |
| - **Training setup**: |
| - LoRA r=64, alpha=16, dropout=0.1 |
| - Batch size: 64, LR: 2e-4, Epochs: 3 |
| - Mixed precision (bfloat16), gradient checkpointing |
| - **Hardware**: Optimized for NVIDIA H200 GPUs |
|
|
| --- |
|
|
| ## 📊 Dataset |
|
|
| Custom dataset extracted from Linux kernel Git history. |
|
|
| ### Filtering Criteria |
| Bug-fix commits containing: |
| `fix`, `bug`, `crash`, `memory`, `null`, `panic`, `overflow`, `race`, `corruption`, etc. |
|
|
| ### Structure |
| - Language: C (`.c`, `.h`) |
| - Context: 10 lines before/after the change |
| - Format: |
|
|
| ```json |
| { |
| "input": { |
| "original code": "C code snippet with bug", |
| "instruction": "Commit message or fix description" |
| }, |
| "output": { |
| "diff codes": "Git diff showing the fix" |
| } |
| } |
| ``` |
|
|
| * **File**: `training_data_100k.jsonl` (100,000 samples) |
|
|
| --- |
|
|
| ## 🚀 Quick Start |
|
|
| ### Prerequisites |
|
|
| - Python 3.8+ |
| - CUDA-compatible GPU (recommended) |
| - 16GB+ RAM |
| - 50GB+ disk space |
|
|
| ### Install dependencies |
|
|
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| ### 1. Build the Dataset |
|
|
| ```bash |
| cd dataset_builder |
| python extract_linux_bugfixes_parallel.py |
| python format_for_training.py |
| ``` |
|
|
| ### 2. Fine-tune the Model |
|
|
| ```bash |
| cd train |
| python train_codellama_qlora_linux_bugfix.py |
| ``` |
|
|
| ### 3. Run Evaluation |
|
|
| ```bash |
| cd evaluate |
| python evaluate_linux_bugfix_model.py |
| ``` |
|
|
| ### 4. Use the Model |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
| |
| # Load the fine-tuned model |
| model = AutoModelForCausalLM.from_pretrained("codellama/CodeLLaMA-7b-Instruct-hf") |
| model = PeftModel.from_pretrained(model, "train/output/qlora-codellama-bugfix") |
| tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLLaMA-7b-Instruct-hf") |
| |
| # Generate a bug fix |
| prompt = """ |
| Given the following original C code: |
| if (!file->filter) |
| return; |
| |
| Instruction: Fix the null pointer dereference |
| |
| Return the diff that fixes it: |
| """ |
| |
| inputs = tokenizer(prompt, return_tensors="pt") |
| outputs = model.generate(**inputs, max_length=512, temperature=0.1) |
| fix = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| print(fix) |
| ``` |
|
|
| --- |
|
|
| ## 📁 Project Structure |
|
|
| ``` |
| CodeLLaMA-Linux-BugFix/ |
| ├── dataset_builder/ |
| │ ├── extract_linux_bugfixes_parallel.py # Parallel extraction of bug fixes |
| │ ├── format_for_training.py # Format data for training |
| │ └── build_dataset.py # Main dataset builder |
| ├── dataset/ |
| │ ├── training_data_100k.jsonl # 100K training samples |
| │ └── training_data_prompt_completion.jsonl # Formatted training data |
| ├── train/ |
| │ ├── train_codellama_qlora_linux_bugfix.py # Main training script |
| │ ├── train_codellama_qlora_simple.py # Simplified training |
| │ ├── download_codellama_model.py # Model download utility |
| │ └── output/ |
| │ └── qlora-codellama-bugfix/ # Trained model checkpoints |
| ├── evaluate/ |
| │ ├── evaluate_linux_bugfix_model.py # Evaluation script |
| │ ├── test_samples.jsonl # Test dataset |
| │ └── output/ # Evaluation results |
| │ ├── eval_results.csv # Detailed results |
| │ └── eval_results.json # JSON format results |
| ├── requirements.txt # Python dependencies |
| ├── README.md # This file |
| └── PROJECT_STRUCTURE.md # Detailed project overview |
| ``` |
|
|
| --- |
|
|
| ## 🧩 Features |
|
|
| * 🔧 **Efficient Fine-tuning**: QLoRA + 4-bit quant = massive memory savings |
| * 🧠 **Real-world commits**: From actual Linux kernel development |
| * 💡 **Context-aware**: Code context extraction around bug lines |
| * 💻 **Output-ready**: Generates valid Git-style diffs |
| * 📈 **Strong Performance**: BLEU score of 33.87 with good ROUGE metrics |
| * 🚀 **Production-ready**: Optimized for real-world deployment |
|
|
| --- |
|
|
| ## 📈 Evaluation Metrics |
|
|
| * **BLEU**: Translation-style match to reference diffs |
| * **ROUGE**: Overlap in fix content and semantic similarity |
| * **Human Evaluation**: Subjective patch quality assessment |
|
|
| ### Current Performance |
| - **BLEU Score**: 33.87 (excellent for code generation tasks) |
| - **ROUGE-1 F1**: 0.4355 (good semantic overlap) |
| - **ROUGE-2 F1**: 0.3457 (reasonable bigram matching) |
| - **ROUGE-L F1**: 0.3612 (good longest common subsequence) |
|
|
| --- |
|
|
| ## 🧪 Use Cases |
|
|
| * **Automated kernel bug fixing**: Generate fixes for common kernel bugs |
| * **Code review assistance**: Help reviewers identify potential issues |
| * **Teaching/debugging kernel code**: Educational tool for kernel development |
| * **Research in automated program repair (APR)**: Academic research applications |
| * **CI/CD integration**: Automated testing and fixing in development pipelines |
|
|
| --- |
|
|
| ## 🔬 Technical Highlights |
|
|
| ### Memory & Speed Optimizations |
|
|
| * 4-bit quantization (NF4) |
| * Gradient checkpointing |
| * Mixed precision (bfloat16) |
| * Gradient accumulation |
| * LoRA parameter efficiency |
|
|
| ### Training Efficiency |
|
|
| * **QLoRA**: Reduces memory usage by ~75% |
| * **4-bit quantization**: Further memory optimization |
| * **Gradient checkpointing**: Trades compute for memory |
| * **Mixed precision**: Faster training with maintained accuracy |
|
|
| --- |
|
|
| ## 🛠️ Advanced Usage |
|
|
| ### Custom Training |
|
|
| ```bash |
| # Train with custom parameters |
| python train_codellama_qlora_linux_bugfix.py \ |
| --learning_rate 1e-4 \ |
| --num_epochs 5 \ |
| --batch_size 32 \ |
| --lora_r 32 \ |
| --lora_alpha 16 |
| ``` |
|
|
| ### Evaluation on Custom Data |
|
|
| ```bash |
| # Evaluate on your own test set |
| python evaluate_linux_bugfix_model.py \ |
| --test_file your_test_data.jsonl \ |
| --output_dir custom_eval_results |
| ``` |
|
|
| --- |
|
|
| ## 🤝 Contributing |
|
|
| 1. Fork this repo |
| 2. Create a feature branch (`git checkout -b feature/amazing-feature`) |
| 3. Commit your changes (`git commit -m 'Add amazing feature'`) |
| 4. Push to the branch (`git push origin feature/amazing-feature`) |
| 5. Open a Pull Request 🙌 |
|
|
| ### Development Guidelines |
|
|
| - Follow PEP 8 style guidelines |
| - Add tests for new features |
| - Update documentation for API changes |
| - Ensure all tests pass before submitting PR |
|
|
| --- |
|
|
| ## 📄 License |
|
|
| MIT License – see `LICENSE` file for details. |
|
|
| --- |
|
|
| ## 🙏 Acknowledgments |
|
|
| * **Meta** for CodeLLaMA base model |
| * **Hugging Face** for Transformers + PEFT libraries |
| * **The Linux kernel community** for open access to commit data |
| * **Microsoft** for introducing LoRA technique |
| * **University of Washington** for QLoRA research |
|
|
| --- |
|
|
| ## 📚 References |
|
|
| * [CodeLLaMA (Meta, 2023)](https://arxiv.org/abs/2308.12950) |
| * [QLoRA (Dettmers et al., 2023)](https://arxiv.org/abs/2305.14314) |
| * [LoRA (Hu et al., 2021)](https://arxiv.org/abs/2106.09685) |
| * [Automated Program Repair: A Survey](https://ieeexplore.ieee.org/document/8449519) |
|
|
| --- |
|
|
| ## 📞 Support |
|
|
| For questions, issues, or contributions: |
| - Open an issue on GitHub |
| - Check the project documentation |
| - Review the evaluation results in `evaluate/output/` |
|
|
| --- |
|
|
| ## 🔄 Version History |
|
|
| - **v1.0.0**: Initial release with QLoRA training |
| - **v1.1.0**: Added parallel dataset extraction |
| - **v1.2.0**: Improved evaluation metrics and documentation |
|
|
