| ---
|
| 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:
|
| ```c
|
| 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
|
| |
