train/output/qlora-codellama-bugfix/README.md

---
license: mit
tags:
  - linux
  - bugfix
  - codellama
  - qlora
  - transformers
  - causal-lm
model_type: causal-lm
library_name: transformers
pipeline_tag: text-generation
base_model: codellama/CodeLLaMA-7b-Instruct-hf
language:
  - en
  - c
---

# CodeLLaMA-Linux-BugFix

A fine-tuned CodeLLaMA-7B-Instruct model specifically designed for Linux kernel bug fixing. This model generates Git diff patches from buggy C code and commit messages.

## Model Description

This model is a QLoRA fine-tuned version of CodeLLaMA-7B-Instruct, trained on a dataset of Linux kernel bug fixes extracted from Git commits. It learns to generate appropriate Git diff patches that can fix bugs in C code.

- **Developed by:** Maaac
- **Model type:** Causal Language Model (QLoRA fine-tuned)
- **Language(s):** English, C
- **License:** MIT
- **Finetuned from model:** codellama/CodeLLaMA-7b-Instruct-hf

## Uses

### Direct Use

This model is designed to:
- Generate Git diff patches for Linux kernel bug fixes
- Assist developers in fixing common kernel bugs
- Provide automated code review suggestions
- Help with learning Linux kernel development patterns

### Downstream Use

The model can be integrated into:
- Automated code review systems
- Development IDEs and editors
- Continuous integration pipelines
- Educational tools for kernel development

### Out-of-Scope Use

This model is not suitable for:
- Non-Linux kernel code
- Non-C programming languages
- Security-critical applications without human review
- Production systems without proper validation

## Bias, Risks, and Limitations

### Limitations
- Focused specifically on Linux kernel C code
- May not generalize to other codebases
- Generated fixes should be reviewed by human developers
- Limited to the patterns present in the training data

### Recommendations

Users should:
- Always review generated patches before applying
- Test fixes in a safe environment first
- Understand the context of the bug being fixed
- Use as a development aid, not a replacement for human expertise

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model
model = AutoModelForCausalLM.from_pretrained("Maaac/CodeLLaMA-Linux-BugFix")
tokenizer = AutoTokenizer.from_pretrained("Maaac/CodeLLaMA-Linux-BugFix")

# Example usage
prompt = """Given the following original C code:
int *ptr = kmalloc(sizeof(int), GFP_KERNEL);
if (!ptr) {
    return -ENOMEM;
}
// ... use ptr ...
// Missing kfree(ptr)

Instruction: Fix memory leak by adding proper cleanup

Return the diff that fixes it:
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

### Training Data

- **Source:** Linux kernel Git repository
- **Size:** 100,000 bug-fix samples
- **Format:** JSONL with prompt-completion pairs
- **Extraction Method:** PyDriller analysis of commit history

### Training Procedure

#### Preprocessing
- Extracted bug-fix commits using keyword filtering
- Captured code context (10 lines before/after bug location)
- Converted to prompt-completion format for supervised learning

#### Training Hyperparameters
- **Base Model:** codellama/CodeLLaMA-7b-Instruct-hf
- **Method:** QLoRA with 4-bit quantization
- **LoRA Config:** r=64, alpha=16, dropout=0.1
- **Training:** 3 epochs, batch size 64, learning rate 2e-4
- **Hardware:** Optimized for H200 GPU with bfloat16

## Evaluation

### Testing Data
- Separate evaluation dataset with known bug-fix pairs
- Focused on common Linux kernel bug patterns

### Metrics
- **BLEU Score:** Measures translation quality of generated diffs
- **ROUGE Score:** Evaluates overlap between predicted and actual fixes
- **Human Evaluation:** Qualitative assessment of fix quality

### Results
The model demonstrates the ability to generate contextually appropriate Git diff patches for Linux kernel bugs, though results should be validated by human developers.

## Technical Specifications

### Model Architecture
- **Base:** CodeLLaMA-7B-Instruct (7 billion parameters)
- **Adapter:** LoRA layers for efficient fine-tuning
- **Output:** Generates Git diff format patches

### Compute Infrastructure
- **Hardware:** H200 GPU
- **Framework:** PyTorch with Transformers
- **Quantization:** 4-bit QLoRA for memory efficiency

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{CodeLLaMA-Linux-BugFix,
  author = {Maaac},
  title = {CodeLLaMA-Linux-BugFix: A Fine-tuned Model for Linux Kernel Bug Fixing},
  year = {2024},
  url = {https://huggingface.co/Maaac/CodeLLaMA-Linux-BugFix}
}
```

## Model Card Authors

- **Author:** Maaac
- **Contact:** [Your contact information]

## Framework Versions

- PEFT 0.16.0
- Transformers 4.53.1
- PyTorch 2.7.1