Maaac
/

CodeLLaMA-Linux-BugFix

@@ -4,205 +4,171 @@ tags:
   - linux
   - bugfix
   - codellama
 model_type: causal-lm
 library_name: transformers
 pipeline_tag: text-generation
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.16.0

   - 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