metadata

license: llama2
base_model:
  - codellama/CodeLlama-13b-Instruct-hf
pipeline_tag: text-generation

CodeLlama-13b-MORepair

CodeLlama-13b-MORepair is a program repair model fine-tuned from CodeLlama-13b-instruct using a novel multi-objective fine-tuning framework called MOREPAIR. This model is specifically designed to improve automated program repair capabilities by learning both code transformations and repair logic reasoning.

Paper | Code

Model Description

Base Model: CodeLlama-13b-instruct
Training Technique: Multi-objective fine-tuning with MOREPAIR framework
Supported Languages: Primarily tested on C++ and Java, but likely generalizes to other languages
Primary Use: Automated program repair
License: Llama 2 Community License

Training Details

Training Data

Dataset: TUTORLLMCODE
Size: 1,600 pairs of buggy and repaired code
Nature: Programming task corrections with LLM-generated repair guidance

Training Approach

The model was trained using MOREPAIR, which employs:

Multi-objective learning with two objectives:
1. Generating repaired code
2. Producing repaired code with explanatory guidance
QLoRA fine-tuning (only 1.84% of parameters modified)
NEFTune for improved generalization
LLM-generated guidance for understanding repair logic

Usage

Here's how to use the model with the Hugging Face Transformers library:

Installation

pip install transformers torch

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# Load model and tokenizer
model_name = "barty/CodeLlama-13B-MORepair"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    load_in_8bit=True  # Enable 8-bit quantization for efficiency
)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

def repair_code(buggy_code, filename="example.java"):
    # Construct prompt in the format model expects
    prompt = f"""[INST] This is an incorrect code({filename}):
```java
{buggy_code}
```
You are a software engineer. Can you repair the incorrect code?
[/INST]
```java
"""
    
    # Calculate token count for length control
    prompt_tokens = len(tokenizer.tokenize(prompt))
    max_new_tokens = 500 - prompt_tokens
    
    # Generate repair
    output = pipe(
        prompt,
        min_length=prompt_tokens + 64,
        max_length=prompt_tokens + max_new_tokens,
        temperature=1.0,
        do_sample=True
    )
    
    # Extract the generated code
    full_text = output[0]['generated_text']
    fixed_code = full_text.split('[/INST]')[1].strip()
    
    return full_text, fixed_code

# Example usage
buggy_code = """
public static int findMinRotated(int[] arr) {
    int left = 0;
    int right = arr.length - 1;
    
    while (left < right) {
        int mid = (left + right) / 2;
        if (arr[mid] > arr[right])
            left = mid;  // Bug: should be mid + 1
        else
            right = mid;
    }
    return arr[left];
}
"""

full_response, fixed_code = repair_code(buggy_code)
print("Fixed code:")
print(fixed_code)

Important Parameters

load_in_8bit=True: Enables 8-bit quantization for efficient inference
temperature=1.0: Controls randomness in generation
do_sample=True: Enables sampling-based generation
min_length: Minimum length of generated text
max_length: Maximum length of generated text

Limitations

Performance varies across different programming languages
May require multiple attempts to generate correct fixes
Should be used with appropriate test cases to validate repairs
May not handle very complex or multi-file program repairs

Technical Specifications

Architecture: Based on CodeLlama-13b-instruct
Parameters: Same as base model (13B)
Fine-tuning Method: QLoRA + NEFTune
Context Window: Same as CodeLlama-13b-instruct
Input Format: Code snippets with optional repair guidance

Citation

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

@article{yang2024multi,
  title={Multi-Objective Fine-Tuning for Enhanced Program Repair with LLMs},
  author={Yang, Boyang and Tian, Haoye and Ren, Jiadong and Zhang, Hongyu and Klein, Jacques and Bissyandé, Tegawendé F. and Le Goues, Claire and Jin, Shunfu},
  journal={arXiv preprint arXiv:2404.12636},
  year={2024}
}

Acknowledgments

This model builds upon the CodeLlama model family developed by Meta AI and incorporates the MOREPAIR framework for enhanced program repair capabilities.