Enhancing Code Generation with RAG Fine-Tuning on Open-Source LLMs

Overview

Large Language Models (LLMs) have revolutionized various applications, including code generation. However, they often suffer from outdated knowledge and hallucinations-issues that can be mitigated through fine-tuning. In this article, we explore how Retrieval-Augmented Generation (RAG) fine-tuning can enhance the performance of open-source LLMs for personalized code assistance. Specifically, we focus on the results of fine-tuning Mistral 7B Instruct v0.2 using Together AI's platform.

The Challenge of Code Generation with LLMs

LLMs are powerful tools for generating code, but they face several challenges:

• Outdated Knowledge: LLMs trained on older datasets may lack the latest coding practices and libraries.
• Hallucinations: These models can generate incorrect or nonsensical code that doesn't align with the user's intent.

To address these issues, researchers at Together AI have developed a method called Retrieval-Augmented Generation (RAG) fine-tuning. This approach combines traditional fine-tuning with retrieval-based augmentation to provide more accurate and contextually relevant code suggestions.

Online Repository-Level Fine-Tuning with Retrieval

The RAG fine-tuning process involves the following steps:

• Data Collection: Gather a dataset of code repositories that are relevant to the specific domain or project.
• Retrieval System: Implement a retrieval system that can efficiently search and retrieve code snippets from these repositories based on user queries.
• Fine-Tuning: Use the retrieved code snippets to fine-tune the LLM, ensuring it learns from the most up-to-date and contextually relevant examples.

Key Benefits of RAG Fine-Tuning:

• Improved Accuracy: By leveraging recent and domain-specific data, RAG fine-tuned models can generate more accurate and useful code.
• Reduced Hallucinations: The retrieval system helps ensure that the generated code is grounded in real-world examples, reducing the likelihood of hallucinations.

Results

Our experiments with RAG fine-tuning on Mistral 7B Instruct v0.2 have yielded impressive results:

• Accuracy: RAG fine-tuned models achieve up to 16% better accuracy compared to Claude 3 Opus.
• Speed: They offer a 3.7x speed improvement over Claude 3 Opus.
• Cost: The cost is reduced by an astounding 150x.

When compared to GPT-4o, the RAG fine-tuned models show:

• Quality Improvement: Up to 19% better quality in generated code.
• Speed: A 1.1x speed improvement.
• Cost Reduction: An impressive 37.5x cost reduction.

Conclusion

RAG fine-tuning represents a significant advancement in the field of code generation with LLMs. By combining retrieval-based augmentation with traditional fine-tuning, it addresses the key challenges of outdated knowledge and hallucinations. The results from our experiments on Mistral 7B Instruct v0.2 demonstrate that this approach can significantly enhance the accuracy, speed, and cost-efficiency of code generation.

Generated Examples

Here are a few examples of code generated by RAG fine-tuned models:

• Example 1: Implementing a function to reverse a string.
  • Input: "Write a Python function to reverse a string."
  • Output:

    def reverse_string(s):
        return s[::-1]
    

• Example 2: Writing a function to find the maximum element in an array.
  • Input: "Write a Python function to find the maximum element in an array."
  • Output:

    def find_max(arr):
        if not arr:
            return None
        max_val = arr[0]
        for num in arr:
            if num > max_val:
                max_val = num
        return max_val
    

Reference

For more details and to try out the RAG fine-tuning process, you can use the Together Fine-tuning API and explore additional resources at Morph Labs and [Morph Code API](https://morph