Fine-tuning large language models for domain-specific tasks requires careful consideration of data quality, parameter efficiency, and evaluation metrics. In this deep dive, we explore how to adapt Llama 3.1 70B for specialized code generation tasks.
The Challenge
While general-purpose models like Llama 3.1 70B excel at broad coding tasks, they often struggle with domain-specific patterns, frameworks, and conventions. Our goal was to create a specialized model that could generate high-quality code for a specific enterprise codebase with unique architectural patterns.
LoRA Configuration Strategy
We implemented Parameter-Efficient Fine-Tuning (PEFT) using LoRA (Low-Rank Adaptation) with the following configuration:
- Rank (r): 16 for optimal balance between efficiency and expressiveness
- Alpha: 32 to control the magnitude of LoRA updates
- Target modules: All attention layers (q_proj, k_proj, v_proj, o_proj)
- Dropout: 0.1 to prevent overfitting
Data Preparation
The training dataset consisted of 50,000 high-quality code examples from the target domain, carefully curated and formatted as instruction-response pairs. Each example included context, requirements, and expected output with detailed comments explaining the reasoning.
Results
After fine-tuning, we achieved a 23% improvement in domain-specific accuracy compared to the base model, with significant improvements in:
- Framework-specific API usage patterns
- Code structure and architectural consistency
- Error handling and edge case coverage
- Documentation and comment quality
The fine-tuned model now serves as the foundation for our code generation pipeline, significantly reducing development time while maintaining high code quality standards.