LLM Fine-Tuning with LoRA: Efficient Parameter-Efficient Training for Domain Adaptation

Abstract

This project demonstrates mastery of Large Language Model (LLM) fine-tuning techniques using Llama 2 and Hugging Face frameworks. The work focuses on implementing LoRA (Low-Rank Adaptation) approaches for efficient, parameter-efficient training, successfully adapting models to specific domains while demonstrating practical understanding of NLP techniques.

Key Contributions

• LoRA Implementation: Developed efficient fine-tuning approaches using Low-Rank Adaptation
• Domain Adaptation: Successfully adapted Llama 2 models to specific domains
• Framework Integration: Mastered Hugging Face frameworks for LLM fine-tuning
• Parameter Efficiency: Demonstrated understanding of parameter-efficient training methods

Technical Details

• Model: Llama 2
• Framework: Hugging Face Transformers
• Method: LoRA (Low-Rank Adaptation)
• Focus: Domain-specific fine-tuning and NLP applications

Impact

This project showcases practical understanding of modern NLP techniques and demonstrates ability to implement cutting-edge AI methodologies for real-world applications.

Recommended citation: Mkhitaryan, D. (2025). LLM Fine-Tuning with LoRA: Efficient Parameter-Efficient Training for Domain Adaptation. Self-Study Project.