Fine-Tuning LLM using LoRA & Unsloth
Parameter-efficient instruction tuning of Large Language Models using LoRA adapters.
- Category
- Machine Learning
- Status
- Personal
- Stack
- Python, LoRA, Unsloth +2
Problem
Fine-tuning foundation models requires significant computational resources. Parameter-efficient techniques reduce memory consumption while maintaining strong performance.
Architecture
Hugging Face Transformers integrated with Unsloth and LoRA adapters for efficient instruction tuning and deployment-ready model optimization.
Key features
- Instruction fine-tuning
- LoRA adapters
- Efficient GPU utilization
- Supervised fine-tuning
- Parameter-efficient model adaptation
My role
Implemented the complete fine-tuning pipeline, dataset preparation, training workflow, and model evaluation.
Impact
Demonstrates practical experience with modern LLM adaptation techniques and efficient fine-tuning workflows.
Stack
AI-powered job application assistant that analyzes resumes, evaluates job compatibility, and generates personalized cover letters using multi-agent AI and RAG.