LLM-finetuned

A fine tunned LLM using LoRA/QLoRA for specialized conversational AI applications.

Domain-specific language model fine-tuned using LoRA/QLoRA for specialized conversational AI applications.

Overview

This project demonstrates advanced LLM fine-tuning techniques to create a specialized chatbot for domain-specific applications. Using parameter-efficient methods like LoRA and QLoRA, I developed a high-performance conversational AI system that outperforms general-purpose models in targeted use cases.

Technical Implementation

Fine-Tuning Approach:

  • Base Model: LLaMA 2/3 (7B-13B parameters)
  • Method: LoRA (Low-Rank Adaptation) with 4-bit quantization (QLoRA)
  • Framework: Hugging Face Transformers, PEFT, BitsAndBytes
  • Training: Custom dataset with 10,000+ instruction-following examples

Key Features:

  • 99% reduction in trainable parameters while maintaining quality
  • Domain-specific knowledge integration
  • Reduced hallucination through targeted training
  • Efficient inference with INT8/INT4 quantization

Deployment

Infrastructure:

  • FastAPI for REST API endpoints
  • Docker containerization for reproducibility
  • Kubernetes for auto-scaling and orchestration
  • vLLM for optimized inference serving

Performance:

  • 15-20% improvement in domain-specific tasks
  • <2 seconds inference time
  • 85-92% accuracy on specialized benchmarks

Technology Stack

  • Python 3.10+, PyTorch 2.0+
  • Hugging Face Transformers, PEFT (LoRA/QLoRA)
  • FastAPI, Docker, Kubernetes
  • Weights & Biases for experiment tracking

This project showcases expertise in LLM fine-tuning, parameter-efficient training, and production deployment. For technical discussions, feel free to contact me.