Project Name: MeAI (Persona-Driven LLM with Adapter Fine-Tuning) #NOT COMPLETE YET (RAG)
Tech Stack: Python, PyTorch, HuggingFace Transformers, PEFT, CUDA

Problem Solved:
Enables the creation of a highly personalized conversational AI agent by fine-tuning a large language model (LLM) with adapter-based methods, capturing nuanced persona traits and professional context for more authentic, context-aware responses.

Key Features / Functionality:

Adapter-based fine-tuning of a pre-trained LLM (Phi-2 Instruct) for efficient persona specialization
Custom prompt engineering to enforce behavioral and stylistic constraints
Automated response generation with configurable decoding parameters (temperature, top-p, top-k, repetition penalty)
Persona emulation with strict adherence to professional and ethical guidelines
Modular checkpoint management for iterative model improvement
Architecture & Implementation Details:

Utilizes HuggingFace Transformers for model and tokenizer management
Loads a base model and applies PEFT adapters from a specified checkpoint (results/checkpoint-450)
Inference pipeline constructs a detailed persona prompt, encodes input, and generates responses using GPU acceleration
Output post-processing enforces persona boundaries and trims extraneous tokens
Designed for future integration with Retrieval-Augmented Generation (RAG)

Outcome / Impact:
Achieved efficient, persona-consistent conversational AI with minimal compute overhead
Demonstrated effective use of adapter-based fine-tuning for rapid persona deployment
Established a robust foundation for further enhancements, including RAG and multi-turn dialogue support