Finetuning an Open-Source Large Language Model
End-to-end finetuning of open-source LLMs (LLaMA, Falcon, GPT-J) for domain-specific tasks using parameter-efficient techniques, from dataset curation to live Streamlit deployment.
Year
2025
Scope
Machine Learning / AI Engineering
Client
Independent Research
Duration
2 weeks
End-to-end LLM finetuning project built to demonstrate hands-on technical depth beyond typical PM skill sets.
Challenge:
Off-the-shelf pretrained models perform poorly on domain-specific tasks. Adapting them efficiently without massive compute resources requires both technical precision and systematic methodology.
Solution:
Applied LoRA and QLoRA fine-tuning techniques to adapt large pre-trained models while optimizing compute usage. Curated and preprocessed custom datasets for supervised transfer learning. Built end-to-end training workflows using Hugging Face Transformers with PyTorch and TensorFlow backends. Deployed an interactive Streamlit demo for real-time LLM inference. Documented methodology, hyperparameters, and evaluation metrics including BLEU, ROUGE, and perplexity scores.
