Project brief
A parameter-efficient LLM fine-tuning project using QLoRA on local GPU hardware, built to make fine-tuning practical without cloud compute budgets or external API dependence. The project covers dataset curation, quantized training, evaluation benchmarking, and local inference deployment in a complete end-to-end workflow.
Problem
Full fine-tuning of large language models requires GPU memory and compute that only makes economic sense at scale or with cloud spend. Most teams that want domain-specific model behavior either accept hallucination from a generic model or pay for hosted fine-tuning with limited control over the process.
Solution
This project applied QLoRA — quantized low-rank adaptation — to reduce the memory footprint of fine-tuning to what is achievable on local consumer-grade GPU hardware. Custom training data was curated to shape the target behavior, and PEFT adapters were used to update only the parameters that matter for the desired output, keeping training stable and iteration fast.
Role
End-to-end implementation: training infrastructure setup, dataset curation and preprocessing, QLoRA and PEFT configuration, training loop management, evaluation design with quality and efficiency comparison, and local inference deployment using Hugging Face tooling.
Challenge
Limited GPU memory means every decision — model size, quantization level, batch size, sequence length — involves tradeoffs between training stability and output quality. Dataset quality is the largest variable in whether the fine-tuned behavior is actually useful, and the gap between training-time quality metrics and practical inference quality requires empirical validation that is time-consuming on constrained hardware.