Fine-tune a Small Language Model
Use LoRA (Low-Rank Adaptation) via Hugging Face PEFT to fine-tune a small open-source language model on a custom dataset, on hardware you can realistically access — including a free Colab GPU.
Prerequisites: Python, basic PyTorch familiarity, a Hugging Face account, and access to a GPU (a free-tier Google Colab GPU is sufficient for a small model).
Full fine-tuning of a language model — updating every weight — requires serious hardware and a clean, large dataset. LoRA changes that calculus: instead of retraining the whole model, you freeze the base model and train small low-rank adapter matrices alongside it, which is dramatically cheaper in compute and storage while achieving performance comparable to full fine-tuning on many tasks.
In this project you'll fine-tune a small open-source model to follow a consistent instruction-response format using LoRA via Hugging Face's PEFT (Parameter-Efficient Fine-Tuning) library and the TRL library's SFTTrainer for supervised fine-tuning. The final adapter checkpoint will be a few tens of megabytes, not gigabytes.
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