Fine-Tuning LLaMA 3.2-Vision for Product Descriptions

Fine-tuning a multimodal large language model to generate short, SEO-friendly product descriptions directly from product photographs.

The project adapts LLaMA 3.2-11B-Vision with LoRA on a dataset of kid’s clothing images, using 4-bit quantization and the Unsloth training stack so that the whole workflow fits on a single consumer GPU. It covers dataset preparation, parameter-efficient fine-tuning, and inference.

The result is a practical demonstration of pulling structured, usable text out of messy visual input under realistic hardware constraints.