Fine-Tuning

A concise, practical walkthrough of how to fine-tune Transformer models for text and multimodal tasks. We’ll cover objectives, data prep, adapter/LoRA methods, evaluation, and pitfalls. Then we’ll do a fast, end-to-end 20-minute code demo that fine-tunes a small text model on a toy dataset clean, copy-pastable, and easy to extend later.

Outcomes:

• Understand fine-tuning objectives for text (causal LM, seq-cls, SFT) and multimodal (contrastive, captioning, VLM instruction-tuning).
• Know when to use full-fine-tune vs adapters/LoRA vs QLoRA and how to size batches, context, and learning rates.
• Be able to run and modify a minimal fine-tuning script interpret metrics.
• Have a checklist for data curation, eval, and lightweight deployment.