5 docs tagged with "fine-tuning"

Decision framework and cost/benefit analysis for fine-tuning vs prompting LLMs.

Instruction-response pairs, data quality, and formatting (Alpaca, ShareGPT) for fine-tuning.

Parameter-efficient fine-tuning, adapter layers, and memory savings with LoRA/QLoRA.

Hands-on: fine-tune a 7B model on Google Colab using the Unsloth framework.

Before/after comparison, catastrophic forgetting detection, and overfitting signals.