Fine-tuning

Fine-tuning updates pretrained weights on your labelled (or instruction) data so the model specialises to a domain or task.

Adapted for this playbook from the 🤗 Transformers documentation by Hugging Face. Official page: https://huggingface.co/docs/transformers/training. Images © Hugging Face (documentation-images) unless noted. This is not a substitute for the upstream docs — verify against the current version.

Covers Hugging Face pages: training

Executive summary

Define the task head, tokenize consistently, monitor eval metrics, and checkpoint often. Prefer PEFT when full fine-tuning is too expensive.

Workflow

1. Pick a base checkpoint and task class (ForSequenceClassification, ForCausalLM, …)
2. Prepare a datasets Dataset with the expected columns
3. Tokenize with the matching tokenizer / processor
4. Configure TrainingArguments (LR, batch, epochs, precision)
5. trainer.train() → evaluate → save_pretrained / Hub upload

Practical tips

• Start with a small subset to validate the pipeline
• Stratify splits; watch for leakage
• Log learning rate, loss, and business metrics — not only training loss
• For LLMs, consider instruction SFT then preference methods via TRL

Key takeaways

• Task head + data format must match.
• Validate on held-out data before production promotion.

What to do next

PEFT.