# Nlp Pretraining

> Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.

- Skill: `enuno/nlp-pretraining` (Agent Skill)
- Install (CLI): `npx skillmds@latest add enuno/nlp-pretraining`
- Raw SKILL.md: https://api.skillmd.com/api/skills/enuno/nlp-pretraining/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: enuno (https://skillmd.com/u/enuno)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/enuno/nlp-pretraining

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## NLP Pretraining/Fine-tuning Best Practice
Fine-tuning recipe:
- Use pre-trained checkpoints (HuggingFace hub)
- AdamW optimizer, lr=2e-5 to 5e-5
- Linear warmup (6% of total steps) + linear decay
- Batch size: 16-32 (use gradient accumulation for larger effective batch)
- 3-5 epochs for classification, 1-2 for generation
- Weight decay: 0.01

Parameter-efficient methods:
- LoRA: r=8-64, alpha=16-128, apply to q/v projections
- Prefix tuning: 10-20 prefix tokens
- Adapters: bottleneck dimension 64-256

Evaluation:
- Classification: accuracy, F1 (macro for imbalanced)
- Generation: perplexity, BLEU/ROUGE, human evaluation
- Use multiple seeds and report mean +/- std