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BytesAgain β€Ί Skills β€Ί Fine-Tuning
πŸ¦€ ClawHub

Fine-Tuning

by @ivangdavila

Fine-tune LLMs with data preparation, provider selection, cost estimation, evaluation, and compliance checks.

Versionv1.0.0
Downloads1,011
Installs5
Stars⭐ 2
#clawhub
TERMINAL
clawhub install fine-tuning

πŸ“– About This Skill

name: Fine-Tuning slug: fine-tuning description: Fine-tune LLMs with data preparation, provider selection, cost estimation, evaluation, and compliance checks.

When to Use

User wants to fine-tune a language model, evaluate if fine-tuning is worth it, or debug training issues.

Quick Reference

| Topic | File | |-------|------| | Provider comparison & pricing | providers.md | | Data preparation & validation | data-prep.md | | Training configuration | training.md | | Evaluation & debugging | evaluation.md | | Cost estimation & ROI | costs.md | | Compliance & security | compliance.md |

Core Capabilities

1. Decide fit β€” Analyze if fine-tuning beats prompting for the use case 2. Prepare data β€” Convert raw data to JSONL, deduplicate, validate format 3. Select provider β€” Compare OpenAI, Anthropic (Bedrock), Google, open source based on constraints 4. Estimate costs β€” Calculate training cost, inference savings, break-even point 5. Configure training β€” Set hyperparameters (learning rate, epochs, LoRA rank) 6. Run evaluation β€” Compare fine-tuned vs base model on task-specific metrics 7. Debug failures β€” Diagnose loss curves, overfitting, catastrophic forgetting 8. Handle compliance β€” Scan for PII, configure on-premise training, generate audit logs

Decision Checklist

Before recommending fine-tuning, ask:

  • [ ] What's the failure mode with prompting? (format, style, knowledge, cost)
  • [ ] How many training examples available? (minimum 50-100)
  • [ ] Expected inference volume? (affects ROI calculation)
  • [ ] Privacy constraints? (determines provider options)
  • [ ] Budget for training + ongoing inference?

    • Fine-Tune vs Prompt Decision

    | Signal | Recommendation | |--------|----------------| | Format/style inconsistency | Fine-tune βœ“ | | Missing domain knowledge | RAG first, then fine-tune if needed | | High inference volume (>100K/mo) | Fine-tune for cost savings | | Requirements change frequently | Stick with prompting | | <50 quality examples | Prompting + few-shot |

    Critical Rules

  • Data quality > quantity β€” 100 great examples beat 1000 noisy ones
  • LoRA first β€” Never jump to full fine-tuning; LoRA is 10-100x cheaper
  • Hold out eval set β€” Always 80/10/10 split; never peek at test data
  • Same precision β€” Train and serve at identical precision (4-bit, 16-bit)
  • Baseline first β€” Run eval on base model before training to measure actual improvement
  • Expect iteration β€” First attempt rarely optimal; plan for 2-3 cycles

    • Common Pitfalls

    | Mistake | Fix | |---------|-----| | Training on inconsistent data | Manual review of 100+ samples before training | | Learning rate too high | Start with 2e-4 for SFT, 5e-6 for RLHF | | Expecting new knowledge | Fine-tuning adjusts behavior, not knowledge β€” use RAG | | No baseline comparison | Always test base model on same eval set | | Ignoring forgetting | Mix 20% general data to preserve capabilities |

    ⚑ When to Use

    User wants to fine-tune a language model, evaluate if fine-tuning is worth it, or debug training issues.

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