# Week 3: Supervised Fine-Tuning on the Hub

Fine-tune and share models on the Hub. Take a base model, train it on your data, and publish the result for the community to use.

## Why This Matters

Fine-tuning is how we adapt foundation models to specific tasks. By sharing fine-tuned models—along with your training methodology—you're giving the community ready-to-use solutions and reproducible recipes they can learn from.

## The Skill

Use `hf-llm-trainer/` for this quest. Key capabilities:

- **SFT** (Supervised Fine-Tuning) — Standard instruction tuning
- **DPO** (Direct Preference Optimization) — Alignment from preference data
- **GRPO** (Group Relative Policy Optimization) — Online RL training
- Cloud GPU training on HF Jobs—no local setup required
- Trackio integration for real-time monitoring
- GGUF conversion for local deployment

Your coding agent uses `hf_jobs()` to submit training scripts directly to HF infrastructure.

## XP Tiers

We'll announce the XP tiers for this quest soon.

## Resources

- [SKILL.md](../hf-llm-trainer/SKILL.md) — Full skill documentation
- [SFT Example](../hf-llm-trainer/scripts/train_sft_example.py) — Production SFT template
- [DPO Example](../hf-llm-trainer/scripts/train_dpo_example.py) — Production DPO template
- [GRPO Example](../hf-llm-trainer/scripts/train_grpo_example.py) — Production GRPO template
- [Training Methods](../hf-llm-trainer/references/training_methods.md) — Method selection guide
- [Hardware Guide](../hf-llm-trainer/references/hardware_guide.md) — GPU selection

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**All quests complete?** Head back to [01_start.md](01_start.md) for the full schedule and leaderboard info.