Fine-tuning LLMs
Fine-tuning adapts a pre-trained language model to your specific use case by training it on domain-specific data. It's more powerful than prompt engineering for specialized tasks.
When to Fine-tune
Good Use Cases
- Domain-specific language (legal, medical)
- Consistent style/tone
- Structured output formats
- Better performance on specific tasks
When NOT to Fine-tune
- Limited training data (<100 examples)
- Prompt engineering works well
- Need frequent updates
- General knowledge tasks
Fine-tuning Methods
Full Fine-tuning
Update all model parameters. Most effective but expensive.
LoRA (Low-Rank Adaptation)
Freeze base model, train small adapter layers. 90% less memory!
QLoRA
LoRA + quantization. Fine-tune 70B models on consumer GPUs.
Training Data Format
Typical format for instruction fine-tuning (JSONL).
python
Output:
Click "Run Code" to see output
Pro Tip: Start with 50-100 high-quality examples. Quality > Quantity!