LLM Fine-tuning
Adapting pre-trained models to specific domains with LoRA, QLoRA, and full fine-tuning.
What is LLM Fine-tuning?
Fine-tuning adjusts pre-trained LLMs for specific tasks or domains. Instead of training from scratch, you leverage the model's existing knowledge and adapt it with domain-specific data, reducing computational costs and data requirements.
💡 When to Fine-tune: When prompting isn't enough to achieve desired behavior, you need consistent output formats, or you want to reduce inference costs by using a smaller specialized model.
📊 Fine-tuning Methods
| Method | Resources | Best For |
|---|---|---|
| Full Fine-tuning | Very High (all parameters) | Maximum customization, large budgets |
| LoRA | Low (adapters only) | Cost-effective adaptation, 7B+ models |
| QLoRA | Very Low (quantized + adapters) | Consumer GPUs, 70B+ models |
| Prompt Tuning | Minimal (soft prompts only) | Quick experiments, multi-task |
🔧 LoRA (Low-Rank Adaptation)
LoRA trains small adapter matrices instead of full model weights. This dramatically reduces memory requirements while maintaining quality.
90%+ Less Memory
Trains fraction of parameters
Swap Adapters
Multiple fine-tunes, one base
Fast Training
Hours instead of days
🆚 Fine-tuning vs RAG
Fine-tuning
- ✅ Consistent behavior & format
- ✅ Reduced inference costs
- ✅ Embedded domain knowledge
- ⚠️ Static knowledge (training cutoff)
- ⚠️ Requires training infrastructure
RAG
- ✅ Always up-to-date knowledge
- ✅ Easy to update data
- ✅ Transparent sources (citations)
- ⚠️ Higher inference latency
- ⚠️ Retrieval quality dependency