Fine-Tuning
Adapting a pre-trained model to a specific task or domain by training it further on specialised data. Creates a new model variant.
In-Depth Explanation
Fine-tuning takes a pre-trained model and continues training it on your specific dataset, specialising its capabilities for your use case. This creates a custom model variant with knowledge and behaviours tailored to your needs.
The fine-tuning process:
- Start with a capable pre-trained model (GPT-4, Llama, etc.)
- Prepare a dataset of examples in your domain
- Train the model on your data (typically for a few epochs)
- The model's weights are adjusted to perform better on your specific task
- You now have a custom model deployment
When to consider fine-tuning:
- Do fine-tune when you need: consistent formatting, domain-specific knowledge baked in, particular tone/style, reduced prompt length, higher throughput
- Don't fine-tune when: prompting achieves good results, data changes frequently, you lack sufficient training examples (<100), or RAG can provide the knowledge
Modern efficient techniques (LoRA, QLoRA) have made fine-tuning much more accessible, reducing compute requirements by 90% or more.
Business Context
Fine-tuning is best when you need consistent, specialised behaviour that can't be achieved through prompting alone. Consider it for high-volume, specialised tasks.
How Clever Ops Uses This
We help Australian businesses determine when fine-tuning is worth the investment versus using RAG or advanced prompting. Often, clever prompting achieves 80% of fine-tuning benefits at 10% of the cost.
Example Use Case
"Fine-tuning a model on your company's writing style for consistent brand voice across all AI-generated content."