Artificial Intelligence
What is Fine-tuning
Additional training of a model on specific data
Fine-tuning — the process of additionally training a pre-trained model on a specific dataset to adapt it for a particular task or domain.
Fine-tuning Approaches
- Full fine-tuning — updating all model weights
- LoRA — Low-Rank Adaptation, training only adapters
- QLoRA — quantized LoRA for memory savings
- Prompt tuning — training only soft prompts
- Adapter tuning — adding small trainable modules
When to Use
- Specific domain — legal, medical texts
- Corporate style — company tone, terminology
- Narrow task — classification, entity extraction
- Formatting — specific response format
Key Parameters
- Learning rate — training speed (usually low: 1e-5 — 5e-5)
- Epochs — number of epochs (usually 1-5)
- Batch size — batch size
- Warmup — gradual learning rate increase
Business Applications
- Corporate chatbots — training on internal documents
- Ticket classification — automatic request routing
- Content generation — brand-style text
- Code assistants — training on company codebase