What is Fine-Tuning?
Fine-tuning means refining a pre-trained LLM model using smaller datasets to execute tasks specific to a particular domain. For example, fine-tuning can help when a company needs an AI chatbot that delivers responses based on the internal documents and adapted to a specific tone of voice.
Why does fine-tuning matter?
- Fine-tuning allows using the most up-to-date information not available for pre-trained models.
- It addresses the model's knowledge gaps and prevents AI hallucinations.
- This technique reduces model biases due to the more balanced additional datasets.
- It lets you create a more tailored solution that aligns with specific industry needs, user requirements, or brand voice.
Types of fine-tuning
The fine-tuning meaning implies a set of tactics used to adapt pre-trained models in different ways.
- Full fine-tuning implies that all the parameters of a pre-trained model are refined for a specific use case.
- Partial fine-tuning involves retraining some parts of the model while others remain untouched.
- Parameter-efficient fine-tuning (PEFT) refines only a small number of additional parameters, while almost the whole model is taken as is.
- Instruction fine-tuning is based on human-style instructions rather than on predicting the next token.
- Reinforcement learning from human feedback (RLHF) uses human ratings of model outputs to train a reward model, then fine-tunes the model via reinforcement learning to better match human preferences.
- Domain adaptation fine-tuning implies using domain-specific data to adapt responses to the niche without altering the models’ algorithms.
- Multi-task fine-tuning involves performing a few related tasks simultaneously to create multi-functional systems.
The best fine-tuning practices
Fine-tuning requires high-quality datasets and accurate response validation to get the relevant outcome. Here are the most important stages and practices for training.
- Use clean, standardized data without typos and duplicates.
- Ensure the data category is represented equally across your datasets.
- Collect enough examples that cover all the information needed for responses.
- Follow consistent structure, style, and labeling rules.
- Choose from the models that best suit your case and are pretrained for your domain.
- Use proper validation and test splits for training.
- Test the results on real-world use cases with both human and automatic evaluation.
- Compare your base model and fine-tuned one to avoid model regression.
- Use version control and continuously refine the model.
Fine-tuning is one of the AI customization and optimization methods. To see how it compares with approaches like Retrieval Augmented Generation (RAG) and prompt engineering, read our article on RAG vs. fine-tuning vs. prompt engineering.
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