19 Tips to Better AI Fine Tuning
AI Summary
Video Summary
- Topic: Fine-tuning language models
- Key Points:
- Fine-tuning adjusts a model’s focus and utilization of existing knowledge, not adding new information.
- It’s like specializing a general practitioner in a specific medical field.
- Fine-tuning is not effective for adding new knowledge; techniques like retrieval-augmented generation (RAG) are better suited for that.
- Fine-tuning is useful for domain adaptation and style matching, not for occasional specific responses or adding current information.
- Overfitting can occur if fine-tuning with too little data, leading to a loss of generalization.
- Quality training data is essential, and it should be consistent, error-free, and relevant.
- Base model selection is crucial, considering size, resources, and licensing.
- Smaller models like GPT-3.2 3B are often adequate and more practical for most projects.
- Upcoming series will cover fine-tuning tools like Axel, UNS sloth, and mlx, each with its own advantages.
Detailed Instructions and Tips (if any)
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