Finetuning Large Language Models
Fine-tuning all layers of a pretrained LLM remains the gold standard for adapting to new target tasks, but there are several efficient alternatives for using pretrained transformers. Methods such as feature-based approaches, in-context learning, and parameter-efficient finetuning techniques enable effective application of LLMs to new tasks while minimizing computational costs and resources.
- In-Context Learning and Indexing
- The 3 Conventional Feature-Based and Finetuning Approaches
- Feature-Based Approach
- Finetuning I – Updating The Output Layers
- Finetuning II – Updating All Layers
- Parameter-Efficient Finetuning
- Reinforcement Learning with Human Feedback
- Conclusion
