In this tutorial, learn how to improve the performance of large language models (LLMs) by utilizing a proxy tuning approach, which enables more efficient fine-tuning and better integration with the AI model.
- Proxy fine-tuning is a method to improve large pre-trained language models without directly accessing their weights.
- It operates on top of black-box LLMs by utilizing only their predictions.
- The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
- Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.
- It operates on top of black-box LLMs by utilizing only their predictions.
- The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
- Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.
