AI Fine Tuning

Tao explains the advantages of full end-to-end fine tuning over partial adjustments, highlighting slower convergence rates when only tuning specific parts. Vivek emphasizes the faster convergence and model quality achieved through end-to-end fine tuning compared to low rank approaches like Lora.

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From this podcast
The Cognitive Revolution: How AI Changes Everything
Google’s Multimodal Med-PaLM with Vivek Natarajan and Tao Tu
Related Questions
- How are Large Language Models (LLMs) fine-tuned post-training?
- How are Large Language Models (LLMs) fine-tuned post-training in the episode Llama 3: Scaling open LLMs to AGI and the clip Fine Tuning Insights?