It achieves this by training only a small subset of the model's parameters, while keeping the rest frozen, significantly reducing the computational cost and resource requirements of fine-tuning.
LoRA, or Low-Rank Adaptation, is a technique in generative AI that allows for more efficient fine-tuning of large language models (LLMs) and other AI models.
The goal is to improve the model's performance on that specific task by teaching it from examples more relevant than the general data it was originally trained on.
LLM fine-tuning refers to the process of taking a pre-trained large language model (LLM), like GPT or BERT, and continuing its training on a specific dataset to adapt it for a particular task, domain, or style.
the people who’ve never done the thing are usually the loudest, giving advice about how to do the thing
the people actually doing the thing are just doing it ™️
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