LoRA: Low-Rank Adaptation of Large Language Models, A Powerful Tool for Fine-Tuning Models【Part I】
Опубликовано:
26 авг 2024
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![Low-rank Adaption of Large Language Models: Explaining the Key Concepts Behind LoRA](/img/0.gif)
19:17
![LoRA explained (and a bit about precision and quantization)](/img/0.gif)
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![НАРИСОВАЛ МЕДВЕБАКСЫ ЧТОБЫ КУПИТЬ КОТА](/img/0.gif)
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![LoRA: Low-Rank Adaptation of LLMs Explained](/img/0.gif)
27:19
![Fine-tuning Large Language Models (LLMs) | w/ Example Code](/img/0.gif)
28:18
![The Attention Mechanism in Large Language Models](/img/0.gif)
21:02
![揭秘QLoRA: 通过对权重矩阵量化的方法,来高效微调大语言模型](/img/0.gif)
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![What is generative AI and how does it work? - The Turing Lectures with Mirella Lapata](/img/0.gif)
46:02
![QLoRA-How to Fine-tune an LLM on a Single GPU (w/ Python Code)](/img/0.gif)
36:58
![AI, Machine Learning, Deep Learning and Generative AI Explained](/img/0.gif)
10:01
![【生成式AI】Finetuning vs. Prompting:對於大型語言模型的不同期待所衍生的兩類使用方式 (2/3)](/img/0.gif)
24:39
![RAG Explained](/img/0.gif)
8:03
![[論文導讀] GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection 用梯度LowRank近似降低模型訓練硬體需求](/img/0.gif)
22:02
![Only I get to bully my sister 😤](/img/0.gif)
00:27