Fit Your LLM in a single GPU with Gradient Checkpointing, LoRA, and Quantization.

Author(s): Jeremy Arancio

Originally published on Towards AI.

Fine-tune an LLM on your personal data: create a “The Lord of the Rings” storyteller.

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Whoever has ever tried to fine-tune a Large Language Model knows how hard it is to handle the GPU memory.

“RuntimeError: CUDA error: out of memory”

This error message has been haunting my nights.

3B, 7B, or even 13B parameters models are large and the fine-tuning is long and tedious. Running out of memory during training can be both frustrating and costly.

But don’t worry, I got you!

In this article, we’re going through 3 techniques you have to know or already use without knowing how they work: Gradient Checkpointing, Low-Rank Adapters, and Quantization.

These… Read the full blog for free on Medium.

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Published via Towards AI