How to Fine-Tune Llama 2 With LoRA

Author(s): Derrick Mwiti

Originally published on Towards AI.

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Until recently, fine-tuning large language models (LLMs) on a single GPU was a pipe dream. This is because of the large size of these models, leading to colossal memory and storage requirements. For example, you need 780 GB of GPU memory to fine-tune a Llama 65B parameter model. The recent shortage of GPUs has also exacerbated the problem due to the current wave of generative models. That all changed with the entry of LoRA, allowing the fine-tuning of large language models on a single GPU such as the ones offered by Google Colab and Kaggle notebooks for free.

This dive will examine the LoRA technique for fine-tuning large language models such as Llama. Later, you’ll also explore the code and try it yourself.

There are three main reasons why you’d consider fine-tuning a large language model:

Reduce hallucinations, particularly when you pose questions the model hasn’t seen in its training dataMake the model suitable for a particular use case, for example, fine-tuning on private company dataTo remove or add undesirable and desirable behavior

Compared to fine-tuning, prompt engineering is less expensive because there is no upfront cost in… Read the full blog for free on Medium.

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Published via Towards AI