rasbt/blog-finetuning-llama-adapters
Supplementary material for "Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to Adapters"
This project helps you understand and apply techniques for making large language models (LLMs) like Llama more efficient for specific tasks, even if you have limited computational resources. You'll learn how to take a general-purpose LLM and adapt it to your unique data, resulting in a specialized model without the need for extensive retraining. This is ideal for researchers, data scientists, and practitioners looking to customize powerful AI models.
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Use this if you want to efficiently adapt powerful large language models to your specific datasets or applications without needing massive computing power.
Not ideal if you're looking for a simple, out-of-the-box solution to apply an LLM without understanding the underlying finetuning mechanisms.
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Apr 12, 2023
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