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Edge 343: Understanding Llama-Adapter Fine-Tuning

Edge 343: Understanding Llama-Adapter Fine-Tuning

One of the most intriguing fine-tuning methods that combines prefix-tuning and PEFT.

Nov 14, 2023
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Edge 343: Understanding Llama-Adapter Fine-Tuning
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A humorous and whimsical image of a cartoon llama sitting at a computer desk, fine tuning an artificial intelligence model. The llama is wearing glasses and looks focused, with one hoof on a mouse and the other typing on a keyboard. The computer screen shows a colorful interface with graphs and code, and prominently displays the words "Llama-Adapter." The setting is a cozy office environment with tech gadgets and a whiteboard filled with mathematical equations and diagrams.
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💡 ML Concept of the Day: What is Llama-Adapter?

In this series about fine-tuning, we have explored concepts such as prefix-tuning or parameter-efficient fine-tuning(PEFT). Today, we would like to dive into a concept that combines ideas from those two methods: Llama-Adapter.

The ideas behind Llama-Adapter come from adapter fine-tuning techniques. The original adapter method is somewhat related to the aforementioned prefix tuning as they also add additional parameters to each transformer block. However, instead of prepending prefixes to the input embeddings, the adapter method adds adapter layers in two places, as illustrated in the figure below.

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