LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

LLaMA-Adapter: Teach Big Language Models New Tricks — Fast and Small

This short note talks about a simple way to make a big language model follow instructions, without changing the whole thing.
The method adds just a tiny set of extra parts, so it stays small and trains very fast.
Instead of re-writing the whole model, it gently nudges the model with learnable prompts, so it keeps what it already knew while it learns new tasks, it were smart and stable.
The result is a model that gives high-quality answers almost like models that were fully re-trained, but costs far less time and power.
It also works when you want the model to use pictures — so the same trick helps for images plus text.
This makes it easier for teams with limited hardware to build helpful assistants, chatbots, or creative tools.
The code is shared so others can try it, and many people will find they can teach big models without heavy work.
Try it if you want fast gains and low cost, you might be surprised how well it does.

Read article comprehensive review in Paperium.net:
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

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