LLaMA-Adapter turns frozen LLaMA 7B into a capable instruction follower using only 1.2M new parameters and zero-init attention, matching Alpaca while extending to image-conditioned reasoning on ScienceQA and COCO.
Exploring versatile generative language model via parameter-efficient transfer learning
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LMNet connects stripped LLMs as nodes with trainable seq2seq edges for dense vector exchange, supporting supervision-efficient learning through differentiable communication.
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LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
LLaMA-Adapter turns frozen LLaMA 7B into a capable instruction follower using only 1.2M new parameters and zero-init attention, matching Alpaca while extending to image-conditioned reasoning on ScienceQA and COCO.
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Language Model Networks: Supervision-Efficient Learning through Dense Communication
LMNet connects stripped LLMs as nodes with trainable seq2seq edges for dense vector exchange, supporting supervision-efficient learning through differentiable communication.