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home / papers / 2023 / LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

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

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2303.16199

Summary

Turning an open LLM into an instruction follower usually means updating every weight. Stanford Alpaca did this for all 7B parameters of LLaMA, which is slow, expensive, and awkward to repeat for each new use case. The authors propose LLaMA-Adapter, which keeps LLaMA 7B frozen and trains only a small set of additions on Alpaca's 52K self-instruct examples. Trainable prompt vectors are prepended to the token sequence in the top transformer layers. Their effect on attention is scaled by a learnable gate that starts at zero, so at first the model behaves exactly like the original LLaMA. The gate then lets instruction signals in gradually, which keeps training stable.

The adapter has about 1.2M parameters and trains in under an hour. Its responses are reported to be on par with fully fine-tuned Alpaca. Adding an image encoder turns the same approach into a multi-modal model. The zero-gated attention idea also carries over to other pretrained backbones. This matters because instruction tuning becomes cheap, and task-specific adapters are small enough to swap in and out.

Key points

  • Training setup: frozen LLaMA 7B, 52K Alpaca self-instruct demonstrations, and roughly 1.2M learnable parameters.
  • Speed: convergence in under one hour on 8 A100 GPUs, about 3× faster than Alpaca's full fine-tuning.
  • Storage: each scenario needs only a ~1.8M adapter rather than a full 13G copy of LLaMA. Adapters can be plugged in for different expert knowledge or input modalities.
  • Method: adaption prompts (length K per layer) are inserted into the topmost L layers. A zero-initialized gating factor inside self-attention controls how much prompt tokens interact with word tokens.
  • Extensions: the multi-modal variant is evaluated on MME, MMBench and LVLM-eHub. The zero-init attention is also tested for parameter-efficient tuning of ViT, RoBERTa and CLIP. Code is at github.com/OpenGVLab/LLaMA-Adapter.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

156 papers of our library cite it (of the 140673 whose bibliographies we read); 24 of them have a page here, 132 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2303.16199 · arXiv 2303.16199

@misc{zhang2023llama,
  title = {{LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention}},
  author = {Zhang, Renrui and Han, Jiaming and Liu, Chris and Gao, Peng and Zhou, Aojun and Hu, Xiangfei and Yan, Shilin and Lu, Pan and Li, Hongsheng and Qiao, Yu},
  year = {2023},
  doi = {10.48550/arxiv.2303.16199},
  eprint = {2303.16199},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2303.16199}
}

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