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Shortened LLaMA Model Card

Shortened LLaMA is a depth-pruned version of LLaMA models & variants for efficient text generation.

Compression Method

After identifying unimportant Transformer blocks, we perform one-shot pruning and light LoRA-based retraining.

Click to see a method figure. method

Model Links

Source
Model
Pruning
Ratio
Pruning
Criterion
HF Models
Link
LLaMA-1-7B 20% PPL nota-ai/st-llama-1-5.5b-ppl
LLaMA-1-7B 20% Taylor+ nota-ai/st-llama-1-5.5b-taylor
Vicuna-v1.3-7B 20% PPL nota-ai/st-vicuna-v1.3-5.5b-ppl
Vicuna-v1.3-7B 20% Taylor+ nota-ai/st-vicuna-v1.3-5.5b-taylor
Vicuna-v1.3-13B 21% PPL nota-ai/st-vicuna-v1.3-10.5b-ppl
Vicuna-v1.3-13B 21% Taylor+ nota-ai/st-vicuna-v1.3-10.5b-taylor

Zero-shot Performance & Efficiency Results

  • EleutherAI/lm-evaluation-harness version 3326c54
results

License

  • All rights related to this repository and the compressed models are reserved by Nota Inc.
  • The intended use is strictly limited to research and non-commercial projects.

Acknowledgments

Citation

@article{kim2024shortened,
  title={Shortened LLaMA: A Simple Depth Pruning for Large Language Models},
  author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu},
  journal={arXiv preprint arXiv:2402.02834},      
  year={2024},
  url={https://arxiv.org/abs/2402.02834}
}
@article{kim2024mefomo,
  title={Shortened LLaMA: A Simple Depth Pruning for Large Language Models},
  author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu},
  journal={ICLR Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo)},
  year={2024},
  url={https://openreview.net/forum?id=18VGxuOdpu}
}