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| 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 |
1@article{kim2024shortened,
2 title={Shortened LLaMA: A Simple Depth Pruning for Large Language Models},
3 author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu},
4 journal={arXiv preprint arXiv:2402.02834},
5 year={2024},
6 url={https://arxiv.org/abs/2402.02834}
7}1@article{kim2024mefomo,
2 title={Shortened LLaMA: A Simple Depth Pruning for Large Language Models},
3 author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu},
4 journal={ICLR Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo)},
5 year={2024},
6 url={https://openreview.net/forum?id=18VGxuOdpu}
7}