nota-ai/st-llama-1-5.5b-ppl

Shortened LLaMA Model Card

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

• Developed by: Nota AI
• License: Non-commercial license
• Repository: https://github.com/Nota-NetsPresso/shortened-llm
• Paper: https://arxiv.org/abs/2402.02834

Compression Method

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

Click to see a method figure.

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

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

• LLM-Pruner, which utilizes LM Evaluation Harness, PEFT, and Alpaca-LoRA. Thanks for the pioneering work on structured pruning of LLMs!
• Meta AI's LLaMA and LMSYS Org's Vicuna. Thanks for the open-source LLMs!

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}
}