Efficient Large Language Model
Collection
Shortened LLMs from Depth Pruning; https://github.com/Nota-NetsPresso/shortened-llm
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Shortened LLM Model Card
Shortened LLM is a depth-pruned version of large language models 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.
- In retraining pruned models for quality recovery, continued pretraining (CPT) on a large corpus markedly outperforms LoRA-based tuning, particularly at severe pruning ratios.
Models from Aggressive Pruning & CPT Retraining (arXiv-v2):
| Source Model |
Pruning Ratio |
Pruning Criterion |
HF Models Link |
|---|---|---|---|
| Vicuna-v1.3-7B | 20% | PPL | nota-ai/cpt_st-vicuna-v1.3-5.5b-ppl |
| Vicuna-v1.3-7B | 45% | PPL | nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl |
| Vicuna-v1.3-7B | 60% | PPL | nota-ai/cpt_st-vicuna-v1.3-2.7b-ppl |
| Vicuna-v1.3-7B | 80% | PPL | nota-ai/cpt_st-vicuna-v1.3-1.5b-ppl |
Experimental Setup for CPT of Pruned Vicuna-7B
- Dataset: SlimPajama-627B
- Training using 8 NVIDIA H100 GPUs.
- 5.5B parameters: 37B training tokens (for 6 days)
- 3.7B parameters: 74B tokens (for 8 days)
- 2.7B parameters: 150B tokens (for 12 days)
- 1.5B parameters: 271B tokens (for 11 days)
- AdamW optimizer with (β1, β2)=(0.9, 0.95); a learning rate of 0.0001; a weight decay of 0.1.
- Global batch size: 512 (micro-batch size of 2 × 32 gradient accumulation steps × 8 GPUs).
Click to see the learning curve:
Zero-shot performance over the course of training for models from Vicuna-7B-v1.3 at different pruning ratios. For each model size, the CPT duration was limited to a two-week period, but additional training could further improve the quality.
Models from Moderate Pruning & LoRA Retraining (arXiv-v1):
| 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 |
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
- Microsoft for Startups Founders Hub and Gwangju AICA for generously providing GPU resources.
- 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: Depth Pruning for Large Language Models with Comparison of Retraining Methods},
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}
}
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