README.md

# Shortened LLM Model Card

Shortened LLM is a depth-pruned version of large language models for efficient text generation.

- **Developed by:** [Nota AI](https://www.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<br>Model | Pruning<br>Ratio | Pruning<br>Criterion | HF Models<br>Link |
  |:---:|:---:|:---:|:---:|
  | Vicuna-v1.3-7B | 20% | PPL | [nota-ai/cpt_st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-1.5b-ppl) |

<details>
<summary>
Click to see the results:
</summary>

- EleutherAI/lm-evaluation-harness version [3326c54](https://github.com/EleutherAI/lm-evaluation-harness/tree/3326c547a733d598b4377e54be96e194861b964c)

<img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st_llm-cpt_results.png" width="100%">

</details>

#### Experimental Setup for CPT of Pruned Vicuna-7B
* Dataset: [SlimPajama-627B](https://huggingface.co/datasets/cerebras/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).

<details>
<summary>
Click to see the learning curve:
</summary>

**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.

<img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st_llm-cpt_learning-curve.png" width="100%">

</details>



## Models from Moderate Pruning & LoRA Retraining (arXiv-v1):
  | Source<br>Model | Pruning<br>Ratio | Pruning<br>Criterion | HF Models<br>Link |
  |:---:|:---:|:---:|:---:|
  | LLaMA-1-7B | 20% | PPL | [nota-ai/st-llama-1-5.5b-ppl](https://huggingface.co/nota-ai/st-llama-1-5.5b-ppl) |
  | LLaMA-1-7B | 20% | Taylor+ | [nota-ai/st-llama-1-5.5b-taylor](https://huggingface.co/nota-ai/st-llama-1-5.5b-taylor) |
  | Vicuna-v1.3-7B | 20% | PPL | [nota-ai/st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-5.5b-ppl) |
  | Vicuna-v1.3-7B | 20% | Taylor+ | [nota-ai/st-vicuna-v1.3-5.5b-taylor](https://huggingface.co/nota-ai/st-vicuna-v1.3-5.5b-taylor) |
  | Vicuna-v1.3-13B | 21% | PPL | [nota-ai/st-vicuna-v1.3-10.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-ppl) |
  | Vicuna-v1.3-13B | 21% | Taylor+ | [nota-ai/st-vicuna-v1.3-10.5b-taylor](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-taylor) |

<details>

<summary>
Click to see the results:
</summary>

- EleutherAI/lm-evaluation-harness version [3326c54](https://github.com/EleutherAI/lm-evaluation-harness/tree/3326c547a733d598b4377e54be96e194861b964c)

<img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st-llama_zero-shot_scores.png" width="100%">

</details>

## 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](https://www.microsoft.com/en-us/startups) and [Gwangju AICA](http://www.aica-gj.kr/main.php) for generously providing GPU resources.
- [LLM-Pruner](https://github.com/horseee/LLM-Pruner), which utilizes [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness), [PEFT](https://github.com/huggingface/peft), and [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). Thanks for the pioneering work on structured pruning of LLMs! 
- Meta AI's [LLaMA](https://github.com/facebookresearch/llama) and  LMSYS Org's [Vicuna](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md). Thanks for the open-source LLMs!

## Citation
```bibtex
@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}
}
```
```bibtex
@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}
}
```