Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


cpt_st-vicuna-v1.3-1.5b-ppl - GGUF
- Model creator: https://huggingface.co/nota-ai/
- Original model: https://huggingface.co/nota-ai/cpt_st-vicuna-v1.3-1.5b-ppl/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q2_K.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q2_K.gguf) | Q2_K | 0.56GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.IQ3_XS.gguf) | IQ3_XS | 0.61GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.IQ3_S.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.IQ3_S.gguf) | IQ3_S | 0.64GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q3_K_S.gguf) | Q3_K_S | 0.64GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.IQ3_M.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.IQ3_M.gguf) | IQ3_M | 0.66GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q3_K.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q3_K.gguf) | Q3_K | 0.7GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q3_K_M.gguf) | Q3_K_M | 0.7GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q3_K_L.gguf) | Q3_K_L | 0.75GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.IQ4_XS.gguf) | IQ4_XS | 0.77GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q4_0.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q4_0.gguf) | Q4_0 | 0.81GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.IQ4_NL.gguf) | IQ4_NL | 0.81GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q4_K_S.gguf) | Q4_K_S | 0.81GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q4_K.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q4_K.gguf) | Q4_K | 0.84GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q4_K_M.gguf) | Q4_K_M | 0.84GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q4_1.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q4_1.gguf) | Q4_1 | 0.88GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q5_0.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q5_0.gguf) | Q5_0 | 0.96GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q5_K_S.gguf) | Q5_K_S | 0.96GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q5_K.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q5_K.gguf) | Q5_K | 0.98GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q5_K_M.gguf) | Q5_K_M | 0.98GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q5_1.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q5_1.gguf) | Q5_1 | 1.04GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q6_K.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q6_K.gguf) | Q6_K | 1.13GB |
| [cpt_st-vicuna-v1.3-1.5b-ppl.Q8_0.gguf](https://huggingface.co/RichardErkhov/nota-ai_-_cpt_st-vicuna-v1.3-1.5b-ppl-gguf/blob/main/cpt_st-vicuna-v1.3-1.5b-ppl.Q8_0.gguf) | Q8_0 | 1.46GB |




Original model description:
# 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}
}
```