README.md

---
license: apache-2.0
---

---

**Paper**: [https://arxiv.org/pdf/2310.06694.pdf](https://arxiv.org/pdf/2310.06694.pdf)  
**Code**: https://github.com/princeton-nlp/LLM-Shearing  
**Models**: [Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B), [Sheared-LLaMA-2.7B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B)  

**License**: Must comply with license of Llama2 since it's a model derived from Llama2.

---

Sheared-LLaMA-2.7B is a model pruned and further pre-trained from [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf). We dynamically load data from different domains in the [RedPajama dataset](https://github.com/togethercomputeub.com/togethercomputer/RedPajama-Data). We use 0.4B tokens for pruning and 50B tokens for continued pre-training the pruned model. This model can be loaded into huggingface via

```
model = AutoModelForCausalLM.from_pretrained("princeton-nlp/Sheared-LLaMA-2.7B")
```

- Smaller-scale
- Same vocabulary as LLaMA1 and LLaMA2
- Derived with a budget of 50B tokens by utilizing existing strong LLMs

## Downstream Tasks

We evaluate on an extensive set of downstream tasks including reasoning, reading comprehension, language modeling and knowledge intensive tasks. Our Sheared-LLaMA models outperform existing large language models. 

| Model               | # Pre-training Tokens | Average Performance |
| ------------------- | --------------------- | ------------------- |
| LLaMA2-7B           | 2T                    | 64.6                |

**1.3B**

| Model               | # Pre-training Tokens | Average Performance |
| ------------------- | --------------------- | ------------------- |
| OPT-1.3B            | 300B                  | 48.2                |
| Pythia-1.4B         | 300B                  | 48.9                |
| Sheared-LLaMA-1.3B  | 50B                   | 51.0                |

**3B**

| Model               | # Pre-training Tokens | Average Performance |
| ------------------- | --------------------- | ------------------- |
| OPT-2.7B            | 300B                  | 51.4                |
| Pythia-2.8B         | 300B                  | 52.5                |
| INCITE-Base-3B      | 800B                  | 54.7                |
| Open-LLaMA-3B-v1    | 1T                    | 55.1                |
| Open-LLaMA-3B-v2    | 1T                    | 55.7                |
| **Sheared-LLaMA-2.7B**  | **50B**                   | **56.7**                |

## Bibtex
```
@article{xia2023sheared,
   title={Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning},
   author={Xia, Mengzhou and Gao, Tianyu, and Zeng, Zhiyuan and Chen, Danqi},
   year={2023}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Sheared-LLaMA-2.7B)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 35.66   |
| ARC (25-shot)         | 41.72          |
| HellaSwag (10-shot)   | 71.01    |
| MMLU (5-shot)         | 26.92         |
| TruthfulQA (0-shot)   | 37.32   |
| Winogrande (5-shot)   | 67.01   |
| GSM8K (5-shot)        | 1.06        |
| DROP (3-shot)         | 4.57         |