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license: apache-2.0 |
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**Paper**: [https://arxiv.org/pdf/2310.06694.pdf](https://arxiv.org/pdf/2310.06694.pdf) |
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**Code**: https://github.com/princeton-nlp/LLM-Shearing |
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**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) |
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**Pruned Models without Continued Pre-training**: [Sheared-LLaMA-1.3B-Pruned](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B-Pruned), [Sheared-LLaMA-2.7B-Pruned](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B-Pruned) |
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**Instruction-tuned Models**: [Sheared-LLaMA-1.3B-ShareGPT](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B-ShareGPT), [Sheared-LLaMA-2.7B-ShareGPT](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT) |
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**License**: Must comply with license of Llama2 since it's a model derived from Llama2. |
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--- |
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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 |
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``` |
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model = AutoModelForCausalLM.from_pretrained("princeton-nlp/Sheared-LLaMA-2.7B") |
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``` |
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- Smaller-scale |
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- Same vocabulary as LLaMA1 and LLaMA2 |
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- Derived with a budget of 50B tokens by utilizing existing strong LLMs |
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## Downstream Tasks |
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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. |
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| Model | # Pre-training Tokens | Average Performance | |
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| ------------------- | --------------------- | ------------------- | |
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| LLaMA2-7B | 2T | 64.6 | |
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**1.3B** |
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| Model | # Pre-training Tokens | Average Performance | |
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| ------------------- | --------------------- | ------------------- | |
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| OPT-1.3B | 300B | 48.2 | |
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| Pythia-1.4B | 300B | 48.9 | |
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| Sheared-LLaMA-1.3B | 50B | 51.0 | |
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**3B** |
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| Model | # Pre-training Tokens | Average Performance | |
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| ------------------- | --------------------- | ------------------- | |
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| OPT-2.7B | 300B | 51.4 | |
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| Pythia-2.8B | 300B | 52.5 | |
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| INCITE-Base-3B | 800B | 54.7 | |
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| Open-LLaMA-3B-v1 | 1T | 55.1 | |
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| Open-LLaMA-3B-v2 | 1T | 55.7 | |
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| **Sheared-LLaMA-2.7B** | **50B** | **56.7** | |
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## Bibtex |
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``` |
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@article{xia2023sheared, |
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title={Sheared llama: Accelerating language model pre-training via structured pruning}, |
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author={Xia, Mengzhou and Gao, Tianyu and Zeng, Zhiyuan and Chen, Danqi}, |
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journal={arXiv preprint arXiv:2310.06694}, |
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year={2023} |
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} |
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``` |
