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license: llama2 |
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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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Sheared-LLaMA-1.3B-Pruned is the model pruned from [meta-llama/Llama-2-7b-hf](https://huggingface.co./meta-llama/Llama-2-7b-hf) **without continued pre-training**. |
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We used roughly 0.4B tokens to perform the pruning experiment. This model could be a good use to study |
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- effective data mixtures for continued pre-training |
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- comparisons to other pruning techniques |
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- extensive evaluations to understand how pruning affects knowledge and reasoning capabilities of LLMs |
