初始化项目，由ModelHub XC社区提供模型

Model: princeton-nlp/Sheared-LLaMA-2.7B-Pruned
Source: Original Platform

README.md

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+license: llama2
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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)  
+**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)  
+**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)
+**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-2.7B-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**. 
+We used roughly 0.4B tokens to perform the pruning experiment. This model could be a good use to study 
+- effective data mixtures for continued pre-training
+- comparisons to other pruning techniques
+- extensive evaluations to understand how pruning affects knowledge and reasoning capabilities of LLMs