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

README.md

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+---
+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)  
+
+
+## Training information
+This is the instruction tuned version of [princeton-nlp/Sheared-LLaMA-2.7B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B). We trained the base model on 10,000 instruction-response pairs
+sampled from the ShareGPT dataset (first-turns only). We use the following prompt to perform instruction tuning. 
+
+> You are a helpful assistant. Write a response that appropriately completes the request.\n\n### Input:\n{input}\n\n### Response:
+
+This model can be loaded through transformers.LlamaModelForCausalLM as follows:
+
+```
+from transformers import LlamaModelForCausalLM
+model = LlamaModelForCausalLM.from_pretrained("princeton-nlp/Sheared-LLaMA-1.3B-ShareGPT")
+```
+
+## Bibtex
+
+If you find our model useful, consider citing us with:
+```
+@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},
+  journal={arXiv preprint arXiv:2310.06694},
+  year={2023}
+}
+```
+