Update README.md

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)  
-**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)  
+ 
 
 **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**
+Pruned-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). 
 
-| 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**                |
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-## Bibtex
+model = AutoModelForCausalLM.from_pretrained("minhchuxuan/pruned-2.7b")
+tokenizer = AutoTokenizer.from_pretrained("minhchuxuan/pruned-2.7b")
 ```
-@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}
-}
-```