Update README.md

README.md

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 license: apache-2.0
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+
+# OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA
+
+In this repo, we release a permissively licensed open-source instruction-following model based on [OpenLLaMA](https://github.com/openlm-research/open_llama). In this release, we release a public preview of the 7B OpenAlpaca model based on [the previewed version of OpenLLaMA](https://huggingface.co/openlm-research/open_llama_3b_600bt_preview) that is 3B model trained with 600 billion tokens. We provide PyTorch weights of OpenAlpaca. Stay tuned for our forthcoming updates!
+
+**[Project Page]** [(https://github.com/yxuansu/OpenAlpaca)](https://github.com/yxuansu/OpenAlpaca)
+
+# Dataset and Training
+
+We train our model on the [dolly 15k dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k) released by Databricks. The training configurations are provided in the table below. The training takes on 8 x A100(40G) GPUs and lasts for around 30 minutes.
+
+|||
+|:-------------:|:-------------:|
+|**Batch Size**|64|
+|**Learning rate**|2e-5|
+|**Epochs**|3|
+|**Max length**|1024|
+
+
+
+# Example Usage
+
+Below shows an example on how to use OpenAlpaca
+
+```python
+import torch
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+# the previewed version of OpenAlpaca
+model_path = r'openllmplayground/openalpaca_3b_600bt_preview'
+tokenizer = LlamaTokenizer.from_pretrained(model_path)
+model = LlamaForCausalLM.from_pretrained(model_path).cuda()
+tokenizer.bos_token_id, tokenizer.eos_token_id = 1,2 # see https://github.com/openlm-research/open_llama#preview-weights-release-and-usage
+
+# same prompt as provided in https://crfm.stanford.edu/2023/03/13/alpaca.html
+instruction = r'What is an alpaca? How is it different from a llama?'
+'''
+instruction = r'Write an e-mail to congratulate new Standford admits and mention that you are excited about meeting all of them in person.'
+instruction = r'What is the capital of Tanzania?'
+instruction = r'Write a well-thought out abstract for a machine learning paper that proves that 42 is the optimal seed for training neural networks.'
+'''
+
+prompt_no_input = f'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:'
+tokens = tokenizer.encode(prompt_no_input)
+
+tokens = torch.LongTensor(tokens).unsqueeze(0)
+instance = {'input_ids': tokens,
+                    'top_k': 50,
+                    'top_p': 0.9,
+                    'generate_len': 128}
+                    
+length = len(tokens[0])
+with torch.no_grad():
+    rest = model.generate(
+            input_ids=tokens, 
+            max_length=length+instance['generate_len'], 
+            use_cache=True, 
+            do_sample=True, 
+            top_p=instance['top_p'], 
+            top_k=instance['top_k']
+        )
+        
+output = rest[0][length:]
+string = tokenizer.decode(output, skip_special_tokens=True)
+print(f'[!] Generation results: {string}')
+```
+
+# License and Usage
+
+OpenAlpaca is permissively licensed under the Apache 2.0 license and can be used freely for academic/commercial purposes.
+
+
+# Contact
+We would love to get feedback from the community. If you have any questions, please open an issue or contact us.
+
+OpenAlpaca is developed by: [Yixuan Su](https://yxuansu.github.io/)<sup>\*</sup>, [Tian Lan](https://github.com/gmftbyGMFTBY)<sup>\*</sup>, and [Deng Cai](https://jcyk.github.io/) (The first two members<sup>\*</sup> contributed equally.)
+
+# Reference:
+
+If you found OpenAlpaca useful in your research or applications, please kindly cite using the following BibTeX:
+```
+@misc{openalpaca,
+  author = {Yixuan Su and Tian Lan and Deng Cai},
+  title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}},
+}
+```
+```
+@software{openlm2023openllama,
+  author = {Xinyang Geng and Hao Liu},
+  title = {OpenLLaMA: An Open Reproduction of LLaMA},
+  month = May,
+  year = 2023,
+  url = {https://github.com/openlm-research/open_llama}
+}
+```
+```
+@misc{alpaca,
+  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
+  title = {Stanford Alpaca: An Instruction-following LLaMA model},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
+}
+```
+```
+@article{touvron2023llama,
+  title={Llama: Open and efficient foundation language models},
+  author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie{-}Anne Lachaux and Timoth{\'{e}}e Lacroix and Baptiste Rozi{\`{e}}re and Naman Goyal and Eric Hambro and Faisal Azhar and Aur{\'{e}}lien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
+  journal={arXiv preprint arXiv:2302.13971},
+  year={2023}
+}
+```
+