Update README.md

README.md

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 ---
 license: apache-2.0
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+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is optimized for Material Science by continuing pertaining on over 1 million Material science academic articles based on LLaMa-2-7b. And further finetuned on materials science instructions.
+
+
+- **Developed by:** [UCSB]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [LLaMa-2]
+
+- **Paper [optional]:** [https://arxiv.org/pdf/2401.01089.pdf]
+- **Demo [optional]:** [More Information Needed]
+
+## How to Get Started with the Model
+```python
+from transformers import LlamaTokenizer, LlamaForCausalLM
+import torch
+
+tokenizer = LlamaTokenizer.from_pretrained("Xianjun/Quokka-7b-instruct ")
+model = LlamaForCausalLM.from_pretrained("Xianjun/Quokka-7b-instruct ").half().to("cuda")
+
+instruction = "How to ..."
+batch = tokenizer(instruction, return_tensors="pt", add_special_tokens=False).to("cuda")
+with torch.no_grad():
+    output = model.generate(**batch, max_new_tokens=512, temperature=0.7, do_sample=True)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+```
+
+## Citation
+If you find Quokka useful in your research, please cite the following paper:
+
+```latex
+@inproceedings{Yang2024QuokkaAO,
+  title={Quokka: An Open-source Large Language Model ChatBot for Material Science},
+  author={Xianjun Yang and Stephen Wilson and Linda Ruth Petzold},
+  year={2024},
+  url={https://api.semanticscholar.org/CorpusID:266725577}
+}
+```
+
+