transformers pytorch llama text-generation arxiv:2401.01089 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
---

# 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-2-13b. 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-13b-instruct ")
model = LlamaForCausalLM.from_pretrained("Xianjun/Quokka-13b-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}
}
```


