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--- |
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license: apache-2.0 |
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tags: |
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- biology |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is optimized for plant science by continuing pertaining on over 1.5 million plant science academic articles based on LLaMa-2. |
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- **Developed by:** [UCSB] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [LLaMa-2] |
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- **Paper [optional]:** [https://arxiv.org/pdf/2401.01600.pdf] |
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- **Demo [optional]:** [More Information Needed] |
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## How to Get Started with the Model |
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```python |
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from transformers import LlamaTokenizer, LlamaForCausalLM |
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import torch |
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tokenizer = LlamaTokenizer.from_pretrained("Xianjun/PLLaMa-7b-base") |
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model = LlamaForCausalLM.from_pretrained("Xianjun/PLLaMa-7b-base").half().to("cuda") |
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instruction = "How to ..." |
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batch = tokenizer(instruction, return_tensors="pt", add_special_tokens=False).to("cuda") |
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with torch.no_grad(): |
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output = model.generate(**batch, max_new_tokens=512, temperature=0.7, do_sample=True) |
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response = tokenizer.decode(output[0], skip_special_tokens=True) |
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``` |
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## Citation |
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If you find PLLaMa useful in your research, please cite the following paper: |
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```latex |
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@inproceedings{Yang2024PLLaMaAO, |
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title={PLLaMa: An Open-source Large Language Model for Plant Science}, |
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author={Xianjun Yang and Junfeng Gao and Wenxin Xue and Erik Alexandersson}, |
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year={2024}, |
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url={https://api.semanticscholar.org/CorpusID:266741610} |
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} |
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``` |
