from transformers import LlamaTokenizer, LlamaForCausalLM
import torch

tokenizer = LlamaTokenizer.from_pretrained("Xianjun/PLLaMa-13b-base")
model = LlamaForCausalLM.from_pretrained("Xianjun/PLLaMa-13b-base").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 PLLaMa useful in your research, please cite the following paper:

latex

@inproceedings{Yang2024PLLaMaAO,
  title={PLLaMa: An Open-source Large Language Model for Plant Science},
  author={Xianjun Yang and Junfeng Gao and Wenxin Xue and Erik Alexandersson},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:266741610}
}