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Xianjun/
PLLaMa-13b-instruct

Name: PLLaMa-13b-instruct
Availability: InStock

text-generation-inference

endpoints_compatible

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Model Card for Model ID

This model is optimized for plant science by continuing pertaining on over 1.5 million plant science academic articles based on LLaMa-2-13b-base. And it undergoes further instruction tuning to make it follow 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/2401.01600.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/PLLaMa-13b-instruct")
model = LlamaForCausalLM.from_pretrained("Xianjun/PLLaMa-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 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}
}