license: cc-by-nc-sa-4.0
widget:
- text: AAAAGCGACATGACCAAACTGCCCCTCACCCGCCGCACTGATGACCGA
tags:
- DNA
- biology
- genomics
datasets:
- zhangtaolab/plant_reference_genomes
Plant foundation DNA large language models
The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes.
All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000 tokens are included in the vocabulary.
Developed by: zhangtaolab
Model Sources
- Repository: Plant DNA LLMs
- Manuscript: Versatile applications of foundation DNA language models in plant genomes
Architecture
The model is trained based on the Google Gemma model with modified config and tokenizer specific for DNA sequence.
How to use
Install the runtime library first:
pip install transformers
Here is a simple code for inference:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = 'plant-dnabert-6mer'
# load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
# example sequence and tokenization
sequences = ['ATATACGGCCGNC','GGGTATCGCTTCCGAC']
tokens = tokenizer(sequences,padding="longest")['input_ids']
print(f"Tokenzied sequence: {tokenizer.batch_decode(tokens)}")
# inference
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
inputs = tokenizer(sequences, truncation=True, padding='max_length', max_length=512,
return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
outs = model(
**inputs,
output_hidden_states=True
)
# get the final layer embeddings and prediction logits
embeddings = outs['hidden_states'][-1].detach().numpy()
logits = outs['logits'].detach().numpy()
Training data
We use CausalLM method to pre-train the model, the tokenized sequence have a maximum length of 512.
Detailed training procedure can be found in our manuscript.
Hardware
Model was pre-trained on a NVIDIA RTX4090 GPU (24 GB).