植物基础DNA大语言模型 (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.
开发者: zhangtaolab
Model Sources
- Repository: Plant DNA LLMs
- Manuscript: [Versatile applications of foundation DNA large language models in plant genomes]()
Architecture
The model is trained based on the InstaDeepAI/agro-nucleotide-transformer-1b model.
This model is fine-tuned for predicting H3K27me3 histone modification.
How to use
Install the runtime library first:
pip install transformers
Here is a simple code for inference:
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = 'agront-1b-H3K27me3'
# load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
# inference
sequences = ['ATCTTTTAAACCCTACTTTTCTTCACATTATTCATAATAGGCACTCTCAACTCATGGTTTAGTGGAGTTACACAATACCCAAGGTTGGGTCAAGGCCAAGACGTGATTGGTTTCTTCATTGGGCACCCTCAACTTCTGATTTTGTCCTAAGTTGAGGTAAACATGTGCAAATCTTGAATCTCCAACACCACCCGACGGAAAACTCTTCCTTTTGCCTAACGCTTTTGCTTAGCGATTGTATATGT',
'GCATAATCGAGCTTGATGCCCATGTTTTTGCACCAGAGTTTTACCTCGTCGGCCGTAAAGTTCGTGCCGTTATCAGTGATGATGTTGTGGGGGACGCCGTAACAGTGTACAACCCCGGATATAAAGTCTATCACCGGTCCAGATTCGGCCGTCTCAACAGGCTTGGCTTCTATCCATTTGGT']
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
trust_remote_code=True, top_k=None)
results = pipe(sequences)
print(results)
Training data
We use EsmForSequenceClassification to fine-tune the model.
Detailed training procedure can be found in our manuscript.
Hardware
Model was trained on a NVIDIA RTX4090 GPU (24 GB).
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