README.md

---
license: cc-by-nc-sa-4.0
widget:
- text: AGTCCAGTGGACGACCAGCCACGGCTCCGGTCTGTAGAACCATCGCGGAAACGGCTCGCAAAACTCTAAACAGCGCAAACGATGCGCGCGCCGAAGCAACCCGGCTCTACTTATAAAAACGTCCAACGGTGAGCACCGAGCAGCTACTACTCGTACTCCCCCCACCGATC
tags:
- DNA
- biology
- genomics
---
# 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](https://github.com/zhangtaolab/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 promoter strength in tobacco leaves system.


### How to use

Install the runtime library first:
```bash
pip install transformers
```

Here is a simple code for inference:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model_name = 'agront-1b-promoter_strength_leaf'
# 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 = ['TACTCTAATCGTATCAGCTGCACTTGCGTACAGGCTACCGGCGTCCTCAGCCACGTAAGAAAAGGCCCAATAAAGGCCCAACTACAACCAGCGGATATATATACTGGAGCCTGGCGAGATCACCCTAACCCCTCACACTCCCATCCAGCCGCCACCAGGTGCAGAGTGTT',
             'ATTTCAAAACTAGTTTTCTATAAACGAAAACTTATATTTATTCCGCTTGTTCCGTTTGATCTGCTGATTCGACACCGTTTTAACGTATTTTAAGTAAGTATCAGAAATATTAATGTGAAGATAAAAGAAAATAGAGTAAATGTAAAGGAAAATGCATAAGATTTTGTTGA']
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
                trust_remote_code=True, function_to_apply="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).