README.md

---
license: cc-by-nc-sa-4.0
widget:
- text: AGTCGCCGCAACCCACACACGGACGGCTCGACGTGGCGATCTTAGCGGCTCATCCGCCCGGCCTCCCTCGCGCTCGATCGCTACGCAGCCTACGCTCGTTTCGCTCGGTTCGGTGGGTCGCCGATCTGGCGCCACGGCGGCTACCAACGACACCGCGATTGAGAAGGGTGCGTGGCCGTGGAGTCGTGGAGAAACGCCCGCGCGCGCGGGTGCGGCGAGGGACGACGACCGCGTCGTGCGGATCGATTGGCGGGGCAGCTCGGCGCCCCG
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 language models in plant genomes]() 

### Architecture

The model is trained based on the State-Space Mamba-130m model with modified tokenizer specific for DNA sequence.

This model is fine-tuned for predicting H3K27ac histone modification.


### How to use

Install the runtime library first:
```bash
pip install transformers
pip install causal-conv1d<=1.2.0
pip install mamba-ssm<2.0.0
```

Since `transformers` library (version < 4.43.0) does not provide a MambaForSequenceClassification function, we wrote a script to train Mamba model for sequence classification.  
An inference code can be found in our [GitHub](https://github.com/zhangtaolab/plant_DNA_LLMs).  
Note that Plant DNAMamba model requires NVIDIA GPU to run.


### Training data
We use a custom MambaForSequenceClassification script to fine-tune the model.  
Detailed training procedure can be found in our manuscript.


#### Hardware
Model was trained on a NVIDIA GTX4090 GPU (24 GB).