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--- |
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license: cc-by-nc-sa-4.0 |
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widget: |
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- text: >- |
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ATGCTTTGTGCTGGCATGCCATGTCATGTTGCATCAGCATTTTCTTTATATTTTCTTTCTGATCTTTTCTGTGCTTCAAAACCTCATTCGTCTGTTTCCTTCTTTCCTACCAGTTATCCACAGACACACCCTATTAGAGTACTCCATGCTTGTTTATTTCTTTTGTCAAATAGAAGGGTCTTTTCTCCTCGCTTTAGTAGGGAATGTTGTCTTCCTCATTTGGGAAAAAAAAATTGTTCCTGCAGTTATGCCAGTCATGGGCTCTTTTTGATTGGTTGCATTGATATATTGTCTACCCCGTTTTCTGTAGGAATGATACATATTCCTGATCCTGAGCCTATTTGA |
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tags: |
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- DNA |
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- biology |
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- genomics |
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datasets: |
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- zhangtaolab/plant-multi-species-lncRNAs |
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metrics: |
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- accuracy |
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base_model: |
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- zhangtaolab/plant-dnamamba-BPE |
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--- |
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# Plant foundation DNA large language models |
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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. |
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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. |
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**Developed by:** zhangtaolab |
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### Model Sources |
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- **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs) |
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- **Manuscript:** [Versatile applications of foundation DNA large language models in plant genomes]() |
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### Architecture |
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The model is trained based on the State-Space Mamba-130m model with modified tokenizer specific for DNA sequence. |
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This model is fine-tuned for predicting lncRNAs. |
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### How to use |
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Install the runtime library first: |
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```bash |
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pip install transformers |
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pip install causal-conv1d<=1.2.0 |
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pip install mamba-ssm<2.0.0 |
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``` |
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Since `transformers` library (version < 4.43.0) does not provide a MambaForSequenceClassification function, we wrote a script to train Mamba model for sequence classification. |
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An inference code can be found in our [GitHub](https://github.com/zhangtaolab/plant_DNA_LLMs). |
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Note that Plant DNAMamba model requires NVIDIA GPU to run. |
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### Training data |
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We use a custom MambaForSequenceClassification script to fine-tune the model. |
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Detailed training procedure can be found in our manuscript. |
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#### Hardware |
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Model was trained on a NVIDIA GTX4090 GPU (24 GB). |
