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README.md CHANGED
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- ---
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- license: cc-by-nc-sa-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: AAAACATAATAATTTGCCGACTTACTCACCCTGTGATTAATCTATTTTCACTGTGTAGTAAGTAGAGAGTGTTACTTACTACAGTATCTATTTTTGTTTGGATGTTTGCCGTGGACAAGTGCTAACTGTCAAAACCCGTTTTGACCTTAAACCCAGCAATAATAATAATGTAAAACTCCATTGGGCAGTGCAACCTACTCCTCACATATTATATTATAATTCCTAAACCTTGATCAGTTAAATTAATAGCTCTGTTCCCTGTGGCTTTATATAAACACCATGGTTGTCAGCAGTTCAGCA
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+ tags:
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+ - DNA
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+ - biology
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+ - genomics
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+ ---
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+ # Plant foundation DNA large language models
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+
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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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+
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+
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+ **Developed by:** zhangtaolab
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+
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+ ### Model Sources
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+
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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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+
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+ ### Architecture
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+
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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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+
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+ This model is fine-tuned for predicting active core promoters.
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+
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+ ### How to use
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+
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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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+
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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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+
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+
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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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+
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+
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+ #### Hardware
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+ Model was trained on a NVIDIA GTX4090 GPU (24 GB).
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