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---
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license: apache-2.0
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---
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## Model Overview
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PlantCaduceus is a DNA language model pre-trained on 16 Angiosperm genomes. Utilizing the [Caduceus](https://caduceus-dna.github.io/) and [Mamba](https://arxiv.org/abs/2312.00752) architectures and a masked language modeling objective, PlantCaduceus is designed to learn evolutionary conservation and DNA sequence grammar from 16 species spanning a history of 160 million years. We have trained a series of PlantCaduceus models with varying parameter sizes:
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- **[PlantCaduceus_l20](https://huggingface.co/kuleshov-group/PlantCaduceus_l20)**: 20 layers, 384 hidden size, 20M parameters
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- **[PlantCaduceus_l24](https://huggingface.co/kuleshov-group/PlantCaduceus_l24)**: 24 layers, 512 hidden size, 40M parameters
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- **[PlantCaduceus_l28](https://huggingface.co/kuleshov-group/PlantCaduceus_l28)**: 28 layers, 768 hidden size, 112M parameters
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- **[PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)**: 32 layers, 1024 hidden size, 225M parameters
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## How to use
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```python
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from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer
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import torch
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model_path = 'kuleshov-group/PlantCaduceus_l24'
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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model = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True, device_map=device)
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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sequence = "ATGCGTACGATCGTAG"
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encoding = tokenizer.encode_plus(
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sequence,
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return_tensors="pt",
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return_attention_mask=False,
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return_token_type_ids=False
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)
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input_ids = encoding["input_ids"].to(device)
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with torch.inference_mode():
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outputs = model(input_ids=input_ids, output_hidden_states=True)
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```
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## Citation
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```bibtex
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@article {Zhai2024.06.04.596709,
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author = {Zhai, Jingjing and Gokaslan, Aaron and Schiff, Yair and Berthel, Ana and Liu, Zong-Yan and Miller, Zachary R and Scheben, Armin and Stitzer, Michelle C and Romay, Cinta and Buckler, Edward S. and Kuleshov, Volodymyr},
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title = {Cross-species plant genomes modeling at single nucleotide resolution using a pre-trained DNA language model},
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elocation-id = {2024.06.04.596709},
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year = {2024},
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doi = {10.1101/2024.06.04.596709},
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URL = {https://www.biorxiv.org/content/early/2024/06/05/2024.06.04.596709},
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eprint = {https://www.biorxiv.org/content/early/2024/06/05/2024.06.04.596709.full.pdf},
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journal = {bioRxiv}
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}
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```
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## Contact
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Jingjing Zhai ([email protected]) |
