Instructions to use monsoon-nlp/dna-blockdiff-papaya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monsoon-nlp/dna-blockdiff-papaya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="monsoon-nlp/dna-blockdiff-papaya", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("monsoon-nlp/dna-blockdiff-papaya", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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# DNA and Block Diffusion
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Using the
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[AgroNT](https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b)'s
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Took [dna-blockdiff-2](https://huggingface.co/monsoon-nlp/dna-blockdiff-2) weights,
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trained on [Papaya genome](https://huggingface.co/datasets/monsoon-nlp/wheat-bees) for one epoch. Output tokens are restricted to avoid outputting single-nucleotide or N (unknown nucleotide) tokens.
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# DNA and Block Diffusion
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Using the **Block Diffusion** architecture ([code](https://github.com/kuleshov-group/bd3lms), [paper](https://arxiv.org/abs/2503.09573)) and
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[AgroNT](https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b)'s 6-nucleotide tokens (4^6 = 4,096).
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Took [dna-blockdiff-2](https://huggingface.co/monsoon-nlp/dna-blockdiff-2) weights,
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trained on [Papaya genome](https://huggingface.co/datasets/monsoon-nlp/wheat-bees) for one epoch. Output tokens are restricted to avoid outputting single-nucleotide or N (unknown nucleotide) tokens.
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