Instructions to use Taykhoom/RNA-MSM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/RNA-MSM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/RNA-MSM", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/RNA-MSM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Hidden-state representations verified identical (max abs diff = 0.00, exact match) to
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the reference implementation at all 11 representation levels (embedding + 10 transformer
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layers), both on padded and unpadded batches. Verified on GPU with PyTorch 2.7 /
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CUDA 12.
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## Related Models
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Hidden-state representations verified identical (max abs diff = 0.00, exact match) to
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the reference implementation at all 11 representation levels (embedding + 10 transformer
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layers), both on padded and unpadded batches. Verified on GPU with PyTorch 2.7 /
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CUDA 12.9.
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## Related Models
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