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title: Nvidia’s new DNA model learns what token prediction misses
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# Nvidia’s new DNA model learns what token prediction misses

**[The New Stack](https://daily.dev/sources/newstack)** · 3 min read · 0 upvotes · 0 comments

## Summary

Nvidia has released JEPA-DNA on Hugging Face, a genomic foundation model that combines masked language modeling (MLM) with a latent-space prediction objective inspired by Yann LeCun's JEPA architecture. Unlike conventional genomic models that rely solely on token reconstruction, JEPA-DNA also supervises the model's global sequence embedding in latent space, enabling it to capture broader functional meaning of DNA sequences rather than just local syntax. Built on top of DNABERT-2 (117M parameters), the model is available for non-commercial research and supports tasks like feature extraction, linear probing, continual pre-training, and zero-shot scoring of DNA sequence changes.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://thenewstack.io/nvidia-jepa-dna-genomics>

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Tags: [#ai](https://daily.dev/tags/ai), [#nvidia](https://daily.dev/tags/nvidia)

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