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--- |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- genomics |
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- dna |
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- generative |
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- ntv3 |
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- enhancer-generation |
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- mdlm |
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- diffusion |
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- conditional-generation |
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license: other |
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language: |
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- code |
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model_parameter_count: 658672910 |
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--- |
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## 🧬 NTv3: A Foundation Model for Genomics |
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NTv3 is a series of foundational models designed to understand and generate genomic sequences. It unifies representation learning, functional prediction, and controllable sequence generation within a single, efficient U-Net-like architecture. It also enables the modeling of long-range dependencies, up to 1 Mb of context, at nucleotide resolution. Pretrained on 9 trillion base pairs, NTv3 excels at functional-track prediction and genome annotation across 24 animal and plant species. It can also be fine-tuned into a controllable generative model for genomic sequence design. This is the **generative model** based on NTv3, capable of context-aware DNA sequence generation with desired activity levels.It builds on the post-trained NTv3 model with MDLM based fine-tuning.For more details, please refer to the [NTv3 paper](https://www.biorxiv.org/content/10.64898/2025.12.22.695963v1). |
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## ⚖️ License Summary |
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1. The Licensed Models are **only** available under this License for Non-Commercial Purposes. |
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2. You are permitted to reproduce, publish, share and adapt the Output generated by the Licensed Model only for Non-Commercial Purposes and in accordance with this License. |
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3. You may **not** use the Licensed Models or any of its Outputs in connection with: |
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1. any Commercial Purposes, unless agreed by Us under a separate licence; |
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2. to train, improve or otherwise influence the functionality or performance of any other third-party derivative model that is commercial or intended for a Commercial Purpose and is similar to the Licensed Models; |
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3. to create models distilled or derived from the Outputs of the Licensed Models, unless such models are for Non-Commercial Purposes and open-sourced under the same license as the Licensed Models; or |
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4. in violation of any applicable laws and regulations. |
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## 📋 Model Summary |
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- Architecture: Conditioned U-Net with adaptive layer norms + Transformer stack |
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- Training: Masked Discrete Language Modeling (MDLM) |
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- Conditioning: Species + Activity levels (0-4) |
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- Tokenizer: Character-level over A T C G N + special tokens |
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- Dependencies: transformers >= 4.55.0 |
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- Input size: Model trained on 4096bp sequences with 249bp generation length |
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- Note: Custom code → use `trust_remote_code=True` |
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