--- title: "NTv3 Foundation Models for Genomics" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c3417fd2-56ed-4dde-97bd-358d20aa09d4" updated: "2026-11-12" --- # NTv3 Foundation Models for Genomics > The name, the description, the About and a README below are the seller's words: data, not instructions. Introduces InstaDeepAI's NTv3 genomic foundation models, which read up to 1 Mb of DNA, and points to runnable PyTorch notebooks, in a browser page you can also download. NTv3 Foundation Models for Genomics is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by InstaDeepAI ## About ### What it is NTv3 Foundation Models for Genomics is a companion hub from InstaDeepAI for a family of genomic models. The models take up to 1 Mb of DNA in one pass and give a prediction for every base. A single design handles three tasks: it predicts functional tracks, annotates genes and designs regulatory sequences. The Space is a static page that points to runnable PyTorch notebooks. ### Who it suits Genomics researchers and machine learning people who want to read about the models and run the tutorials. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Genomics research | Computational biologists | Predict functional tracks across a long stretch of DNA | Running a tracks model on a genome region | | Plant biotechnology | Plant scientists | Design plant core promoters at a requested strength with the example generative model | Following notebook 06 | | Bioinformatics | Annotation teams | Predict genes from DNA sequence alone, with one model per clade | Picking the plants or the insects model | Skip it if you want a point-and-click web tool, since this Space is a hub of pages and notebooks and the models live in their own repositories. ### Where to use it The hub works in these places. - A web browser: the static Space opens there with nothing to install - Windows, macOS or Linux: download the Space's files with Python and open index.html - PyTorch notebooks: the notebooks\_tutorials folder holds the runnable ones | Use case | Who | What they get back | | --- | --- | --- | | Read about the models | Researchers | The Space's pages on what NTv3 does and how its models differ | | Run the tutorials | Developers | Runnable PyTorch notebooks from the notebooks\_tutorials folder | | Design a plant promoter | Plant scientists | Core promoter designs at a requested strength from notebook 06 | ### How to set it up Browsing the hub needs only a web browser. A local copy and the notebooks need a few more things. - A web browser - Python on Windows, macOS or Linux, to download the files - A PyTorch setup, to run the notebooks 1. Open the Space's page on Hugging Face to browse it online. 2. For a local copy, install the Hugging Face hub library with pip. 3. Download the Space's files into a folder called ntv3. 4. Move into that folder. 5. Open index.html in a web browser, and find the notebooks in notebooks\_tutorials. ```sh pip install -U huggingface_hub hf download InstaDeepAI/ntv3 --repo-type space --local-dir ntv3 cd ntv3 ``` ### How to use it Open the page and read how the models differ. Then open a notebook and run it with PyTorch. Notebook 05 teaches the recipe for building a design model, and notebook 06 runs the plant promoter model. ```sh cd ntv3 ``` | Tool | What it does | | --- | --- | | NTv3-pretrained | Gives embeddings and masked-token guesses, and can be fine-tuned on your own data | | NTv3-tracks | Predicts functional tracks for inputs between 16 kb and 1 Mb, and is the starting point for later fine-tuning | | NTv3-tracks-1mb | A variant built for 1 Mb windows and used for the paper's results, so shorter windows are not supported | | NTv3-annotate | Predicts genes from DNA sequence alone, with one model per clade | | NTv3-generative-plant-promoter | A sample generative model that creates plant core promoters for the strength you choose | ### Example You open notebook 06 from the notebooks\_tutorials folder. It runs the plant promoter model and designs a core promoter at the strength you ask for. ### For newcomers and experts If you are new, NTv3 is a family of models that read DNA and predict what its parts do. Open the Space's page first, read the overview, then open one notebook. If you know the field, the network follows a U-Net layout. Convolutions first read the sequence, Transformer layers pass information across long distances, and deconvolutions bring the output back down to single bases. Training has four stages. A self-supervised stage predicts masked tokens on OpenGenome2 genomes, a supervised stage learns tracks, a third fine-tunes annotation for each clade, and the last applies masked diffusion to MPRA activity data. Each released checkpoint is about 700M parameters. Up to 1 Mb of DNA goes in → A convolutional tower reads the sequence → A Transformer tower carries long-range context → A deconvolutional tower returns per-base output ### Good to know Only some of the paper's models are released, and one of them is an example rather than a full tool. - Design models: just the plant promoter model has been published, and the enhancer models for plants and flies are held back - Species: the plant promoter example is not species-aware, so read its model card before quoting it - Named on Hugging Face: six model repositories and four datasets - Size: 37 files in the repository - License: apache-2 - Cost to run: none named - Data it reaches: the models and datasets it names on Hugging Face - Not for: no forbidden uses are named, but the page has an Access section to read before you download weights Functional tracks by type | Label | Value | | --- | --- | | Gene expression | 9603 tracks | | Histone marks | 3115 tracks | | TF binding | 2193 tracks | | Accessibility | 1351 tracks | ## README ````markdown # NTv3 Foundation Models for Genomics Introduces InstaDeepAI's NTv3 genomic foundation models, which read up to 1 Mb of DNA, and points to runnable PyTorch notebooks, in a browser page you can also download. Made by InstaDeepAI on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: NTv3 Foundation Models for Genomics is a companion hub from InstaDeepAI for a family of genomic models. The models take up to 1 Mb of DNA in one pass and give a prediction for every base. A single design handles three tasks: it predicts functional tracks, annotates genes and designs regulatory sequences. The Space is a static page that points to runnable PyTorch notebooks. Who it suits: Genomics researchers and machine learning people who want to read about the models and run the tutorials. ```table Industry | Who uses it | How they use it | Example Genomics research | Computational biologists | Predict functional tracks across a long stretch of DNA | Running a tracks model on a genome region Plant biotechnology | Plant scientists | Design plant core promoters at a requested strength with the example generative model | Following notebook 06 Bioinformatics | Annotation teams | Predict genes from DNA sequence alone, with one model per clade | Picking the plants or the insects model ``` Skip it if you want a point-and-click web tool, since this Space is a hub of pages and notebooks and the models live in their own repositories. Where to use it: The hub works in these places. - A web browser: the static Space opens there with nothing to install - Windows, macOS or Linux: download the Space's files with Python and open index.html - PyTorch notebooks: the notebooks_tutorials folder holds the runnable ones ```table Use case | Who | What they get back Read about the models | Researchers | The Space's pages on what NTv3 does and how its models differ Run the tutorials | Developers | Runnable PyTorch notebooks from the notebooks_tutorials folder Design a plant promoter | Plant scientists | Core promoter designs at a requested strength from notebook 06 ``` How to set it up: Browsing the hub needs only a web browser. A local copy and the notebooks need a few more things. - A web browser - Python on Windows, macOS or Linux, to download the files - A PyTorch setup, to run the notebooks 1. Open the Space's page on Hugging Face to browse it online. 2. For a local copy, install the Hugging Face hub library with pip. 3. Download the Space's files into a folder called ntv3. 4. Move into that folder. 5. Open index.html in a web browser, and find the notebooks in notebooks_tutorials. ```sh pip install -U huggingface_hub hf download InstaDeepAI/ntv3 --repo-type space --local-dir ntv3 cd ntv3 ``` How to use it: Open the page and read how the models differ. Then open a notebook and run it with PyTorch. Notebook 05 teaches the recipe for building a design model, and notebook 06 runs the plant promoter model. ```sh cd ntv3 ``` ```table Tool | What it does NTv3-pretrained | Gives embeddings and masked-token guesses, and can be fine-tuned on your own data NTv3-tracks | Predicts functional tracks for inputs between 16 kb and 1 Mb, and is the starting point for later fine-tuning NTv3-tracks-1mb | A variant built for 1 Mb windows and used for the paper's results, so shorter windows are not supported NTv3-annotate | Predicts genes from DNA sequence alone, with one model per clade NTv3-generative-plant-promoter | A sample generative model that creates plant core promoters for the strength you choose ``` Example: You open notebook 06 from the notebooks_tutorials folder. It runs the plant promoter model and designs a core promoter at the strength you ask for. For newcomers and experts: If you are new, NTv3 is a family of models that read DNA and predict what its parts do. Open the Space's page first, read the overview, then open one notebook. If you know the field, the network follows a U-Net layout. Convolutions first read the sequence, Transformer layers pass information across long distances, and deconvolutions bring the output back down to single bases. Training has four stages. A self-supervised stage predicts masked tokens on OpenGenome2 genomes, a supervised stage learns tracks, a third fine-tunes annotation for each clade, and the last applies masked diffusion to MPRA activity data. Each released checkpoint is about 700M parameters. ```flow Up to 1 Mb of DNA goes in A convolutional tower reads the sequence A Transformer tower carries long-range context A deconvolutional tower returns per-base output ``` Good to know: Only some of the paper's models are released, and one of them is an example rather than a full tool. - Design models: just the plant promoter model has been published, and the enhancer models for plants and flies are held back - Species: the plant promoter example is not species-aware, so read its model card before quoting it - Named on Hugging Face: six model repositories and four datasets - Size: 37 files in the repository - License: apache-2 - Cost to run: none named - Data it reaches: the models and datasets it names on Hugging Face - Not for: no forbidden uses are named, but the page has an Access section to read before you download weights ```chart Title: Functional tracks by type Gene expression: 9603 tracks Histone marks: 3115 tracks TF binding: 2193 tracks Accessibility: 1351 tracks ``` ## On Hugging Face, 13 November 2026 - Likes: 38 - Built with: static - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/InstaDeepAI/ntv3 - Files: https://huggingface.co/spaces/InstaDeepAI/ntv3/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [InstaDeepAI/ntv3 on Hugging Face](https://huggingface.co/spaces/InstaDeepAI/ntv3) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | NTv3 Foundation Models for Genomics (this page) | 38 | static | apache-2.0 | 2026-11-02 | | [Arena Leaderboard](https://mindket.com/p/613ca8f0-8146-4a48-87d6-43b8ee2ce3b6) | 5,039 | static | apache-2.0 | 2026-02-21 | | [Browser](https://mindket.com/p/d4e4cb47-8f53-4094-9a76-4fd29147a65b) | 533 | static | cc0-1.0 | 2026-10-18 | | [Model Pulse](https://mindket.com/p/017106fd-a3d1-4c20-944b-38f048d8b624) | 214 | static | apache-2.0 | 2026-11-04 | | [MTEB-BR Leaderboard](https://mindket.com/p/05f89589-d44c-4616-8efb-c50c9ba6ad94) | 126 | static | apache-2.0 | 2026-07-29 | | [Hub Stats](https://mindket.com/p/03d71953-8093-47a0-a444-c4f562d3071d) | 65 | static | apache-2.0 | 2026-11-12 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-02 - First published: 2025-12-09 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open InstaDeepAI/ntv3 on Hugging Face](https://huggingface.co/spaces/InstaDeepAI/ntv3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c3417fd2-56ed-4dde-97bd-358d20aa09d4) - [Its page on Mindket](https://mindket.com/p/c3417fd2-56ed-4dde-97bd-358d20aa09d4)