Evo2mind Vo Github

Leo Migdal

-Nov 18, 2025, 4:45 AM

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Workflow Engine with Dynamic Flow and Database Centric capabilities Evo 2 is a state of the art DNA language model for long context modeling and design. Evo 2 models DNA sequences at single-nucleotide resolution at up to 1 million base pair context length using the StripedHyena 2 architecture. Evo 2 was pretrained using Savanna. Evo 2 was trained autoregressively on OpenGenome2, a dataset containing 8.8 trillion tokens from all domains of life. We describe Evo 2 in the preprint: "Genome modeling and design across all domains of life with Evo 2".

This repo is for running Evo 2 locally for inference or generation, using our Vortex inference code. For training and finetuning, see the section here. You can run Evo 2 without any installation using the Nvidia Hosted API. You can also self-host an instance using Nvidia NIM. See the Nvidia NIM section for more information. Evo 2 is built on the Vortex inference repo, see the Vortex github for more details and Docker option.

FP8 requirements: The 40B and 1B models require FP8 for numerical accuracy, and low accuracy has been reported on Blackwell hardware or without FP8. The 7B models can run without FP8 by modifying the config. Always validate model outputs after configuration changes or on different hardware by using the tests. Workflow Engine with Dynamic Flow and Database Centric capabilities This repo contains the content that's used to create the Hugging Face course. The course teaches you about applying Transformers to various tasks in natural language processing and beyond.

Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It's completely free and open-source! As part of our mission to democratise machine learning, we'd love to have the course available in many more languages! Please follow the steps below if you'd like to help translate the course into your language 🙏. To get started, navigate to the Issues page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the Translation template from the New issue button.

Once an issue is created, post a comment to indicate which chapters you'd like to work on and we'll add your name to the list. Since it can be difficult to discuss translation details quickly over GitHub issues, we have created dedicated channels for each language on our Discord server. If you'd like to join, follow the instructions at this channel 👉: https://discord.gg/JfAtkvEtRb There was an error while loading. Please reload this page. Building an AI project that can be anything and everything

Evo 2 is a state of the art DNA language model for long context modeling and design. Evo 2 models DNA sequences at single-nucleotide resolution at up to 1 million base pair context length using the StripedHyena 2 architecture. Evo 2 was pretrained using Savanna. Evo 2 was trained autoregressively on OpenGenome2, a dataset containing 8.8 trillion tokens from all domains of life. We describe Evo 2 in the preprint: "Genome modeling and design across all domains of life with Evo 2". Evo 2 is based on StripedHyena 2 which requires python>=3.11.

Evo 2 uses Trasnformer Engine FP8 for some layers which requires an H100 (or other GPU with compute capability ≥8.9). We are actively investigating ways to avoid this requirement. You can also run Evo 2 without any installation using the Nvidia Hosted API. Please clone and install from GitHub. We recommend using a new conda environment with python>=3.11. There was an error while loading.

Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs. To my knowledge, the only available code to fine-tune evo2 is provided via the NVIDIA BioNeMo Framework (i.e, here). Here, I describe how to install this framework on a cluster with access to Apptainer, while also including a step to step guidance on how to run the actual code (see Evo2 fine-tuning tutorial... As a reference, cluster I refer to the CRG cluster, which has Apptainer installed.

We first need to set-up the NVIDIA BioNeMo Framework , which includes all the API (e.g, scripts) to fine-tune evo2. Fortunately, the Bionemo framework is provided as a Docker container, including all the necessary dependencies. Although most clusters do not have Docker installed, we can use this container via Apptainer (Singularity), which should be available in most clusters (see here for more info on how to run docker Bionemo... In order to access this (Bionemo framework) container, we need to perform the following steps: Create a free account on NGC (just insert your email address to create a new account if don't have one) Open Source Project Management based on Scrum

Evo2mind Vo Github

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