Post
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I ported Evo 2 inference to plain PyTorch and Transformers so the official checkpoints load through AutoModelForCausalLM with no Vortex, Transformer Engine, or custom kernels required.
What is included:
- GitHub repo with the port, a Vortex to HF converter, and a gene completion repro script: https://github.com/Aquiles-ai/Evo2-transformers
- Aquiles-ai/Evo2-1B-Base: the 1B base checkpoint (8k context) in Transformers format: Aquiles-ai/Evo2-1B-Base
- Aquiles-ai/Evo2-7B: the 7B checkpoint (1M context) in Transformers format: Aquiles-ai/Evo2-7B
Both repos vendor the modeling files, so loading needs trust_remote_code=True. The tokenizer matches the original byte level behavior, including the vortex_tokenize and vortex_detokenize helpers.
One honest note: this port is less efficient than the original Vortex stack. It has no FlashAttention, no FP8 path, and no recurrent state decoding, so generation recomputes the prefix and long contexts are slow. For large scale or 1M context work, use the original implementation or NVIDIA NIM.
All credit for the model, data, and research goes to the Evo 2 team at Arc Institute and collaborators. Original project: https://github.com/ArcInstitute/evo2. Paper: https://www.nature.com/articles/s41586-026-10176-5
Feedback and corrections are welcome as GitHub issues on the port repo.
What is included:
- GitHub repo with the port, a Vortex to HF converter, and a gene completion repro script: https://github.com/Aquiles-ai/Evo2-transformers
- Aquiles-ai/Evo2-1B-Base: the 1B base checkpoint (8k context) in Transformers format: Aquiles-ai/Evo2-1B-Base
- Aquiles-ai/Evo2-7B: the 7B checkpoint (1M context) in Transformers format: Aquiles-ai/Evo2-7B
Both repos vendor the modeling files, so loading needs trust_remote_code=True. The tokenizer matches the original byte level behavior, including the vortex_tokenize and vortex_detokenize helpers.
One honest note: this port is less efficient than the original Vortex stack. It has no FlashAttention, no FP8 path, and no recurrent state decoding, so generation recomputes the prefix and long contexts are slow. For large scale or 1M context work, use the original implementation or NVIDIA NIM.
All credit for the model, data, and research goes to the Evo 2 team at Arc Institute and collaborators. Original project: https://github.com/ArcInstitute/evo2. Paper: https://www.nature.com/articles/s41586-026-10176-5
Feedback and corrections are welcome as GitHub issues on the port repo.