license: apache-2.0
tags:
- stripedhyena
- long context
- deep signal processing
- hybrid
- biology
- genomics
Evo-1 (Phase 1)
About
Evo is a biological foundation model capable of long-context modeling and design.
Evo uses the StripedHyena architecture to enable modeling of sequences at a single-nucleotide, byte-level resolution with near-linear scaling of compute and memory relative to context length. Evo has 7 billion parameters and is trained on OpenGenome, a prokaryotic whole-genome dataset containing ~300 billion tokens.
Technical details about Evo can be found in our preprint and our accompanying blog posts. Evo was collaboratively developed by the Arc Institute and TogetherAI.
As part of our commitment to open science, we release weights of 15 intermediate pretraining checkpoints for phase 1 and phase 2 of pretraining. The checkpoints are available as branches of the corresponding HuggingFace repository.
Evo-1 (Phase 1) is our first model in the Evo family, trained at a context length of 8k.
| Checkpoint Name | Description |
|---|---|
evo-1-phase-1 |
A model pretrained with 8,192 context. We use this model as the base model for molecular-scale finetuning tasks. |
evo-1-phase-2 |
A model pretrained with 131,072 context using evo-1-phase-1 as the initialization. We use this model to reason about and generate sequences at the genome scale. |
Model Architecture
StripedHyena is a deep signal processing, hybrid architecture composed of multi-head attention and gated convolutions arranged in Hyena blocks, improving over decoder-only Transformers.
Some highlights of the architecture:
- Efficient autoregressive generation via a recurrent mode (>500k generation with a single 80GB GPU)
- Significantly faster training and finetuning at long context (>3x at 131k)
- Improved scaling laws over state-of-the-art architectures (e.g., Transformer++) on both natural language and biological sequences.
- Robust to training beyond the compute-optimal frontier e.g., training way beyond Chinchilla-optimal token amounts (see preprint for details -- more details to come)
Example
Parametrization for Inference and Finetuning
One of the advantages of deep signal processing models is their flexibility. Different parametrizations of convolutions can be used depending on the memory, expressivity and causality requirements of pretraining, finetuning or inference workloads.
The main classes are:
- Modal: unconstrained poles (reference, reference), or constrained poles (reference, reference)
- Canonical / Rational: TBA
- Hypernetworks: hypernetwork (reference), modulated hypernetwork (reference).
- Explicit: modulated explicit (reference)
StripedHyena is a mixed precision model. Make sure to keep your poles and residues in float32 precision, especially for longer prompts or training.
Disclaimer
To use StripedHyena outside of the playground, you will need to install custom kernels. Please follow the instructions from the standalone repository.