AminoBert
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Harvard Medical School,Nabla Bio,Columbia University
- Organisation type
- Academia,Industry,Academia
- Country
- United States of America
- Published
- 3 October 2022
- Authors
- Ratul Chowdhury, Nazim Bouatta, Surojit Biswas, Christina Floristean, Anant Kharkar, Koushik Roy, Charlotte Rochereau, Gustaf Ahdritz, Joanna Zhang, George M. Church, Peter K. Sorger, Mohammed AlQuraishi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 78,000,000,000 tokens
260,000,000 sequences × 300 tokens/sequence = 78,000,000,000 tokens (7.8 × 10¹⁰)
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Hosted access (no API)
- Training code
- Open (non-commercial)
RGN2 is available freely as a standalone tool from https://github.com/aqlaboratory/rgn2. Users can make structure predictions using a Python3-based web user interface by uploading the protein sequence in FASTA format (https://colab.research.google.com/github/aqlaboratory/rgn2/blob/master/rgn2_prediction.ipynb). no clear license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 343
Sources
Where this record came from and when it was last checked.
- Reference
- Single-sequence protein structure prediction using a language model and deep learning
- Last updated
- 28 November 2025
What the numbers mean
What this model is
AminoBert was published by Harvard Medical School,Nabla Bio,Columbia University, in United States of America, in October 2022. The organisation is categorised as academia,Industry,Academia.
It works in Biology, and is recorded as doing protein folding prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Around 78,000,000,000 tokens went into training it.
Answers
AminoBert — common questions
How many parameters does AminoBert have?
No parameter count has been published for AminoBert, which is why no memory or speed figure appears on this page.
Who created AminoBert?
AminoBert was published by Harvard Medical School,Nabla Bio,Columbia University, based in United States of America, categorised as academia,Industry,Academia.
When was AminoBert released?
AminoBert was published in October 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is AminoBert used for?
AminoBert works in Biology, and is recorded as handling protein folding prediction. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run AminoBert?
None. AminoBert is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is AminoBert open source?
No. AminoBert has not had its weights published, so it exists only as a service controlled by its owner.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.