TranceptEve
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
- University of Oxford,Harvard Medical School
- Organisation type
- Academia,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 10 December 2022
- Authors
- Pascal Notin, Lood Van Niekerk, Aaron W Kollasch, Daniel Ritter, Yarin Gal, Debora S. Marks
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein pathogenicity prediction
- Base model
- Tranception
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
- tokens
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
- Unreleased
- Training code
- Unreleased
https://www.pascalnotin.com/publication/trancepteve/
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
"Besides its broader application scope, it achieves state-of- the-art performance for mutation effects prediction, both in terms of correlation with experimental assays and with clinical annotations from ClinVar."
Sources
Where this record came from and when it was last checked.
- Reference
- TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction
- Last updated
- 28 November 2025
What the numbers mean
About this model
TranceptEve was published by University of Oxford,Harvard Medical School, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during December 2022. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of proteins, Protein pathogenicity prediction.
Rather than being trained from scratch, it is derived from Tranception. That is the usual way a specialised model is produced.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Its inclusion criterion: sOTA improvement.
Answers
TranceptEve — common questions
TranceptEve— what GPU do I need to run it?
None. This 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.
TranceptEve— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
TranceptEve— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
TranceptEve— who created it?
It was published by University of Oxford,Harvard Medical School, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia,Academia.
TranceptEve— when was it released?
It was published in December 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.
TranceptEve— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of proteins, Protein pathogenicity prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.