scFormer
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 Toronto,Vector Institute,University Health Network,Microsoft Research
- Organisation type
- Academia,Academia,Industry
- Country
- Canada, United States of America
- Published
- 22 November 2022
- Authors
- Haotian Cui, Chloe Wang, Hassaan Maan, Nan Duan, Bo Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Representation learning
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
- Epochs
- 30
Each cell has M genes, not specified precisely but set to 1200 for UMAP plots. Total cells: 3005+7982+17001=27988 Estimated training tokens: 27988*1200=33585600
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 10
Sources
Where this record came from and when it was last checked.
- Reference
- scFormer: A Universal Representation Learning Approach for Single-Cell Data Using Transformers
- Last updated
- 1 December 2025
What the numbers mean
What this model is
scFormer was published by University of Toronto,Vector Institute,University Health Network,Microsoft Research, in the country recorded as Canada, during November 2022. It comes out of an organisation categorised as academia,Academia,Industry.
It works in the domain of Biology, and is recorded as performing the task of representation learning.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
scFormer — common questions
scFormer— 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.
scFormer— who created it?
It was published by University of Toronto,Vector Institute,University Health Network,Microsoft Research, based in Canada, an organisation categorised as academia,Academia,Industry.
scFormer— when was it released?
It was published in November 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.
scFormer— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of representation learning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
scFormer— 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.
scFormer— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.