CPCProt
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
- Organisation type
- Academia
- Country
- Canada
- Published
- 10 November 2020
- Authors
- Amy X. Lu, Haoran Zhang, Marzyeh Ghassemi, Alan Moses
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
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.
- Parameters
- 1.7M
- Training data
- 2,963,049,428 tokens
- Epochs
- 19
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 95
Sources
Where this record came from and when it was last checked.
- Reference
- Self-Supervised Contrastive Learning of Protein Representations By Mutual Information Maximization
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
CPCProt was published by University of Toronto, in the country recorded as Canada, during November 2020. The publishing organisation is categorised as academia.
It works in the domain of Biology, and is recorded as performing the task of protein or nucleotide language model (pLM/nLM).
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training consumed a corpus of around 2,963,049,428 tokens of text.
Answers
CPCProt — common questions
CPCProt— how many parameters does it have?
It has a parameter count of 1.7M. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
CPCProt— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
CPCProt— when was it released?
It was published in November 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
CPCProt— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein or nucleotide language model (pLM/nLM). 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.
CPCProt— 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.
CPCProt— 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.