Structure-Informed Protein Language Model
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
- Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal,IBM Research,HEC Montreal,CIFAR AI Research
- Organisation type
- Academia,Academia,Industry,Academia,Research collective
- Country
- Canada, United States of America
- Published
- 7 February 2024
- Authors
- Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, Jian Tang
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)
- Base model
- ESM2-650M
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
- 650M
- Training data
- tokens
- Epochs
- 50
Fine-tuning: 12,312 proteins × 300 residues = 3.7 × 10^6 tokens
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
- 15
Sources
Where this record came from and when it was last checked.
- Reference
- Structure-Informed Protein Language Model
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Structure-Informed Protein Language Model was published by Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal,IBM Research,HEC Montreal,CIFAR AI Research, in the country recorded as Canada, during February 2024. It comes out of an organisation categorised as academia,Academia,Industry,Academia,Research collective.
It works in the domain of Biology, and is recorded as performing the task of protein or nucleotide language model (pLM/nLM).
Its starting point was an existing base model, ESM2-650M. That is why it shares the base model's general shape and size.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Structure-Informed Protein Language Model — common questions
Structure-Informed Protein Language Model— when was it released?
It was published in February 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Structure-Informed Protein Language Model— 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). These are the areas it was designed around; they describe intent rather than a hard boundary.
Structure-Informed Protein Language Model— 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.
Structure-Informed Protein Language Model— 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.
Structure-Informed Protein Language Model— how many parameters does it have?
It has a parameter count of 650M. 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.
Structure-Informed Protein Language Model— who created it?
It was published by Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal,IBM Research,HEC Montreal,CIFAR AI Research, based in Canada, an organisation categorised as academia,Academia,Industry,Academia,Research collective.
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.