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 Canada, in February 2024. It comes out of academia,Academia,Industry,Academia,Research collective.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Its starting point was ESM2-650M — most models at this scale are adapted from an existing base rather than built from nothing.
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
When was Structure-Informed Protein Language Model released?
Structure-Informed Protein Language Model 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.
What is Structure-Informed Protein Language Model used for?
Structure-Informed Protein Language Model works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Structure-Informed Protein Language Model?
None. Structure-Informed Protein Language Model 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 Structure-Informed Protein Language Model open source?
The licensing for Structure-Informed Protein Language Model was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Structure-Informed Protein Language Model have?
Structure-Informed Protein Language Model has 650M parameters. 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.
Who created Structure-Informed Protein Language Model?
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, based in Canada, 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.