MIF-ST
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
- Microsoft Research,OpenBioML,University of Chicago
- Organisation type
- Industry,Research collective,Academia
- Country
- United States of America, Multinational
- Published
- 26 October 2022
- Authors
- Kevin K Yang, Niccolò Zanichelli, Hugh Yeh
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein generation
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
42M × 300 = 42,000,000 × 300 = 12,600,000,000 = 1.26e10 datapoints
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.5 × 10²² FLOP
- How it was established
- Hardware
1. Hardware setup: 200x NVIDIA V100 GPUs with 1.30×10^14 FLOP/s per GPU 2. Training duration: Estimated 4 weeks based on "multiple weeks" phrase (4 weeks × 7 days × 24 hours × 3600 seconds = 2,419,200 seconds) 3. Utilization rate: 40% 4. Calculation: 1.30×10^14 FLOP/s × 200 GPUs × 2.4192×10^6 seconds × 0.4 = 2.5×10^22 FLOP
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
- 113
Sources
Where this record came from and when it was last checked.
- Reference
- Masked inverse folding with sequence transfer for protein representation learning
- Last updated
- 1 January 2026
What the numbers mean
About this model
MIF-ST was published by Microsoft Research,OpenBioML,University of Chicago, in United States of America, in October 2022. industry,Research collective,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing proteins, Protein generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 2.5 × 10²² FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
MIF-ST — common questions
What is MIF-ST used for?
MIF-ST works in Biology, and is recorded as handling proteins, Protein generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train MIF-ST?
Around 2.5 × 10²² FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run MIF-ST?
None. MIF-ST 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 MIF-ST open source?
The licensing for MIF-ST 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 MIF-ST have?
No parameter count has been published for MIF-ST, which is why no memory or speed figure appears on this page.
Who created MIF-ST?
MIF-ST was published by Microsoft Research,OpenBioML,University of Chicago, based in United States of America, categorised as industry,Research collective,Academia.
When was MIF-ST released?
MIF-ST was published in October 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.
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.