HJRSS
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 Washington,Microsoft
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 1 September 2021
- Authors
- Sanaa Mansoor, Minkyung Baek, Umesh Madan, Eric Horvitz
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design
- Base model
- ESM1b
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
- 16M
- Training data
- tokens
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA V100
- Chips used
- 1
- Power draw
- 333 W
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
- 21
Sources
Where this record came from and when it was last checked.
- Reference
- Toward More General Embeddings for Protein Design: Harnessing Joint Representations of Sequence and Structure
- Last updated
- 1 December 2025
What the numbers mean
Background
HJRSS was published by University of Washington,Microsoft, in the country recorded as United States of America, during September 2021. The category the publisher falls under is academia,Industry.
It works in the domain of Biology, and is recorded as performing the task of protein design.
Its starting point was an existing base model, ESM1b. 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
HJRSS — common questions
HJRSS— 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.
HJRSS— 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.
HJRSS— how many parameters does it have?
It has a parameter count of 16M. 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.
HJRSS— who created it?
It was published by University of Washington,Microsoft, based in United States of America, an organisation categorised as academia,Industry.
HJRSS— when was it released?
It was published in September 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
HJRSS— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein design. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.