Adaptive Subgrad
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
- Technion - Israel Institute of Technology,Google,University of California (UC) Berkeley
- Organisation type
- Academia,Industry,Academia
- Country
- Israel, United States of America
- Published
- 3 October 2011
- Authors
- J Duchi, E Hazan, Y Singer
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text classification
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
- 800,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 11,018
Sources
Where this record came from and when it was last checked.
- Reference
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
Adaptive Subgrad was published by Technion - Israel Institute of Technology,Google,University of California (UC) Berkeley, in Israel, in October 2011. The organisation is categorised as academia,Industry,Academia.
It works in Language, and is recorded as doing text classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training set ran to roughly 800,000 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Adaptive Subgrad — common questions
Is Adaptive Subgrad open source?
The licensing for Adaptive Subgrad 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 Adaptive Subgrad have?
No parameter count has been published for Adaptive Subgrad, which is why no memory or speed figure appears on this page.
Who created Adaptive Subgrad?
Adaptive Subgrad was published by Technion - Israel Institute of Technology,Google,University of California (UC) Berkeley, based in Israel, categorised as academia,Industry,Academia.
When was Adaptive Subgrad released?
Adaptive Subgrad was published in October 2011. 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 Adaptive Subgrad used for?
Adaptive Subgrad works in Language, and is recorded as handling text classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Adaptive Subgrad?
None. Adaptive Subgrad 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.
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.