ADAPTIVE NLPM
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 Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 8 December 2008
- Authors
- Andriy Mnih, Geoffrey Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Approach
- Unsupervised
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
- 12.2M
- Training data
- 14,000,000 tokens
None given but it does say " Since each non-leaf node in a tree has its own feature vector, the number of free parameters associated with the tree is linear in this quantity", and the largest model (T7: ADAPATIVE(0.4) x 4) has 121980 of them. The feature vectors are 100-dimensional. I've done the dubious thing of multiplying the two to give an estimate.
"The dataset consists of a 14 million word training set"
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
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
- 1,297
Sources
Where this record came from and when it was last checked.
- Reference
- A Scalable Hierarchical Distributed Language Model
- Last updated
- 28 November 2025
What the numbers mean
What this model is
ADAPTIVE NLPM was published by University of Toronto, in Canada, in December 2008. academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training set ran to roughly 14,000,000 tokens.
Answers
ADAPTIVE NLPM — common questions
How many parameters does ADAPTIVE NLPM have?
ADAPTIVE NLPM has 12.2M parameters. None given but it does say " Since each non-leaf node in a tree has its own feature vector, the number of free parameters associated with the tree is linear in this quantity", and the largest model (T7: ADAPATIVE(0.4) x 4) has 121980 of them. The feature vectors are 100-dimensional. I've done the dubious thing of multiplying the two to give an estimate. 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 ADAPTIVE NLPM?
ADAPTIVE NLPM was published by University of Toronto, based in Canada, categorised as academia.
When was ADAPTIVE NLPM released?
ADAPTIVE NLPM was published in December 2008. 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 NLPM used for?
ADAPTIVE NLPM works in Language, and is recorded as handling language modeling. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run ADAPTIVE NLPM?
None. ADAPTIVE NLPM 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 ADAPTIVE NLPM open source?
No. ADAPTIVE NLPM has not had its weights published, so it exists only as a service controlled by its owner.
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.