Mogrifier (d2, MoS2, MC) + dynamic eval
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
- DeepMind,University of Oxford
- Organisation type
- Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 4 September 2019
- Authors
- Gábor Melis, Tomáš Kočiský, Phil Blunsom
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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
- 35M
- Training data
- 2,000,000 tokens
- Epochs
- 145
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
- Training code
- Unreleased
Github has dead link: https://github.com/google-deepmind/lamb/blob/master/experiment/mogrifier/README.md
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
- SOTA improvement
- Record confidence
- Confident
- Citations
- 109
- Benchmark data
- Mogrifier (d2, MoS2, MC) + dynamic eval
"We establish a new state of the art on all datasets with the exception of Enwik8"
Sources
Where this record came from and when it was last checked.
- Reference
- Mogrifier LSTM
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Mogrifier (d2, MoS2, MC) + dynamic eval was published by DeepMind,University of Oxford, in United Kingdom of Great Britain and Northern Ireland, in September 2019. industry,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
It was trained on about 2,000,000 tokens of text.
Its inclusion criterion is sOTA improvement.
Answers
Mogrifier (d2, MoS2, MC) + dynamic eval — common questions
What is Mogrifier (d2, MoS2, MC) + dynamic eval used for?
Mogrifier (d2, MoS2, MC) + dynamic eval 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 Mogrifier (d2, MoS2, MC) + dynamic eval?
None. Mogrifier (d2, MoS2, MC) + dynamic eval 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 Mogrifier (d2, MoS2, MC) + dynamic eval open source?
No. Mogrifier (d2, MoS2, MC) + dynamic eval has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Mogrifier (d2, MoS2, MC) + dynamic eval have?
Mogrifier (d2, MoS2, MC) + dynamic eval has 35M 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 Mogrifier (d2, MoS2, MC) + dynamic eval?
Mogrifier (d2, MoS2, MC) + dynamic eval was published by DeepMind,University of Oxford, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry,Academia.
When was Mogrifier (d2, MoS2, MC) + dynamic eval released?
Mogrifier (d2, MoS2, MC) + dynamic eval was published in September 2019. 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.