Verbatim Memory Transformer (117M)
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
- Johns Hopkins University,New York University (NYU)
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 24 October 2022
- Authors
- Kristijan Armeni, Christopher Honey, Tal Linzen
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
- 117M
- Training data
- 10,666,666,667 tokens
Table 3
40 (GB text data) Table 3
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
- Open source
CC BY 4.0 for code: https://github.com/KristijanArmeni/verbatim-memory-in-NLMs
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
- 9
- Benchmark data
- Characterizing Verbatim Short-Term Memory in Neural Language Models (117M)
Sources
Where this record came from and when it was last checked.
- Reference
- Characterizing Verbatim Short-Term Memory in Neural Language Models
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Verbatim Memory Transformer (117M) was published by Johns Hopkins University,New York University (NYU), in United States of America, in October 2022. It comes out of academia,Academia.
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
The training set ran to roughly 10,666,666,667 tokens.
Answers
Verbatim Memory Transformer (117M) — common questions
How many parameters does Verbatim Memory Transformer (117M) have?
Verbatim Memory Transformer (117M) has 117M parameters. Table 3. 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 Verbatim Memory Transformer (117M)?
Verbatim Memory Transformer (117M) was published by Johns Hopkins University,New York University (NYU), based in United States of America, categorised as academia,Academia.
When was Verbatim Memory Transformer (117M) released?
Verbatim Memory Transformer (117M) 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.
What is Verbatim Memory Transformer (117M) used for?
Verbatim Memory Transformer (117M) 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 Verbatim Memory Transformer (117M)?
None. Verbatim Memory Transformer (117M) 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 Verbatim Memory Transformer (117M) open source?
No. Verbatim Memory Transformer (117M) 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.