TOME
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 Southern California,Google
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 12 October 2021
- Authors
- Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Fei Sha, William Cohen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering
- Numerical format
- FP32
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
- 220M
- Training data
- tokens
- Epochs
- 157.29
- Batch size
- 524,288
220M from Table 1 entry for TOME
Per Wikipedia, the site had 3.755B words as of the most recent checkpoint prior to the paper's publication: https://en.wikipedia.org/wiki/Wikipedia:Size_of_Wikipedia#Yearly_statistics 3.755B * 4/3 = 5B tokens
"All models are pre-trained on [...] batch size of 4096. Each passage in the batch has length T = 128, excluding entity tokens."
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1 × 10²¹ FLOP
- How it was established
- Operation counting
We have information about hardware "All models are pre-trained on 128 TPUs using AdamW optimizer (Loshchilov& Hutter, 2019) with learning rate 1e-4 and batch size of 4096.", but no information about training time and exact model of TPUs. There is information about training 1.5M steps with batch size 4096 * 128 citation from appendix A: "The Mention Encoder and BATCH-TOME are pre-trained for 1 million steps with 50k warmup steps, and TOME is trained for 500k additional steps with 25k warmup step…
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
Apache 2.0 (repo license) https://github.com/google-research/language/tree/master/language/mentionmemory
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 55
Sources
Where this record came from and when it was last checked.
- Reference
- Mention Memory: incorporating textual knowledge into Transformers through entity mention attention
- Last updated
- 25 May 2026
What the numbers mean
Background
TOME was published by University of Southern California,Google, in United States of America, in October 2021. The organisation is categorised as academia,Industry.
It works in Language, and is recorded as doing question answering.
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
Producing it required around 1 × 10²¹ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Answers
TOME — common questions
How much compute was used to train TOME?
Around 1 × 10²¹ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run TOME?
None. TOME 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 TOME open source?
No. TOME has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does TOME have?
TOME has 220M parameters. 220M from Table 1 entry for TOME. 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 TOME?
TOME was published by University of Southern California,Google, based in United States of America, categorised as academia,Industry.
When was TOME released?
TOME was published in October 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.
What is TOME used for?
TOME works in Language, and is recorded as handling question answering. 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.