DOC + Finetune∗ + Partial Shuffle (PTB)
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 Washington
- Organisation type
- Academia
- Country
- United States of America
- Published
- 11 March 2019
- Authors
- Ofir Press
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.
- Training data
- 929,000 tokens
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 (non-commercial)
no license specified: https://github.com/ofirpress/PartialShuffle
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
- 6
- Benchmark data
- DOC + Finetune∗ + Partial Shuffle (PTB)
Sources
Where this record came from and when it was last checked.
- Reference
- Partially Shuffling the Training Data to Improve Language Models
- Last updated
- 11 February 2026
What the numbers mean
What this model is
DOC + Finetune∗ + Partial Shuffle (PTB) was published by University of Washington, in United States of America, in March 2019. 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 929,000 tokens of text.
Answers
DOC + Finetune∗ + Partial Shuffle (PTB) — common questions
What is DOC + Finetune∗ + Partial Shuffle (PTB) used for?
DOC + Finetune∗ + Partial Shuffle (PTB) 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 DOC + Finetune∗ + Partial Shuffle (PTB)?
None. DOC + Finetune∗ + Partial Shuffle (PTB) 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 DOC + Finetune∗ + Partial Shuffle (PTB) open source?
No. DOC + Finetune∗ + Partial Shuffle (PTB) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DOC + Finetune∗ + Partial Shuffle (PTB) have?
No parameter count has been published for DOC + Finetune∗ + Partial Shuffle (PTB), which is why no memory or speed figure appears on this page.
Who created DOC + Finetune∗ + Partial Shuffle (PTB)?
DOC + Finetune∗ + Partial Shuffle (PTB) was published by University of Washington, based in United States of America, categorised as academia.
When was DOC + Finetune∗ + Partial Shuffle (PTB) released?
DOC + Finetune∗ + Partial Shuffle (PTB) was published in March 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.