True-Regularization+Finetune+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
- Mobvoi,Williams College
- Organisation type
- Industry,Academia
- Country
- China, United States of America
- Published
- 8 April 2019
- Authors
- Yangyang Shi, Mei-Yuh Hwang, Xin Lei, Haoyu Sheng
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
- 7M
- 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
- Unreleased
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
- 26
- Benchmark data
- True-Regularization+Finetune+Dynamic-Eval
"In the first experiment, the student model achieves state-of-the-art perplexity results on the Penn Treebank dataset [1] with a model size one third of that of the previously published best model"
Sources
Where this record came from and when it was last checked.
- Reference
- Knowledge Distillation For Recurrent Neural Network Language Modeling With Trust Regularization
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
True-Regularization+Finetune+Dynamic-Eval was published by Mobvoi,Williams College, in China, in April 2019. It comes out of industry,Academia.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on about 929,000 tokens of text.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
True-Regularization+Finetune+Dynamic-Eval — common questions
Who created True-Regularization+Finetune+Dynamic-Eval?
True-Regularization+Finetune+Dynamic-Eval was published by Mobvoi,Williams College, based in China, categorised as industry,Academia.
When was True-Regularization+Finetune+Dynamic-Eval released?
True-Regularization+Finetune+Dynamic-Eval was published in April 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.
What is True-Regularization+Finetune+Dynamic-Eval used for?
True-Regularization+Finetune+Dynamic-Eval works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run True-Regularization+Finetune+Dynamic-Eval?
None. True-Regularization+Finetune+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 True-Regularization+Finetune+Dynamic-Eval open source?
No. True-Regularization+Finetune+Dynamic-Eval has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does True-Regularization+Finetune+Dynamic-Eval have?
True-Regularization+Finetune+Dynamic-Eval has 7M 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.
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.