Quantized ADMM
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
- Chinese University of Hong Kong (CUHK),Microsoft
- Organisation type
- Academia,Industry
- Country
- Hong Kong, United States of America
- Published
- 29 November 2021
- Authors
- Junhao Xu, Xie Chen, Shoukang Hu, Jianwei Yu, Xunying Liu, Helen Meng
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
- tokens
- Epochs
- 50
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.
- Record confidence
- Unknown
- Citations
- 9
- Benchmark data
- Quantized ADMM
Sources
Where this record came from and when it was last checked.
- Reference
- Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Quantized ADMM was published by Chinese University of Hong Kong (CUHK),Microsoft, in Hong Kong, in November 2021. It comes out of academia,Industry.
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.
Answers
Quantized ADMM — common questions
Is Quantized ADMM open source?
No. Quantized ADMM has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Quantized ADMM have?
No parameter count has been published for Quantized ADMM, which is why no memory or speed figure appears on this page.
Who created Quantized ADMM?
Quantized ADMM was published by Chinese University of Hong Kong (CUHK),Microsoft, based in Hong Kong, categorised as academia,Industry.
When was Quantized ADMM released?
Quantized ADMM was published in November 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 Quantized ADMM used for?
Quantized ADMM works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Quantized ADMM?
None. Quantized ADMM 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.
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.