Amended-DARTS
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
- Tsinghua University,Huawei,Tongji University
- Organisation type
- Academia,Industry,Academia
- Country
- China
- Published
- 25 October 2019
- Authors
- Kaifeng Bi, Changping Hu, Lingxi Xie, Xin Chen, Longhui Wei, Qi Tian
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Neural Architecture Search - NAS, 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
- 23M
- Training data
- 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.
- Citations
- 54
- Benchmark data
- Amended-DARTS
Sources
Where this record came from and when it was last checked.
- Reference
- Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Amended-DARTS was published by Tsinghua University,Huawei,Tongji University, in China, in October 2019. academia,Industry,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing neural Architecture Search - NAS, Language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Amended-DARTS — common questions
When was Amended-DARTS released?
Amended-DARTS was published in October 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 Amended-DARTS used for?
Amended-DARTS works in Language, and is recorded as handling neural Architecture Search - NAS, 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 Amended-DARTS?
None. Amended-DARTS 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 Amended-DARTS open source?
No. Amended-DARTS has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Amended-DARTS have?
Amended-DARTS has 23M 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.
Who created Amended-DARTS?
Amended-DARTS was published by Tsinghua University,Huawei,Tongji University, based in China, categorised as academia,Industry,Academia.
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.