GRU + p-tHSM (pretrain via Brown) (WT103)
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
- Beihang University,University of Montreal / Université de Montréal,Chongqing University
- Organisation type
- Academia,Academia,Academia
- Country
- China, Canada
- Published
- 19 August 2017
- Authors
- Nan Jiang, Wenge Rong, Min Gao, Yikang Shen, Zhang Xiong
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
- 206M
- 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
code, looks like just WT2. no clear license: https://github.com/jiangnanhugo/lmkit
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 6
- Benchmark data
- GRU + p-tHSM (pretrain via Brown) (WT103)
Sources
Where this record came from and when it was last checked.
- Reference
- Exploration of Tree-based Hierarchical Softmax for Recurrent Language Models
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
GRU + p-tHSM (pretrain via Brown) (WT103) was published by Beihang University,University of Montreal / Université de Montréal,Chongqing University, in China, in August 2017. academia,Academia,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
GRU + p-tHSM (pretrain via Brown) (WT103) — common questions
Who created GRU + p-tHSM (pretrain via Brown) (WT103)?
GRU + p-tHSM (pretrain via Brown) (WT103) was published by Beihang University,University of Montreal / Université de Montréal,Chongqing University, based in China, categorised as academia,Academia,Academia.
When was GRU + p-tHSM (pretrain via Brown) (WT103) released?
GRU + p-tHSM (pretrain via Brown) (WT103) was published in August 2017. 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 GRU + p-tHSM (pretrain via Brown) (WT103) used for?
GRU + p-tHSM (pretrain via Brown) (WT103) 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 GRU + p-tHSM (pretrain via Brown) (WT103)?
None. GRU + p-tHSM (pretrain via Brown) (WT103) 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 GRU + p-tHSM (pretrain via Brown) (WT103) open source?
No. GRU + p-tHSM (pretrain via Brown) (WT103) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GRU + p-tHSM (pretrain via Brown) (WT103) have?
GRU + p-tHSM (pretrain via Brown) (WT103) has 206M 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.