base LM+GNN (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
- Shannon.AI,Nanjing University,Nanyang Technological University,Zhejiang University (ZJU)
- Organisation type
- Industry,Academia,Academia,Academia
- Country
- China, Singapore
- Published
- 17 October 2021
- Authors
- Yuxian Meng, Shi Zong, Xiaoya Li, Xiaofei Sun, Tianwei Zhang, Fei Wu, Jiwei Li
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Base model
- Transformer (Adaptive Input Embeddings) WT103
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
- 247M
- Training data
- 103,000,000 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 source
MIT: https://github.com/ShannonAI/GNN-LM
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
- 46
- Benchmark data
- base LM+GNN
Sources
Where this record came from and when it was last checked.
- Reference
- GNN-LM: Language Modeling based on Global Contexts via GNN
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
base LM+GNN (WT103) was published by Shannon.AI,Nanjing University,Nanyang Technological University,Zhejiang University (ZJU), in the country recorded as China, during October 2021. It comes out of an organisation categorised as industry,Academia,Academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its starting point was an existing base model, Transformer (Adaptive Input Embeddings) WT103. That is the usual way a specialised model is produced.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training set ran to roughly 103,000,000 tokens of text.
Answers
base LM+GNN (WT103) — common questions
base LM+GNN (WT103)— when was it released?
It was published in October 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.
base LM+GNN (WT103)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
base LM+GNN (WT103)— what GPU do I need to run it?
None. This 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.
base LM+GNN (WT103)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
base LM+GNN (WT103)— how many parameters does it have?
It has a parameter count of 247M. 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.
base LM+GNN (WT103)— who created it?
It was published by Shannon.AI,Nanjing University,Nanyang Technological University,Zhejiang University (ZJU), based in China, an organisation categorised as industry,Academia,Academia,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.