GSM
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
- Peking University,Microsoft Research
- Organisation type
- Academia,Industry
- Country
- China, United States of America
- Published
- 30 July 2017
- Authors
- Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, Ming Zhou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering
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
- 215,570 tokens
from https://paperswithcode.com/dataset/squad SQuAD have 107,785 question-answer pairs download-ed dataset from: https://www.kaggle.com/datasets/stanfordu/stanford-question-answering-dataset?resource=download wc -w on train-v.1.1 returns 4017471 words so around 4M words
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
- Likely
- Citations
- 806
"At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model."
Sources
Where this record came from and when it was last checked.
- Reference
- Gated Self-Matching Networks for Reading Comprehension and Question Answering
- Last updated
- 28 November 2025
What the numbers mean
Background
GSM was published by Peking University,Microsoft Research, in China, in July 2017. The organisation is categorised as academia,Industry.
It works in Language, and is recorded as doing question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Around 215,570 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
GSM — common questions
Is GSM open source?
The licensing for GSM was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does GSM have?
No parameter count has been published for GSM, which is why no memory or speed figure appears on this page.
Who created GSM?
GSM was published by Peking University,Microsoft Research, based in China, categorised as academia,Industry.
When was GSM released?
GSM was published in July 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 GSM used for?
GSM works in Language, and is recorded as handling question answering. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run GSM?
None. GSM 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.