ONLSTM-SYD
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
- Westlake University,Institute for Advanced Study,McGill University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),CIFAR AI Research,University of Montreal / Université de Montréal
- Organisation type
- Academia,Academia,Academia,Academia,Research collective,Academia
- Country
- China, United States of America, Canada
- Published
- 12 May 2020
- Authors
- Wenyu Du, Zhouhan Lin, Yikang Shen, Timothy J. O'Donnell, Yoshua Bengio, Yue Zhang
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
- 25M
- Training data
- 912,344 tokens
- Epochs
- 1,000
25M Table 1
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.4 × 10¹⁷ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 25 * 10^6 parameters * 912 344 tokens * 1000 epochs = 1.368516e+17 FLOP
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
BSD-3 license: https://github.com/wenyudu/SDLM
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
- 15
- Benchmark data
- ONLSTM-SYD
Sources
Where this record came from and when it was last checked.
- Reference
- Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
ONLSTM-SYD was published by Westlake University,Institute for Advanced Study,McGill University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),CIFAR AI Research,University of Montreal / Université de Montréal, in the country recorded as China, during May 2020. It comes out of an organisation categorised as academia,Academia,Academia,Academia,Research collective,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took a computation budget of roughly 1.4 × 10¹⁷ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 912,344 tokens of text.
Answers
ONLSTM-SYD — common questions
ONLSTM-SYD— how many parameters does it have?
It has a parameter count of 25M. 25M Table 1. 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.
ONLSTM-SYD— who created it?
It was published by Westlake University,Institute for Advanced Study,McGill University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),CIFAR AI Research,University of Montreal / Université de Montréal, based in China, an organisation categorised as academia,Academia,Academia,Academia,Research collective,Academia.
ONLSTM-SYD— when was it released?
It was published in May 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
ONLSTM-SYD— 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.
ONLSTM-SYD— how much compute was used to train it?
Training consumed around 1.4 × 10¹⁷ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
ONLSTM-SYD— 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.
ONLSTM-SYD— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.