Segatron -XL base, M=150 + HCP
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
- Microsoft Research,University of Waterloo
- Organisation type
- Industry,Academia
- Country
- United States of America, Canada
- Published
- 21 March 2022
- Authors
- He Bai, Tong Wang, Alessandro Sordoni, Peng Shi
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
- 151M
- Training data
- 103,000,000 tokens
- Epochs
- 18.64
Table 1 reports WikiText results, Table 2 arxiv. WikiText experiments use the larger model of the two.
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
- 1.7 × 10¹⁸ FLOP
"base model: 16 layers, 10 heads, hidden size 410, batch size 64, training steps 200k;" "The input lengths are 150 for the base model and 384 for the large model. " 6ND estimate: 6*200000*64*150*151000000=1.73952e+18
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 (non-commercial)
code, no license: https://github.com/richardbaihe/robustLM
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
- 17
- Benchmark data
- Segatron -XL base, M=150 + HCP
Sources
Where this record came from and when it was last checked.
- Reference
- Better Language Model with Hypernym Class Prediction
- Last updated
- 25 May 2026
What the numbers mean
About this model
Segatron -XL base, M=150 + HCP was published by Microsoft Research,University of Waterloo, in the country recorded as United States of America, during March 2022. The publishing organisation is categorised as industry,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.
What went into building it
The training run consumed about 1.7 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 103,000,000 tokens of text.
Answers
Segatron -XL base, M=150 + HCP — common questions
Segatron -XL base, M=150 + HCP— 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.
Segatron -XL base, M=150 + HCP— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Segatron -XL base, M=150 + HCP— how many parameters does it have?
It has a parameter count of 151M. Table 1 reports WikiText results, Table 2 arxiv. WikiText experiments use the larger model of the two. 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.
Segatron -XL base, M=150 + HCP— who created it?
It was published by Microsoft Research,University of Waterloo, based in United States of America, an organisation categorised as industry,Academia.
Segatron -XL base, M=150 + HCP— when was it released?
It was published in March 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Segatron -XL base, M=150 + HCP— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
Segatron -XL base, M=150 + HCP— how much compute was used to train it?
Training consumed around 1.7 × 10¹⁸ 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.
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.