Weight Decay
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.
- Published
- 2 December 1991
- Authors
- A. Krogh, J. Hertz
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech synthesis
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
- 8.4K
- Training data
- 25,000 tokens
- Epochs
- 300
7*26*40+40+40*26+26=8386 "The network had 7 x 26 input units, 40 hidden units and 26 output units"
"It was trained on 400 to 5000 random words from the data base of around 20.000 words,"
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
- 7.5 × 10¹⁰ FLOP
- How it was established
- Operation counting
2*8386*3*1500000=75474000000=7.55e10 "It was trained on 400 to 5000 random words from the data base of around 20.000 words," "The top full line corresponds to the generalization error after 300 epochs"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Highly cited,Historical significance
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- A Simple Weight Decay Can Improve Generalization
- Last updated
- 28 November 2025
What the numbers mean
Background
Weight Decay was published by its authors, during December 1991.
It works in the domain of Speech, and is recorded as performing the task of speech synthesis.
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
Producing it required arithmetic totalling around 7.5 × 10¹⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 25,000 tokens of text.
The reason it appears in this catalogue at all: highly cited,Historical significance.
Answers
Weight Decay — common questions
Weight Decay— when was it released?
It was published in December 1991. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Weight Decay— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech synthesis. These are the areas it was designed around; they describe intent rather than a hard boundary.
Weight Decay— how much compute was used to train it?
Training consumed around 7.5 × 10¹⁰ 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.
Weight Decay— 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.
Weight Decay— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Weight Decay— how many parameters does it have?
It has a parameter count of 8.4K. 7*26*40+40+40*26+26=8386 "The network had 7 x 26 input units, 40 hidden units and 26 output units". 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.