EI-REHN-1000D
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
- Korea Advanced Institute of Science and Technology (KAIST)
- Organisation type
- Academia
- Country
- Korea (Republic of)
- Published
- 14 August 2017
- Authors
- Hyunsin Park, Chang D. Yoo
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Numerical format
- FP32
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
- 19M
- Training data
- 929,000 tokens
- Epochs
- 100
19M (Table 4)
"For all the experiments in this paper, Tensorflow toolkit [15] was used. For training the network, Adam optimizer [16] was adopted with 20 mini-batch size, 100 epochs, and 0.01 learning rate"
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
- 1.1 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 19000000 parameters * 929000 tokens * 100 epochs = 1.05906e+16 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
- Unreleased
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
- Confident
- Citations
- 6
- Benchmark data
- EI-REHN-1000D
"The proposed networks showed better performance than other state-of-the-art recurrent networks in all three experiments."
Sources
Where this record came from and when it was last checked.
- Reference
- Early Improving Recurrent Elastic Highway Network
- Last updated
- 28 November 2025
What the numbers mean
About this model
EI-REHN-1000D was published by Korea Advanced Institute of Science and Technology (KAIST), in Korea (Republic of), in August 2017. The organisation is categorised as academia.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Producing it required around 1.1 × 10¹⁶ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
It was trained on about 929,000 tokens of text.
Its inclusion criterion is sOTA improvement.
Answers
EI-REHN-1000D — common questions
Who created EI-REHN-1000D?
EI-REHN-1000D was published by Korea Advanced Institute of Science and Technology (KAIST), based in Korea (Republic of), categorised as academia.
When was EI-REHN-1000D released?
EI-REHN-1000D was published in August 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 EI-REHN-1000D used for?
EI-REHN-1000D works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train EI-REHN-1000D?
Around 1.1 × 10¹⁶ 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.
What GPU do I need to run EI-REHN-1000D?
None. EI-REHN-1000D 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.
Is EI-REHN-1000D open source?
No. EI-REHN-1000D has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does EI-REHN-1000D have?
EI-REHN-1000D has 19M parameters. 19M (Table 4). 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.