Decaying Fast Weights Transformer (WT-103)
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
- Jenni
- Organisation type
- Industry
- Country
- Singapore
- Published
- 9 October 2022
- Authors
- Huanru Henry Mao
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Base model
- Transformer (Adaptive Input Embeddings) WT103
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
- 242M
- Training data
- 103,000,000 tokens
- Epochs
- 192.12
"We fine-tune starting from the checkpoint provided by Baevski and Auli (2019)8, which has 242M parameters"
"For both models, we trained with a batch size of 26 for 100,000 iterations. "
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
- 7.9 × 10¹⁹ FLOP
- How it was established
- Hardware
- Fine-tuning compute
- 5.7 × 10¹⁸ FLOP
Pre-trained model is Transformer (Adaptive Input Embeddings) which was 7.3e19. This is from 8 * 67 V100-hours. fine-tuning: "Training was performed on a single NVIDIA A40 GPU for 35 hours" 35h, 1 GPU, 149.7e12, 30% = 5.7e18 FLOP" 5.7e18 + 7.3e19 is 7.9e19
"Training was performed on a single NVIDIA A40 GPU for 35 hours" 35h, 1 GPU, 149.7e12, 30% 5.7e18 FLOP"
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA A40 PCIe
- Chips used
- 1
- Wall-clock time
- 35 hours
- Power draw
- 330 W
"Training was performed on a single NVIDIA A40 GPU for 35 hours."
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
They have CUDA kernels, don't see pretrain code: https://github.com/jenni-ai/T2FW
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
- 30
- Benchmark data
- Decaying Fast Weights Transformer
Sources
Where this record came from and when it was last checked.
- Reference
- Fine-Tuning Pre-trained Transformers into Decaying Fast Weights
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Decaying Fast Weights Transformer (WT-103) was published by Jenni, in Singapore, in October 2022. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
It is derived from Transformer (Adaptive Input Embeddings) WT103 rather than trained from scratch, which is the usual way a specialised model is produced.
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 7.9 × 10¹⁹ FLOP of arithmetic, on NVIDIA A40 PCIe, which is a statement about the training budget rather than about inference.
The training set ran to roughly 103,000,000 tokens.
Answers
Decaying Fast Weights Transformer (WT-103) — common questions
What is Decaying Fast Weights Transformer (WT-103) used for?
Decaying Fast Weights Transformer (WT-103) 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 Decaying Fast Weights Transformer (WT-103)?
Around 7.9 × 10¹⁹ FLOP, on NVIDIA A40 PCIe. 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 Decaying Fast Weights Transformer (WT-103)?
None. Decaying Fast Weights Transformer (WT-103) 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 Decaying Fast Weights Transformer (WT-103) open source?
No. Decaying Fast Weights Transformer (WT-103) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Decaying Fast Weights Transformer (WT-103) have?
Decaying Fast Weights Transformer (WT-103) has 242M parameters. "We fine-tune starting from the checkpoint provided by Baevski and Auli (2019)8, which has 242M parameters". 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.
Who created Decaying Fast Weights Transformer (WT-103)?
Decaying Fast Weights Transformer (WT-103) was published by Jenni, based in Singapore, categorised as industry.
When was Decaying Fast Weights Transformer (WT-103) released?
Decaying Fast Weights Transformer (WT-103) was published in October 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.
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.