6-Layer-Tensor-Transformer+AdaHessian
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
- NERSC, Lawrence Berkeley National Laboratory,University of California (UC) Berkeley
- Organisation type
- Government,Academia
- Country
- United States of America
- Published
- 1 June 2020
- Authors
- Zhewei Yao, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, Michael W. Mahoney
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
- 85.5M
- Training data
- 103,000,000 tokens
- Epochs
- 30
" Following [35], a three-layer tensorized transformer core-1 for PTB and a six-layer tensorized transformer core-1 for Wikitext-103 are used in the experiments" [35] Ma, X.; Zhang, P.; Zhang, S.; Duan, N.; Hou, Y.; Zhou, M.; and Song, D. 2019. A tensorized transformer for language modeling. In Advances in Neural Information Processing Systems, 2229–2239. https://arxiv.org/pdf/1906.09777 Tensorized transformer has 85.5M parameters
batch size 120 10^7 steps (Figure 10)
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
- 1.6 × 10¹⁸ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 85500000 parameters * 103000000 tokens * 30 epochs = 1.58517e+18 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
MIT for code. doesn't have training script for WT103 but looks fairly adaptable: https://github.com/amirgholami/ADAHESSIAN
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
- 365
- Benchmark data
- 6-Layer-Tensor-Transformer+AdaHessian
Sources
Where this record came from and when it was last checked.
- Reference
- ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
6-Layer-Tensor-Transformer+AdaHessian was published by NERSC, Lawrence Berkeley National Laboratory,University of California (UC) Berkeley, in the country recorded as United States of America, during June 2020. It comes out of an organisation categorised as government,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
Producing it required arithmetic totalling around 1.6 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 103,000,000 tokens of text.
Answers
6-Layer-Tensor-Transformer+AdaHessian — common questions
6-Layer-Tensor-Transformer+AdaHessian— who created it?
It was published by NERSC, Lawrence Berkeley National Laboratory,University of California (UC) Berkeley, based in United States of America, an organisation categorised as government,Academia.
6-Layer-Tensor-Transformer+AdaHessian— when was it released?
It was published in June 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.
6-Layer-Tensor-Transformer+AdaHessian— 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.
6-Layer-Tensor-Transformer+AdaHessian— how much compute was used to train it?
Training consumed around 1.6 × 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.
6-Layer-Tensor-Transformer+AdaHessian— 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.
6-Layer-Tensor-Transformer+AdaHessian— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
6-Layer-Tensor-Transformer+AdaHessian— how many parameters does it have?
It has a parameter count of 85.5M. " Following [35], a three-layer tensorized transformer core-1 for PTB and a six-layer tensorized transformer core-1 for Wikitext-103 are used in the experiments" [35] Ma, X.; Zhang, P.; Zhang, S.; Duan, N.; Hou, Y.; Zhou, M.; and Song, D. 2019. A tensorized transformer for language modeling. In Advances in Neural Information Processing Systems, 2229–2239. https://arxiv.org/pdf/1906.09777 Tensorized transformer has 85.5M 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.
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.