DiffQ Transformer (16L)
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
- Meta AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 20 April 2021
- Authors
- Alexandre Défossez, Yossi Adi, Gabriel Synnaeve
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Approach
- Self-supervised learning
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
- 247M
- Training data
- 103,000,000 tokens
They base their architecture off of "Transformer (Adaptive Input Embeddings) WT103", a 16 layer transformer with 16 heads per MHA layer, d_model=1024 and d_ff=4096.
WikiText-103, which has 103M training tokens
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
- 3 × 10¹⁹ FLOP
- How it was established
- Operation counting
The authors say they train their base model following Baevski & Auli (2019). That paper trained for 286k steps in batches of 65,536 tokens. This suggests training the base model took 6 * 247M * 286k * 65536 = 2.778E19 FLOPs. However, DiffQ introduces some additional computational overhead during training: "Using DiffQ usually increase the training time by some amount. On the language modeling task, the time per batch went from 115ms to 125ms." This suggests 125/115 - 1 = 8.7% overhead, relati…
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 V100
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)
training code (non-commercial): https://github.com/facebookresearch/diffq/blob/main/examples/FAIRSEQ_README.md
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
- 63
- Benchmark data
- DiffQ Transformer (16L)
Sources
Where this record came from and when it was last checked.
- Reference
- Differentiable Model Compression via Pseudo Quantization Noise
- Last updated
- 25 May 2026
What the numbers mean
Background
DiffQ Transformer (16L) was published by Meta AI, in United States of America, in April 2021. industry is the category the publisher falls under.
It works in Language, and is recorded as doing 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 3 × 10¹⁹ FLOP, on NVIDIA V100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 103,000,000 tokens went into training it.
Answers
DiffQ Transformer (16L) — common questions
How many parameters does DiffQ Transformer (16L) have?
DiffQ Transformer (16L) has 247M parameters. They base their architecture off of "Transformer (Adaptive Input Embeddings) WT103", a 16 layer transformer with 16 heads per MHA layer, d_model=1024 and d_ff=4096. 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 DiffQ Transformer (16L)?
DiffQ Transformer (16L) was published by Meta AI, based in United States of America, categorised as industry.
When was DiffQ Transformer (16L) released?
DiffQ Transformer (16L) was published in April 2021. 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 DiffQ Transformer (16L) used for?
DiffQ Transformer (16L) works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train DiffQ Transformer (16L)?
Around 3 × 10¹⁹ FLOP, on NVIDIA V100. 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 DiffQ Transformer (16L)?
None. DiffQ Transformer (16L) 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 DiffQ Transformer (16L) open source?
No. DiffQ Transformer (16L) has not had its weights published, so it exists only as a service controlled by its owner.
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.