CODA
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
- The University of Hong Kong,Sun Yat-sen University,Shanghai AI Lab
- Organisation type
- Academia,Academia,Academia
- Country
- Hong Kong, China
- Published
- 31 May 2021
- Authors
- Lin Zheng, Zhiyong Wu, Lingpeng Kong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Translation
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
- 246.9M
- Training data
- 103,000,000 tokens
Table 1
"Wikitext-103 [...] consists of articles from Wikipedia with the token number around 103M/218K/246K for the training/validation/testing splits respectively."
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: https://github.com/LZhengisme/CODA
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
- 3
- Benchmark data
- CODA
Sources
Where this record came from and when it was last checked.
- Reference
- Cascaded Head-colliding Attention
- Last updated
- 1 December 2025
What the numbers mean
Background
CODA was published by The University of Hong Kong,Sun Yat-sen University,Shanghai AI Lab, in the country recorded as Hong Kong, during May 2021. The publishing organisation is categorised as academia,Academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling, Translation.
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
Training consumed a corpus of around 103,000,000 tokens of text.
Answers
CODA — common questions
CODA— who created it?
It was published by The University of Hong Kong,Sun Yat-sen University,Shanghai AI Lab, based in Hong Kong, an organisation categorised as academia,Academia,Academia.
CODA— when was it released?
It was published in May 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.
CODA— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling, Translation. These are the areas it was designed around; they describe intent rather than a hard boundary.
CODA— 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.
CODA— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
CODA— how many parameters does it have?
It has a parameter count of 246.9M. Table 1. 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.