We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation.
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.
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.
- Training data
- tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Last updated
- 28 November 2025
What the numbers mean
What this model is
We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation. was published by its authors.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation. — common questions
What GPU do I need to run We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation.?
None. We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation. 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 We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation. open source?
The licensing for We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation. was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation. have?
No parameter count has been published for We employ various filters for data filtering and progressively increase their thresholds to build 4 training datasets, i.e., 256p, 360p, 540p, and 720p, while the final SFT dataset is built through manual annotation., which is why no memory or speed figure appears on this page.
Source
Record last updated 28 November 2025
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.