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Parameter descriptions:

Base Model Data
Are datasources for training the base model comprehensively documented and freely made available? In case a distinction between base (foundation) and end (user) model is not applicable, this mirrors the end model data entries.
End User Model Data
Are datasources for training the model that the enduser interacts with comprehensively documented and freely made available?
Base Model Weights
Are the weights of the base models made freely available? In case a distinction between base (foundation) and end (user) model is not applicable, this mirrors the end model data entries.
End User Model Weights
Are the weights of the model that the enduser interacts with made freely available?
Training Code
Is the source code of datasource processing, model training and tuining comprehensively and freely made available?
Code Documentation
Is the source code of datasource processing, model training and tuning comprehensively documented?
Hardware Architecture
Is the hardware architecture used for datasource processing and model training comprehensively documented?
Preprint
Are archived preprint(s) are available that detail all major parts of the system including datasource processing, model training and tuning steps?
Paper
Are peer-reviewed scientific publications available that detail all major parts of the system including datasource processing, model training and tuning steps?
Modelcard
Is a model card in standardized format available that provides comprehensive insight on model architecture, training, fine-tuning, and evaluation are available?
Datasheet
Is a datasheet as defined in "Datasheets for Datasets" (Gebru et al. 2021) available?
Package
Is a packaged release of the model available on a software repository (e.g. a Python Package Index, Homebrew)?
API and Meta Prompts
Is an API available that provides unrestricted access to the model (other than security and CDN restrictions)? If applicable, this entry also collects information on the use and availability of meta prompts.
Licenses
Is the project fully covered by Open Source Initiative (OSI)-approved licenses, including all data sources and training pipeline code?

CascadeV

by ByteDance

Video generation model implementing the Würschten architecture.
Video
Limited
https://huggingface.co/ByteDance/CascadeV
PixArt-Sigma
CascadeV
CreativeML Open RAIL++-M License
ByteDance, the parent company of TikTok.
https://www.bytedance.com/
September 2024
Availability
Training Code
Training code published on GitHub.
https://github.com/bytedance/CascadeV
Base Model Data
Internal dataset, apparantly undisclosed.
End User Model Data
No data sources found.
Base Model Weights
Model made available on GitHub.
https://github.com/PixArt-alpha/PixArt-sigma
End User Model Weights
Weights made available on HuggingFace.
https://huggingface.co/ByteDance/CascadeV
Documentation
Code Documentation
Training code sparsely documented.
Hardware Architecture
No hardware architecture information found.
Preprint
Preprint available on arXiv.
https://arxiv.org/pdf/2306.00637
Paper
Paper submitted to ICLR 2024 oral.
https://openreview.net/forum?id=gU58d5QeGv
Modelcard
Model card contains most required information, but lacks details on training data and fine-tuning.
https://huggingface.co/ByteDance/CascadeV
Datasheet
No datasheet found.
Access
Package
No package found.
API and Meta Prompts
No API found.
Licenses
CreativeML Open RAIL++-M License. Not an OSI-approved license.
https://huggingface.co/ByteDance/CascadeV
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