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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?

Infinity-Instruct

by Beijing Academy of Artificial Intelligence

Open-source model designed to test the performance of the Infinity-Instruct dataset.
Text
Limited
https://huggingface.co/BAAI/Infinity-Instruct-7M-Gen-Llama3_1-70B
Llama-3.1-70B
Infinity-Instruct-7M-Gen-Llama3.1-70B
Llama 3.1 Community License Agreement
Chinese non-profit AI lab.
https://www.baai.ac.cn/en/
August 2024
Availability
Training Code
soon to be released
Base Model Data
based on Llama3
End User Model Data
partially on HF
https://huggingface.co/datasets/BAAI/Infinity-Instruct
Base Model Weights
End User Model Weights
https://huggingface.co/BAAI/Infinity-Instruct-7M-Gen-Llama3_1-70B
Documentation
Code Documentation
Hardware Architecture
Preprint
dead link
Paper
Modelcard
https://huggingface.co/BAAI/Infinity-Instruct-7M-Gen-Llama3_1-70B
Datasheet
Access
Package
API and Meta Prompts
Licenses
weights under meta custom license
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