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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?

K2

by LLM360

Large fully-reproducible model.
Text
Full
https://huggingface.co/LLM360/K2-Chat
K2
K2-Chat
Apache-2.0
LLM360, an initiative to fully open-source LLMs.
https://www.llm360.ai/index.html
May 2024
Availability
Training Code
https://github.com/LLM360
Base Model Data
https://huggingface.co/LLM360/K2-Chat
End User Model Data
https://huggingface.co/LLM360/K2-Chat
Base Model Weights
https://huggingface.co/LLM360/K2-Chat
End User Model Weights
https://huggingface.co/LLM360/K2-Chat
Documentation
Code Documentation
https://github.com/LLM360
Hardware Architecture
https://github.com/LLM360
Preprint
https://arxiv.org/abs/2501.07124
Paper
Modelcard
https://huggingface.co/LLM360/K2-Chat
Datasheet
some found
https://huggingface.co/LLM360/K2-Chat
Access
Package
API and Meta Prompts
Licenses
code and models under Apache 2, datasets complicated
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