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Parameter descriptions:

Base Model Data
Are datasources for training the base model comprehensively documented and 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 end user interacts with comprehensively documented and 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 end user interacts with made freely available?
Training Code
Is the source code of dataset processing, model training and tuning comprehensively 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 available in standardized format that provides comprehensive insight on model architecture, training, fine-tuning, and evaluation?
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?

Koala

by BAIR

From the documentation 'Koala is fine-tuned on freely available interaction data scraped from the web, but with a specific focus on data that includes interaction with highly capable closed-source models such as ChatGPT.'
Text
Full
https://bair.berkeley.edu/blog/2023/04/03/koala/
Llama-13B
Koala
Mixed
Berkeley Artificial Intelligence Research
https://bair.berkeley.edu/
March 2023
Availability
Base Model Data
Based on LLaMA whose pretraining data has nowhere been disclosed or documented.
https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#training-dataset
End User Model Data
Datasets described in blog post but not all made available
https://bair.berkeley.edu/blog/2023/04/03/koala/#datasets-and-traininghttps://github.com/young-geng/koala_data_pipeline
Base Model Weights
Based on LLaMA weights, which are not openly available though a leaked versions is in wide circulation.
End User Model Weights
Model weights ostensibly made available as a diff against LLaMA, however the folder containing the weights is empty.
https://drive.google.com/drive/folders/10f7wrlAFoPIy-TECHsx9DKIvbQYunCfl
Training Code
Code scattered across projects and repositories
https://github.com/young-geng/EasyLM
Documentation
Code Documentation
Code scattered across various repositories and not systematically documented.
https://github.com/young-geng/EasyLM
Hardware Architecture
Architecture visually illustrated and described in some detail.
https://github.com/young-geng/EasyLM
Preprint
No preprint available
Paper
No peer-reviewed paper
Modelcard
No model card available
Datasheet
No datasheet found
Access
Package
No package available
API and Meta Prompts
No API available
Licenses
Licensing is 'subject to the model License of LLaMA, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT'
https://huggingface.co/young-geng/koala#license
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