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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?

NexusRaven

by NexusFlow

Function-calling LLM.
Code
Limited
https://huggingface.co/Nexusflow/NexusRaven-V2-13B
Llama-2-13B
NexusRaven-V2-13B
NexusFlow community license
Company developing GenAI agents for enterprise workflows.
https://huggingface.co/Nexusflow
December 2023
Availability
Training Code
Repo exists, but does not contain training code.
https://github.com/nexusflowai/NexusRaven-V2
Base Model Data
Data nowhere disclosed or documented, and described only in the vaguest terms in a corporate preprint released by Meta
End User Model Data
Proprietary dataset used.
Base Model Weights
Gated model available on HuggingFace.
https://huggingface.co/meta-llama/Llama-2-70b-hf
End User Model Weights
Weights made available through HuggingFace.
https://huggingface.co/Nexusflow/NexusRaven-V2-13B
Documentation
Code Documentation
No training code, so no documentation.
Hardware Architecture
No architecture information found.
Preprint
Model only publicized through blog post.
https://nexusflow.ai/blogs/ravenv2
Paper
No paper found.
Modelcard
Model card contains general information, but none about model architecture, training, or fine-tuning.
https://huggingface.co/Nexusflow/NexusRaven-V2-13B
Datasheet
No datasheet found.
Access
Package
Package made available on PyPi.
https://pypi.org/project/nexusraven/
API and Meta Prompts
No API found.
Licenses
NexusFlow community license, not an OSI-approved license.
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