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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?

Eurus

by OpenBMB

Reasoning MoE model.
Text
Limited
https://huggingface.co/openbmb/Eurux-8x22b-nca
Mixtral-8x22B-v0.1
Eurux-8x22B-NCA
Apache 2.0
OpenBMB (Open Lab for Big Model Base), founded by TsinghuaNLP & ModelBest Inc (面壁智能), aims to build foundation models and systems towards AGI.
https://www.openbmb.cn/
April 2024
Availability
Training Code
No training code provided.
Base Model Data
No data sources were disclosed for the training of CodeLlama. The paper merely refers to abstract 'code'.
End User Model Data
Data mixture used is laid out consisely and clearly in source paper.
https://arxiv.org/abs/2404.02078
Base Model Weights
CodeLlama weights published on HuggingFace as a gated model.
https://huggingface.co/meta-llama/CodeLlama-70b-hf/tree/main
End User Model Weights
Weights published on HuggingFace.
https://huggingface.co/openbmb/Eurus-70b-nca/tree/main
Documentation
Code Documentation
Only evaluation code found in the repositories, which is documented to a small extent.
https://github.com/OpenBMB/Eurus/tree/main
Hardware Architecture
Architecture described in detail in the paper.
https://arxiv.org/abs/2404.02078
Preprint
Preprint published on ArXiV
https://arxiv.org/abs/2404.02078
Paper
No peer-reviewed paper found.
Modelcard
Model card provides a decent overview of the model.
https://huggingface.co/openbmb/Eurus-70b-nca
Datasheet
Some data sheets are provided, however no data sheet for the combined mixtures. Some linked sheets lead to 404 pages.
https://huggingface.co/datasets/openbmb/UltraFeedbackhttps://huggingface.co/datasets/openbmb/UltraInteract_sft
Access
Package
No package found.
API and Meta Prompts
No API found.
Licenses
Apache 2.0
https://huggingface.co/openbmb/Eurus-70b-nca/tree/main
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