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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?

Teuken

by OpenGPT-X

Open-source multilingual LLM that claims to support all 24 official languages of the European Union.
Text
Full
https://huggingface.co/openGPT-X/Teuken-7B-instruct-commercial-v0.4
Teuken-7B-base
Teuken-7B-instruct
Apache-2.0
Project aiming to develop LLMs in Germany.
https://opengpt-x.de/en/
September 2024
Availability
Base Model Data
Dataset described as deriving from the CommonCrawl, but no filtered dataset provided. Either a filtered dataset or a fully reproducible and persistent data pipeline would be preferred here.
https://arxiv.org/pdf/2410.08800
End User Model Data
The Huggingface shows a table with all datasets used for the end model.
https://huggingface.co/openGPT-X/Teuken-7B-instruct-commercial-v0.4#instruction-tuning-data
Base Model Weights
Available via Huggingface repository.
https://huggingface.co/openGPT-X/Teuken-7B-base-v0.6
End User Model Weights
Available via Huggingface repository.
https://huggingface.co/openGPT-X/Teuken-7B-instruct-commercial-v0.4
Training Code
SBATCH script with training code available at fork of Megatron-LM. However, no easily visible and easily navigable repository containing the code used to train the model is available. Making the repository more easily visible would alleviate this.
https://github.com/OpenGPTX/Megatron-LM/blob/main/examples/7B_EU24_juwels_part_3_fw_after3T.sbatch
Documentation
Code Documentation
README of containing training code is unchanged from base repo. More elaborate documentation would be warranted. A good example for a good documentation style would be the repository for the OLMo model: https://github.com/allenai/OLMo
Hardware Architecture
Preprint shows architecture, providing details about design decisions and hyperparameters.
https://arxiv.org/abs/2410.03730
Preprint
Three corresponding preprints, detailing the models, data, and evaluation.
https://arxiv.org/abs/2410.03730https://arxiv.org/abs/2410.08928https://arxiv.org/abs/2410.08800
Paper
Peer-reviewed paper published in ECAI. Other publications only available as preprints.
https://ecai2025.org/accepted-papers/
Modelcard
Detailed modelcard showing training details, data, technical specifications, and example usage.
https://huggingface.co/openGPT-X/Teuken-7B-instruct-commercial-v0.4
Datasheet
No datasheet containing a detailed description of data collection and curation is found attached to a persistent version of the model data, as would be preferred here. A persistent version of the filtered data with attached the information in the data preprint at https://arxiv.org/abs/2410.08800 would be sufficient here.
Access
Package
Available through either Huggingface API or vLLM library, no proprietary release.
https://huggingface.co/openGPT-X/Teuken-7B-instruct-commercial-v0.4#how-to-get-started-with-the-model
API and Meta Prompts
No API found.
Licenses
Apache 2.0, an OSI-approved license.
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