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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?

Minerva

by Sapienza Natural Language Processing Group

LLM pretrained from scratch on Italian.
Text
Full
https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0
Minerva-7B-base-v1.0
Minerva-7B-instruct-v1.0
Apache 2.0
Sapienza NLP, a university research group.
https://nlp.uniroma1.it/
November 2024
Availability
Training Code
Model card contains very broad description of how the model was trained.
https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0
Base Model Data
Data mixture is shared, but not all data in listed datasets is used and no data set containing selected data is provided.
https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0#training-data
End User Model Data
Data mixture is provided with links, though again it is not always indicated which parts of the datasets are used.
https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0#sft-training
Base Model Weights
Base LLM model made available for download
https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
End User Model Weights
Instruct version of the model made available
https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0
Documentation
Code Documentation
No explicit code is provided, so consequently no documentation of code exists.
Hardware Architecture
Architecture described at a high level in model card.
https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0#model-architecture
Preprint
No preprint found.
Paper
No peer reviewed paper available
Modelcard
Model card is made available and provides insights into necessary components.
https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0
Datasheet
No datasheet available.
Access
Package
No package found.
API and Meta Prompts
No API available.
Licenses
Apache 2.0
https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0
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