The IndexGuidesNews
AboutContribute

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?

Mistral NeMo

by Mistral AI, NVIDIA

Multilingual model trained by Mistral AI.
Text
Full
https://mistral.ai/news/mistral-nemo/
Mistral NeMo
unspecified
Apache 2.0 (model weights only)
Jointly trained by Mistral, a French AI company, and NVIDIA, a major chip manufacturer.
[ "https://mistral.ai/", "https://www.nvidia.com" ]
July 2024
Availability
Training Code
repository provides 'minimal code to run our models'
https://github.com/mistralai/mistral-inference
Base Model Data
No information provided except 'Trained on a large proportion of multilingual and code data'
https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
End User Model Data
No information provided except 'Trained on a large proportion of multilingual and code data'
https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407
Base Model Weights
Base LLM model made available for download
https://models.mistralcdn.com/mistral-nemo-2407/mistral-nemo-base-2407.tar
End User Model Weights
Instruct version of the model made available but no information on fine-tuning procedure provided
https://models.mistralcdn.com/mistral-nemo-2407/mistral-nemo-instruct-2407.tar
Documentation
Code Documentation
repository contains minimal code to run the models; also open source code, althought it is mostly uncommented and not documented very well.
https://github.com/mistralai/mistral-inference/tree/main/src/mistral_inference
Hardware Architecture
Some information on architecture provided in github repo and in release blogpost
https://github.com/mistralai/mistral-inference
Preprint
No preprint found
Paper
No peer reviewed paper available
Modelcard
Model cards available for both the base and end models, although they are both severely limited.
https://huggingface.co/mistralai/Mistral-Nemo-Base-2407https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407
Datasheet
No datasheet available
Access
Package
Docker image shared on github
https://docs.mistral.ai/quickstart/
API and Meta Prompts
API specification provided by vLLM
https://docs.mistral.ai/api
Licenses
Apache 2.0
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/README.md
Is this information not up to date?
Contribute here ->

Supported by the Centre for Language Studies and the Dutch Research Council. Website design & development © 2024 by BSTN. This version of the index generated 09 Apr 2025, website content last updated 23 Apr 2025.