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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?

MobileLLM

by Meta

Small reasoning model.
Text,code
Full
https://huggingface.co/facebook/MobileLLM-R1-950M
MobileLLM-R1-950M-base
MobileLLM-R1-950M
FAIR Noncommercial Research License v1
Meta, a major technology company.
https://ai.meta.com/
September 2025
Availability
Base Model Data
Datasources listed on model card.
https://huggingface.co/facebook/MobileLLM-R1-950M
End User Model Data
Datasources listed on model card.
https://huggingface.co/facebook/MobileLLM-R1-950M
Base Model Weights
Gated model available on HuggingFace.
https://huggingface.co/facebook/MobileLLM-R1-950M-base
End User Model Weights
Gated model available on HuggingFace.
https://huggingface.co/facebook/MobileLLM-R1-950M
Training Code
Repo containing training code available on GitHub.
https://github.com/facebookresearch/MobileLLM
Documentation
Code Documentation
Repository is well-documented.
https://github.com/facebookresearch/MobileLLM
Hardware Architecture
Hardware architecture described in preprint and on GitHub.
https://arxiv.org/pdf/2402.14905https://github.com/facebookresearch/MobileLLM
Preprint
Preprint made available on arXiv.
https://arxiv.org/pdf/2402.14905
Paper
Paper published in ICML.
https://dl.acm.org/doi/10.5555/3692070.3693386
Modelcard
Model card provides the requisite information.
https://huggingface.co/facebook/MobileLLM-R1-950M
Datasheet
No datasheet found.
Access
Package
No package found.
API and Meta Prompts
No API found.
Licenses
FAIR Noncommercial Research License v1, not an OSI recognised open license.
https://huggingface.co/facebook/MobileLLM-R1-950M
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