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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?

Intestella

by AMD

Open LLM trained entirely on AMD GPUs.
Text
Full
https://huggingface.co/amd/Instella-3B-Instruct
Instella-3B
Instella-3B-Instruct
Research-only RAIL-M5
AMD, a major chip manufacturer.
https://www.amd.com/en.html
March 2025
Availability
Training Code
Training code published on GitHub.
https://github.com/AMD-AIG-AIMA/Instella
Base Model Data
Data sources laid out in model card.
https://huggingface.co/amd/Instella-3B-Instruct
End User Model Data
Data sources laid out in model card.
https://huggingface.co/amd/Instella-3B-Instruct
Base Model Weights
Weights published on HuggingFace.
https://huggingface.co/amd/Instella-3B
End User Model Weights
Weights published on HuggingFace.
https://huggingface.co/amd/Instella-3B-Instruct
Documentation
Code Documentation
Code is well-documented.
https://github.com/AMD-AIG-AIMA/Instella
Hardware Architecture
Hardware architecture outlined in blog post.
https://rocm.blogs.amd.com/artificial-intelligence/introducing-instella-3B/README.html
Preprint
Model only publicized through blog post.
https://rocm.blogs.amd.com/artificial-intelligence/introducing-instella-3B/README.html
Paper
No peer-reviewed paper found.
Modelcard
Model card contains the requisite detail.
https://huggingface.co/amd/Instella-3B-Instruct
Datasheet
All datasets used are open and documented.
https://huggingface.co/amd/Instella-3B-Instruct
Access
Package
No package found.
API and Meta Prompts
No API found.
Licenses
Research-only RAIL-M5, not an OSI-approved license.
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