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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?

OLMo

by Ai2

Open LLM trained from scratch by Allen AI.
Text
Full
https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc
OLMo-2-0325-32B
OLMo-2-0325-32B-Instruct
Apache-2.0
Allen Institute for AI (non-profit research institute)
https://allenai.org
November 2024
Availability
Base Model Data
Training data for base model released and use documented.
https://huggingface.co/datasets/allenai/olmo-mix-1124https://huggingface.co/datasets/allenai/dolmino-mix-1124
End User Model Data
Data for fine-tuning published in a well-organized manner.
https://huggingface.co/datasets/allenai/tulu-3-sft-olmo-2-mixturehttps://huggingface.co/datasets/allenai/olmo-2-1124-13b-preference-mixhttps://huggingface.co/datasets/allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
Base Model Weights
Model weights available in many stages.
https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc
End User Model Weights
Model weights available in many stages.
https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc
Training Code
Multiple repos with training, architecture and fine-tuning code available.
https://github.com/allenai/OLMo
Documentation
Code Documentation
Repositories and code well-described, commented and documented.
https://github.com/allenai/OLMo
Hardware Architecture
Architecture documented in requisite detail.
https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct#model-sources
Preprint
Pre-print goes into impressive detail about the data, training process, architecture, and evaluation.
https://arxiv.org/abs/2501.00656
Paper
2024 ACL paper documents important parts of design, architecture, and processing pipelines.
https://aclanthology.org/2024.acl-long.841/
Modelcard
Model card provides broad overview and links to full details.
https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct
Datasheet
Data sheets are well-documented and provide requisite info.
https://huggingface.co/datasets/allenai/tulu-3-sft-olmo-2-mixturehttps://huggingface.co/datasets/allenai/olmo-2-1124-13b-preference-mixhttps://huggingface.co/datasets/allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
Access
Package
Model available on Ollama.
https://ollama.com/library/olmo2
API and Meta Prompts
Available through HuggingFace though model does not run on free inference API. Inference endpoints are available.
https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct
Licenses
Apache 2.0, an OSI-approved license.
https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct#license-and-use
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