The Allen Institute for AI (AI2) has introduced OLMo, a groundbreaking actually openLLM - it stands out by offering unprecedented access to its pre-training data, training code, and model weights. Something other “open” models had never done. Not going into the numbers, but this level of openness is designed to accelerate scientific understanding and technological advancements by allowing the community to build upon previous work, ensuring AI development is more inclusive, efficient, and reducing overall carbonfootprint.
This resonates strongly with the feedback from exchanges earlier last Tuesday by Mick LEVY and Didier Gaultier, Peter Martey Addo, PhD from AFD, and exchanges by Albert Meige and Philippe Limantour at HECAlumni.
Don’t get me started on how OLMo could help address some of the challenges raised in these exchanges:
- With shared training data and methodologies public, OLMo could ethical concerns regarding bias, fairness, and the responsible use of AI, and this transparency helps the community identify and mitigate potential issues in AI models, with increased trust.
- A corollary: reproducibility. Full access to model weights and training logs allows researchers to replicate the training process and validate the results reported by AI2,
- Its open nature collaboration among researchers, institutions, and industries. This shared ecosystem could accelerate the pace of discovery and development in AI, leading to more robust and versatile AI solutions.
- Greener AI: it reduces the need for individual researchers or organizations to conduct their own resource-intensive training runs from scratch and reducing redundant training sessions.
- This initiative addresses the critical need for open-source models to prevent the monopolization of AI technology and ensure its safe and responsible development, globally (possibly addressing some of the LLM localization challenges presented by Peter Addo earlier this week at AFD).
Really great news ahead!
OLMo opensource llm AFD
- https://sprou.tt/1cXeJol8u0D
