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OLMo: Accelerating the Science of Language Models

About

Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation.

Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi• 2024

Related benchmarks

TaskDatasetResultRank
Commonsense ReasoningHellaSwag
Accuracy76.4
1460
Mathematical ReasoningGSM8K
Accuracy1.7
983
Multi-task Language UnderstandingMMLU
Accuracy47.3
842
Commonsense ReasoningWinoGrande
Accuracy67.9
776
Question AnsweringARC Challenge
Accuracy48.5
749
Question AnsweringOpenBookQA
Accuracy50.4
465
Question AnsweringARC Easy
Normalized Acc65.4
385
Physical Commonsense ReasoningPIQA
Accuracy78.4
329
Boolean Question AnsweringBoolQ
Accuracy73.4
307
Question AnsweringSciQ
Accuracy93.8
226
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Other info

Code

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