OLMoE: Open Mixture-of-Experts Language Models
Keywords: large language models, mixture-of-experts, open-source
TL;DR: A state-of-the-art Mixture-of-Experts LLM with 1B active and 7B total parameters trained for 5T tokens that is 100% open-source
Abstract: We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input token. We pretrain it on 5 trillion tokens and further adapt it to create OLMoE-1B-7B-Instruct. Our models outperform all available models with similar active parameters, even surpassing larger ones like Llama2-13B-Chat and DeepSeekMoE-16B. We present novel findings on MoE training, define and analyze new routing properties showing high specialization in our model, and open-source all our work: model weights, training data, code, and logs.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 211
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