Grove MoE uses unequal-size adjugate experts with complexity-based activation to run 33B-parameter models at roughly 3.1 to 3.3B active parameters while matching larger open models in benchmarks.
MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation
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Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
Grove MoE uses unequal-size adjugate experts with complexity-based activation to run 33B-parameter models at roughly 3.1 to 3.3B active parameters while matching larger open models in benchmarks.