most citedFSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models

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cs.CL2026

F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World

Ziyin Zhang, Zihan Liao, Hang Yu +2

We present F2LLM-v2, a new family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a newly curated composite of 60 million…