Looks like these were released six days ago. Did a search and didn't see a post about them.
https://huggingface.co/AIDC-AI/Marco-Mini-Instruct
https://huggingface.co/AIDC-AI/Marco-Nano-Instruct
Pretty wild parameter/active ratio, should be lightning fast.
Marco-Mini-Instruct is the instruction-tuned variant of Marco-Mini-Base, a highly sparse Mixture-of-Experts (MoE) multilingual language model from the Marco-MoE family, developed by Alibaba International Digital Commerce. It activates only 0.86B out of 17.3B total parameters (5% activation ratio) per token. Marco-Mini-Instruct achieves the best average performance across English, multilingual general, and multilingual cultural benchmarks when compared against instruct models with up to 12B activated parameters, including Qwen3-4B-Instruct, Ministral3-8B-Instruct, Gemma3-12B-Instruct, LFM2-24B-A2B, and Granite4-Small-Instruct.
Marco-Nano-Instruct is the post-trained variant of Marco-Nano-Base, a highly sparse Mixture-of-Experts (MoE) multilingual language model from the Marco-MoE family, developed by Alibaba International Digital Commerce. It activates only 0.6B out of 8B total parameters (7.5% activation ratio) per token. Despite its extreme sparsity, Marco-Nano-Instruct achieves the best average performance across English, multilingual general, and multilingual cultural benchmarks among all comparable instruct models up to 3.84B activated parameters.
https://xcancel.com/ModelScope2022/status/2042084482661191942
https://pbs.twimg.com/media/HFbvyB-WsAAayv1.jpg?name=orig
Meet Marco-Mini-Instruct: a highly sparse MoE multilingual model from Alibaba International. 17.3B total params, only 0.86B active (5% activation ratio). 🚀
Beats Qwen3-4B, Gemma3-12B, Granite4-Small on English, multilingual general, and cultural benchmarks — with a fraction of their active params.
🌍 29 languages: Arabic, Turkish, Kazakh, Bengali, Nepali and more
🧠 256 experts, 8 active per token. Drop-Upcycling from Qwen3-0.6B-Base.
🎯 2-stage post-training: SFT + Online Policy Distillation (Qwen3-30B → Qwen3-Next-80B cascade)
✅ Apache 2.0
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