📋 Cerebras, MiniMax-M2-REAP-162B-A10B 출시: Long Context Coding Agent를 위한 MiniMax-M2의 메모리 효율적인 버전 완벽가이드
✨ Cerebras, MiniMax-M2-REAP-162B-A10B 출시: Long Context Coding Agent를 위한 MiniMax-M2의 메모리 효율적인 버전
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Cerebras has released MiniMax-M2-REAP-162B-A10B, a compressed Sparse Mixture-of-Experts (SMoE) Causal Language Model derived from MiniMax-M2, using the new Router weighted Expert Activation Pruning (REAP) method. The model keeps the behavior of the original 230B total, 10B active MiniMax M2, while p
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Cerebras has released MiniMax-M2-REAP-162B-A10B, a compressed Sparse Mixture-of-Experts (SMoE) Causal Language Model derived from MiniMax-M2, using the new Router weighted Expert Activation Pruning (REAP) method. The model keeps the behavior of the original 230B total, 10B active MiniMax M2, while pruning experts and reducing memory for deployment focused workloads such as coding agents and tool […]
The post Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents appeared first on MarkTechPost.