As of 2025-11-19, create a fresh Python environment and run: 𓌳 REAP𓌳 the Experts: Why Pruning Prevails for One-Shot MoE Compression Introducing MiniMax-M2-REAP-162B-A10B, a memory-efficient compressed variant of MiniMax-M2 that maintains near-identical performance while being 30% lighter. This model was created using REAP (Router-weighted Expert Activation Pruning), a novel expert pruning method that selectively removes redundant experts while preserving the router’s independent control over remaining experts. Key features include: — Near-Lossless Performance: Maintains almost identical accuracy on code generation, agentic coding, and function calling tasks compared to the full 230B model — 30% Memory Reduction: Compressed from 230B to 162B parameters, significantly lowering deployment costs and memory requirements — Preserved Capabilities: Retains all core functionalities including code generation, math & reasoning and tool calling. — Drop-in Compatibility: Works with vanilla vLLM — no source modifications or custom patches required — Optimized for Real-World Use: Particularly effective for resource-constrained environments, local deployments, and academic research Benchmark…
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Генерация текста
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Задача: Генерация текста
Автор: QuantTrio
Теги: minimax_m2, vLLM, AWQ, conversational, custom_code, endpoints_compatible, 4-bit, awq
Лайков: 3 | Загрузок: 29
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
