MiniMax-M3-REAP22-Coder A JANG-quantized MiniMax-M3 — coding/agentic + multimodal — for the vMLX engine (Apple Silicon / MLX). > ⚠️ Requires vMLX engine v1.5.67 or newer. > This is a JANG-format model (JANG affine-mixed + AWQ quantization, REAP expert pruning, and the > MiniMax-M3 MSA / Lightning-Indexer runtime). It will NOT load with transformers, vLLM, or generic MLX > loaders — it needs vMLX’s JANG loader + the M3 runtime. Coder support lands in vMLX ≥ 1.5.67. JANG is vMLX’s quantization + packing format: mixed-precision affine quantization (per-projection bit widths) + AWQ activation-aware scaling + REAP expert pruning, described by a jangconfig.json. Weights stay quantized in GPU memory and are loaded by vMLX’s JANG loader. Because the format and the MiniMax-M3 runtime (MSA dual-cache, Lightning Indexer, partial RoPE, vision tower) are vMLX-specific, these models run only on vMLX ≥ 1.5.67.** 1. Install/update vMLX 1.5.67+ — https://mlx.studio (or pip install -U vmlx). 2. App: Server → New Session → pick/download this model → Start → chat. 3. CLI: vmlx-engine serve JANGQ-AI/MiniMax-M3-REAP22-Coder —reasoning-parser minimaxm3 —tool-call-parser minimaxm3 — Coding: HumanEval…
Модальности:
Генерация текста Мультимодальность
Области применения:
Генерация кода Диалог / чат
Задача: Генерация текста
Автор: JANGQ-AI
Теги: mlx, minimax_m3_vl, vmlx, jang, reap, awq, moe, code
Лайков: 4 | Загрузок: 274
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
