MiniMax-M3 · REAP-40 · JANG_2L
⚠️ Requires vMLX ≥ v1.5.62
Earlier vMLX builds contain a runtime cache bug that causes repetition loops on long output. This is an engine issue, not a weights issue — update vMLX to v1.5.62 or later before running this model. On v1.5.62+ generation is clean.
A space-efficient MiniMax-M3 bundle for Apple Silicon: 40 % REAP expert pruning + JANG_2L mixed-precision quantization, ~95 GB — the smaller, lower-RAM sibling of REAP-32 (more pruning, less resident memory). Runs comfortably on a single 128 GB Mac via vMLX / MLX.
What this is
-
Base: MiniMax-M3 (
model_type=minimax_m3_vl) — MoE, GQA-4, MSA Lightning Indexer, vision tower. -
Pruning: REAP saliency pruning, 40 % of routed experts removed (77 of 128 kept per MoE layer), highest-saliency experts retained.
-
Quantization (JANG_2L, affine, group size 64):
tensor bits routed experts gate_proj/up_proj2 routed experts down_proj3 shared experts 6 dense MLP (layers 0–2) 6 attention q/k/v/o 8 embeddings 6 lm_head 8 vision tower + projectors 8 norms, router gate, MSA indexer fp16 down_projis kept at 3-bit (the rest of the routed experts are 2-bit) for stable long-form coherency. The full per-module bit map is written intoconfig.json(quantization) and applied automatically by the loader.
Which one to use
- REAP-40 (this repo, ~95 GB): smaller, lower resident RAM, more headroom.
- REAP-32 (~105 GB): keeps more experts; higher quality ceiling, closer to the RAM limit on 128 GB machines.
Usage
Load in vMLX (v1.5.62+); the engine autodetects minimax_m3_vl and applies the correct
settings (native MSA cache, paged cache off, per-module quant map). Sampling defaults ship
in generation_config.json (temperature=1.0, top_p=0.95).
Attribution
- Quantization & packaging: Jinho Jang · eric@jangq.ai
- Base model © MiniMax, used under the MiniMax-M3 license.