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gauravmm/MiniMax-M2.5-REAP-139B-A10B-NVFP4
MiniMax-M2.5-REAP-139B-A10B-NVFP4 is a text generation model from gauravmm. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
NVFP4 weight-only (W4A16) quantization of Cerebras' MiniMax-M2.5-REAP-139B-A10B — a REAP (router-weighted expert-activation pruning) prune of MiniMax-M2 down to 139B total / 10B active (154 experts, pruned from 256; 6…
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From the Hugging Face model README
NVFP4 weight-only (W4A16) quantization of Cerebras'
MiniMax-M2.5-REAP-139B-A10B
— a REAP (router-weighted expert-activation pruning) prune of MiniMax-M2 down to 139B total /
10B active (154 experts, pruned from 256; 62 layers). This is an agentic/reasoning MoE. The
weights are quantized to NVFP4 (4-bit float E2M1, block-16 FP8-E4M3 micro-scale + FP32
per-tensor global scale, ≈4.5 bits/value) with BF16 activations, so it fits and serves on a
single 128 GB DGX Spark / GB10. Unlike most REAP targets there is no BF16/FP16 source —
the only upstream is block-FP8 (float8_e4m3fn + weight_scale_inv [128,128]); the
shard-by-shard streaming quantizer dequantizes FP8→BF16 on the fly per shard
(w_bf16 = w_fp8 × weight_scale_inv) before computing NVFP4, never materializing a giant
intermediate (peak RAM ≈ one shard). Output is the standard compressed-tensors
nvfp4-pack-quantized format — drop-in for vLLM, no custom modeling code (vLLM resolves
MiniMaxM2ForCausalLM natively). On-disk size is 75 GB (down from 131 GB block-FP8); served
runtime footprint ≈109 GB. Benchmarked on vLLM at prefill 128 / decode 120 tok/s (ShareGPT,
concurrency 32, 64K context, 0 errors). The fused parallel projections (q/k/v→qkv,
gate/up→gate_up, expert w1/w3) share one weight_global_scale per group — required for
correct output under vLLM's fused MoE/attention layers.
vllm serve gauravmm/MiniMax-M2.5-REAP-139B-A10B-NVFP4 \
--quantization compressed-tensors \
--moe-backend marlin \
--trust-remote-code \
--max-model-len 65536 \
--kv-cache-dtype fp8
--moe-backend marlinis mandatory on this NVFP4 W4A16 checkpoint — the flashinfer_cutlass MoE backend rejects W4A16 NVFP4 and crashes engine-core init.
MiniMax-M2 (MiniMax AI, modified-MIT) → REAP prune (Cerebras) → NVFP4 W4A16 (this repo).
License inherited from upstream (see LICENSE): MIT with a high-revenue attribution clause
(display "MiniMax M2" if the deploying product exceeds the stated annual-recurring-revenue
threshold).