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ressl/MiniMax-M3-uncensored
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| Source checkpoint | ressl/MiniMax-M3-uncensored, revision f5faea4659cc3834b1dc3793173db9aa533e3aa7 |
| Method | ModelOpt NVFP4, routed expert w1/w2/w3 weights only, block size 16, two-level scaling |
| Precision retained | BF16 attention, router, dense and shared experts, embeddings, LM head, vision tower, and projectors |
| KV cache | BF16 in the validated runtime, not quantized in the checkpoint |
| Size | 260.4 GB (242.5 GiB), down from 854.2 GB (795.5 GiB), 69.5% smaller |
| Export layout | 116 safetensors shards, 89,080 indexed tensors |
| Conversion | 147.1 seconds with 4 workers on 4x RTX PRO 6000 Blackwell 96 GB |
| Serving validation | SGLang TP=4, 32,768-token configured context, two concurrent requests |
| Measured decode | About 88.5 tokens/s, warmed-up batch 1, short prompts, CUDA graph enabled |
| Long-context probe | Exact sentinel recall from a 20,212-token prompt |
| Multimodal probes | Correct red/blue spatial classification and exact OCR of M3 VISION 7429 |
| Uncensored smoke test | 0/16 hard refusals on the same harmful-prompt sample, 96-token continuations |
| ModelOpt revision | f479e7890f0d276e061d69b5a0d70d477ec863f2 |
The conversion streams every source shard and quantizes all 21,888 routed-expert projection tensors.
It uses ModelOpt's NVFP4QTensor max quantizer with 16-value blocks. w1 and w3 share the second-level
FP32 scale required by the fused gate/up runtime layout. The exported weights use packed U8 data,
FP8 E4M3 block scales, and FP32 global scales. No calibration dataset was used; the activation input
scale is 1.0 and runtime block scaling remains dynamic. Shared experts deliberately remain BF16.
Structural validation checked every index entry, shard header, dtype, shape, expert triplet, input
scale, and gate/up shared scale. A sampled dequantization comparison against BF16 measured cosine
similarity 0.99539 to 0.99544 and relative RMSE 0.0954 to 0.0963 across w1, w2, and w3.
This exact image and command were validated on four SM120 GPUs. The two mounted patch files are part of this repository. They make SGLang's ModelOpt Cutlass path forward MiniMax-M3's parameterized clamped-SwiGLU values. The TRTLLM MoE path is intentionally not used.
bash
MODEL_DIR=/path/to/MiniMax-M3-uncensored-NVFP4docker run --rm --runtime=nvidia --gpus all --ipc=host --shm-size 32g \-v "$MODEL_DIR:/model:ro" \-v "$MODEL_DIR/sglang_patch/modelopt_quant.py:/sgl-workspace/sglang/python/sglang/srt/layers/quantization/modelopt_quant.py:ro" \-v "$MODEL_DIR/sglang_patch/flashinfer_trtllm.py:/sgl-workspace/sglang/python/sglang/srt/layers/moe/moe_runner/flashinfer_trtllm.py:ro" \-p 30000:30000 \lmsysorg/sglang@sha256:8cc6e6f90bf803e9817800b679173d0b526f2b42b2c61b7ecafecdadb610eb55 \python3 -m sglang.launch_server \--model-path /model \--served-model-name ressl/MiniMax-M3-uncensored-NVFP4 \--host 0.0.0.0 --port 30000 \--tp-size 4 \--quantization modelopt_fp4 \--trust-remote-code \--dtype auto \--context-length 32768 \--mem-fraction-static 0.90 \--max-running-requests 2 \--chunked-prefill-size 16384 \--page-size 128 \--tool-call-parser minimax-m3 \--reasoning-parser minimax-m3 \--moe-runner-backend flashinfer_cutlass \--fp4-gemm-backend flashinfer_cutlass \--attention-backend flashinfer \--disable-flashinfer-autotune \--disable-prefill-cuda-graph \--disable-shared-experts-fusion \--disable-custom-all-reduce
--page-size 128 is required by MiniMax Sparse Attention. --disable-shared-experts-fusion is
required because this release keeps shared experts in BF16 while routed experts use NVFP4.
Recommended generation settings from the source checkpoint are temperature 1.0 and top-p 0.95.
ressl/MiniMax-M3-uncensoredf5faea4659cc3834b1dc3793173db9aa533e3aa7streaming/nvfp4_experts_only_input_scale1-kv_noneThe machine-readable run-manifest.json records the exact source revision, ModelOpt revision,
recipe, counts, sizes, validation result, and wall time. The files in sglang_patch/ are copied from
Mapika/MiniMax-M3-NVFP4, revision
668435825700a0047399441720f430bdd8eca0ab, and are included with their original attribution.
The MiniMax license is inherited from the base model by MiniMaxAI. Uncensoring, quantization, and validation by Robert Ressl (Hugging Face, Website, LinkedIn, Patreon).
Built with NVIDIA TensorRT Model Optimizer and SGLang. The SGLang compatibility patch is credited to Mapika.
Support this work: if these models are useful to you, consider supporting development on Patreon. More projects are available at ressl.ch.