beamster/MiMo-V2.6-Flash-Sushi-2.3bpw

Model

MiMo-V2.6-Flash-Sushi-2.3bpw

2

6 commits

2 linked in READMEs

updated Oct 7, 2026

See the code

README

sushi

MiMo-V2.6-Flash-Sushi-2.3bpw

XiaomiMiMo/MiMo-V2.6-Flash-MOPD packed for sushi, a native inference engine for Apple Silicon. Target: a 128 GB Mac. The weights alone take about 90 GB of GPU memory, so it does not fit on a 96 GB Mac.

parthow it is stored
routed experts (47 MoE layers x 256, top-8)EXL3 at 2.25 bits per weight (the last layer at 4), made with our own converter
dense part (attention, layer-0 MLP)FP8 as released, with its 128x128 block scales; output projections, embedding and lm_head 8-bit affine
vision tower (MiMo-ViT)included: image input
MTP headsincluded (3 layers)
contextup to 1,048,576 tokens
disk~96 GiB

sushi only. This pack needs sushi v1.1.0 or later on Apple Silicon with macOS 26.2 or later. It does not load in transformers, vLLM, mlx-lm or exllamav3. Audio and video input are not supported.

How to run it (install, launch flags, memory and context per Mac): sushi usage.

dflash/ (the DFlash drafter) and audio_tokenizer/ are Xiaomi's originals, unchanged, for runtimes that use them; sushi does not load either.

Quality

KLD against the original MOPD checkpoint (its FP8 dense part as stored, no quantized KV of its own): 16 prompts x 512 tokens, scored to the first EOS (8067 positions), KV cache at kv8. Lower is better.

packweights in GPU memoryKLDtop-1 agreement
MiMo-V2.6-Flash-Sushi-2.3bpw~90 GB0.086091.95%

Hard questions

LLMProbe Hard 50: 84.0%

LLMProbe's Hard 50 set (MMLU-Pro, OlympiadBench, LiveBench, NIST Juliet): 42 correct, 7 incorrect, 1 partial (hit the token cap), 84.0%. llmprobe 0.6.12, xhigh effort, 12k reasoning / 16k output tokens, 8-bit KV and MTP.

Credits and license

  • Xiaomi MiMo team: the base model, XiaomiMiMo/MiMo-V2.6-Flash-MOPD, released under the MIT License; this pack is a derivative under the same license.
  • turboderp: the EXL3 format.
  • ddalcu: mlx-serve. sushi is a fork of it.
apple-silicon
conversational
custom_code
exl3
image-text-to-text
mimo_v2
safetensors
sushi

beamster/MiMo-V2.6-Flash-Sushi-2.3bpw

Model

MiMo-V2.6-Flash-Sushi-2.3bpw

2

6 commits

2 linked in READMEs

updated Oct 7, 2026

See the code

README

sushi

MiMo-V2.6-Flash-Sushi-2.3bpw

XiaomiMiMo/MiMo-V2.6-Flash-MOPD packed for sushi, a native inference engine for Apple Silicon. Target: a 128 GB Mac. The weights alone take about 90 GB of GPU memory, so it does not fit on a 96 GB Mac.

parthow it is stored
routed experts (47 MoE layers x 256, top-8)EXL3 at 2.25 bits per weight (the last layer at 4), made with our own converter
dense part (attention, layer-0 MLP)FP8 as released, with its 128x128 block scales; output projections, embedding and lm_head 8-bit affine
vision tower (MiMo-ViT)included: image input
MTP headsincluded (3 layers)
contextup to 1,048,576 tokens
disk~96 GiB

sushi only. This pack needs sushi v1.1.0 or later on Apple Silicon with macOS 26.2 or later. It does not load in transformers, vLLM, mlx-lm or exllamav3. Audio and video input are not supported.

How to run it (install, launch flags, memory and context per Mac): sushi usage.

dflash/ (the DFlash drafter) and audio_tokenizer/ are Xiaomi's originals, unchanged, for runtimes that use them; sushi does not load either.

Quality

KLD against the original MOPD checkpoint (its FP8 dense part as stored, no quantized KV of its own): 16 prompts x 512 tokens, scored to the first EOS (8067 positions), KV cache at kv8. Lower is better.

packweights in GPU memoryKLDtop-1 agreement
MiMo-V2.6-Flash-Sushi-2.3bpw~90 GB0.086091.95%

Hard questions

LLMProbe Hard 50: 84.0%

LLMProbe's Hard 50 set (MMLU-Pro, OlympiadBench, LiveBench, NIST Juliet): 42 correct, 7 incorrect, 1 partial (hit the token cap), 84.0%. llmprobe 0.6.12, xhigh effort, 12k reasoning / 16k output tokens, 8-bit KV and MTP.

Credits and license

  • Xiaomi MiMo team: the base model, XiaomiMiMo/MiMo-V2.6-Flash-MOPD, released under the MIT License; this pack is a derivative under the same license.
  • turboderp: the EXL3 format.
  • ddalcu: mlx-serve. sushi is a fork of it.
apple-silicon
conversational
custom_code
exl3
image-text-to-text
mimo_v2
safetensors
sushi