MiMo-V2.5-DFlash β†’ PrismaQuant MXFP4/BF16 (170 GB) β€” custom-code artifacts

WIP: custom-code adaptation preserved before final export. Target: a 170 GiB weight compressed-tensors checkpoint of XiaomiMiMo/MiMo-V2.5-DFlash, servable on stock vLLM 0.11+, DFlash/MTP speculative decoding intact.

Committed quant config (no KL measurement β€” operator override, time-boxed)

  • Experts (256 routed, layers 1-47, ~302.8B params) β†’ MXFP4 (W4).
  • Sensitive set (attention qkv_proj+o_proj, dense layer-0 MLP) + MTP β†’ BF16.
  • router/gate + norms β†’ FP32; embed + lm_head β†’ BF16; in-model vision/audio towers β†’ BF16 passthrough.
  • audio_tokenizer/ DROPPED; dflash/ drafter β†’ BF16, separate (not counted in 170).
  • Measured footprint (safetensors headers): experts MXFP4 + all-BF16 sensitive β‰ˆ 164.7 GiB (≀170 GiB).

Files

  • mimo_v2.py β€” PrismaQuant MiMoV2Profile (nested per-expert MoE, fused qkv, FP8-source, 3 dense MTP layers). Verified: detect_profileβ†’MiMoV2Profile, 256 experts collapse per layer, dense-0 scored separately.
  • mimo_v2.json β€” declarative model-structure spec.
  • registry.patched.py β€” profile registration (before DeepseekV4 + DefaultProfile).
  • calib/*.jsonl β€” domain calibration set (JSON/tool-call/tender-extraction), disjoint gate split.
  • step1_keymatch.py β€” state-dict key-match diagnostic.

BLOCKER (documented for reproduction)

MiMo-V2.5-DFlash ships config-only (configuration_mimo_v2.py, no modeling_*.py; auto_map=AutoConfig only). transformers v5.0 built-in MiMoV2FlashForCausalLM (model_type mimo_v2_flash) is STRUCTURALLY INCOMPATIBLE: batched-3D experts (vs 72,192 separate per-expert tensors), split q/k/v (vs fused qkv_proj), no MTP. => A vendored modeling_mimo_v2.py (name-matching the checkpoint: nested 256-expert ModuleList w/ separate gate/up/down_proj; fused qkv_proj + attention_sink_bias on 39 SWA layers; model.mtp.layers.{0,1,2}; dense layer-0) is required for the transformers-based tooling path. MXFP4 being data-free also permits a direct tensor-level requant that bypasses model-loading. dflash/dflash.py may seed the MTP/attention shim.

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