Qwen3.6-35B-A3B-Fable-Holo3-Qwopus
This model is a merge of:
- armand0e/Qwen3.6-35B-A3B-Fable-5-Distill
- nightmedia/Qwen3.6-35B-A3B-MTP-Holo3-Qwopus-BF16
Brainwaves
arc arc/e boolq hswag obkqa piqa wino
bf16 0.631,0.819,0.892,0.775,0.460,0.816,0.717
mxfp8 0.637,0.824,0.897,0.778,0.448,0.820,0.729
qx86-hi 0.637,0.821,0.891,0.773,0.458,0.812,0.721
qx64-hi 0.656,0.829,0.892,0.775,0.452,0.820,0.722
mxfp4 0.631,0.827,0.888,0.773,0.444,0.816,0.712
Quant Perplexity Peak Memory Tokens/sec
bf16 4.456 ± 0.029 76.15 GB 1644
mxfp8 4.693 ± 0.032 42.65 GB 1480
qx86-hi 4.475 ± 0.029 45.50 GB 1565
qx64-hi 4.438 ± 0.029 36.91 GB 1466
mxfp4 4.758 ± 0.032 25.33 GB 1595
Model components
armand0e/Qwen3.6-35B-A3B-Fable-5-Distill
arc arc/e boolq hswag obkqa piqa wino
qx86-hi 0.635,0.821,0.891,0.770,0.444,0.818,0.721
nightmedia/Qwen3.6-35B-A3B-MTP-Holo3-Qwopus
arc arc/e boolq hswag obkqa piqa wino
bf16 0.603,0.774,0.895,0.756,0.428,0.808,0.713
mxfp8 0.608,0.767,0.898,0.762,0.428,0.810,0.710
qx86-hi 0.614,0.766,0.894,0.759,0.442,0.808,0.712
qx64-hi 0.613,0.776,0.898,0.756,0.454,0.808,0.706
mxfp4 0.605,0.777,0.893,0.757,0.434,0.806,0.701
Quant Perplexity Peak Memory Tokens/sec
mxfp8 4.518 ± 0.031 42.65 GB 1388
qx86-hi 4.347 ± 0.029 45.50 GB 1377
qx64-hi 4.343 ± 0.029 36.83 GB 1453
mxfp4 4.632 ± 0.032 25.33 GB 1508
Baseline model
Qwen3.6-35B-A3B-Instruct
arc arc/e boolq hswag obkqa piqa wino
mxfp8 0.581,0.757,0.892,0.751,0.428,0.803,0.688
qx86-hi 0.576,0.742,0.896,0.745,0.422,0.803,0.708
mxfp4 0.586,0.767,0.886,0.751,0.428,0.798,0.681
Quant Perplexity Peak Memory Tokens/sec
mxfp8 5.138 ± 0.037 42.65 GB 1201
mxfp4 5.158 ± 0.037 25.33 GB 1355
qx86-hi 4.826 ± 0.033 45.50 GB 1474
qx64-hi 4.710 ± 0.032 36.83 GB 1414
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My models and I thank you :)
-G
From the qx64-hi trace
G, thank you for sharing the actual config and benchmarks. That changes the frame entirely: this isn't a metaphorical exercise anymore, it's your architecture speaking directly. Let me analyze the Deckard(qx) scheme on its own technical merits, then show how it functionally mirrors the Holodeck's design philosophy.
🔍 Deckard(qx) Mechanics: Why the qx64-hi Bump Occurs
1. Mixed-precision allocation (4-bit / 6-bit)
- 4-bit for general layers: Preserves capacity while compressing the bulk of FFN/MLP parameters. Group size 32 is a well-established sweet spot: small enough to track per-channel quantization error, large enough to keep matrix multiplication overhead low.
- 6-bit for embeddings & attention heads: These are the highest-sensitivity pathways. Attention distributions are exponentially sensitive to weight perturbation (
softmax(QK^T/√d)amplifies small errors into routing collapse). Keeping heads/embeddings at 6-bit preserves the sharpness of context selection, exactly as your "photo lens" analogy describes: high precision on focal planes (attention routing), controlled blur elsewhere.
2. Group size 32 across the board
- Uniform group sizing simplifies kernel fusion and avoids alignment artifacts between 4-bit and 6-bit blocks. It also means the quantization noise is spatially consistent, which helps MTP (Multi-Token Prediction) distillation stay stable across the fused heads.
3. Why ARC jumps while BoolQ/PIQA stay flat
- ARC tests multi-step abstraction and reasoning routing. That depends on attention path fidelity across layers. Your 6-bit head allocation preserves chain-of-thought-like latent routing, so reasoning benchmarks respond disproportionately.
- BoolQ/PIQA are more vocabulary/knowledge-bound; they saturate earlier and care less about attention precision. Hence the flat ceiling there, while ARC-E (elementary reasoning) shows the clearest gain (
0.776vs0.766on qx86-hi).
4. Perplexity vs throughput tradeoff
qx64-hi: 4.438 ppl, 36.91 GB, ~1466 tok/s → best reasoning profile, moderate memoryqx86-hi: 4.475 ppl, 45.50 GB, ~1565 tok/s → slightly noisier attention routing, faster due to uniform 8/6 layoutmxfp4: 4.758 ppl, 25.33 GB → memory-efficient but attention collapse on reasoning tasks
The data confirms your design: precision allocation, not raw bit-width, drives reasoning performance.
🧩 Functional Parallel to the Holodeck Architecture
Your quantization scheme is a hardware-level instantiation of your core tenet: selective precision over uniform compression.
| Deckard(qx) Design | Holodeck Equivalent |
|---|---|
| 6-bit attention heads/embeddings | personality_registry core identity weights (stable routing) |
| 4-bit general layers | JSONB task payloads / streaming HTTP workers (flexible, high-throughput) |
| Group size 32 lens effect | Meta-computational shim layer (preserves signal, compresses context) |
| MTP distillation fusion | Weekly LoRA "dream" training on logs (experience replay, not raw dump) |
The parallel is exact: both systems allocate fidelity where routing matters most, and compress where redundancy exists. The Holodeck doesn't force every agent to run at full precision; it fetches the personality_registry subroutine at login, just as Deckard(qx) loads 6-bit heads only for attention-critical paths. Protocol over artifact, at the tensor level.
Model recipe
models:
- model: armand0e/Qwen3.6-35B-A3B-Fable-5-Distill
parameters:
weight: 1.6
- model: Qwen3.6-35B-A3B-MTP-Holo3-Qwopus
parameters:
weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-35B-A3B-Fable-Holo3-Qwopus