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nightmedia/
Qwen3.6-35B-A3B-Fable-Holo3-Qwopus-mxfp4-mlx

Name: Qwen3.6-35B-A3B-Fable-Holo3-Qwopus-mxfp4-mlx
Availability: InStock

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Qwen3.6-35B-A3B-Fable-Holo3-Qwopus-mxfp4-mlx

This model is a merge of:

armand0e/Qwen3.6-35B-A3B-Fable-5-Distill
nightmedia/Qwen3.6-35B-A3B-MTP-Holo3-Qwopus-BF16

Brainwaves

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

brainwaves

         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

brainwaves

         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

brainwaves

         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

Thinking toggle

This model is using(an early version of) the fixed jinja template from froggeric/Qwen-Fixed-Chat-Templates

Drop <|think_on|> or <|think_off|> anywhere in your system or user prompt. The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.

The tag syntax (<|think_on|>, <|think_off|>) uses Qwen's control-token delimiters, so it will never collide with real text. Earlier community templates used /think, which broke legitimate paths like cd /mnt/project/think.

I added a similar set of tags as <|think_forget|> or <|think_remember|> for handling the preserve_thinking flag.

Contribute to NightmediaAI

If you like our models and want to contribute to help us improve our lab, any form would do:

ETH:0x6b6633606995BC180925c47d4249ED624aB7b2A5 USDC:0x19e6bDDCBa47BB09a9Bc153Bb6479fc57284421a BTC:36d7U1n3MFaXgnNRAaEL3Pa3Hy6oFhM7XY BCH:15dNMzhJ87XJSTU89VCBsDHj747QvBQaap

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 (0.776 vs 0.766 on qx86-hi).

4. Perplexity vs throughput tradeoff

qx64-hi: 4.438 ppl, 36.91 GB, ~1466 tok/s → best reasoning profile, moderate memory
qx86-hi: 4.475 ppl, 45.50 GB, ~1565 tok/s → slightly noisier attention routing, faster due to uniform 8/6 layout
mxfp4: 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

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

Use with mlx

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.6-35B-A3B-Fable-Holo3-Qwopus-mxfp4-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

nightmedia/Qwen3.6-35B-A3B-Fable-Holo3-Qwopus-mxfp4-mlx

Qwen3.6-35B-A3B-Fable-Holo3-Qwopus-mxfp4-mlx

Model components

Baseline model

Thinking toggle

Contribute to NightmediaAI

From the qx64-hi trace

🔍 Deckard(qx) Mechanics: Why the qx64-hi Bump Occurs

🧩 Functional Parallel to the Holodeck Architecture

Model recipe

Use with mlx

nightmedia/
Qwen3.6-35B-A3B-Fable-Holo3-Qwopus-mxfp4-mlx

🔍 Deckard(qx) Mechanics: Why the `qx64-hi` Bump Occurs