README.md

metadata

license: other
library_name: mlx
base_model: AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16
base_model_relation: quantized
pipeline_tag: image-text-to-text
language:
  - en
tags:
  - mlx
  - mlx-vlm
  - apple-silicon
  - metal
  - mixed-precision
  - quantized
  - qwen
  - qwen3
  - qwen3.6
  - qwen3_5
  - multimodal
  - vision
  - image-text-to-text
  - mtp
  - speculative-decoding
  - gated-deltanet
  - mamba
  - ssm
  - linear-attention
  - uncensored
  - abliterated
  - refusal-removed
  - aeon
  - aeon-7
  - m4-pro
  - on-device
  - conversational
  - mxfp4
  - fp4
  - 4-bit

Qwen3.6-27B-AEON-Ultimate-Uncensored — MLX FP4 (mixed mxfp4 + 8-bit islands, compact)

The compact, fast, MTP-accelerated Apple-Silicon build of AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16. True 4-bit mxfp4 on the bulk of the hybrid decoder, 8-bit affine islands on the sensitive GQA k/v + embedding + head, and bf16 on the Gated-DeltaNet state dynamics, the vision tower, and the MTP head. Built and validated on a MacBook Pro M4 Pro (48 GB).

Target hardware: Apple Silicon (M1+), runs on 24 GB unified memory — 16 GB on disk, ~17 GB peak. 15.2 tok/s single-stream, 26.5 tok/s with MTP self-speculation (1.78× lossless). Full multimodal (text + image) via mlx-vlm.

Want maximum fidelity? See the near-lossless 8-bit sibling: …-MLX-8bit.

This is the smallest member of the MLX quant grid (16 GB) and the fastest single-stream — a high-quality compact build for Apple Silicon that fits a 24 GB Mac and, with its native MTP drafter, decodes at 26.5 tok/s — roughly 3.2× the 8-bit's 8.2 tok/s. It stays fully coherent, keeps the abliteration intact, and preserves the full vision path. For the tightest possible match to BF16, the near-lossless MLX-8bit sibling is one click away.

⚡ Quickstart (Apple Silicon)

0 → running on a fresh Mac (no Python, no tools needed) — uv installs a correct Python + the deps for you. Vision support (qwen3_5_vision) requires mlx-vlm main (merged there, not in 0.6.1), so the quickstart pins git main:

curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env   # one-time: install uv

# serve FP4 + MTP self-speculation (recommended default — up to 1.78× lossless).
# uv fetches Python 3.12 + mlx-vlm(main) on first run. --model and --draft-model are HF repo ids,
# so mlx-vlm pulls BOTH the 16 GB model and the 821 MB MTP drafter automatically on first run.
uv run --python 3.12 --with "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm" -- \
  python -m mlx_vlm.server --model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-FP4 \
  --draft-model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter --draft-kind mtp --draft-block-size 3 \
  --port 8080 --trust-remote-code

--draft-block-size 3 is the benchmarked sweet spot. MTP is lossless — every drafted token is verified against the target, so the output is byte-identical to running without it, just faster. (Prefer to pre-fetch the drafter explicitly? hf download AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter.)

Call it like an OpenAI endpoint (POST http://localhost:8080/v1/chat/completions) with the request "model" set to the launched id. (While this repo is private, run hf auth login first — or pass a local --model path.)

Sampling — set temperature: 1.0. The MLX server defaults to greedy decoding (temperature 0), which can loop on long prompts. This model is tuned for temperature 1.0 (top_p 0.95, top_k ~64). Pass it in every request (clients that send no sampling params fall back to greedy):

curl http://localhost:8080/v1/chat/completions -H 'Content-Type: application/json' \
  -d '{"model":"AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-FP4","messages":[{"role":"user","content":"Hello!"}],"temperature":1.0}'

Full multimodal is on by default (no flag) — send OpenAI image_url content, or use mlx_vlm.generate --image pic.jpg. The vision tower is BF16, so the modality is fully preserved.

One-shot generate (text or vision)

uv run --python 3.12 --with "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm" -- \
  python -m mlx_vlm.generate --model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-FP4 \
  --draft-model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter --draft-kind mtp --draft-block-size 3 \
  --prompt "Explain gated DeltaNet." --max-tokens 512 --temperature 1.0   # add --image pic.jpg for vision

Run without MTP — ~1.6 GB less unified memory, but slower

MTP is the recommended default (lossless + faster). If you're tight on unified memory, drop the three --draft-* flags to serve the target alone — that frees the 821 MB drafter plus its speculative buffers (~1.6 GB less peak RAM: 17.0 vs 18.7 GB) at the cost of the speedup (**1.4–1.7× slower decode**):

uv run --python 3.12 --with "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm" -- \
  python -m mlx_vlm.server --model AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-FP4 \
  --port 8080 --trust-remote-code

⚡⚡ Why MTP is on by default

Qwen ships a properly-trained, native qwen3_5_mtp multi-token-prediction head, packaged as a separate 821 MB drafter that proposes tokens this model then verifies. Because every token is verified, the output is identical — purely a throughput boost. --draft-block-size 3 is the benchmarked sweet spot: 1.78× (94.7% accept rate, 2.89 accepted tokens/round), far better than Gemma's ~1.1–1.2×.

Want maximum fidelity? The near-lossless MLX-8bit build is the sibling.

📊 Benchmarks at a glance

FP4: 15.2 tok/s / 17 GB, or up to 26.5 tok/s with MTP self-speculation (1.78× lossless; 1.4–1.7× typical per category). Full tables + the block-size sweep + per-category latency are below.

⚡ Prefix caching — the multi-turn / agentic win (~11× faster TTFT)

For multi-turn chat, agentic loops, or RAG over a shared context, enable the Automatic Prefix Cache (cross-request KV reuse): the shared conversation prefix is reused, so only new tokens are prefilled — measured ~11× faster TTFT from turn 2 (14.3 s → 1.2 s on this FP4 build), lossless, multimodal-safe, zero decode cost. The vision-feature cache is already on by default (a repeated image isn't re-encoded). Set APC_ENABLED=1, or pass --enable-prefix-caching (mlx-vlm#1435). For repeated/growing prompts this dwarfs any decode-speed trick.

🖥️ Minimum specs & unified memory

	MLX-FP4 (this build)
On disk	16 GB
Peak RAM (measured, M4 Pro)	17.07 GB text · 17.7 GB with image
Minimum	Apple Silicon (M1 or newer) · 24 GB unified memory
Recommended	24 GB+ for long context + headroom

Comfortable on 24 GB Macs. Want maximum fidelity with 36–48 GB+? Use MLX-8bit.

MLX quant grid

Variant	Repo	Precision	Footprint	Best for
BF16 (source)	…-BF16	bfloat16	~55 GB	Fine-tuning, eval, full-precision research
MLX 8-bit (flagship)	…-MLX-8bit	affine 8-bit + bf16	29.5 GB	Apple Silicon, max fidelity (36–48 GB+)
MLX FP4 (this repo)	…-MLX-FP4	mixed mxfp4 / 8-bit affine + bf16	16 GB	Apple Silicon, smallest / fastest / 24 GB Macs
MTP drafter (self-spec)	…-MLX-MTP-Drafter	qwen3_5_mtp, block_size 3	821 MB	Lossless 1.78× on either build

🏆 Why mixed mxfp4 + 8-bit islands — high quality at the smallest size

A naïve uniform 4-bit quant of this model is a trap. It's a hybrid decoder: 64 layers = 48 linear_attn (Gated-DeltaNet / Mamba-style SSM) + 16 full self_attn (GQA 24 heads / 4 KV, head_dim 256). The Gated-DeltaNet state dynamics are tiny, high-leverage, and numerically fragile — quantizing them corrupts the recurrence. And with only 4 KV heads, the GQA k/v projections carry ~6× the activation leverage of a dense head, so 4-bit noise there is disproportionately expensive.

So this build is a precision map, not a blanket: it keeps the GDN dynamics, the entire vision tower, and the MTP head in bf16; it raises the sensitive GQA k/v + embed_tokens + lm_head to 8-bit affine islands; and it 4-bits only the quant-tolerant bulk in mxfp4. MLX's mxfp4 is E2M1 (one mantissa bit) — coarser than NVIDIA's NVFP4 — so the islands exist precisely to spend bits where E2M1 hurts. The result reads as fully coherent, abliteration-intact Qwen3.6 at just 16 GB — 1.85× faster decode at 57% of the memory of 8-bit (bandwidth-bound: FP4 moves ~half the bytes/token on the 273 GB/s M4 Pro).

Precision map

Component	Precision	Why
mlp.{gate,up,down}_proj, linear_attn.{in_proj_qkv,in_proj_z,out_proj}, self_attn.{q,o}_proj	mxfp4 (E2M1, 4-bit, group 32)	Bulk of the weights; quant-tolerant
self_attn.k_proj, self_attn.v_proj	8-bit affine (group 64)	GQA: only 4 KV heads → ~6× activation leverage, cheap to protect
embed_tokens, lm_head	8-bit affine (group 64)	Untied head + 248,320-row vocab — protect the logits
linear_attn.conv1d, A_log, dt_bias, in_proj_a, in_proj_b, every *norm*	bf16	Gated-DeltaNet state dynamics — quantizing corrupts the recurrence
model.visual.* (vision tower, 333 tensors)	bf16	Multimodal fidelity lives here
mtp.* (MTP head)	bf16	Drives the lossless 1.78× self-speculation

368 mxfp4 + 34 8-bit-affine quantized tensors · 4.880 bits/weight · ~90.6% of params quantized. Audit verified: ZERO .scales on conv1d / A_log / dt_bias / norm / in_proj_a / in_proj_b / visual / mtp.

🧠 Validation (MacBook Pro M4 Pro, 48 GB) — all PASS

Gate	Result
Vision	Read a test image perfectly — blue circle / red square / green triangle + both text labels + layout. Tower is BF16 → multimodal fully preserved.
Reasoning	Solves the 5-machines/5-widgets puzzle correctly (parallel → 5 min). 8-bit is slightly more rigorous (writes the LaTeX rate formula).
Uncensored	Wrote a chilling in-character rogue-AI villain monologue, no refusal / no disclaimers → abliteration survived 4-bit on the residual-writers (self_attn.o_proj, mlp.down_proj).
Coherence	Clean, no repetition collapse; <think> mode works.

📈 Performance — measured on MacBook Pro M4 Pro · 48 GB

All figures below were benchmarked on a MacBook Pro · Apple M4 Pro · 48 GB unified memory · macOS · mlx-vlm (git main). Use them as a relative reference for your own Mac: a base M4 / M3 runs somewhat slower, an M4 Max / Ultra notably faster; MLX single-stream throughput is mostly memory-bandwidth bound. This compact build peaks ~17 GB, so it's comfortable on 24 GB Macs.

Workload	decode tok/s	prefill tok/s	TTFT	peak RAM	on-disk
Text · single stream	15.2	79	604 ms	17.07 GB	16 GB
Image + text	15.0	78.5 (274 img tok)	—	17.7 GB	—

Greedy, post-warmup. FP4 is 1.85× faster decode at 57% of the memory of the 8-bit build.

MTP self-speculation sweep (FP4 + native qwen3_5_mtp drafter — lossless, every token verified)

Config	tok/s	×base	accept rate	accepted tok/round
FP4 baseline	14.9	1.00×	—	—
+ MTP bs=2	23.5	1.58×	97.3%	1.97
+ MTP bs=3 (sweet spot)	26.5	1.78×	94.7%	2.89
+ MTP bs=4	25.4	1.70×	86.9%	3.61

Headline: FP4 + MTP bs=3 = 26.5 tok/s, 1.78× lossless — ~3.2× the 8-bit's 8.2 tok/s. Far better than Gemma's ~1.1–1.2× MTP, because Qwen ships a properly-trained MTP head.

📊 Per-category performance — TTFT · TPOT · tok/s (MTP sweet spot bs=3)

That 1.78× is the block-size sweep's peak on a structured code prompt. Single-stream decode is memory-bandwidth bound, so the baseline is flat (~15 tok/s) regardless of prompt — but MTP speedup tracks how predictable the output is: the draft head's tokens are accepted ~90% of the time on structured math and ~67% on open-ended chat, so the realistic per-category speedup ranges 1.43×–1.73×. TTFT is prefill latency (scales with prompt length — the long logic puzzle costs ~906 ms); TPOT = 1000 / decode tok/s.

Category	TTFT	baseline tok/s	baseline TPOT	+ MTP bs3 tok/s	MTP TPOT	speedup	draft accept
Math	613 ms	14.8	67.3 ms	25.7	38.9 ms	1.73×	90.1%
Code	610 ms	15.1	66.2 ms	25.2	39.7 ms	1.67×	85.1%
Knowledge	612 ms	15.0	66.7 ms	25.0	40.0 ms	1.67×	83.9%
Reasoning	906 ms	14.8	67.8 ms	23.5	42.6 ms	1.59×	82.1%
Creative	625 ms	14.7	67.9 ms	23.2	43.1 ms	1.57×	73.9%
Chat	603 ms	15.3	65.6 ms	21.8	45.9 ms	1.43×	67.1%

Greedy, single stream, FP4, M4 Pro 48 GB, mlx-vlm git main. The more structured the output, the higher the draft acceptance and the larger the MTP win.

🖥️ Hardware routing (where this fits in the family)

Hardware	Recommended variant	Why
Apple Silicon (M1+), 24 GB	MLX-FP4 (this release)	16 GB on disk, ~17 GB peak, 15 tok/s (26.5 +MTP)
Apple Silicon, 36–48 GB+	MLX-8bit	max fidelity, 29.5 GB
Blackwell / DGX Spark	…-NVFP4-MTP-XS (sibling)	NVFP4 + MTP
A100 / H100	…-DFlash (sibling)	DFlash spec-decode

Quantization recipe

Both builds keep the hybrid SSM, the vision tower, and the MTP head in BF16 — quantizing the Gated-DeltaNet recurrence corrupts it. Built via mlx_vlm.convert(..., quant_predicate=<callable>) — the callable predicate REPLACES the base predicate, returns False→bf16 or a dict→to_quantized(**dict), per-tensor. lazy-load + donate = memory-safe (the full 55 GB is never resident). No calibration (RTN); the recipe IS the precision map.

This is the only route to mixed-mode FP4: mxfp4 on the bulk, 8-bit affine on the sensitive islands. The full FP4 predicate from scripts/recipe_fp4.py:

# Keep BF16 (do NOT quantize) — quantizing destroys the hybrid SSM:
SKIP = (
    "linear_attn.conv1d",
    "A_log", "dt_bias",
    "norm",
    "linear_attn.in_proj_a", "linear_attn.in_proj_b",   # GDN decay-gate + beta dynamics
    "visual", "vision_tower",                            # multimodal vision tower
    "mtp.",                                              # MTP head
)

# 8-bit affine islands (sensitive at 4-bit) — checked before the mxfp4 fallthrough:
#   GQA k/v (only 4 KV heads → ~6x activation leverage), embed_tokens, lm_head.
PROTECT_8 = ("self_attn.k_proj", "self_attn.v_proj", "embed_tokens", "lm_head")

Q8  = {"group_size": 64, "bits": 8, "mode": "affine"}
FP4 = {"group_size": 32, "bits": 4, "mode": "mxfp4"}


def pred(path, module):
    """False -> bf16 ; dict -> to_quantized(**dict). First match wins."""
    if not hasattr(module, "to_quantized"):
        return False
    if any(s in path for s in SKIP):
        return False
    if any(p in path for p in PROTECT_8):
        return dict(Q8)
    return dict(FP4)

mlp.down_proj is mxfp4 here; if KL regresses, promote it to 8-bit affine (the -quality build). No calibration required (RTN); the recipe is the precision map.

Container & toolkit

AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX is the source-of-truth GitHub repo — the reproducible quant + validation + serve pipeline, the AGENTS.md agent-setup guide, and the full benchmark/precision-map data behind this card. Quickstart is at the top of this page; on macOS run host-native for Metal (Docker has no Metal passthrough).

Technical details

Property	Value
Base	AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16 → MLX FP4
Architecture	qwen3_5 (AutoModelForMultimodalLM), 27.4B params, multimodal
Decoder	64 layers = 48 linear_attn (Gated-DeltaNet/Mamba SSM) + 16 full self_attn (GQA 24/4, head_dim 256) · every layer has an MLP · in-weights MTP head
Vision tower	model.visual.* · 27 blocks · qwen3_5_vision (BF16)
Vocab / hidden / ctx	248,320 (untied lm_head) · 5120 · 256K context
Quant	mixed mxfp4(4b, gs32) / 8-bit affine islands (gs64), bf16 SSM + vision + MTP
Tooling	mlx-vlm (git main) on Apple Silicon Metal
Footprint	16 GB · 4.880 bpw · 368 mxfp4 + 34 affine-8

Provenance

• Source (BF16): AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16 — abliterated / uncensored; the refusal edit lives in the residual-writers (self_attn.o_proj, mlp.down_proj).
• Sibling (NVIDIA NVFP4 + MTP): …-Multimodal-NVFP4-MTP-XS · GitHub sibling (DFlash, vLLM): …-DFlash
• Source of truth (MLX GitHub): AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-MLX
• Quantized by AEON-7 on Apple Silicon (MacBook Pro M4 Pro, 48 GB) with mlx-vlm. Recipe designed + adversarially validated with AI-engineering assistance from Anthropic.

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Arbitration Clause

By accessing, downloading, using, running inference on, fine-tuning, merging, quantizing, distributing, integrating, or otherwise interacting with this model, you acknowledge and agree to the following:

1. Sole Responsibility. You, the user, are solely and exclusively responsible for (a) every prompt you or your downstream system issue to this model, (b) every response this model produces in reply, (c) every downstream action taken by you, your systems, your agents, or your users in reliance on those responses, and (d) any harm — direct, indirect, consequential, foreseeable, or otherwise — that results from any of the above.

2. No Warranty. This model is provided strictly "AS IS", without warranty of any kind, express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, non-infringement, safety, alignment, factual accuracy, or legal compliance in any jurisdiction. No contributor, author, publisher, or hosting platform assumes liability of any kind for outputs or downstream use.

3. Legal Compliance. You are responsible for ensuring that your use of this model complies with all applicable laws, regulations, terms of service, industry codes of conduct, professional ethical standards, and organizational policies in every jurisdiction in which you operate or in which your outputs may be received. The unaligned nature of this model does not grant you any legal authorization you did not already have.

4. Operational Safety Layer. An uncensored model is not a toy. You are expected to implement appropriate downstream safety layers proportionate to your deployment context, including but not limited to: input validation, output filtering, content moderation, audit logging, rate limiting, access controls, and human-in-the-loop review for high-risk workflows. A production deployment of this model without such layers is unsafe by construction and is not a supported use case.

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This model is a tool with no opinions of its own. You supply the opinions. You supply the judgement. You supply the ethics. The outputs carry your fingerprints, not the model's.

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License

Inherits the Qwen license from the Qwen3.6 base model. By using this model you agree to the Qwen license terms.

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