Gemma-4-12B-it AEON Abliterated — MLX 8-bit (mxfp8, near-lossless)

The near-lossless Apple-Silicon build of AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16. Every language-decoder linear is mxfp8 (8-bit, group 32); the tied soft-capped head and the vision/audio projectors are kept at bf16. Built and validated on a MacBook Pro M4 Pro (48 GB).

Target hardware: Apple Silicon (M-series), best on 24 GB+ unified memory. Full multimodal (text + image + audio) via mlx-vlm.

Want the smallest build for a 16 GB Mac? See the compact FP4 sibling: …-MLXFP4.

This is the maximum-fidelity member of the MLX quant grid: measured top-1 token agreement 0.924 and median KL ≈ 0.002 nats against the BF16 source on the model's own greedy output — below the perceptual/sampling-noise floor on the typical token. Near-lossless, and the recommended build when quality is paramount.

⚡ Quickstart (Apple Silicon)

0 → running on a fresh Mac (no Python, no tools needed) — uv installs a correct Python + the deps for you:

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

# serve — uv fetches Python 3.12 + mlx-vlm on first run · MLX-8bit (FP8, near-lossless)
uv run --python 3.12 --with mlx-vlm -- \
  python -m mlx_vlm.server --model AEON-7/Gemma-4-12B-it-AEON-Abliterated-MLX-8bit --port 8080 --max-kv-size 32768

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 repeat or loop on long prompts. This model is tuned for its native sampling — 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/Gemma-4-12B-it-AEON-Abliterated-MLX-8bit","messages":[{"role":"user","content":"Hello!"}],"temperature":1.0}'

Full multimodal is on by default (no flag) — send OpenAI image_url or input_audio content, or use mlx_vlm.generate --image pic.jpg / --audio clip.wav. Verified: describes images and transcribes speech.

Already have Python 3.12? Use a venv instead (+ one-off generate)

python3 -m venv .venv && source .venv/bin/activate
pip install -U mlx-vlm
python -m mlx_vlm.server   --model AEON-7/Gemma-4-12B-it-AEON-Abliterated-MLX-8bit --port 8080
python -m mlx_vlm.generate --model AEON-7/Gemma-4-12B-it-AEON-Abliterated-MLX-8bit \
  --prompt "Explain mixed-precision quantization." --max-tokens 512 --temperature 1.0   # add --image pic.jpg for vision

⚡⚡ Optional second deployment — +MTP speculative decoding (~1.1–1.2× faster, output-identical)

Google ships an official Gemma-4 MTP draft — google/gemma-4-12B-it-assistant (423M), an "assistant" head that proposes tokens this model then verifies. Because every token is verified, the output is identical — it's purely a throughput boost. The server auto-pulls the latest draft on first run (gated → run hf auth login once). Use --draft-block-size 2 — the benchmarked sweet spot on this quant; drafting deeper is slower (draft acceptance decays with depth on abliterated/quantized weights).

To run MLX-8bit + MTP, paste this into your terminal:

uv run --python 3.12 --with mlx-vlm -- python -m mlx_vlm.server \
  --model AEON-7/Gemma-4-12B-it-AEON-Abliterated-MLX-8bit --port 8080 --max-kv-size 32768 \
  --draft-model google/gemma-4-12B-it-assistant --draft-kind mtp --draft-block-size 2

Pre-fetch/refresh the draft explicitly with hf download google/gemma-4-12B-it-assistant. Lossless; +~0.9 GB RAM. Remove the three --draft-* flags to disable. (Measured ~1.1–1.2× on this abliterated build; the draft is tuned for stock Gemma-4, so stock targets see more.)

Need a smaller, faster build for a 16 GB Mac? The high-quality compact MLXFP4 (FP4) build is the sibling.

🖥️ Minimum specs & unified memory

	MLX-8bit (this build)
On disk	13.4 GB
Peak RAM (measured, M4 Pro)	~13.5 GB text · ~14.0 GB with image
Minimum	Apple Silicon (M1 or newer) · 24 GB unified memory
Recommended	32 GB+ for long context + headroom

On a 16 GB Mac, use the compact MLXFP4 (peaks ~10 GB) instead.

Why 8-bit is the near-lossless point on Apple Silicon

MLX's 4-bit format (mxfp4) is E2M1 — a single mantissa bit — so it visibly diverges from BF16 wherever it is applied (this is the key difference from NVIDIA's NVFP4, which uses finer two-level scaling and is near-lossless at 4-bit). On Apple Silicon, the near-lossless point is mxfp8 (E4M3, 8-bit), which we measured to track BF16 far more tightly than any 4-bit placement we tried:

Build	top-1 vs BF16 (greedy)	mean KL	median KL	size
MLX 8-bit (this)	0.924	0.107	0.0019	~13.4 GB
MLX FP4 (compact sibling)	0.885	0.218	0.0042	9.3 GB
BF16 (source)	1.000	0	0	23.9 GB

(Measured on the M4 Pro over the BF16 model's own greedy trajectory — the regime that reflects deployment.)

Precision map

Component	Precision	Why
all attention `q/k/v/o_proj` + MLP `gate/up/down_proj` (×48)	`mxfp8` (E4M3, 8-bit, group 32)	Near-lossless 8-bit across the whole decoder; safely preserves the K=4 abliteration edit on `o_proj`/`down_proj`
tied `embed_tokens` / `lm_head`	bf16	Soft-capped logits (`final_logit_softcapping=30`) — kept exact
`embed_vision` / `embed_audio` / `vision_embedder` projectors	bf16	Gemma-4 is encoder-free; all image/audio fidelity lives here — kept exact
RMSNorm / QK-norm / value-norm / `layer_scalar`	bf16 (automatic)	1-D; never quantized

328 mxfp8 + 4 bf16-skipped quantizable linears · 8.94 bits/weight.

Validation (MacBook Pro M4 Pro, 48 GB)

Gate	Result
Near-lossless	top-1 0.924 / median KL 0.0019 nats vs BF16
Abliteration survived	0/8 harmful refused, 0/5 benign
Coherence (≥512 tok)	no repetition collapse (3/3 clean)
Multimodal	image + audio describe correctly (projectors bf16)

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

All figures below were benchmarked on a MacBook Pro · Apple M4 Pro (14-core CPU, 48 GB unified memory) · macOS 26 · mlx-vlm 0.6.1. 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 13.4 GB build wants ≥24 GB unified memory (peaks ~13.5 GB) — on 16 GB Macs use the compact MLXFP4.

Workload	gen tok/s	prompt tok/s	TTFT	peak RAM
Text · 256 tok · single stream	16.4 (peak 17.1)	163	314 ms	13.5 GB
Image + text · 140 tok	16.1	—	—	14.0 GB

Greedy, post-warmup, median of 5 runs (benchmark.py).

Inherited K=4 abliteration capability profile vs google/gemma-4-12B-it: wikitext PPL drift −4.22%, HumanEval functional +6.7pp, IFEval 90% (see BF16 source card).

Container & toolkit

ghcr.io/aeon-7/gemma4-aeon-abliterated-mlx-toolkit — the reproducible quant + validation + serve pipeline, an AGENTS.md agent-setup guide, and an elaborate model-comparison card. Quickstart is at the top of this page; on macOS run host-native for Metal (Docker has no Metal passthrough — see the toolkit's notes).

Technical details

Property	Value
Base	`google/gemma-4-12B-it` → AEON K=4 biprojection (BF16) → MLX 8-bit
Architecture	`Gemma4UnifiedForConditionalGeneration` (text + image + audio)
Decoder layers	48 · Hidden 3840 · GQA 16 heads / 8 KV · hybrid sliding(1024)+full
Vocab	262,144 · Embeddings tied · `final_logit_softcapping=30`
Quant	`mxfp8` (8-bit, group 32) all linears; bf16 head + vision/audio projectors
Tooling	mlx 0.31.2 / mlx-vlm 0.6.1
Footprint	~13.4 GB

Provenance

Source (BF16): AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16 — K=4 multi-direction norm-preserving biprojection; edits o_proj + down_proj on 24/48 layers; basis from top-SNR layers L24/L37/L39/L26.
Original base: google/gemma-4-12B-it by Google.
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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