license: gemma
library_name: mlx
base_model: AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16
base_model_relation: quantized
pipeline_tag: image-text-to-text
language:

• en
  tags:
• 12b
• 8-bit
• abliterated
• aeon
• aeon-7
• agentic
• apple-silicon
• audio
• chat
• coding
• conversational
• english
• function-calling
• gemma
• gemma-4
• gemma-4-12B
• gemma4
• gemma4_unified
• google
• heretic
• instruct
• k4-biprojection
• long-context
• m4-pro
• metal
• mlx
• mlx-vlm
• multimodal
• mxfp8
• near-lossless
• on-device
• quantized
• reasoning
• refusal-removed
• thinking
• tool-calling
• uncensored
• unfiltered
• vision
• vision-language

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.

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

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:

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

Precision map

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

Validation (MacBook Pro M4 Pro, 48 GB)

⏱️ 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.

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

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.

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.

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License

Inherits the Gemma license from the base model. By using this model you agree to Google's Gemma license terms.

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