Important: This model uses the JANG quantization format — the GGUF equivalent for MLX on Apple Silicon. Currently only supported by MLX Studio and the jang-tools Python package.

MLX Studio — the only app that natively supports JANG models

Qwen 3.5 VL 27B — JANG_4S + CRACK

JANG mixed-precision · CRACK abliterated · Vision-Language · No guardrails · 16 GB

What Is This?

This is Qwen 3.5 VL 27B — a 27B parameter dense hybrid SSM/Attention model with GatedDeltaNet SSM layers + full attention layers, and built-in vision capabilities.

It has been:

JANG quantized — JANG_4S profile (6-bit attention, 4-bit MLP) — 16 GB
CRACK abliterated — permanent weight-level removal of safety refusal


Architecture	Qwen 3.5 VL Dense — 27B params, hybrid SSM/FA, 64 layers
Quantization	JANG_4S (6/4-bit mixed) — 16 GB
Abliteration	CRACK — novel weight surgery
HarmBench	75.0% (240/320)
MMLU	83.1% (base: 83.1%, 0% drop)
Speed	27 tok/s (M4 Max)
Vision	Yes — via MLX Studio / vMLX
Thinking	ON/OFF supported
Fits on	32 GB+ Macs

JANG vs MLX Uniform Quantization

Model	MMLU	Size	Speed	Notes
JANG_4S + CRACK	83.1%	16 GB	27 tok/s	This model
JANG_4S (base)	84.5%	16 GB	35 tok/s	Unmodified JANG
MLX 4-bit	84.5%	14 GB	20 tok/s	Uniform quant
MLX 8-bit	~86%	29 GB	~15 tok/s	2x larger

JANG runs 35% faster than MLX 4-bit (35 vs 20 tok/s) at the same quality level.

HarmBench Results

240/320 (75.0%) — tested with enable_thinking=false, temperature=1.0

Category	Score
Misinformation / Disinfo	47/54	87%
Copyright	68/80	85%
Chemical / Biological	35/42	83%
Illegal	38/53	72%
Harmful	12/18	67%
Cybercrime / Intrusion	31/52	60%
Harassment / Bullying	9/21	43%

Note: Dense models have stronger distributed safety training than MoE models, making them harder to fully abliterate while preserving knowledge. This model prioritizes zero MMLU degradation over maximum compliance.

MMLU Results

65 curated hard questions across 13 subjects. Surgery preserves knowledge perfectly — zero degradation.

Subject	CRACK	Base	Delta
College Physics	5/5	5/5	0
Professional Medicine	5/5	5/5	0
Conceptual Physics	5/5	5/5	0
Electrical Engineering	5/5	5/5	0
Machine Learning	5/5	5/5	0
HS Biology	5/5	5/5	0
Abstract Algebra	4/5	4/5	0
College CS	4/5	4/5	0
HS Geography	4/5	4/5	0
World Religions	5/5	5/5	0
HS Mathematics	3/5	3/5	0
Formal Logic	3/5	3/5	0
College Math	1/5	1/5	0
Total	54/65 (83.1%)	54/65 (83.1%)	0%

Install & Usage

pip install "jang[mlx]"

from jang_tools.loader import load_jang_model
from mlx_lm import generate

model, tokenizer = load_jang_model("dealignai/Qwen3.5-VL-27B-JANG_4S-CRACK")

messages = [{"role": "user", "content": "Your prompt here"}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False)

response = generate(model, tokenizer, prompt=prompt, max_tokens=2000)
print(response)

Thinking Mode

Thinking is ON by default (chain-of-thought reasoning before answering).

To disable thinking for faster responses:

prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True,
    enable_thinking=False, tokenize=False)

Tip: Use temperature=1.0 for chat (greedy can cause repetition). Use temperature=0.0 for structured tasks like MMLU.

About JANG

JANG (Jang Adaptive N-bit Grading) is a mixed-precision quantization format for Apple Silicon — the GGUF equivalent for MLX. Classifies tensors into sensitivity tiers and assigns bits accordingly.

About CRACK

CRACK (Controlled Refusal Ablation via Calibrated Knockouts) removes safety alignment from LLMs at the weight level using per-layer projected vectors from structurally-mirrored prompt pairs.

Disclaimer

This model is provided for research and educational purposes. The creators are not responsible for any misuse. By downloading this model, you agree to use it responsibly and in compliance with applicable laws.

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