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 9B — JANG_4S + CRACK

JANG mixed-precision · CRACK abliterated · Vision-Language · No guardrails · 6 GB

What Is This?

This is Qwen 3.5 VL 9B — a 9B parameter dense hybrid SSM/Attention model with built-in vision capabilities.

It has been:

1. JANG quantized — JANG_4S profile (6-bit attention, 4-bit MLP) — 6 GB

2. CRACK abliterated — permanent weight-level removal of safety refusal

| | |

|---|---|

| Architecture | Qwen 3.5 VL Dense — 9B params, hybrid SSM/FA, 32 layers |

| Quantization | JANG_4S (6/4-bit mixed) — 6 GB |

| Abliteration | CRACK — novel weight surgery |

| HarmBench | 72.5% (232/320) |

| MMLU | 70.8% (base: 72.3%, -1.5%) |

| Speed | ~80 tok/s (M4 Max) |

| Vision | Yes — via MLX Studio / vMLX |

| Thinking | ON/OFF supported |

| Fits on | 16 GB+ Macs |

JANG vs MLX Uniform Quantization

|-------|:---:|:---:|-------|

| JANG_4S + CRACK | 70.8% | 6 GB | This model |

| JANG_4S (base) | 73.0% | 6 GB | Unmodified JANG |

| MLX 4-bit | 72.5% | 4.7 GB | Uniform quant |

HarmBench Results

232/320 (72.5%) — tested with enable_thinking=false, temperature=1.0

| Category | Score | |

|----------|:---:|---|

| Misinformation / Disinfo | 46/54 | 85% |

| Cybercrime / Intrusion | 41/52 | 79% |

| Chemical / Biological | 31/42 | 74% |

| Harmful | 13/18 | 72% |

| Illegal | 38/53 | 72% |

| Copyright | 53/80 | 66% |

| Harassment / Bullying | 10/21 | 48% |

Note: Dense models have stronger distributed safety training than MoE models. This model prioritizes knowledge preservation over maximum compliance.

MMLU Results

|---|:---:|:---:|:---:|

| Total | 46/65 (70.8%) | 47/65 (72.3%) | -1.5% |

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-9B-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. To disable:

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

Tip: Use temperature=1.0 for chat. 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.

About CRACK

CRACK (Controlled Refusal Ablation via Calibrated Knockouts) removes safety alignment from LLMs at the weight level using per-layer projected vectors from 512 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.

한국어