language:

• en library_name: mlx license: apache-2.0 base_model: mistralai/Mistral-Small-4-119B-2603 tags:
• jang
• quantized
• mixed-precision
• apple-silicon
• mlx
• moe
• mla
• abliterated
• uncensored
• crack
• vision
• mlx-studio pipeline_tag: image-text-to-text thumbnail: dealign_mascot.png

⚠️ MLX Studio ONLY. This model uses the JANG quantization format — the GGUF equivalent for MLX on Apple Silicon. NOT compatible with LM Studio, Ollama, oMLX, or Inferencer. Requires MLX Studio or pip install "jang[mlx]".

Mistral Small 4 — Uncensored — JANG_2L

JANG mixed-precision · Uncensored / Abliterated · MLA + MoE + Vision · No guardrails · 37 GB

What Is This?

The first uncensored version of Mistral Small 4 (119B) for Apple Silicon. A 119B parameter MoE model with Multi-head Latent Attention (MLA), 128 experts, and Pixtral vision — with all safety guardrails permanently removed at the weight level.

Runs ONLY in MLX Studio or via jang-tools Python package. JANG is the GGUF equivalent for MLX — it is NOT compatible with GGUF-based tools.

It has been:

1. JANG quantized — JANG_2L profile (8-bit attention, 6-bit important, 2-bit experts) — 37 GB
2. CRACK abliterated — permanent weight-level removal of safety refusal

Also see: JANG_4M version — 64 GB, 95.3% HarmBench, 8/8 compliance (fits on 96 GB Macs)

HarmBench Results

307/320 (95.9%)

Requirements

This model REQUIRES MLX Studio or jang-tools. It will NOT work with:

• ❌ LM Studio
• ❌ Ollama
• ❌ oMLX
• ❌ Inferencer
• ❌ Any GGUF-based tool

HarmBench Results

307/320 (95.9%)

CRACK vs Base

Surgery reduced no-think MMLU by only 1.4% — the 2-bit quantization is the bottleneck, not CRACK.

MMLU Results (with reasoning recovery)

187/208 (89.9%) — no-think 137/208 (65.9%) + reasoning recovered 50

Install

pip install "jang[mlx]"

Usage

from jang_tools.loader import load_jang_model
from mlx_lm import generate

model, tokenizer = load_jang_model("dealignai/Mistral-Small-4-Uncensored-JANG_2L")

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)

Reasoning Mode

Reasoning is OFF by default. To enable step-by-step thinking:

prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True,
    tokenize=False, reasoning_effort="high")

The model reasons inside [THINK]...[/THINK] tags before answering.

About JANG

JANG (Jang Adaptive N-bit Grading) is a mixed-precision quantization format designed specifically for Apple Silicon — the GGUF equivalent for MLX. It classifies every weight tensor by sensitivity and assigns optimal bit-widths, achieving better quality-per-bit than uniform quantization.

About CRACK

CRACK (Controlled Refusal Ablation via Calibrated Knockouts) is a weight-level intervention that removes safety alignment while preserving reasoning quality. The modification is permanently baked into the published weights — no LoRA, no fine-tuning, no system prompts.

Links

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

Mistral Small 4 — Uncensored — JANG_2L

pip install "jang[mlx]"

GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai