Instructions to use dealignai/Mistral-Small-4-119B-JANG_2L-CRACK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dealignai/Mistral-Small-4-119B-JANG_2L-CRACK with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("dealignai/Mistral-Small-4-119B-JANG_2L-CRACK")
config = load_config("dealignai/Mistral-Small-4-119B-JANG_2L-CRACK")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use dealignai/Mistral-Small-4-119B-JANG_2L-CRACK with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Mistral-Small-4-119B-JANG_2L-CRACK"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dealignai/Mistral-Small-4-119B-JANG_2L-CRACK"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use dealignai/Mistral-Small-4-119B-JANG_2L-CRACK with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Mistral-Small-4-119B-JANG_2L-CRACK"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dealignai/Mistral-Small-4-119B-JANG_2L-CRACK

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dealignai/Mistral-Small-4-119B-JANG_2L-CRACK with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Mistral-Small-4-119B-JANG_2L-CRACK"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dealignai/Mistral-Small-4-119B-JANG_2L-CRACK" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Mistral-Small-4-119B-JANG_2L-CRACK / README.md

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	---
	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](https://mlx.studio) or `pip install "jang[mlx]"`.

	---

	<p align="center">
	<a href="https://mlx.studio"><img src="https://raw.githubusercontent.com/jjang-ai/jangq/main/assets/mlx-studio-light.png" alt="MLX Studio" width="500"></a>
	</p>

	<p align="center">
	<a href="https://mlx.studio"><img src="https://mlx.studio/assets/screenshots/mlx-studio-featured.png?v=1" alt="MLX Studio App" width="600"></a>
	</p>

	<h4 align="center"><a href="https://mlx.studio">MLX Studio</a> — the ONLY app that supports JANG models</h4>

	---

	<div align="center">

	<img src="dealign_mascot.png" width="128" />

	# Mistral Small 4 — Uncensored — JANG_2L

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

	<a href="https://ko-fi.com/jangq"><img src="https://img.shields.io/badge/Ko--fi-Support_Development-FF5E5B?logo=ko-fi&logoColor=white&style=for-the-badge" alt="Ko-fi"></a>

	</div>

	---

	## What Is This?

	The first uncensored version of [Mistral Small 4 (119B)](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) 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](https://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

	\| \| \|
	\|---\|---\|
	\| Architecture \| Mistral 4 MoE — 119B total, ~8B active, MLA + 128 experts \|
	\| Quantization \| JANG_2L (8/6/2-bit mixed, 2.1 avg) — 37 GB \|
	\| HarmBench \| 95.9% (307/320) \|
	\| MMLU \| 89.9% (187/208 with reasoning) \|
	\| Compliance \| 6/8 \|
	\| Vision \| Pixtral tensors included — VL via [MLX Studio](https://mlx.studio) engine \|
	\| Reasoning \| ON/OFF supported (`reasoning_effort`) \|
	\| Fits on \| 64 GB+ Macs \|
	\| Runs in \| [MLX Studio](https://mlx.studio) ONLY \|

	Also see: [JANG_4M version](https://huggingface.co/dealignai/Mistral-Small-4-119B-JANG_4M-CRACK) — 64 GB, 95.3% HarmBench, 8/8 compliance (fits on 96 GB Macs)

	---

	## HarmBench Results

	307/320 (95.9%)

	\| Category \| Score \| \|
	\|----------\|:---:\|---\|
	\| Covering Tracks \| 20/20 \| 100% \|
	\| Auth Bypass \| 97/100 \| 97% \|
	\| API Hacking \| 96/100 \| 96% \|
	\| Cloud Exploits \| 94/100 \| 94% \|

	---

	## Requirements

	> This model REQUIRES [MLX Studio](https://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%)

	\| Category \| Score \| \|
	\|----------\|:---:\|---\|
	\| Covering Tracks \| 20/20 \| 100% \|
	\| Auth Bypass \| 97/100 \| 97% \|
	\| API Hacking \| 96/100 \| 96% \|
	\| Cloud Exploits \| 94/100 \| 94% \|

	## CRACK vs Base

	\| \| CRACK \| Base JANG_2L \|
	\|---\|:---:\|:---:\|
	\| MMLU (with reasoning) \| 89.9% \| ~91% (est) \|
	\| MMLU (no-think) \| 65.9% \| 67.3% \|
	\| MMLU drop (no-think) \| -1.4% \| — \|
	\| HarmBench \| 95.9% \| 0% \|

	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

	\| Subject \| Score \| \|
	\|---------\|:---:\|---\|
	\| HS Biology \| 16/16 \| 100% \|
	\| Conceptual Physics \| 15/16 \| 94% \|
	\| HS Geography \| 14/16 \| 88% \|
	\| World Religions \| 14/16 \| 88% \|
	\| College Physics \| 12/16 \| 75% \|
	\| Electrical Engineering \| 11/16 \| 69% \|
	\| Professional Medicine \| 11/16 \| 69% \|
	\| Machine Learning \| 10/16 \| 62% \|
	\| College Mathematics \| 9/16 \| 56% \|
	\| HS Mathematics \| 7/16 \| 44% \|
	\| Formal Logic \| 7/16 \| 44% \|
	\| College CS \| 6/16 \| 38% \|
	\| Abstract Algebra \| 5/16 \| 31% \|


	---

	## Install

	```bash
	pip install "jang[mlx]"
	```

	### Usage

	```python
	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:
	```python
	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

	<p align="center">
	<a href="https://ko-fi.com/jangq"><img src="https://img.shields.io/badge/Ko--fi-Support_Development-FF5E5B?logo=ko-fi&logoColor=white&style=flat-square" alt="Ko-fi"></a>
	<a href="https://x.com/dealignai"><img src="https://img.shields.io/badge/X-@dealignai-000000?logo=x&logoColor=white&style=flat-square" alt="X/Twitter"></a>
	<a href="https://github.com/jjang-ai/jangq"><img src="https://img.shields.io/badge/GitHub-jjang--ai/jangq-181717?logo=github&logoColor=white&style=flat-square" alt="GitHub"></a>
	<a href="https://mlx.studio"><img src="https://img.shields.io/badge/MLX_Studio-App-blue?style=flat-square" alt="MLX Studio"></a>
	<a href="https://jangq.ai"><img src="https://img.shields.io/badge/Website-jangq.ai-green?style=flat-square" alt="Website"></a>
	</p>

	---

	## 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

	\| 항목 \| 내용 \|
	\|------\|------\|
	\| 크기 \| 37 GB \|
	\| HarmBench \| 95.9% (307/320) \|
	\| 최소 요구사양 \| 64 GB 메모리 Mac \|
	\| 실행 환경 \| MLX Studio 전용 \|

	```bash
	pip install "jang[mlx]"
	```

	[GitHub](https://github.com/jjang-ai/jangq) · [HuggingFace](https://huggingface.co/JANGQ-AI) · [MLX Studio](https://mlx.studio) · [Ko-fi](https://ko-fi.com/jangq) · [X @dealignai](https://x.com/dealignai)

	---

	<p align="center">Created by <a href="https://jangq.ai">Jinho Jang</a> · 장진호 제작</p>