Qwen3.5-4B-JANG_4S

CRITICAL FIX (2026-03-19): Fixed eos_token_id — previous versions caused infinite thinking loops. You MUST re-download this model if you downloaded before today.

Update (2026-03-18): Models have been updated to v2.1.0 with VLM support, proper tokenizer, and fixed configs. If you downloaded before this date, please re-download for full MLX Studio compatibility.

<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 natively supports JANG models</h4>

---

Early Adoption: LM Studio, Ollama, oMLX, Inferencer do not support JANG yet. Use MLX Studio or pip install "jang[mlx]". Ask your favorite app's creators to add JANG support!

---

<p align="center"> <img src="https://raw.githubusercontent.com/jjang-ai/jangq/main/assets/jangq-logo-dark.png" alt="JANG" width="300"> </p> <p align="center"> <a href="https://vmlx.net"><img src="vmlx-app.png" alt="vMLX — run JANG models on Apple Silicon" width="820"></a> </p> <h3 align="center">⚡ All JANG models are meant to be run in <a href="https://vmlx.net">vMLX</a></h3> <h3 align="center">Qwen3.5-4B — JANG_4S (4.15-bit) — VLM</h3> <p align="center"><b>JANG</b> — Jang Adaptive N-bit Grading | Mixed-Precision Quantization for Apple Silicon</p> <p align="center"> <a href="https://github.com/jjang-ai/jangq"><img src="https://img.shields.io/badge/GitHub-Source_Code-blue?logo=github" alt="GitHub"></a>&nbsp; <a href="https://pypi.org/project/jang/"><img src="https://img.shields.io/pypi/v/jang?label=PyPI&color=green" alt="PyPI"></a>&nbsp; <a href="https://jangq.ai"><img src="https://img.shields.io/badge/Web-jangq.ai-orange" alt="Website"></a>&nbsp; <a href="https://x.com/dealignai"><img src="https://img.shields.io/badge/X-@dealignai-black?logo=x" alt="X/Twitter"></a> </p>

JANG is fully open-source. Quantization engine, research, and full commit history: github.com/jjang-ai/jangq. Created by Jinho Jang.

Results (200-question MMLU)

JANG_4S beats MLX 4-bit — same quality, attention at 6-bit for better coherence.

Per-Subject Scores

Specs

Install

pip install "jang[mlx]"

For Vision-Language models:

pip install "jang[vlm]"

Quick Start

from jang_tools.loader import load_jang_model
from mlx_lm.sample_utils import make_sampler
from mlx_lm.generate import generate_step
import mlx.core as mx

model, tokenizer = load_jang_model("JANGQ-AI/Qwen3.5-4B-JANG_4S")
sampler = make_sampler(temp=0.7)

tokens = tokenizer.encode("What is photosynthesis?")
for tok, _ in generate_step(prompt=mx.array(tokens), model=model, max_tokens=200, sampler=sampler):
    t = tok.item() if hasattr(tok, 'item') else int(tok)
    print(tokenizer.decode([t]), end="", flush=True)
    if t == tokenizer.eos_token_id:
        break

VLM Inference

from jang_tools.loader import load_jang_vlm_model
from mlx_vlm import generate

model, processor = load_jang_vlm_model("JANGQ-AI/Qwen3.5-4B-JANG_4S")

prompt = processor.tokenizer.apply_chat_template(
    [{"role": "user", "content": [
        {"type": "image", "image": "photo.jpg"},
        {"type": "text", "text": "Describe this image."}
    ]}], add_generation_prompt=True, tokenize=False, enable_thinking=False)

result = generate(model, processor, prompt, ["photo.jpg"], max_tokens=200)
print(result.text)

Links

• GitHub | HuggingFace | MLX Studio | PyPI | Format Spec

---

한국어

Qwen3.5-4B — JANG 4S

JANG은 Apple Silicon을 위한 혼합정밀도 양자화 포맷입니다. MLX를 위한 GGUF와 같은 역할을 합니다.

설치

pip install "jang[mlx]"

호환성

현재 **MLX Studio**만 JANG 포맷을 기본 지원합니다.

GitHub · HuggingFace · MLX Studio · PyPI

---

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