JANGQ-AI/Qwen3.5-122B-A10B-JANG_3L

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 updated to v2.1 with VLM support and fixed configs. Re-download if you got this before today.

MLX Studio — the only app that natively supports JANG models

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Early Adoption: LM Studio, Ollama, oMLX, Inferencer do not support JANG yet. Use MLX Studio or pip install "jang[mlx]".

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⚡ All JANG models are meant to be run in vMLX

Qwen3.5-122B-A10B — JANG_3L (3-bit, 8-bit attention) — VLM

JANG — Jang Adaptive N-bit Grading | Mixed-Precision Quantization for Apple Silicon

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_3L: 81.5% at 49 GB — fits on 64 GB Macs. 8-bit attention preserves quality at 3-bit compression.

Per-Subject Scores

Specs

Install

pip install "jang[mlx]"

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/Qwen3.5-122B-A10B-JANG_3L")
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/Qwen3.5-122B-A10B-JANG_3L")
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

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한국어

Qwen3.5-122B — JANG_3L

JANG은 Apple Silicon을 위한 혼합정밀도 양자화 포맷입니다.

pip install "jang[mlx]"

GitHub · HuggingFace · MLX Studio

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장진호 제작 · Created by Jinho Jang — jangq.ai · @dealignai