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Qwen3.5-35B-A3B-JANG_4K
OsaurusAI/Qwen3.5-35B-A3B-JANG_4K
Qwen3.5-35B-A3B-JANG_4K is a image-text-to-text model from OsaurusAI. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as apache-2.0.
<p align="center" <a href="https://osaurus.ai"<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/69d00705ce8872981c6c4fce/GWKjOwezSOhW5iuKpDwq.png" alt="Osaurus AI" width="120"</a </p
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553
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Parameters
47.5B
19.7 GB on disk
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1
Public
Repo makeup
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.safetensors19.6 GB · 100%
Parameter types
How the weights are stored.
U3247B · 99%
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Base models
Model card
From the Hugging Face model README
<p align="center">
<a href="https://osaurus.ai"><img src="https://cdn-avatars.huggingface.co/v1/production/uploads/69d00705ce8872981c6c4fce/GWKjOwezSOhW5iuKpDwq_.png" alt="Osaurus AI" width="120"></a>
</p>
<h3 align="center">Qwen 3.5 35B-A3B — JANG_4K (Mixed-Precision, 4-bit)</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://osaurus.ai"><img src="https://img.shields.io/badge/Web-osaurus.ai-blue" alt="Website"></a>
<a href="https://github.com/jjang-ai/jangq"><img src="https://img.shields.io/badge/GitHub-JANG_Source-blue?logo=github" alt="GitHub"></a>
<a href="https://pypi.org/project/jang/"><img src="https://img.shields.io/pypi/v/jang?label=PyPI&color=green" alt="PyPI"></a>
<a href="https://huggingface.co/OsaurusAI"><img src="https://img.shields.io/badge/HF-OsaurusAI-yellow?logo=huggingface" alt="OsaurusAI"></a>
</p>
<p align="center">Quantized by <a href="https://osaurus.ai">Osaurus AI</a> using <a href="https://github.com/jjang-ai/jangq">JANG</a></p>
Osaurus natively supports JANG models. Download at osaurus.ai.
Model Details
| Property | Value |
|---|---|
| Base Model | Qwen 3.5 VL 35B-A3B |
| Architecture | MoE Transformer + Vision |
| Total Parameters | 35B (3B active per token) |
| Profile | JANG_4K |
| Avg Bits/Weight | 3.98 |
| Bit Widths Used | 3, 4, 5, 8 |
| Model Size | 16.4 GB |
| Vision | Yes |
| Format | JANG v2 (MLX-native safetensors) |
Benchmarks
200-question MMLU (20 per subject x 10 subjects). Thinking OFF (enable_thinking=False), greedy decoding (temp=0.0).
| Model | MMLU | Size |
|---|---|---|
| JANG_4K (this) | 77.5% | 16.4 GB |
| MLX 4-bit | 75.5% | 18 GB |
| MLX 2-bit | ~20% | 10 GB |
JANG_4K beats MLX 4-bit by +2 MMLU while being smaller (16.4 GB vs 18 GB). Budget-neutral bit redistribution boosts attention quality without increasing total size.
JANG_4K Profile
JANG_4K is a balanced 4-bit mixed-precision profile that provides near-original quality. Critical layers (attention, routing, embeddings) are kept at 8-bit, with expert MLP weights at 3-5 bit depending on importance scoring. Best quality-to-size ratio for most use cases.
Usage
# Requires Osaurus (https://osaurus.ai)
osaurus serve OsaurusAI/Qwen3.5-35B-A3B-JANG_4K
Requirements
- Apple Silicon Mac with 24+ GB unified memory
- MLX framework with Qwen 3.5 MoE support
<p align="center">Quantized by <a href="https://osaurus.ai">Osaurus AI</a> using <a href="https://github.com/jjang-ai/jangq">JANG</a></p>
- Task
- Image-Text-to-Text
- Library
- mlx
- Type
- qwen3_5_moe
- License
- apache-2.0
- Languages
- en
- Created
- Apr 6, 2026
- Updated
- Apr 17, 2026