Ornith-1.0-9B-JANG_6M

JANGQ-AI/Ornith-1.0-9B-JANG_6M

Ornith-1.0-9B-JANG_6M is a image-text-to-text model from JANGQ-AI. 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 mit.

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8.6 GB on disk

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.safetensors8.6 GB · 100%

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From the Hugging Face model README

<p align="center"> <img src="mlx-studio-logo.png" alt="MLX Studio" width="400"/> </p> <p align="center"> <img src="jangq-logo.png" alt="JANGQ" width="200"/> </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> <div align="center">

Ornith-1.0-9B · JANG_6M

Vision-language · Qwen3.5 hybrid backbone · 6-bit near-lossless mixed precision · ~8.0 GB

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⚠️ Requires MLX Studio (or the vMLX runtime) to run. Standard mlx_lm cannot load JANG bundles correctly — they store the Qwen3.5 RMSNorm un-shifted and rely on the runtime's +1 scale_shift + per-layer bit detection. MLX Studio includes the JANG loader.

JANG_6M = 8-bit attention + 6-bit MLP (affine mixed precision, group-size 64); vision tower kept fp16. A near-lossless JANG profile from JANGQ-AI.

Architecture


Family	`qwen3_5` (dense, hybrid)
Text layers	32 — 24 Gated-DeltaNet + 8 full-attention
MoE / dims	hidden 4096 · untied lm_head
Vision	ViT tower (`model.visual`) preserved fp16
Cache	hybrid (GDN state + KV for attention layers)
Parsers	reasoning `qwen3` · tools `qwen`

Provenance

Quantization: JANG · JANG_6M (8-bit attention + 6-bit MLP, group-size 64; vision tower fp16) · [email protected]

Task: Image-Text-to-Text
Library: mlx
Type: qwen3_5
License: mit
Created: Jun 27, 2026
Updated: Sep 8, 2026