Downloads · 30 days
71
18% of all-time downloads
sr29/Mage-VL-mlx-4bit
Mage-VL-mlx-4bit is a image-text-to-text model from sr29. 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.
An MLX (4-bit) conversion of microsoft/Mage-VL — a 5B image/video vision-language model (Qwen3-4B text backbone + a from-scratch Mage-ViT "Codec-ViT" vision encoder) — that runs on Apple Silicon.
Downloads · 30 days
71
18% of all-time downloads
All-time downloads
402
Public
Parameters
4.7B
3.2 GB on disk
Likes
1
Public
Click a slice to open those files.
.safetensors3.1 GB · 99%
How the weights are stored.
U324.4B · 93%
From the Hugging Face model README
An MLX (4-bit) conversion of microsoft/Mage-VL — a 5B image/video vision-language model (Qwen3-4B text backbone + a from-scratch Mage-ViT "Codec-ViT" vision encoder) — that runs on Apple Silicon.
Port code, converter, and validators: https://github.com/rsravanreddy/Mage-VL-MLX
3.1 GB (4-bit, group size 64)# 1. install the MLX stack + register the mage_vl plugin
pip install mlx mlx-lm mlx-vlm numpy pillow tokenizers jinja2 av
git clone https://github.com/rsravanreddy/Mage-VL-MLX && cd Mage-VL-MLX
ln -s "$PWD/mage_vl" "$(python -c 'import mlx_vlm,os;print(os.path.dirname(mlx_vlm.__file__))')/models/mage_vl"
# 2. download these weights
hf download sr29/Mage-VL-mlx-4bit --local-dir mage-vl-mlx
# 3. run (image or video)
python scripts/generate.py --mlx mage-vl-mlx --tokenizer-src mage-vl-mlx \
--image path/to/image.jpg --prompt "Describe this image."
python scripts/generate.py --mlx mage-vl-mlx --tokenizer-src mage-vl-mlx \
--video path/to/video.mp4 --num-frames 8 --prompt "What is happening?"
| model | weights | image decode | image peak RAM |
|---|---|---|---|
| 4-bit | 3.1 GB | 30.6 tok/s | 4.65 GB |
| 8-bit | 5.0 GB | 19.1 tok/s | 6.55 GB |
4-bit is recommended for 16GB; 8-bit gives richer output if you have RAM.
Qwen2VLImageProcessor (max_abs_diff 0.0).max_abs_diff 3.0e-4, fp32, full 24 layers).4.3e-7).Mage-VL's proactive event gate (streammind_gate) is ported
(mage_vl/streaming.py). See scripts/stream.py for a per-frame silent/speak
timeline. Note: the gate weights (streammind_gate.safetensors) are separate and
downloaded from the upstream Mage-VL repo.
Apache-2.0. Derivative of microsoft/Mage-VL (Apache-2.0); reuses the Qwen3 language model from mlx-vlm. Weights converted, not retrained.