Feature Extraction
Transformers
Safetensors
MLX
qwen3_vl
image-text-to-text
multimodal embedding
qwen
embedding
4-bit precision
Instructions to use arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit") model = AutoModelForImageTextToText.from_pretrained("arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit") - MLX
How to use arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-VL-Embedding-2B-mlx-4bit arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit
This model was converted to MLX format from Qwen/Qwen3-VL-Embedding-2B using mlx-vlm version 0.3.11.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
- Downloads last month
- 133
Model size
0.7B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
Model tree for arthurcollet/Qwen3-VL-Embedding-2B-mlx-4bit
Base model
Qwen/Qwen3-VL-2B-Instruct