Feature Extraction
Transformers
Safetensors
MLX
qwen3_vl
image-text-to-text
multimodal embedding
qwen
embedding
Instructions to use mlx-community/Qwen3-VL-Embedding-2B-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/Qwen3-VL-Embedding-2B-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mlx-community/Qwen3-VL-Embedding-2B-8bit", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("mlx-community/Qwen3-VL-Embedding-2B-8bit") model = AutoModelForMultimodalLM.from_pretrained("mlx-community/Qwen3-VL-Embedding-2B-8bit", device_map="auto") - MLX
How to use mlx-community/Qwen3-VL-Embedding-2B-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-VL-Embedding-2B-8bit mlx-community/Qwen3-VL-Embedding-2B-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- 515ea6325c7f0723b7c1344a3fd2f2abd6630970b2d54bf56aee18e43da5f6f9
- Size of remote file:
- 11.4 MB
- SHA256:
- 93623af029cdc69b87f2864d3b2cc2424fdf16684f15e139b5b9d08ec34ced91
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