Image Feature Extraction
Transformers
Safetensors
qwen3_vl
multimodal
vision-language
embeddings
image-retrieval
visual-grounding
Instructions to use fushh7/ObjEmbed-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fushh7/ObjEmbed-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="fushh7/ObjEmbed-2B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, WeDetectEmbedding processor = AutoProcessor.from_pretrained("fushh7/ObjEmbed-2B") model = WeDetectEmbedding.from_pretrained("fushh7/ObjEmbed-2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fushh7/ObjEmbed-2B: direct link, hf CLI and curl.
- Browser
- Download file 8.06 kB
-
https://huggingface.co/fushh7/ObjEmbed-2B/resolve/main/training_args.bin
- Command line
-
hf download hf://fushh7/ObjEmbed-2B/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fushh7/ObjEmbed-2B/resolve/main/training_args.bin
8.06 kB
- Xet hash:
- 1cb99ac1ec3b24386f53929d86da1eeb75026ba115fb75a79b0583e5ea3f66d7
- Size of remote file:
- 8.06 kB
- SHA256:
- bc0e4c1bd13e9302eca7bf063591140b68aa2ef761f141b44542473b90ee30f1
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