Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
custom_code
Instructions to use vllm-sr/d3-edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3-edge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3-edge", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3-edge", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3-edge", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/banner.png from vllm-sr/d3-edge: direct link, hf CLI and curl.
- Browser
- Download file 2.24 MB
-
https://huggingface.co/vllm-sr/d3-edge/resolve/main/assets/banner.png
- Command line
-
hf download hf://vllm-sr/d3-edge/assets/banner.png
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curl -L -o banner.png https://huggingface.co/vllm-sr/d3-edge/resolve/main/assets/banner.png
2.24 MB

- Xet hash:
- 5c40e59ac97b72f29f82b2d87520da76c9ff5302fa746b6957148bf8ffa63aa4
- Size of remote file:
- 2.24 MB
- SHA256:
- 352353b5ac097e31e6447ac0db8185439894e9e6c66376565e30f33c39d27913
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