Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
custom_code
Eval Results
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/index-areas.png from vllm-sr/d3-edge: direct link, hf CLI and curl.
- Browser
- Download file 124 kB
-
https://huggingface.co/vllm-sr/d3-edge/resolve/main/assets/index-areas.png
- Command line
-
hf download hf://vllm-sr/d3-edge/assets/index-areas.png
-
curl -L -o index-areas.png https://huggingface.co/vllm-sr/d3-edge/resolve/main/assets/index-areas.png
124 kB

- Xet hash:
- 89baf93f0bafab2df84a7e9acceb96b13fbec44f212f19d96acaead18c1ba477
- Size of remote file:
- 124 kB
- SHA256:
- b2659c49dd9b990ae61e9f26ff3dd2d1e76e0d9a993301b8707f06e89b0b32e5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.