Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
object-detection
custom_code
Eval Results
Instructions to use vllm-sr/d3-ultra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3-ultra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3-ultra", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3-ultra", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3-ultra", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/examples/starship.png from vllm-sr/d3-ultra: direct link, hf CLI and curl.
- Browser
- Download file 171 kB
-
https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/examples/starship.png
- Command line
-
hf download hf://vllm-sr/d3-ultra/assets/examples/starship.png
-
curl -L -o starship.png https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/examples/starship.png
171 kB

- Xet hash:
- cf28fa153bb47657a8ecd11c163fb36dc59dc19cc7df130d0dbe20d3586c1b90
- Size of remote file:
- 171 kB
- SHA256:
- 7f25cbcacc421147018a7bf4185e68c09e01cc38d607e178fce0d851af1ae9d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.