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/ask-once.png from vllm-sr/d3-ultra: direct link, hf CLI and curl.
- Browser
- Download file 212 kB
-
https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/ask-once.png
- Command line
-
hf download hf://vllm-sr/d3-ultra/assets/ask-once.png
-
curl -L -o ask-once.png https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/ask-once.png
212 kB

- Xet hash:
- 813b562999231f301eedab7ca8303d9c79ae826875ee1b3658e87d641013e96b
- Size of remote file:
- 212 kB
- SHA256:
- de25de9c5a99aa1bf675d920839a98ffcc8f8183ed35b4d940bcb25c82793933
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.