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/curiosity.png from vllm-sr/d3-ultra: direct link, hf CLI and curl.
- Browser
- Download file 991 kB
-
https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/examples/curiosity.png
- Command line
-
hf download hf://vllm-sr/d3-ultra/assets/examples/curiosity.png
-
curl -L -o curiosity.png https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/examples/curiosity.png
991 kB

- Xet hash:
- bbf51d6e29a747f520df91acc5d1bafa164ad07c39e160dc9f64489e6f518f84
- Size of remote file:
- 991 kB
- SHA256:
- 969f3fbb6e026f41efdb724de6aaf69236558170431f1fc0a36c3eab27d8afaa
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