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/banner@2x.png from vllm-sr/d3-ultra: direct link, hf CLI and curl.
- Browser
- Download file 3.99 MB
-
https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/banner%402x.png
- Command line
-
hf download hf://vllm-sr/d3-ultra/assets/banner@2x.png
-
curl -L -o banner@2x.png https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/banner%402x.png
3.99 MB

- Xet hash:
- 00e40bf2410c674c2dd3978e8446d8815c62555d920d79c7d54c9f4229cc28b9
- Size of remote file:
- 3.99 MB
- SHA256:
- 6a0542c8e338304e74cf54e954d0757c4dcae925b744e3c20655340367f414d2
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