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/eval-overall.png from vllm-sr/d3-ultra: direct link, hf CLI and curl.
- Browser
- Download file 175 kB
-
https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/eval-overall.png
- Command line
-
hf download hf://vllm-sr/d3-ultra/assets/eval-overall.png
-
curl -L -o eval-overall.png https://huggingface.co/vllm-sr/d3-ultra/resolve/main/assets/eval-overall.png
175 kB

- Xet hash:
- cff1d64f8a1fdeb938d89c9ae85a76bf2e427de546dcb14c3f6bfc4d2751d88d
- Size of remote file:
- 175 kB
- SHA256:
- 235ae9a4811f7b2186d8d790d86049876c5cec6f9e01652563a93908e15cdb7e
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