Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
custom_code
Instructions to use vllm-sr/d3-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3-nano with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3-nano", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3-nano", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3-nano", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/serving-throughput.png from vllm-sr/d3-nano: direct link, hf CLI and curl.
- Browser
- Download file 175 kB
-
https://huggingface.co/vllm-sr/d3-nano/resolve/main/assets/serving-throughput.png
- Command line
-
hf download hf://vllm-sr/d3-nano/assets/serving-throughput.png
-
curl -L -o serving-throughput.png https://huggingface.co/vllm-sr/d3-nano/resolve/main/assets/serving-throughput.png
175 kB

- Xet hash:
- 379ff69553ca9e7781cb4afbc86846c9f02fdcda51cf783782c848e7fb6714e6
- Size of remote file:
- 175 kB
- SHA256:
- 4603c2c30230fc1467f850e8b706a13f2861d753a02eb40efb4ad1eab8798e0f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.