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-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3-flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3-flash", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3-flash", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3-flash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/serving-throughput.png from vllm-sr/d3-flash: direct link, hf CLI and curl.
- Browser
- Download file 190 kB
-
https://huggingface.co/vllm-sr/d3-flash/resolve/main/assets/serving-throughput.png
- Command line
-
hf download hf://vllm-sr/d3-flash/assets/serving-throughput.png
-
curl -L -o serving-throughput.png https://huggingface.co/vllm-sr/d3-flash/resolve/main/assets/serving-throughput.png
190 kB

- Xet hash:
- bd0d1c7f4f0a121fa73e9ed9bf7c06e255c99bde019f4b53f6748f4b2ba22e56
- Size of remote file:
- 190 kB
- SHA256:
- f6edbe42ac050d0ddf938435d8ba76d614229a45bf4f1da851e52df23a509312
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