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

- Xet hash:
- 41152140a282ecbd914eff34c68332fd27031e51dfb2f9a7011c32809d1bd70e
- Size of remote file:
- 175 kB
- SHA256:
- e324024e87c1d1538def9813bc64d6fd9117ad1f5f81f357771717cd678bd804
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