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/index-areas.png from vllm-sr/d3-flash: direct link, hf CLI and curl.
- Browser
- Download file 130 kB
-
https://huggingface.co/vllm-sr/d3-flash/resolve/main/assets/index-areas.png
- Command line
-
hf download hf://vllm-sr/d3-flash/assets/index-areas.png
-
curl -L -o index-areas.png https://huggingface.co/vllm-sr/d3-flash/resolve/main/assets/index-areas.png
130 kB

- Xet hash:
- 719f8798cdb1c8031f5b898e4a52dea2f299f2df26643511ab60306c2fdb3fb2
- Size of remote file:
- 130 kB
- SHA256:
- b6029ad013682ec3f413b6350a6e919088125e16038df55d08e28a1ad00c950d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.