Zero-Shot Classification
Transformers
qwen3_5
feature-extraction
decision-model
system-one
semantic-router
vela
token-classification
named-entity-recognition
relation-extraction
pii-detection
hallucination-detection
content-safety
multimodal
custom_code
Instructions to use vllm-sr/vela3-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/vela3-flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/vela3-flash", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/vela3-flash", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/vela3-flash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/family.png from vllm-sr/vela3-flash: direct link, hf CLI and curl.
- Browser
- Download file 160 kB
-
https://huggingface.co/vllm-sr/vela3-flash/resolve/main/assets/family.png
- Command line
-
hf download hf://vllm-sr/vela3-flash/assets/family.png
-
curl -L -o family.png https://huggingface.co/vllm-sr/vela3-flash/resolve/main/assets/family.png
160 kB

- Xet hash:
- 3741897df312f3d7dee75f8389948d561a90d6d99d8e6d006935d2c340c97dd7
- Size of remote file:
- 160 kB
- SHA256:
- 11a45523862721022b1877ff15b44a3bfe4f02ef32d7918c398aadb845efec1a
·
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