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
Eval Results
Instructions to use vllm-sr/vela3-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/vela3-turbo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/vela3-turbo", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/vela3-turbo", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/vela3-turbo", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/banner.png from vllm-sr/vela3-turbo: direct link, hf CLI and curl.
- Browser
- Download file 1.26 MB
-
https://huggingface.co/vllm-sr/vela3-turbo/resolve/main/assets/banner.png
- Command line
-
hf download hf://vllm-sr/vela3-turbo/assets/banner.png
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curl -L -o banner.png https://huggingface.co/vllm-sr/vela3-turbo/resolve/main/assets/banner.png
1.26 MB

- Xet hash:
- 8926ea8de01c0137f92c4d9e9a7b756e574bfd0ed9a6be0849b7a81a03992dd5
- Size of remote file:
- 1.26 MB
- SHA256:
- 9fc8f4cdd72485553409a7065c5f78f62fcfc77b2e441585b8425ae57dc6c875
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