Zero-Shot Classification
Transformers
Safetensors
English
ultrabert
feature-extraction
decision-model
system-one
semantic-router
vela
encoder
token-classification
named-entity-recognition
relation-extraction
pii-detection
hallucination-detection
content-safety
custom_code
Instructions to use vllm-sr/vela3-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/vela3-fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/vela3-fast", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vllm-sr/vela3-fast", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/vela3-fast", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from vllm-sr/vela3-fast: direct link, hf CLI and curl.
- Browser
- Download file 3.52 MB
-
https://huggingface.co/vllm-sr/vela3-fast/resolve/main/tokenizer.json
- Command line
-
hf download hf://vllm-sr/vela3-fast/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vllm-sr/vela3-fast/resolve/main/tokenizer.json
3.52 MB
File too large to display, you can check the raw version instead.