Text Classification
Transformers
ONNX
Safetensors
modernbert
semantic-router
vela
text-embeddings-inference
Instructions to use vllm-sr/Vela-1.0-Encoder-307M-Guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/Vela-1.0-Encoder-307M-Guard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vllm-sr/Vela-1.0-Encoder-307M-Guard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-Guard") model = AutoModelForSequenceClassification.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-Guard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from vllm-sr/Vela-1.0-Encoder-307M-Guard: direct link, hf CLI and curl.
- Browser
- Download file 1.23 GB
-
https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Guard/resolve/fafcd4febd55695edc62241cc6e59026a48e7b92/model.safetensors
- Command line
-
hf download hf://vllm-sr/Vela-1.0-Encoder-307M-Guard@fafcd4febd55695edc62241cc6e59026a48e7b92/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Guard/resolve/fafcd4febd55695edc62241cc6e59026a48e7b92/model.safetensors
1.23 GB
- Xet hash:
- e909a9f5f92090dd0fb72e5ff0c334db424cfc37816cd4dc9a65436636539e96
- Size of remote file:
- 1.23 GB
- SHA256:
- dc9cc72df54f08d22c3a232d001f53b5d22263d26e354f37d95bff3d6f13d937
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