Token Classification
Transformers
Safetensors
modernbert
semantic-router
vela
hallucination-detection
Instructions to use vllm-sr/Vela-1.0-Encoder-307M-Halu 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-Halu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vllm-sr/Vela-1.0-Encoder-307M-Halu")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-Halu") model = AutoModelForTokenClassification.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-Halu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from vllm-sr/Vela-1.0-Encoder-307M-Halu: direct link, hf CLI and curl.
- Browser
- Download file 1.23 GB
-
https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Halu/resolve/main/model.safetensors
- Command line
-
hf download hf://vllm-sr/Vela-1.0-Encoder-307M-Halu/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Halu/resolve/main/model.safetensors
1.23 GB
- Xet hash:
- 7df613a1f2f55310890946cb6ffc8cdb76469b4865e7fcceefbee003b969cd24
- Size of remote file:
- 1.23 GB
- SHA256:
- 0f51bf4a6e1462e88733c36e1e59fa96c1188c8c470de7829b51c95298247be6
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