Instructions to use adamtc/v-HSv2q with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adamtc/v-HSv2q with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="adamtc/v-HSv2q")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("adamtc/v-HSv2q") model = AutoModel.from_pretrained("adamtc/v-HSv2q", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from adamtc/v-HSv2q: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/adamtc/v-HSv2q/resolve/main/tokenizer.json
- Command line
-
hf download hf://adamtc/v-HSv2q/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/adamtc/v-HSv2q/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- a7030cf2e58dead38199a68a8cd6f6f1a609a6072d7fb38ba5f85b3bb7e21557
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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