Instructions to use vargr/yt-curiosity-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vargr/yt-curiosity-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vargr/yt-curiosity-quantized")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vargr/yt-curiosity-quantized") model = AutoModelForSequenceClassification.from_pretrained("vargr/yt-curiosity-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Upload tokenizer.json with huggingface_hub
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Upload vocab.txt with huggingface_hub
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Upload special_tokens_map.json with huggingface_hub
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Upload tokenizer_config.json with huggingface_hub
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Upload config.json with huggingface_hub
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Upload ort_config.json with huggingface_hub
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Upload model_optimized_quantized.onnx with huggingface_hub
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initial commit
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