Instructions to use vargr/yt-desire-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vargr/yt-desire-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vargr/yt-desire-quantized")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vargr/yt-desire-quantized") model = AutoModelForSequenceClassification.from_pretrained("vargr/yt-desire-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_optimized_quantized.onnx from vargr/yt-desire-quantized: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/vargr/yt-desire-quantized/resolve/main/model_optimized_quantized.onnx
- Command line
-
hf download hf://vargr/yt-desire-quantized/model_optimized_quantized.onnx
-
curl -L -o model_optimized_quantized.onnx https://huggingface.co/vargr/yt-desire-quantized/resolve/main/model_optimized_quantized.onnx
110 MB
- Xet hash:
- 3dc52e5218001e5d45973cc3b56acee03d9850e4874ced5748df184869eb57ff
- Size of remote file:
- 110 MB
- SHA256:
- 93f3f397956c245ff31982da7dc2f7ab706582e0668ee56dafcea48198599465
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