Instructions to use Evan-Lin/trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evan-Lin/trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Evan-Lin/trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("Evan-Lin/trainer") model = AutoModelForAudioClassification.from_pretrained("Evan-Lin/trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Evan-Lin/trainer: direct link, hf CLI and curl.
- Browser
- Download file 345 MB
-
https://huggingface.co/Evan-Lin/trainer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Evan-Lin/trainer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Evan-Lin/trainer/resolve/main/pytorch_model.bin
345 MB
- Xet hash:
- 4d0cff932b9bf2a0be269510b44b591e455b8138966cf08dedcdb2c0e8c43ad6
- Size of remote file:
- 345 MB
- SHA256:
- 55a582f06ac0f6b7875af008c69daf0bdd21d7e584d3758f2fb7b4e9765b63b8
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