Instructions to use hfnlpmodels/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfnlpmodels/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hfnlpmodels/tmp_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hfnlpmodels/tmp_trainer") model = AutoModelForAudioClassification.from_pretrained("hfnlpmodels/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hfnlpmodels/tmp_trainer: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/hfnlpmodels/tmp_trainer/resolve/main/training_args.bin
- Command line
-
hf download hf://hfnlpmodels/tmp_trainer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hfnlpmodels/tmp_trainer/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- 302c55318a8682180112c1d6fd94df1054574f6a842d68a2d57fd37462903b33
- Size of remote file:
- 4.86 kB
- SHA256:
- bbaad8f28162d5c396760fe4fcb5b58ece79c385e57a56ac249ecc563195e387
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.