Instructions to use hf-internal-testing/tiny-random-HubertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-HubertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-HubertForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-HubertForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-HubertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-HubertForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 135 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-HubertForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-HubertForSequenceClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-HubertForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
135 kB
- Xet hash:
- f5ac8505c74ef838a3137098dcecc09ebed580a59d48b12e76803aa06284e3ac
- Size of remote file:
- 135 kB
- SHA256:
- 14265067f1a9eae9ddf7a021432e0ce4064a7e2d68add8b7ab574027d3bd5e66
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.