Instructions to use hf-tiny-model-private/tiny-random-WavLMForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-WavLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-tiny-model-private/tiny-random-WavLMForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-tiny-model-private/tiny-random-WavLMForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 141 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-WavLMForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-WavLMForSequenceClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-WavLMForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
141 kB
- Xet hash:
- c30de79e606a7762b6b27045ed7baba35d2b22c7203214c0992fd79835124a4e
- Size of remote file:
- 141 kB
- SHA256:
- 87740f13b01f8f3edff4c38261374a08bf9104fdafb7b82b717ec6a72930b21f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.