Instructions to use hsali/m_seq_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsali/m_seq_1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("hsali/m_seq_1") model = Wav2Vec2ForSpeechClassification.from_pretrained("hsali/m_seq_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hsali/m_seq_1: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/hsali/m_seq_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hsali/m_seq_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hsali/m_seq_1/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 3182f4ab6f3d7856d1028e69032f3af6db664756db50978b2814a85241009de6
- Size of remote file:
- 378 MB
- SHA256:
- 703943c7a69431916927b1a08dcad64cb3287d1808b34c690c590dc08c76e557
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