Instructions to use Sjdan/msl_dummy1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/msl_dummy1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/msl_dummy1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/msl_dummy1") model = AutoModelForCTC.from_pretrained("Sjdan/msl_dummy1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/msl_dummy1: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/msl_dummy1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/msl_dummy1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/msl_dummy1/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 7b9c493cb72631593b381f974bb24d4c884a912c5afa800082a34009e34bbccb
- Size of remote file:
- 1.26 GB
- SHA256:
- 36276b18fc5f88bee5c18b2e678bbdfa716dec2bcd1542f83e7a467ef09ac385
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