Instructions to use Sjdan/m11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/m11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/m11")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/m11") model = AutoModelForCTC.from_pretrained("Sjdan/m11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/m11: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/m11/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/m11@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/m11/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.26 GB
- Xet hash:
- dc05e5d92fb59c743db0474c630d37e9c88d5f54a24bf0bdbde6d8499e4bb753
- Size of remote file:
- 1.26 GB
- SHA256:
- 12252a14e794142e0a89b1241696086831b9e9424d69071a84cd64c4ad388744
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