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 training_args.bin from Sjdan/m11: direct link, hf CLI and curl.
- Browser
- Download file 3 kB
-
https://huggingface.co/Sjdan/m11/resolve/main/training_args.bin
- Command line
-
hf download hf://Sjdan/m11/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sjdan/m11/resolve/main/training_args.bin
3 kB
- Xet hash:
- 51937f98a00584d029e7dc3654a6527a06d4ea87661e1b6f5339588bcbcf5425
- Size of remote file:
- 3 kB
- SHA256:
- 0d234f0f37c544466341e699a99cb607f7372cb2376e8a1335096149580ae288
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.