Instructions to use Sjdan/mst_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/mst_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/mst_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/mst_1") model = AutoModelForCTC.from_pretrained("Sjdan/mst_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Sjdan/mst_1: direct link, hf CLI and curl.
- Browser
- Download file 3 kB
-
https://huggingface.co/Sjdan/mst_1/resolve/main/training_args.bin
- Command line
-
hf download hf://Sjdan/mst_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sjdan/mst_1/resolve/main/training_args.bin
3 kB
- Xet hash:
- fb62d2c385c497664873fe7ca427293767dff4e78b27a06ac4e16ba22e786943
- Size of remote file:
- 3 kB
- SHA256:
- b04c6ae2157c5868b447bca63244e5de1c99c09c2d32c816903a19a8a7561dbb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.