Instructions to use Sjdan/C6_A_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/C6_A_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/C6_A_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/C6_A_2") model = AutoModelForCTC.from_pretrained("Sjdan/C6_A_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/C6_A_2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/C6_A_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/C6_A_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/C6_A_2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- b9d274fbb8b30a1bb910325f5c4d3637c93f5f93b0d0493c440a505caf757b86
- Size of remote file:
- 1.26 GB
- SHA256:
- a40584a67d2588a55f6e7d0844245595a7483a7079b4740610c388deee5c3441
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.