Instructions to use ROGRANMAR/output_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ROGRANMAR/output_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ROGRANMAR/output_dir")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ROGRANMAR/output_dir") model = AutoModelForSpeechSeq2Seq.from_pretrained("ROGRANMAR/output_dir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ROGRANMAR/output_dir: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/ROGRANMAR/output_dir/resolve/main/training_args.bin
- Command line
-
hf download hf://ROGRANMAR/output_dir/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ROGRANMAR/output_dir/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- abc50ec983512e63879ab9287830019cc4a4b8c628de123f07fcf69acd0cdc01
- Size of remote file:
- 4.03 kB
- SHA256:
- 74db45b34ef960e3ced026445a877e56885b99cd64c527df2d515229d0e7f7d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.