Instructions to use Eimhin03/outout_model_29Jan26_TestingGeneralisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eimhin03/outout_model_29Jan26_TestingGeneralisation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Eimhin03/outout_model_29Jan26_TestingGeneralisation")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Eimhin03/outout_model_29Jan26_TestingGeneralisation") model = AutoModelForSpeechSeq2Seq.from_pretrained("Eimhin03/outout_model_29Jan26_TestingGeneralisation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Eimhin03/outout_model_29Jan26_TestingGeneralisation: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/Eimhin03/outout_model_29Jan26_TestingGeneralisation/resolve/main/training_args.bin
- Command line
-
hf download hf://Eimhin03/outout_model_29Jan26_TestingGeneralisation/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Eimhin03/outout_model_29Jan26_TestingGeneralisation/resolve/main/training_args.bin
5.33 kB
- Xet hash:
- 08a8558e06651ddbadd5ecaf25d76bcedba7dcca67af9e70b4894f512f157ed0
- Size of remote file:
- 5.33 kB
- SHA256:
- fb8adb80a70f95b18c5f64d687533462d2d0f1256d5c88000c43ce95a0f3be2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.