Instructions to use mkbackup/final_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkbackup/final_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mkbackup/final_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mkbackup/final_model") model = AutoModelForSpeechSeq2Seq.from_pretrained("mkbackup/final_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mkbackup/final_model: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/mkbackup/final_model/resolve/main/training_args.bin
- Command line
-
hf download hf://mkbackup/final_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mkbackup/final_model/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- c1aee136604e8fe4c5cc166b15291e9be3f18331d0cfcfbed2a8f164c4a49cd8
- Size of remote file:
- 4.86 kB
- SHA256:
- 4835b36e5b6763abf679f7b2dc991d84865154cb0293a76a4372bcd8a476f2ad
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