Instructions to use rajat99/Fine_Tuning_XLSR_300M_testing_4_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajat99/Fine_Tuning_XLSR_300M_testing_4_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rajat99/Fine_Tuning_XLSR_300M_testing_4_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rajat99/Fine_Tuning_XLSR_300M_testing_4_model") model = AutoModelForCTC.from_pretrained("rajat99/Fine_Tuning_XLSR_300M_testing_4_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rajat99/Fine_Tuning_XLSR_300M_testing_4_model: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/rajat99/Fine_Tuning_XLSR_300M_testing_4_model/resolve/main/training_args.bin
- Command line
-
hf download hf://rajat99/Fine_Tuning_XLSR_300M_testing_4_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rajat99/Fine_Tuning_XLSR_300M_testing_4_model/resolve/main/training_args.bin
2.86 kB
- Xet hash:
- fc29e08b08ead09e38bdb594c06ad06b702e7f4276a2d61e5db8af587a2f887b
- Size of remote file:
- 2.86 kB
- SHA256:
- ec33bf816c40b98e06ee6e69ce2240ea72fe8196e8c02fd8800a0b617e8e9d73
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