Instructions to use hadiqa123/train_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hadiqa123/train_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hadiqa123/train_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hadiqa123/train_model") model = AutoModelForCTC.from_pretrained("hadiqa123/train_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hadiqa123/train_model: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/hadiqa123/train_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hadiqa123/train_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hadiqa123/train_model/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- d95d0638cffe2476c52fe33d4dcf73cf2df3b1bdadfa1b40f39e0a21451447cf
- Size of remote file:
- 1.26 GB
- SHA256:
- 74ba09f662a76411ab40320327c6b377cd087bc8f1f343f40e4d6f90a68959cb
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