Instructions to use xdwang/tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xdwang/tmp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="xdwang/tmp")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("xdwang/tmp") model = AutoModelForCTC.from_pretrained("xdwang/tmp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xdwang/tmp: direct link, hf CLI and curl.
- Browser
- Download file 144 kB
-
https://huggingface.co/xdwang/tmp/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://xdwang/tmp@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/xdwang/tmp/resolve/refs%2Fpr%2F1/pytorch_model.bin
144 kB
- Xet hash:
- 4a0d0f8cb0a735b5f7faf0924a19a2857d949cd74a90548a5e5aaf606a7002e8
- Size of remote file:
- 144 kB
- SHA256:
- 970efe983b294e62d8946b1eaab4482bbcdf06944d6db07cb6b3c4da1c346582
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