Instructions to use marma/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marma/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="marma/test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("marma/test") model = AutoModelForCTC.from_pretrained("marma/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from marma/test: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/marma/test/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://marma/test@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/marma/test/resolve/refs%2Fpr%2F1/flax_model.msgpack
1.26 GB
- Xet hash:
- 374629809bdde2559b6b764c4d90c5bc77bdc50b675132bd2e84ef68e012e0c0
- Size of remote file:
- 1.26 GB
- SHA256:
- e77331afd00b912e7950d9f5dcd5b72e3cd0621b810d263234ccc29ad1252234
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.