Instructions to use gciaffoni/modelLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gciaffoni/modelLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gciaffoni/modelLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("gciaffoni/modelLM") model = AutoModelForCTC.from_pretrained("gciaffoni/modelLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download language_model/5gram.bin from gciaffoni/modelLM: direct link, hf CLI and curl.
- Browser
- Download file 2.12 GB
-
https://huggingface.co/gciaffoni/modelLM/resolve/main/language_model/5gram.bin
- Command line
-
hf download hf://gciaffoni/modelLM/language_model/5gram.bin
-
curl -L -o 5gram.bin https://huggingface.co/gciaffoni/modelLM/resolve/main/language_model/5gram.bin
2.12 GB
- Xet hash:
- c9957107980cb084cc26e5e80ef127765ac5cd1d55fea1310909e4c623720982
- Size of remote file:
- 2.12 GB
- SHA256:
- 63f740213bc98fd413acd9f00d36f8ef93ff2842e3cfe5722d753080281ca762
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