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 pytorch_model.bin from gciaffoni/modelLM: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/gciaffoni/modelLM/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gciaffoni/modelLM/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gciaffoni/modelLM/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 4fbb926e15923aadf51ccb2b4091fe365964de96a46a6903bc38132c680cdfee
- Size of remote file:
- 1.26 GB
- SHA256:
- 9b2409c4defcb8c884378f8ad8feeeb0396b8f5a7fff95bcba8fc4e83bb51ccd
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