Instructions to use cmcmaster/transcript_model_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmcmaster/transcript_model_lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cmcmaster/transcript_model_lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download adapter_model.safetensors from cmcmaster/transcript_model_lora: direct link, hf CLI and curl.
- Browser
- Download file 195 MB
-
https://huggingface.co/cmcmaster/transcript_model_lora/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://cmcmaster/transcript_model_lora/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/cmcmaster/transcript_model_lora/resolve/main/adapter_model.safetensors
195 MB
- Xet hash:
- 72c72546e17eaf310d01516ac7259228d70602b3ef1c49a3b93cb7298b5eba91
- Size of remote file:
- 195 MB
- SHA256:
- 6f178468e8f3ef702437ff88d26806a43b56aef24aada07c53b46fb68e8276f6
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