Instructions to use Jignesh2619/gemma4-fatigue-detection-lora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jignesh2619/gemma4-fatigue-detection-lora-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jignesh2619/gemma4-fatigue-detection-lora-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download adapter_model.safetensors from Jignesh2619/gemma4-fatigue-detection-lora-v2: direct link, hf CLI and curl.
- Browser
- Download file 120 MB
-
https://huggingface.co/Jignesh2619/gemma4-fatigue-detection-lora-v2/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://Jignesh2619/gemma4-fatigue-detection-lora-v2/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/Jignesh2619/gemma4-fatigue-detection-lora-v2/resolve/main/adapter_model.safetensors
120 MB
- Xet hash:
- 5b2f29e9bc434c8d2507110dabbe01eec593e10c72101b6a3f219b8b78536e24
- Size of remote file:
- 120 MB
- SHA256:
- f939575eaafff5baf8c46ecb8c3c1513015fbe80e353f16962f333d6dd395768
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.