Instructions to use gdevakumar/lora_model_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gdevakumar/lora_model_3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gdevakumar/lora_model_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download adapter_model.safetensors from gdevakumar/lora_model_3: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/gdevakumar/lora_model_3/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://gdevakumar/lora_model_3/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/gdevakumar/lora_model_3/resolve/main/adapter_model.safetensors
168 MB
- Xet hash:
- 2abcab4e99dce23c0845090c1155fdf3a3684a38479be8198a7c9fa3ef854842
- Size of remote file:
- 168 MB
- SHA256:
- 63009b32caac927803a9ff39ccc0cf18b97b1827689e3581eccd13c234758dd3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.