Instructions to use Schinchann/lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Schinchann/lora_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Schinchann/lora_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from Schinchann/lora_model: direct link, hf CLI and curl.
- Browser
- Download file 3.62 MB
-
https://huggingface.co/Schinchann/lora_model/resolve/main/tokenizer.json
- Command line
-
hf download hf://Schinchann/lora_model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Schinchann/lora_model/resolve/main/tokenizer.json
3.62 MB
- Xet hash:
- f9a530a1c16ddb1ccfcdef3f0e0c5c5442a64832a929d47df3ec856b63dedb68
- Size of remote file:
- 3.62 MB
- SHA256:
- c88bda6bdd84543eadebdf4bd2ec325ae43b71a3ff1fead6a766562c00b29bd8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.