Instructions to use Cem13/lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cem13/lora_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cem13/lora_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from Cem13/lora_model: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Cem13/lora_model/resolve/34c81aafd74b24f28f4e7db02e1fe1c4de6aa191/tokenizer.json
- Command line
-
hf download hf://Cem13/lora_model@34c81aafd74b24f28f4e7db02e1fe1c4de6aa191/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Cem13/lora_model/resolve/34c81aafd74b24f28f4e7db02e1fe1c4de6aa191/tokenizer.json
11.4 MB
- Xet hash:
- 62f2b3f78a2d97ddcf9e0c11e96678df15ed4d2a34e34a0cb35d01652359be83
- Size of remote file:
- 11.4 MB
- SHA256:
- 9479047c22047670077878de944c696032b1a81049beeb6e99fbda7fb93e395a
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