Instructions to use Daksh1/multi_class_LoRA_weights_t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Daksh1/multi_class_LoRA_weights_t with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Daksh1/multi_class_LoRA_weights_t", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Daksh1/multi_class_LoRA_weights_t: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/Daksh1/multi_class_LoRA_weights_t/resolve/main/tokenizer.json
- Command line
-
hf download hf://Daksh1/multi_class_LoRA_weights_t/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Daksh1/multi_class_LoRA_weights_t/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 39548a145a47e1cec1dc4388f9aaedb4ae6e29f53741b1beb99063d3ce455c0b
- Size of remote file:
- 34.4 MB
- SHA256:
- f559f2189f392b4555613965f089e7c4d300b41fbe080bf79da0d676e33ee7f0
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