Instructions to use podsni/kaggle-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use podsni/kaggle-lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("podsni/kaggle-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from podsni/kaggle-lora: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/podsni/kaggle-lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://podsni/kaggle-lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/podsni/kaggle-lora/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- ca84e602162bd86bf1f4274524b5bc77232a8b1003ac4149a0a6f01293a24f96
- Size of remote file:
- 17.2 MB
- SHA256:
- cca43616769fed4807470fb41ccf7f5eebdd08624e7b9adafc09d99332cf31eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.