Instructions to use shawhin/gemma-3-1b-tool-use with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shawhin/gemma-3-1b-tool-use with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shawhin/gemma-3-1b-tool-use", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from shawhin/gemma-3-1b-tool-use: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/shawhin/gemma-3-1b-tool-use/resolve/a68c9c97da1ad9f340b5398e5825ebab8707ac71/tokenizer.json
- Command line
-
hf download hf://shawhin/gemma-3-1b-tool-use@a68c9c97da1ad9f340b5398e5825ebab8707ac71/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/shawhin/gemma-3-1b-tool-use/resolve/a68c9c97da1ad9f340b5398e5825ebab8707ac71/tokenizer.json
33.4 MB
- Xet hash:
- fa66c8c017ade193f7cc37a2b421a1fc461563e803f179a496f7bdda2ec42c21
- Size of remote file:
- 33.4 MB
- SHA256:
- 4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.