Instructions to use google/gemma-4-12B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-12B-it with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/gemma-4-12B-it") model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-12B-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
Download tokenizer.json from google/gemma-4-12B-it: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/google/gemma-4-12B-it/resolve/6f339ec620ef447df0a8a0cef77794fd6e480db0/tokenizer.json
- Command line
-
hf download hf://google/gemma-4-12B-it@6f339ec620ef447df0a8a0cef77794fd6e480db0/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/google/gemma-4-12B-it/resolve/6f339ec620ef447df0a8a0cef77794fd6e480db0/tokenizer.json
32.2 MB
- Xet hash:
- c62336ad134cad6f154d84eb0e5a5fa9ca17cd665ef3ba5ac4fd02b1486760b4
- Size of remote file:
- 32.2 MB
- SHA256:
- cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
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