Instructions to use google/gemma-4-E2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-E2B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/gemma-4-E2B") model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-E2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from google/gemma-4-E2B: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/google/gemma-4-E2B/resolve/refs%2Fpr%2F13/tokenizer.json
- Command line
-
hf download hf://google/gemma-4-E2B@refs/pr/13/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/google/gemma-4-E2B/resolve/refs%2Fpr%2F13/tokenizer.json
32.2 MB
- Xet hash:
- b21a1edb9e6e0cd55366224414300a8d78c5fa767bf99274309cd0b245978ee7
- Size of remote file:
- 32.2 MB
- SHA256:
- 9cad58a1ba9824a35574be51e8c9dcc2164a662cdfe56b1b008e06a231f80f0b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.