Any-to-Any
Transformers
Safetensors
gemma4
image-text-to-text
gemma
google
paroquant
4-bit precision
Instructions to use Jeethu/gemma-4-E2B-it-PARO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeethu/gemma-4-E2B-it-PARO with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Jeethu/gemma-4-E2B-it-PARO") model = AutoModelForMultimodalLM.from_pretrained("Jeethu/gemma-4-E2B-it-PARO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 349290553c8a5b2c30e1a8ee60b7a8c287852a89ac9b1fc95bfeebbf619d3059
- Size of remote file:
- 7.47 GB
- SHA256:
- 320978cbd570cc52238fe2c3f7b4bfdd6d9547fc9bea73a2d19fac8a5e208eb5
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