Instructions to use edornd/gemma-4-12B-it-FP8D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edornd/gemma-4-12B-it-FP8D with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("edornd/gemma-4-12B-it-FP8D") model = AutoModelForMultimodalLM.from_pretrained("edornd/gemma-4-12B-it-FP8D") - Notebooks
- Google Colab
- Kaggle
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
This repository contains a FP8 dynamic version of
gemma-4-12B-it, quantized to run on <=16GB VRAM hardware. Given the quantization method, it requires Ada-based or later architectures.
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