Instructions to use google/gemma-4-E4B-it-qat-q4_0-unquantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-E4B-it-qat-q4_0-unquantized with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/gemma-4-E4B-it-qat-q4_0-unquantized") model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-E4B-it-qat-q4_0-unquantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add Technical Report
Browse files
README.md
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<p align="center">
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<a href="https://huggingface.co/collections/google/gemma-4" target="_blank">Hugging Face</a> |
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<a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
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<a href="https://blog.google/innovation-and-ai/technology/developers-tools/
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<a href="https://ai.google.dev/gemma/docs/core" target="_blank">Documentation</a>
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<br>
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<b>License</b>: <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a>
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</p>
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### **Benefits**
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At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models.
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<p align="center">
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<a href="https://huggingface.co/collections/google/gemma-4-qat-q4-0" target="_blank">Hugging Face</a> |
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<a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
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<a href="https://blog.google/innovation-and-ai/technology/developers-tools/quantization-aware-training-gemma-4/" target="_blank">Launch Blog</a> |
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<a href="https://ai.google.dev/gemma/docs/core" target="_blank">Documentation</a> |
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<a href="https://arxiv.org/abs/2607.02770" target="_blank">Technical Report</a>
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<br>
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<b>License</b>: <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a>
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</p>
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### **Benefits**
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At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models.
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## **Citation**
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If you find our work helpful, please consider citing it:
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```bibtex
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@misc{gemmateam2026gemma4,
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title={Gemma 4 Technical Report},
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author={Gemma Team},
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year={2026},
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eprint={2607.02770},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2607.02770},
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}
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```
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