gemma 3 12b it qat q4 0 unquantized (HF) EU AI Act Compliance Profile

Gemma 3 model card

Model Page: Gemma

[!Note] This repository corresponds to the 12B instruction-tuned version of the Gemma 3 model using Quantization Aware Training (QAT).

The checkpoint in this repository is unquantized, please make sure to quantize with Q4_0 with your favorite tool

Thanks to QAT, the model is able to preserve similar quality as bfloat16 while significantly reducing the memory requirements to load the model.

Resources and Technical Documentation:

• [Gemma 3 Technical Report][g3-tech-report]
• [Responsible Generative AI Toolkit][rai-toolkit]
• [Gemma on Kaggle][kaggle-gemma]
• [Gemma on Vertex Model Garden][vertex-mg-gemma3]

Terms of Use: [Terms][terms]

Authors: Google DeepMind

Model Information

Summary description and brief definition of inputs and outputs.

Description

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.

Inputs and outputs

• Input:
  • Text string, such as a question, a prompt, or a document to be summarized
  • Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
  • Total inpu

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