unsloth/gemma-3-12b-it-qat-int4

VISIONConcurrency Cost:1Model Size:12BQuant:FP8Ctx Length:32kPublished:Apr 22, 2025License:gemmaArchitecture:Transformer0.0K Cold

The unsloth/gemma-3-12b-it-qat-int4 model is a 12 billion parameter instruction-tuned variant of Google DeepMind's Gemma 3 family, optimized for quantization-aware training (QAT) to reduce memory footprint while maintaining quality. This multimodal model handles text and image inputs, generating text outputs, and features a large 128K context window with multilingual support across 140+ languages. It excels at diverse tasks including question answering, summarization, reasoning, and image understanding, making it suitable for deployment in resource-constrained environments.

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Overview

unsloth/gemma-3-12b-it-qat-int4 is a 12 billion parameter instruction-tuned model from Google DeepMind's Gemma 3 family. This specific version is optimized using Quantization Aware Training (QAT), allowing it to maintain high quality while significantly reducing memory requirements when quantized (e.g., to Q4_0). The Gemma 3 models are multimodal, accepting both text and image inputs (images normalized to 896x896 resolution, encoded to 256 tokens each) and generating text outputs. It boasts a substantial 128K token context window and supports over 140 languages.

Key Capabilities

  • Multimodal Understanding: Processes both text and image inputs for comprehensive analysis.
  • Extensive Context: Utilizes a 128K token context window, enabling processing of long documents and complex queries.
  • Multilingual Support: Trained on data in over 140 languages, enhancing its global applicability.
  • Quantization Optimized: Designed for efficient deployment with reduced memory footprint through QAT.
  • Diverse Task Performance: Strong performance across reasoning, factuality, STEM, code, and multimodal benchmarks.

Good for

  • Resource-Constrained Environments: Its QAT optimization makes it ideal for deployment on laptops, desktops, or private cloud infrastructure.
  • Text Generation: Generating creative text formats, code, marketing copy, and email drafts.
  • Conversational AI: Powering chatbots, virtual assistants, and interactive applications.
  • Summarization: Creating concise summaries of documents, research papers, or reports.
  • Image Data Extraction: Interpreting and summarizing visual data for text communications.
  • Research and Education: Serving as a foundation for VLM and NLP research, language learning tools, and knowledge exploration.

Finetunes
4 models