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Gemma 3 27b It Qat Q4 0 Gguf

Developed by google
Gemma is a lightweight open-source multimodal model series launched by Google. It supports text and image inputs and generates text outputs. It has a 128K large context window and supports over 140 languages.
Downloads 69.29k
Release Time : 3/20/2025

Model Overview

Gemma 3 is a multimodal model built on the same technology as Gemini. It offers pre-trained and instruction-tuned variants and is suitable for various tasks such as question answering, summarization, and reasoning. The quantized version maintains performance while reducing memory requirements.

Model Features

Multimodal support
Supports both text and image inputs and can perform cross-modal tasks such as visual question answering and image description generation
Large context window
A context window of 128K tokens supports processing long documents and complex tasks
Quantization-aware training
The GGUF format optimized with QAT technology maintains performance close to the original accuracy while reducing memory requirements
Multilingual ability
The training data covers over 140 languages, with strong cross-lingual understanding and generation capabilities

Model Capabilities

Text generation
Image content analysis
Multilingual text processing
Code generation and understanding
Mathematical reasoning
Document summarization
Visual question answering

Use Cases

Content generation
Creative writing
Generate creative text content such as poems and stories
Can generate coherent text in line with literary styles
Technical document summarization
Automatically generate concise summaries of long technical documents
Performs well in NLP benchmark tests
Visual understanding
Image description generation
Generate natural language descriptions for input images
Scores 116 in the COCOcap benchmark test
Document information extraction
Extract structured information from scanned documents or images
Achieves an accuracy of 85.6% in the DocVQA benchmark test
Educational assistance
Mathematical problem solving
Solve mathematical problems step by step and explain the reasoning process
Achieves an accuracy of 82.6% in the GSM8K benchmark test
Programming teaching
Explain programming concepts and generate example code
Achieves a pass rate of 48.8% in the HumanEval benchmark test
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