gemma-3-27b-it-GGUF

Gemma 3 27B IT GGUF

Original model: Gemma 3 27B IT

Model creator: Google DeepMind

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 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.

This repo contains GGUF format model files for Google DeepMind’s Gemma 3 27B IT (instruction-tuned).

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023.

Converted with llama.cpp build b4875 (revision 7841fc7), using autogguf-rs.

Prompt template: Gemma Instruct

{{system_prompt}}
<start_of_turn>user
{{prompt}}<end_of_turn>
<start_of_turn>model

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Download & run with cnvrs on iPhone, iPad, and Mac!

cnvrs is the best app for private, local AI on your device:

• create & save Characters with custom system prompts & temperature settings
• download and experiment with any GGUF model you can find on HuggingFace!
  • or, use an API key with the chat completions-compatible model provider of your choice -- ChatGPT, Claude, Gemini, DeepSeek, & more!
• make it your own with custom Theme colors
• powered by Metal ⚡️ & Llama.cpp, with haptics during response streaming!
• try it out yourself today, on Testflight!
  • if you already have the app, download Gemma 3 27B IT now!
  • cnvrsai:///models/search/hf?id=brittlewis12/gemma-3-27b-it-GGUF
• follow cnvrs on twitter to stay up to date

Gemma 3 27B IT in cnvrs on macOS

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Original Model Evaluation

These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation:

Reasoning and factuality

STEM and code

Multilingual