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gemma-3-27b-it-GGUF

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metadata
base_model: google/gemma-3-27b-it
pipeline_tag: text-generation
inference: true
language:
  - en
license: gemma
model_creator: google
model_name: gemma-3-27b-it
model_type: gemma3
quantized_by: brittlewis12
tags:
  - gemma

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

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

cnvrs.ai

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

  • create & save Characters with custom system prompts & temperature settings
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  • 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!
  • follow cnvrs on twitter to stay up to date

Gemma 3 27B IT in cnvrs on macOS

gemma-3 in cnvrs

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

Benchmark Metric Gemma 3 PT 1B Gemma 3 PT 4B Gemma 3 PT 12B Gemma 3 PT 27B
HellaSwag 10-shot 62.3 77.2 84.2 85.6
BoolQ 0-shot 63.2 72.3 78.8 82.4
PIQA 0-shot 73.8 79.6 81.8 83.3
SocialIQA 0-shot 48.9 51.9 53.4 54.9
TriviaQA 5-shot 39.8 65.8 78.2 85.5
Natural Questions 5-shot 9.48 20.0 31.4 36.1
ARC-c 25-shot 38.4 56.2 68.9 70.6
ARC-e 0-shot 73.0 82.4 88.3 89.0
WinoGrande 5-shot 58.2 64.7 74.3 78.8
BIG-Bench Hard few-shot 28.4 50.9 72.6 77.7
DROP 1-shot 42.4 60.1 72.2 77.2

STEM and code

Benchmark Metric Gemma 3 PT 4B Gemma 3 PT 12B Gemma 3 PT 27B
MMLU 5-shot 59.6 74.5 78.6
MMLU (Pro COT) 5-shot 29.2 45.3 52.2
AGIEval 3-5-shot 42.1 57.4 66.2
MATH 4-shot 24.2 43.3 50.0
GSM8K 8-shot 38.4 71.0 82.6
GPQA 5-shot 15.0 25.4 24.3
MBPP 3-shot 46.0 60.4 65.6
HumanEval 0-shot 36.0 45.7 48.8

Multilingual

Benchmark Gemma 3 PT 1B Gemma 3 PT 4B Gemma 3 PT 12B Gemma 3 PT 27B
MGSM 2.04 34.7 64.3 74.3
Global-MMLU-Lite 24.9 57.0 69.4 75.7
WMT24++ (ChrF) 36.7 48.4 53.9 55.7
FloRes 29.5 39.2 46.0 48.8
XQuAD (all) 43.9 68.0 74.5 76.8
ECLeKTic 4.69 11.0 17.2 24.4
IndicGenBench 41.4 57.2 61.7 63.4