gguf gemma text-generation en arxiv:1905.07830 arxiv:1905.10044 arxiv:1911.11641 arxiv:1904.09728 arxiv:1705.03551 arxiv:1911.01547 arxiv:1907.10641 arxiv:1903.00161 arxiv:2009.03300 arxiv:2304.06364 arxiv:2103.03874 arxiv:2110.14168 arxiv:2311.12022 arxiv:2108.07732 arxiv:2107.03374 arxiv:2210.03057 arxiv:2106.03193 arxiv:1910.11856 arxiv:2502.12404 arxiv:2502.21228 arxiv:2404.16816 base_model:google/gemma-3-27b-it base_model:quantized:google/gemma-3-27b-it license:gemma endpoints_compatible region:us imatrix conversational
---
base_model: google/gemma-3-27b-it
language:
- en
license: gemma
model_name: gemma-3-27b-it
pipeline_tag: text-generation
tags:
- gemma
inference: true
model_creator: google
model_type: gemma3
quantized_by: brittlewis12
---

# Gemma 3 27B IT GGUF

**Original model**: [Gemma 3 27B IT](https://huggingface.co/google/gemma-3-27b-it)

**Model creator**: [Google DeepMind](https://huggingface.co/google)

> 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](https://github.com/ggml-org/llama.cpp/commits/7841fc723e059d1fd9640e5c0ef19050fcc7c698)),
using [autogguf-rs](https://github.com/brittlewis12/autogguf-rs).

### Prompt template: [Gemma Instruct](https://huggingface.co/google/gemma-3-27b-it/raw/main/tokenizer_config.json)

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


```

---

## Download & run with [cnvrs](https://twitter.com/cnvrsai) on iPhone, iPad, and Mac!

![cnvrs.ai](https://pbs.twimg.com/profile_images/1744049151241797632/0mIP-P9e_400x400.jpg)

[cnvrs](https://testflight.apple.com/join/sFWReS7K) 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](https://huggingface.co/models?library=gguf)!
    * 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](https://github.com/ggml-org/llama.cpp), with **haptics** during response streaming!
- **try it out** yourself today, on [Testflight](https://testflight.apple.com/join/sFWReS7K)!
    * 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](https://twitter.com/cnvrsai) to stay up to date

### Gemma 3 27B IT in cnvrs on macOS

![gemma-3 in cnvrs](https://cdn-uploads.huggingface.co/production/uploads/63b64d7a889aa6707f155cdb/hf_Z7YjT26hK8fXo2EPah.png)

---

## 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][hellaswag]         | 10-shot        |      62.3      |      77.2     |      84.2      |      85.6      |
| [BoolQ][boolq]                 | 0-shot         |      63.2      |      72.3     |      78.8      |      82.4      |
| [PIQA][piqa]                   | 0-shot         |      73.8      |      79.6     |      81.8      |      83.3      |
| [SocialIQA][socialiqa]         | 0-shot         |      48.9      |      51.9     |      53.4      |      54.9      |
| [TriviaQA][triviaqa]           | 5-shot         |      39.8      |      65.8     |      78.2      |      85.5      |
| [Natural Questions][naturalq]  | 5-shot         |      9.48      |      20.0     |      31.4      |      36.1      |
| [ARC-c][arc]                   | 25-shot        |      38.4      |      56.2     |      68.9      |      70.6      |
| [ARC-e][arc]                   | 0-shot         |      73.0      |      82.4     |      88.3      |      89.0      |
| [WinoGrande][winogrande]       | 5-shot         |      58.2      |      64.7     |      74.3      |      78.8      |
| [BIG-Bench Hard][bbh]          | few-shot       |      28.4      |      50.9     |      72.6      |      77.7      |
| [DROP][drop]                   | 1-shot         |      42.4      |      60.1     |      72.2      |      77.2      |

[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161

#### STEM and code

| Benchmark                      | Metric         | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu]                   | 5-shot         |      59.6     |      74.5      |      78.6      |
| [MMLU][mmlu] (Pro COT)         | 5-shot         |      29.2     |      45.3      |      52.2      |
| [AGIEval][agieval]             | 3-5-shot       |      42.1     |      57.4      |      66.2      |
| [MATH][math]                   | 4-shot         |      24.2     |      43.3      |      50.0      |
| [GSM8K][gsm8k]                 | 8-shot         |      38.4     |      71.0      |      82.6      |
| [GPQA][gpqa]                   | 5-shot         |      15.0     |      25.4      |      24.3      |
| [MBPP][mbpp]                   | 3-shot         |      46.0     |      60.4      |      65.6      |
| [HumanEval][humaneval]         | 0-shot         |      36.0     |      45.7      |      48.8      |

[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374

#### Multilingual

| Benchmark                            | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm]                         |      2.04     |      34.7     |      64.3     |      74.3     |
| [Global-MMLU-Lite][global-mmlu-lite] |      24.9     |      57.0     |      69.4     |      75.7     |
| [WMT24++][wmt24pp] (ChrF)            |      36.7     |      48.4     |      53.9     |      55.7     |
| [FloRes][flores]                     |      29.5     |      39.2     |      46.0     |      48.8     |
| [XQuAD][xquad] (all)                 |      43.9     |      68.0     |      74.5     |      76.8     |
| [ECLeKTic][eclektic]                 |      4.69     |      11.0     |      17.2     |      24.4     |
| [IndicGenBench][indicgenbench]       |      41.4     |      57.2     |      61.7     |      63.4     |

[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816