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---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-4b-pt
tags:
- mlx
---

# NexaAI/gemma-3-4b-it-8bit-MLX

## Quickstart

Run them directly with [nexa-sdk](https://github.com/NexaAI/nexa-sdk) installed
In nexa-sdk CLI:

```bash
NexaAI/gemma-3-4b-it-8bit-MLX
```

## Overview

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 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. 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.

### Inputs and outputs

-   **Input:**
    -  Text string, such as a question, a prompt, or a document to be summarized
    -  Images, normalized to 896 x 896 resolution and encoded to 256 tokens
       each
    -  Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
       32K tokens for the 1B size
-   **Output:**
    -   Generated text in response to the input, such as an answer to a
        question, analysis of image content, or a summary of a document
    -   Total output context of 8192 tokens

## Benchmark Results

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

#### Multimodal

| Benchmark                      | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap]            |      102      |      111       |      116       |
| [DocVQA][docvqa] (val)         |      72.8     |      82.3      |      85.6      |
| [InfoVQA][info-vqa] (val)      |      44.1     |      54.8      |      59.4      |
| [MMMU][mmmu] (pt)              |      39.2     |      50.3      |      56.1      |
| [TextVQA][textvqa] (val)       |      58.9     |      66.5      |      68.6      |
| [RealWorldQA][realworldqa]     |      45.5     |      52.2      |      53.9      |
| [ReMI][remi]                   |      27.3     |      38.5      |      44.8      |
| [AI2D][ai2d]                   |      63.2     |      75.2      |      79.0      |
| [ChartQA][chartqa]             |      63.6     |      74.7      |      76.3      |
| [VQAv2][vqav2]                 |      63.9     |      71.2      |      72.9      |
| [BLINK][blinkvqa]              |      38.0     |      35.9      |      39.6      |
| [OKVQA][okvqa]                 |      51.0     |      58.7      |      60.2      |
| [TallyQA][tallyqa]             |      42.5     |      51.8      |      54.3      |
| [SpatialSense VQA][ss-vqa]     |      50.9     |      60.0      |      59.4      |
| [CountBenchQA][countbenchqa]   |      26.1     |      17.8      |      68.0      |

[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/

## Reference

**Original model card**: [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it)