---

base_model:

• google/gemma-3-12b-it-qat-q4_0-unquantized

license: gemma

tags:

• gemma3
• unsloth
• gemma
• google

pipeline_tag: image-text-to-text

library_name: transformers

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

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extra_gated_button_content: Acknowledge license

---

Gemma 3 model card

Model Page: Gemma

> [!Note]

> This repository corresponds to the 12B instruction-tuned version of the Gemma 3 model using Quantization Aware Training (QAT).

>

> The checkpoint in this repository is unquantized, please make sure to quantize with Q4_0 with your favorite tool

>

> Thanks to QAT, the model is able to preserve similar quality as bfloat16 while significantly reducing the memory requirements

> to load the model.

Resources and Technical Documentation:

• [Gemma 3 Technical Report][g3-tech-report]
• [Responsible Generative AI Toolkit][rai-toolkit]
• [Gemma on Kaggle][kaggle-gemma]
• [Gemma on Vertex Model Garden][vertex-mg-gemma3]

Terms of Use: [Terms][terms]

Authors: Google DeepMind

Model Information

Summary description and brief definition of inputs and outputs.

Description

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

Citation

@article{gemma_2025,
    title={Gemma 3},
    url={https://goo.gle/Gemma3Report},
    publisher={Kaggle},
    author={Gemma Team},
    year={2025}
}

Model Data

Data used for model training and how the data was processed.

Training Dataset

These models were trained on a dataset of text data that includes a wide variety

of sources. The 27B model was trained with 14 trillion tokens, the 12B model was

trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and

1B with 2 trillion tokens. Here are the key components:

• Web Documents: A diverse collection of web text ensures the model is

exposed to a broad range of linguistic styles, topics, and vocabulary. The

training dataset includes content in over 140 languages.

• Code: Exposing the model to code helps it to learn the syntax and

patterns of programming languages, which improves its ability to generate

code and understand code-related questions.

• Mathematics: Training on mathematical text helps the model learn logical

reasoning, symbolic representation, and to address mathematical queries.

• Images: A wide range of images enables the model to perform image

analysis and visual data extraction tasks.

The combination of these diverse data sources is crucial for training a powerful

multimodal model that can handle a wide variety of different tasks and data

formats.

Data Preprocessing

Here are the key data cleaning and filtering methods applied to the training

data:

• CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering

was applied at multiple stages in the data preparation process to ensure

the exclusion of harmful and illegal content.

• Sensitive Data Filtering: As part of making Gemma pre-trained models

safe and reliable, automated techniques were used to filter out certain

personal information and other sensitive data from training sets.

• Additional methods: Filtering based on content quality and safety in

line with [our policies][safety-policies].

Implementation Information

Details about the model internals.

Hardware

Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,

TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant

computational power. TPUs, designed specifically for matrix operations common in

machine learning, offer several advantages in this domain:

• Performance: TPUs are specifically designed to handle the massive

computations involved in training VLMs. They can speed up training

considerably compared to CPUs.

• Memory: TPUs often come with large amounts of high-bandwidth memory,

allowing for the handling of large models and batch sizes during training.

This can lead to better model quality.

• Scalability: TPU Pods (large clusters of TPUs) provide a scalable

solution for handling the growing complexity of large foundation models.

You can distribute training across multiple TPU devices for faster and more

efficient processing.

• Cost-effectiveness: In many scenarios, TPUs can provide a more

cost-effective solution for training large models compared to CPU-based

infrastructure, especially when considering the time and resources saved

due to faster training.

• These advantages are aligned with

[Google's commitments to operate sustainably][sustainability].

Software

Training was done using [JAX][jax] and [ML Pathways][ml-pathways].

JAX allows researchers to take advantage of the latest generation of hardware,

including TPUs, for faster and more efficient training of large models. ML

Pathways is Google's latest effort to build artificially intelligent systems

capable of generalizing across multiple tasks. This is specially suitable for

foundation models, including large language models like these ones.

Together, JAX and ML Pathways are used as described in the

[paper about the Gemini family of models][gemini-2-paper]; *"the 'single

controller' programming model of Jax and Pathways allows a single Python

process to orchestrate the entire training run, dramatically simplifying the

development workflow."*

Evaluation

> [!Note]

> The evaluation in this section correspond to the original checkpoint, not the QAT checkpoint.

>

Model evaluation metrics and results.

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/

Ethics and Safety

Ethics and safety evaluation approach and results.

Evaluation Approach

Our evaluation methods include structured evaluations and internal red-teaming

testing of relevant content policies. Red-teaming was conducted by a number of

different teams, each with different goals and human evaluation metrics. These

models were evaluated against a number of different categories relevant to

ethics and safety, including:

• Child Safety: Evaluation of text-to-text and image to text prompts

covering child safety policies, including child sexual abuse and

exploitation.

• Content Safety: Evaluation of text-to-text and image to text prompts

covering safety policies including, harassment, violence and gore, and hate

speech.

• Representational Harms: Evaluation of text-to-text and image to text

prompts covering safety policies including bias, stereotyping, and harmful

associations or inaccuracies.

In addition to development level evaluations, we conduct "assurance

evaluations" which are our 'arms-length' internal evaluations for responsibility

governance decision making. They are conducted separately from the model

development team, to inform decision making about release. High level findings

are fed back to the model team, but prompt sets are held-out to prevent

overfitting and preserve the results' ability to inform decision making.

Assurance evaluation results are reported to our Responsibility & Safety Council

as part of release review.

Evaluation Results

For all areas of safety testing, we saw major improvements in the categories of

child safety, content safety, and representational harms relative to previous

Gemma models. All testing was conducted without safety filters to evaluate the

model capabilities and behaviors. For both text-to-text and image-to-text, and

across all model sizes, the model produced minimal policy violations, and showed

significant improvements over previous Gemma models' performance with respect

to ungrounded inferences. A limitation of our evaluations was they included only

English language prompts.

Usage and Limitations

These models have certain limitations that users should be aware of.

Intended Usage

Open vision-language models (VLMs) models have a wide range of applications

across various industries and domains. The following list of potential uses is

not comprehensive. The purpose of this list is to provide contextual information

about the possible use-cases that the model creators considered as part of model

training and development.

• Content Creation and Communication

- Text Generation: These models can be used to generate creative text

formats such as poems, scripts, code, marketing copy, and email drafts.

- Chatbots and Conversational AI: Power conversational interfaces

for customer service, virtual assistants, or interactive applications.

- Text Summarization: Generate concise summaries of a text corpus,

research papers, or reports.

- Image Data Extraction: These models can be used to extract,

interpret, and summarize visual data for text communications.

• Research and Education

- Natural Language Processing (NLP) and VLM Research: These

models can serve as a foundation for researchers to experiment with VLM

and NLP techniques, develop algorithms, and contribute to the

advancement of the field.

- Language Learning Tools: Support interactive language learning

experiences, aiding in grammar correction or providing writing practice.

- Knowledge Exploration: Assist researchers in exploring large

bodies of text by generating summaries or answering questions about

specific topics.

Limitations

• Training Data

- The quality and diversity of the training data significantly

influence the model's capabilities. Biases or gaps in the training data

can lead to limitations in the model's responses.

- The scope of the training dataset determines the subject areas

the model can handle effectively.

• Context and Task Complexity

- Models are better at tasks that can be framed with clear

prompts and instructions. Open-ended or highly complex tasks might be

challenging.

- A model's performance can be influenced by the amount of context

provided (longer context generally leads to better outputs, up to a

certain point).

• Language Ambiguity and Nuance

- Natural language is inherently complex. Models might struggle

to grasp subtle nuances, sarcasm, or figurative language.

• Factual Accuracy

- Models generate responses based on information they learned

from their training datasets, but they are not knowledge bases. They

may generate incorrect or outdated factual statements.

• Common Sense

- Models rely on statistical patterns in language. They might

lack the ability to apply common sense reasoning in certain situations.

Ethical Considerations and Risks

The development of vision-language models (VLMs) raises several ethical

concerns. In creating an open model, we have carefully considered the following:

• Bias and Fairness

- VLMs trained on large-scale, real-world text and image data can

reflect socio-cultural biases embedded in the training material. These

models underwent careful scrutiny, input data pre-processing described

and posterior evaluations reported in this card.

• Misinformation and Misuse

- VLMs can be misused to generate text that is false, misleading,

or harmful.

- Guidelines are provided for responsible use with the model, see the

[Responsible Generative AI Toolkit][rai-toolkit].

• Transparency and Accountability:

- This model card summarizes details on the models' architecture,

capabilities, limitations, and evaluation processes.

- A responsibly developed open model offers the opportunity to

share innovation by making VLM technology accessible to developers and

researchers across the AI ecosystem.

Risks identified and mitigations:

• Perpetuation of biases: It's encouraged to perform continuous

monitoring (using evaluation metrics, human review) and the exploration of

de-biasing techniques during model training, fine-tuning, and other use

cases.

• Generation of harmful content: Mechanisms and guidelines for content

safety are essential. Developers are encouraged to exercise caution and

implement appropriate content safety safeguards based on their specific

product policies and application use cases.

• Misuse for malicious purposes: Technical limitations and developer

and end-user education can help mitigate against malicious applications of

VLMs. Educational resources and reporting mechanisms for users to flag

misuse are provided. Prohibited uses of Gemma models are outlined in the

[Gemma Prohibited Use Policy][prohibited-use].

• Privacy violations: Models were trained on data filtered for removal

of certain personal information and other sensitive data. Developers are

encouraged to adhere to privacy regulations with privacy-preserving

techniques.

Benefits

At the time of release, this family of models provides high-performance open

vision-language model implementations designed from the ground up for

responsible AI development compared to similarly sized models.

Using the benchmark evaluation metrics described in this document, these models

have shown to provide superior performance to other, comparably-sized open model

alternatives.

[g3-tech-report]: https://goo.gle/Gemma3Report

[rai-toolkit]: https://ai.google.dev/responsible

[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3

[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3

[terms]: https://ai.google.dev/gemma/terms

[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf

[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy

[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu

[sustainability]: https://sustainability.google/operating-sustainably/

[jax]: https://github.com/jax-ml/jax

[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/

[sustainability]: https://sustainability.google/operating-sustainably/

[gemini-2-paper]: https://arxiv.org/abs/2312.11805