• Fine-tune Gemma 3 (270M) for free using our Google Colab notebook here!
• Read our Blog about Gemma 3 support: unsloth.ai/blog/gemma3
• View the rest of our notebooks in our docs here.

Unsloth supports	Free Notebooks	Performance	Memory use
Gemma 3 (4B)	▶️ Start on Colab	2x faster	80% less
Gemma-3n-E4B	▶️ Start on Colab	2x faster	60% less
Gemma-3n-E4B (Audio)	▶️ Start on Colab	2x faster	60% less
GRPO with Gemma 3 (1B)	▶️ Start on Colab	2x faster	80% less
Gemma 3 (4B) Vision	▶️ Start on Colab	2x faster	60% less

Gemma 3 model card

Model Page: Gemma

Resources and Technical Documentation:

• Gemma 3 Technical Report
• Responsible Generative AI Toolkit
• Gemma on Kaggle
• Gemma on Vertex Model Garden

Terms of Use: 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 and 270M sizes.

• 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 up to 128K tokens for the 4B, 12B, and 27B sizes,
    and 32K tokens for the 1B and 270M sizes per request, subtracting the
    request input tokens

Citation

@article{gemma_2025,
    title={Gemma 3},
    url={https://arxiv.org/abs/2503.19786},
    publisher={Google DeepMind},
    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,
the 1B with 2 trillion tokens, and the 270M with 6 trillion tokens. The
knowledge cutoff date for the training data was August 2024. 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.

Implementation Information

Details about the model internals.

Hardware

Gemma was trained using Tensor Processing Unit (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.

Software

Training was done using JAX and 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; "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

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. Evaluation results marked
with IT are for instruction-tuned models. Evaluation results marked with
PT are for pre-trained models.

Gemma 3 model card

Model Page: Gemma

Resources and Technical Documentation:

• Gemma 3 Technical Report
• Responsible Generative AI Toolkit
• Gemma on Kaggle
• Gemma on Vertex Model Garden

Terms of Use: 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, for the 4B, 12B, and 27B sizes.
  • Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
    32K tokens for the 1B and 270M sizes.

• 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 up to 128K tokens for the 4B, 12B, and 27B sizes,
    and 32K tokens for the 1B and 270M sizes per request, subtracting the
    request input tokens

Citation

@article{gemma_2025,
    title={Gemma 3},
    url={https://arxiv.org/abs/2503.19786},
    publisher={Google DeepMind},
    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,
the 1B with 2 trillion tokens, and the 270M with 6 trillion tokens. The
knowledge cutoff date for the training data was August 2024. 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.

Implementation Information

Details about the model internals.

Hardware

Gemma was trained using Tensor Processing Unit (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.

Software

Training was done using JAX and 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; "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

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. Evaluation results marked
with IT are for instruction-tuned models. Evaluation results marked with
PT are for pre-trained models.

Gemma 3 270M

Benchmark	n-shot	Gemma 3 PT 270M
HellaSwag	10-shot	40.9
BoolQ	0-shot	61.4
PIQA	0-shot	67.7
TriviaQA	5-shot	15.4
ARC-c	25-shot	29.0
ARC-e	0-shot	57.7
WinoGrande	5-shot	52.0

Benchmark	n-shot	Gemma 3 IT 270m
HellaSwag	0-shot	37.7
PIQA	0-shot	66.2
ARC-c	0-shot	28.2
WinoGrande	0-shot	52.3
BIG-Bench Hard	few-shot	26.7
IF Eval	0-shot	51.2

Gemma 3 1B, 4B, 12B & 27B

Reasoning and factuality

Benchmark	n-shot	Gemma 3 IT 1B	Gemma 3 IT 4B	Gemma 3 IT 12B	Gemma 3 IT 27B
GPQA Diamond	0-shot	19.2	30.8	40.9	42.4
SimpleQA	0-shot	2.2	4.0	6.3	10.0
FACTS Grounding	-	36.4	70.1	75.8	74.9
BIG-Bench Hard	0-shot	39.1	72.2	85.7	87.6
BIG-Bench Extra Hard	0-shot	7.2	11.0	16.3	19.3
IFEval	0-shot	80.2	90.2	88.9	90.4

Benchmark	n-shot	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	n-shot	Gemma 3 IT 1B	Gemma 3 IT 4B	Gemma 3 IT 12B	Gemma 3 IT 27B
MMLU (Pro)	0-shot	14.7	43.6	60.6	67.5
LiveCodeBench	0-shot	1.9	12.6	24.6	29.7
Bird-SQL (dev)	-	6.4	36.3	47.9	54.4
Math	0-shot	48.0	75.6	83.8	89.0
HiddenMath	0-shot	15.8	43.0	54.5	60.3
MBPP	3-shot	35.2	63.2	73.0	74.4
HumanEval	0-shot	41.5	71.3	85.4	87.8
Natural2Code	0-shot	56.0	70.3	80.7	84.5
GSM8K	0-shot	62.8	89.2	94.4	95.9

Benchmark	n-shot	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	n-shot	Gemma 3 IT 1B	Gemma 3 IT 4B	Gemma 3 IT 12B	Gemma 3 IT 27B
Global-MMLU-Lite	0-shot	34.2	54.5	69.5	75.1
ECLeKTic	0-shot	1.4	4.6	10.3	16.7
WMT24++	0-shot	35.9	46.8	51.6	53.4

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

Multimodal

Benchmark	Gemma 3 IT 4B	Gemma 3 IT 12B	Gemma 3 IT 27B
MMMU (val)	48.8	59.6	64.9
DocVQA	75.8	87.1	86.6
InfoVQA	50.0	64.9	70.6
TextVQA	57.8	67.7	65.1
AI2D	74.8	84.2	84.5
ChartQA	68.8	75.7	78.0
VQAv2 (val)	62.4	71.6	71.0
MathVista (testmini)	50.0	62.9	67.6

Benchmark	Gemma 3 PT 4B	Gemma 3 PT 12B	Gemma 3 PT 27B
COCOcap	102	111	116
DocVQA (val)	72.8	82.3	85.6
InfoVQA (val)	44.1	54.8	59.4
MMMU (pt)	39.2	50.3	56.1
TextVQA (val)	58.9	66.5	68.6
RealWorldQA	45.5	52.2	53.9
ReMI	27.3	38.5	44.8
AI2D	63.2	75.2	79.0
ChartQA	63.6	74.7	76.3
VQAv2	63.9	71.2	72.9
BLINK	38.0	35.9	39.6
OKVQA	51.0	58.7	60.2
TallyQA	42.5	51.8	54.3
SpatialSense VQA	50.9	60.0	59.4
CountBenchQA	26.1	17.8	68.0

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.
• 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.
• 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.