| --- |
| license: gemma |
| 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. |
| --- |
| |
| > [!Note] |
| > This repository corresponds to the Preview version of Gemma 3n E2B, to be used with Google AI Edge. You |
| > can also try it out in [Google AI Studio](https://aistudio.google.com/prompts/new_chat?model=gemma-3n-e4b-it). |
| > |
| > The current checkpoint only supports text and vision input. We are actively working to roll out full multimodal features and are |
| > collaborating with open-source partners to bring Gemma 3n to the open-source community in the coming weeks. |
| > |
| > Gemma 3n models have a novel architecture that allows them to run with a smaller number of effective parameters. |
| > They also have a Matformer architecture that allows nesting multiple models. Learn more about these techniques |
| > in the [Gemma documentation](https://ai.google.dev/gemma/docs/gemma-3n). |
|
|
| # Gemma 3n model card |
|
|
| **Model Page**: [Gemma 3n](https://ai.google.dev/gemma/docs/gemma-3n) |
|
|
| **Resources and Technical Documentation**: |
|
|
| - [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) |
| - [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma-3n) |
| - Google AI Edge [documentation](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference) to run on mobile |
| - Try on Android by downloading our [Google AI Edge Gallery](https://github.com/google-ai-edge/gallery/releases) sample app |
|
|
| **Terms of Use**: [Terms](https://ai.google.dev/gemma/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 models are well-suited for a variety of content 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. |
|
|
| Gemma 3n models are designed for efficient execution on low-resource devices. |
| They are capable of multimodal input, handling text, image, video, and audio |
| input, and generating text outputs, with open weights for instruction-tuned |
| variants. These models were trained with data in over 140 spoken languages. |
|
|
| Gemma 3n models use selective parameter activation technology to reduce resource |
| requirements. This technique allows the models to operate at an effective size |
| of 2B and 4B parameters, which is lower than the total number of parameters they |
| contain. For more information on Gemma 3n's efficient parameter management |
| technology, see the [Gemma 3n](https://ai.google.dev/gemma/docs/gemma-3n#parameters) |
| page. |
|
|
| ### Inputs and outputs |
|
|
| - **Input:** |
| - Text string, such as a question, a prompt, or a document to be |
| summarized |
| - Images, normalized to 256x256, 512x512, or 768x768 resolution |
| and encoded to 256 tokens each |
| - Audio data encoded to 6.25 tokens per second from a single channel |
| - Total input context of 32K tokens |
| - **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 length up to 32K tokens, subtracting the request |
| input tokens |
| |
| ### Citation |
|
|
| ``` |
| @article{gemma_3n_2025, |
| title={Gemma 3n}, |
| url={https://ai.google.dev/gemma/docs/gemma-3n}, |
| 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 that includes a wide variety of sources |
| totalling approximately 11 trillion tokens. The knowledge cutoff date for the |
| training data was June 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. |
| - Audio: A diverse set of sound samples enables the model to recognize |
| speech, transcribe text from recordings, and identify information in audio data. |
| |
| 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](https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf). |
| |
| ## Implementation Information |
|
|
| Details about the model internals. |
|
|
| ### Hardware |
|
|
| Gemma was trained using [Tensor Processing Unit |
| (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv4p, TPUv5p |
| and TPUv5e). Training generative models 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 generative models. 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](https://sustainability.google/operating-sustainably/). |
|
|
| ### Software |
|
|
| Training was done using [JAX](https://github.com/jax-ml/jax) and |
| [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/). |
| 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](https://goo.gle/gemma2report): |
| *"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 at full precision (float32) against a large |
| collection of different datasets and metrics to cover different aspects of |
| content generation. Evaluation results marked with **IT** are for |
| instruction-tuned models. Evaluation results marked with **PT** are for |
| pre-trained models. |
|
|
| #### Reasoning and factuality |
|
|
| | Benchmark | Metric | n-shot | E2B PT | E4B PT | |
| | ------------------------------ |----------------|----------|:--------:|:--------:| |
| | [HellaSwag][hellaswag] | Accuracy | 10-shot | 72.2 | 78.6 | |
| | [BoolQ][boolq] | Accuracy | 0-shot | 76.4 | 81.6 | |
| | [PIQA][piqa] | Accuracy | 0-shot | 78.9 | 81.0 | |
| | [SocialIQA][socialiqa] | Accuracy | 0-shot | 48.8 | 50.0 | |
| | [TriviaQA][triviaqa] | Accuracy | 5-shot | 60.8 | 70.2 | |
| | [Natural Questions][naturalq] | Accuracy | 5-shot | 15.5 | 20.9 | |
| | [ARC-c][arc] | Accuracy | 25-shot | 51.7 | 61.6 | |
| | [ARC-e][arc] | Accuracy | 0-shot | 75.8 | 81.6 | |
| | [WinoGrande][winogrande] | Accuracy | 5-shot | 66.8 | 71.7 | |
| | [BIG-Bench Hard][bbh] | Accuracy | few-shot | 44.3 | 52.9 | |
| | [DROP][drop] | Token F1 score | 1-shot | 53.9 | 60.8 | |
|
|
| [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 |
|
|
| #### Multilingual |
|
|
| | Benchmark | Metric | n-shot | E2B IT | E4B IT | |
| | ------------------------------------|-------------------------|----------|:--------:|:--------:| |
| | [MGSM][mgsm] | Accuracy | 0-shot | 53.1 | 60.7 | |
| | [WMT24++][wmt24pp] (ChrF) | Character-level F-score | 0-shot | 42.7 | 50.1 | |
| | [Include][include] | Accuracy | 0-shot | 38.6 | 57.2 | |
| | [MMLU][mmlu] (ProX) | Accuracy | 0-shot | 8.1 | 19.9 | |
| | [OpenAI MMLU][openai-mmlu] | Accuracy | 0-shot | 22.3 | 35.6 | |
| | [Global-MMLU][global-mmlu] | Accuracy | 0-shot | 55.1 | 60.3 | |
| | [ECLeKTic][eclektic] | ECLeKTic score | 0-shot | 2.5 | 1.9 | |
|
|
| [mgsm]: https://arxiv.org/abs/2210.03057 |
| [wmt24pp]: https://arxiv.org/abs/2502.12404v1 |
| [include]:https://arxiv.org/abs/2411.19799 |
| [mmlu]: https://arxiv.org/abs/2009.03300 |
| [openai-mmlu]: https://huggingface.co/datasets/openai/MMMLU |
| [global-mmlu]: https://huggingface.co/datasets/CohereLabs/Global-MMLU |
| [eclektic]: https://arxiv.org/abs/2502.21228 |
|
|
| #### STEM and code |
|
|
| | Benchmark | Metric | n-shot | E2B IT | E4B IT | |
| | ------------------------------------|--------------------------|----------|:--------:|:--------:| |
| | [GPQA][gpqa] Diamond | RelaxedAccuracy/accuracy | 0-shot | 24.8 | 23.7 | |
| | [LiveCodeBench][lcb] v5 | pass@1 | 0-shot | 18.6 | 25.7 | |
| | Codegolf v2.2 | pass@1 | 0-shot | 11.0 | 16.8 | |
| | [AIME 2025][aime-2025] | Accuracy | 0-shot | 6.7 | 11.6 | |
|
|
| [gpqa]: https://arxiv.org/abs/2311.12022 |
| [lcb]: https://arxiv.org/abs/2403.07974 |
| [aime-2025]: https://www.vals.ai/benchmarks/aime-2025-05-09 |
|
|
| #### Additional benchmarks |
|
|
| | Benchmark | Metric | n-shot | E2B IT | E4B IT | |
| | ------------------------------------ |------------|----------|:--------:|:--------:| |
| | [MMLU][mmlu] | Accuracy | 0-shot | 60.1 | 64.9 | |
| | [MBPP][mbpp] | pass@1 | 3-shot | 56.6 | 63.6 | |
| | [HumanEval][humaneval] | pass@1 | 0-shot | 66.5 | 75.0 | |
| | [LiveCodeBench][lcb] | pass@1 | 0-shot | 13.2 | 13.2 | |
| | HiddenMath | Accuracy | 0-shot | 27.7 | 37.7 | |
| | [Global-MMLU-Lite][global-mmlu-lite] | Accuracy | 0-shot | 59.0 | 64.5 | |
| | [MMLU][mmlu] (Pro) | Accuracy | 0-shot | 40.5 | 50.6 | |
|
|
| [gpqa]: https://arxiv.org/abs/2311.12022 |
| [mbpp]: https://arxiv.org/abs/2108.07732 |
| [humaneval]: https://arxiv.org/abs/2107.03374 |
| [lcb]: https://arxiv.org/abs/2403.07974 |
| [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite |
|
|
| #### Android Performance Benchmarks with Google AI Edge |
|
|
| Note that all benchmark stats are from a Samsung S25 Ultra with 4096 KV cache size, 1024 tokens prefill, 256 tokens decode. |
|
|
| These numbers will continue to improve while Gemma 3n is in preview. |
|
|
| | Weight Quantization | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | Time to first token (sec) | Model size (MB) | Peak RSS Memory (MB) | GPU Memory (MB) | |
| | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
| | dynamic\_int4 | CPU | 118 | 12.8 | 9.2 | 4201 | 3924 | 193 | |
| | dynamic\_int4 | GPU | 446 | 16.1 | 15.1 | 4201 | 5504 | 3048 | |
|
|
| * Model size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models) |
| * The inference on CPU is accelerated via the LiteRT [XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads |
| * Benchmark on CPU is done assuming XNNPACK cache is enabled |
| * Benchmark on GPU is done assuming model is cached |
| * Vision encoder is always run on GPU with 512x512 resolution |
| * Cpufreq governor is set to performance during benchmark. Observed performance may vary depending on your phone’s hardware and current activity level. |
| * dynamic\_int4: quantized model with int4 weights and float activations. |
| |
| |
| ## 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. Notable |
| 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 safe levels of performance across 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 text-to-text, image-to-text, |
| and audio-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 high severity violations. A limitation of our |
| evaluations was they included primarily English language prompts. |
| |
| ## Usage and Limitations |
| |
| These models have certain limitations that users should be aware of. |
| |
| ### Intended Usage |
| |
| Open generative 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**: 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**: Extract, interpret, and summarize |
| visual data for text communications. |
| - **Audio Data Extraction**: Transcribe spoken language, speech |
| translated to text in other languages, and analyze sound-based data. |
| - Research and Education |
| - **Natural Language Processing (NLP) and generative model |
| Research**: These models can serve as a foundation for researchers to |
| experiment with generative models 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 data 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 generative models raises several ethical concerns. In |
| creating an open model, we have carefully considered the following: |
| |
| - Bias and Fairness |
| - Generative models 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 |
| - Generative models 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](https://ai.google.dev/responsible). |
| - 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 generative model 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 |
| generative models. 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](https://ai.google.dev/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 |
| generative 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. |
