| --- |
| license: gemma |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| extra_gated_button_content: Acknowledge license |
| base_model: google/gemma-3n-E4B-it |
| tags: |
| - automatic-speech-recognition |
| - automatic-speech-translation |
| - audio-text-to-text |
| - video-text-to-text |
| - matformer |
| --- |
| |
| ``` |
| # score for test-submodel-4, which is a slice of Gemma3n-E4B-it |
| mtx@mtx-cv-m13-gpu-master-14:~/Desktop/personal/vlms/evals/VLMEvalKit$ cat outputs/Gemma3n-E2B-it-ts4/*/*.json |
| { |
| "en_text_recognition": 0.34489594363685844, |
| "en_text_detection": 0.14555663691294746, |
| "en_text_spotting": 0.0, |
| "en_element_parsing": 0.02, |
| "en_mathematical_calculation": 0.20092149837002324, |
| "en_visual_text_understanding": 0.5540588220515938, |
| "en_knowledge_reasoning": 0.44284559706420745, |
| "English Overall Score": 0.2440397854336615 |
| } |
| |
| # score for Gemma3n-E2B-it, google's official published submodel of Gemma3n-E4B-it |
| mtx@mtx-cv-m13-gpu-master-14:~/Desktop/personal/vlms/evals/VLMEvalKit$ cat outputs/Gemma3n-E2B-it/T202*/*.json |
| { |
| "en_text_recognition": 0.6096878412118829, |
| "en_text_detection": 0.23172620479653033, |
| "en_text_spotting": 0.0, |
| "en_element_parsing": 0.04, |
| "en_mathematical_calculation": 0.3240026170865559, |
| "en_visual_text_understanding": 0.7103983997859018, |
| "en_knowledge_reasoning": 0.5074402603391522, |
| "English Overall Score": 0.34617933188857475 |
| } |
| ``` |
|
|
| > [!Note] |
| > This is a submodel derived from `google/gemma-3n-E4B-it`. It has been modified by slicing specific layers and resizing FFN dimensions. It is not the original model. |
| > To learn more about MatFormers, please review the [launch blog](https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide) and generate your own submodels |
| with the [MatFormer Lab](https://goo.gle/gemma3n-matformer-lab). |
| > |
| |
| Skipped layers: [20, 21, 22, 23, 24] |
|
|
| FFN hidden dimensions: [4096, 4096, 4096, 4096, 16384, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 8192, 4096, 4096, 4096, 4096, 4096] |
|
|
|
|
| > [!Note] |
| > This repository corresponds to the launch version of Gemma 3n E4B IT (Instruct), to be used with Hugging Face `transformers`, |
| > supporting text, audio, and vision (image and video) inputs. |
| > |
| > Gemma 3n models have multiple architecture innovations: |
| > * They are available in two sizes based on [effective parameters](https://ai.google.dev/gemma/docs/gemma-3n#parameters). While the raw parameter count of this model is 8B, the architecture design allows the model to be run with a memory footprint comparable to a traditional 4B model by offloading low-utilization matrices from the accelerator. |
| > * They use a MatFormer architecture that allows nesting sub-models within the E4B model. We provide one sub-model (an [E2B](https://huggingface.co/google/gemma-3n-E2B-it)), or you can access a spectrum of custom-sized models using the [Mix-and-Match method](https://goo.gle/gemma3n-matformer-lab). |
| > |
| > Learn more about these techniques in the [technical blog post](https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide) |
| > and 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) |
| - [Gemma on HuggingFace](https://huggingface.co/collections/google/gemma-3n-685065323f5984ef315c93f4) |
| - [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3n) |
|
|
| **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 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 pre-trained and |
| 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 |
| |
| ### Usage |
|
|
| Below, there are some code snippets on how to get quickly started with running |
| the model. First, install the Transformers library. Gemma 3n is supported |
| starting from transformers 4.53.0. |
|
|
| ```sh |
| $ pip install -U transformers |
| ``` |
|
|
| Then, copy the snippet from the section that is relevant for your use case. |
|
|
| #### Running with the `pipeline` API |
|
|
| You can initialize the model and processor for inference with `pipeline` as |
| follows. |
|
|
| ```python |
| from transformers import pipeline |
| import torch |
| |
| pipe = pipeline( |
| "image-text-to-text", |
| model="google/gemma-3n-e4b-it", |
| device="cuda", |
| torch_dtype=torch.bfloat16, |
| ) |
| ``` |
|
|
| With instruction-tuned models, you need to use chat templates to process our |
| inputs first. Then, you can pass it to the pipeline. |
|
|
| ```python |
| messages = [ |
| { |
| "role": "system", |
| "content": [{"type": "text", "text": "You are a helpful assistant."}] |
| }, |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, |
| {"type": "text", "text": "What animal is on the candy?"} |
| ] |
| } |
| ] |
| |
| output = pipe(text=messages, max_new_tokens=200) |
| print(output[0]["generated_text"][-1]["content"]) |
| # Okay, let's take a look! |
| # Based on the image, the animal on the candy is a **turtle**. |
| # You can see the shell shape and the head and legs. |
| ``` |
|
|
| #### Running the model on a single GPU |
|
|
| ```python |
| from transformers import AutoProcessor, Gemma3nForConditionalGeneration |
| from PIL import Image |
| import requests |
| import torch |
| |
| model_id = "google/gemma-3n-e4b-it" |
| |
| model = Gemma3nForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16,).eval() |
| |
| processor = AutoProcessor.from_pretrained(model_id) |
| |
| messages = [ |
| { |
| "role": "system", |
| "content": [{"type": "text", "text": "You are a helpful assistant."}] |
| }, |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, |
| {"type": "text", "text": "Describe this image in detail."} |
| ] |
| } |
| ] |
| |
| inputs = processor.apply_chat_template( |
| messages, |
| add_generation_prompt=True, |
| tokenize=True, |
| return_dict=True, |
| return_tensors="pt", |
| ).to(model.device) |
| |
| input_len = inputs["input_ids"].shape[-1] |
| |
| with torch.inference_mode(): |
| generation = model.generate(**inputs, max_new_tokens=100, do_sample=False) |
| generation = generation[0][input_len:] |
| |
| decoded = processor.decode(generation, skip_special_tokens=True) |
| print(decoded) |
| |
| # **Overall Impression:** The image is a close-up shot of a vibrant garden scene, |
| # focusing on a cluster of pink cosmos flowers and a busy bumblebee. |
| # It has a slightly soft, natural feel, likely captured in daylight. |
| ``` |
|
|
| ### 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 |
|
|
| ## 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, translate speech |
| 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. |
