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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:

Learn more about these techniques in the technical blog post and the Gemma documentation.

Gemma 3n model card

Model Page: Gemma 3n

Resources and Technical Documentation:

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 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 page.

Inputs and outputs 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.

$ 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.

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.

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"])
Running the model on a single GPU 
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)
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:

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:

Implementation Information

Details about the model internals.

Hardware

Gemma was trained using Tensor Processing Unit (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:

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 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 Multilingual Benchmark Metric n-shot E2B IT E4B IT MGSM Accuracy 0-shot 53.1 60.7 WMT24++ (ChrF) Character-level F-score 0-shot 42.7 50.1 Include Accuracy 0-shot 38.6 57.2 MMLU (ProX) Accuracy 0-shot 8.1 19.9 OpenAI MMLU Accuracy 0-shot 22.3 35.6 Global-MMLU Accuracy 0-shot 55.1 60.3 ECLeKTic ECLeKTic score 0-shot 2.5 1.9 STEM and code Benchmark Metric n-shot E2B IT E4B IT GPQA Diamond RelaxedAccuracy/accuracy 0-shot 24.8 23.7 LiveCodeBench v5 pass@1 0-shot 18.6 25.7 Codegolf v2.2 pass@1 0-shot 11.0 16.8 AIME 2025 Accuracy 0-shot 6.7 11.6 Additional benchmarks Benchmark Metric n-shot E2B IT E4B IT MMLU Accuracy 0-shot 60.1 64.9 MBPP pass@1 3-shot 56.6 63.6 HumanEval pass@1 0-shot 66.5 75.0 LiveCodeBench pass@1 0-shot 13.2 13.2 HiddenMath Accuracy 0-shot 27.7 37.7 Global-MMLU-Lite Accuracy 0-shot 59.0 64.5 MMLU (Pro) Accuracy 0-shot 40.5 50.6 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:

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.

Limitations Ethical Considerations and Risks

The development of generative models raises several ethical concerns. In creating an open model, we have carefully considered the following:

Risks identified and mitigations:

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.

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