Gemma 3n e4b-it Overview

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.

This model is ready for commercial/non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. It has been produced to a third-party's requirements for this application and use-case. See the external card: Gemma 3n e4b-it Model Card.

License and Terms of Use:

GOVERNING TERMS: This trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Community Model License. Additional Information: Gemma Terms of Use

Deployment Geography:

Global

Use Case:

Content Creation and Communication (Text Generation, Chatbots, Summarization, Image/Audio Data Extraction), Research and Education (NLP Research, Language Learning, Knowledge Exploration)

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.

Release Date:

Build.NVIDIA.com: 06/26/2025 via (link)

Hugging Face: 06/26/2025 via (link)

References:

• Gemma 3n model overview
• Gemma 3n's efficient parameter management technolog
• Responsible Generative AI Toolkit
• Gemma on Kaggle
• Gemma on HuggingFace
• Gemma on Vertex Model Garden

Model Architecture:

• Architecture Type: Matryoshka Transformer
  
• Network Architecture: Matryoshka Transformer (MatFormer)
  
• Parameter Count: 4B (base model), ~6.8B (with Per-Layer Embeddings)
  
• Number of Layers: 35
  
• Notable Architectural Features: Selective parameter activation technology
  
• Base Model: google/gemma-3n-e4b
  
• Additional Notes: The model's full parameter count is higher than its base model size due to Per-Layer Embeddings (PLE). Standard implementations will load all parameters, including PLE, into VRAM.
  

Input

• Input Type(s): Text, Image, Audio
  
• Input Formats: Text string, Images (normalized to 256x256, 512x512, or 768x768), Audio data (single channel)
  
• Input Parameters: 1D, 2D, 3D
  
• Other Properties Related to Input: Total input context of 32K tokens. Images are encoded to 256 tokens each. Audio data is encoded to 6.25 tokens per second from a single channel.
  

Output

• Output Type(s): Text
  
• Output Formats: Text
  
• Output Parameters: 1D
  
• Other Properties Related to Output: Total output length up to 32K tokens, subtracting the request input tokens.

Software Integration

Supported Hardware Microarchitecture Compatibility:

NVIDIA GPU Micro-architectures Suitable for Serving Gemma 3n in Production

(≥ 16 GB VRAM + Tensor-core /mixed-precision support)

Recommendation: Start with Ampere or newer for production workloads that demand real-time multimodal responses or higher concurrency. Turing/Volta can host smaller replica pools; Pascal is generally not advised for new deployments.

Model Version:

gemma-3n-e4b-it v1.0

Training, Testing, and Evaluation Datasets:

Training Dataset:

• Data Collection Method: Hybrid: Automated, Synthetic, Human
  
• Labeling Method: Undisclosed
  
• Properties: A diverse collection of web text in over 140 languages, code, mathematics, images, and audio, totalling approximately 11 trillion tokens. Knowledge cutoff date is June 2024.
  

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.

Testing Dataset:

• Data Collection Method by dataset: Hybrid: Automated, Synthetic, Human
  
• Labeling Method by dataset: Undisclosed
  
• Properties: Undisclosed
  

Evaluation Benchmark Results:

• Data Collection Method: Undisclosed
  
• Labeling Method: Undisclosed
  
• Properties: Benchmarks include: HellaSwag, BoolQ, PIQA, SocialIQA, TriviaQA, Natural Questions, ARC-c, ARC-e, WinoGrande, BIG-Bench Hard, DROP, MGSM, WMT24++, Include, MMLU, GPQA Diamond, LiveCodeBench, Codegolf, AIME 2025, MBPP, HumanEval, HiddenMath, Global-MMLU-Lite.
  

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

STEM and code

Additional benchmarks

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.

Inference:

Acceleration Engine: vLLM

Test Hardware: L40s

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,
)
output = pipe(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
    text="<image_soft_token> in this image, there is"
)

print(output)
# [{'input_text': '<image_soft_token> in this image, there is',
# 'generated_text': '<image_soft_token> in this image, there is a beautiful flower and a bee is sucking nectar and pollen from the flower.'}]

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="cuda", torch_dtype=torch.bfloat16,).eval()

processor = AutoProcessor.from_pretrained(model_id)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"
image = Image.open(requests.get(url, stream=True).raw)
prompt = "<image_soft_token> in this image, there is"
model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)

input_len = model_inputs["input_ids"].shape[-1]

with torch.inference_mode():
    generation = model.generate(**model_inputs, max_new_tokens=10)
    generation = generation[0][input_len:]

decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
# one picture of flowers which shows that the flower is

Additional Details:

Usage and Limitations

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

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.

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.
• 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.

Ethical Considerations:

NVIDIA believes Trustworthy Al is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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}
}