huggingface--transformers/docs/source/en/model_doc/gemma3n.md

This model was published in HF papers on 2024-11-12 and contributed to Hugging Face Transformers on 2025-06-26.

Gemma3n

Overview

Gemma3n is a multimodal model with pretrained and instruction-tuned variants, available in E4B and E2B sizes. While large portions of the language model architecture are shared with prior Gemma releases, there are many new additions in this model, including Alternating Updates (AltUp), Learned Augmented Residual Layer (LAuReL), MatFormer, Per-Layer Embeddings (PLE), Activation Sparsity with Statistical Top-k, and KV cache sharing. The language model uses a similar attention pattern to Gemma 3 with alternating 4 local sliding window self-attention layers for every global self-attention layer with a maximum context length of 32k tokens. Gemma 3n introduces MobileNet v5 as the vision encoder, using a default resolution of 768x768 pixels, and adds a newly trained audio encoder based on the Universal Speech Model (USM) architecture.

The instruction-tuned variant was post-trained with knowledge distillation and reinforcement learning.

You can find all the original Gemma 3n checkpoints under the Gemma 3n release.

Tip

Click on the Gemma 3n models in the right sidebar for more examples of how to apply Gemma to different vision, audio, and language tasks.

The example below demonstrates how to generate text based on an image with [Pipeline] or the [AutoModel] class.

from transformers import pipeline


pipeline = pipeline(
    task="image-text-to-text",
    model="google/gemma-3n-e4b",
    device=0,
)
pipeline(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
    text="<start_of_image> What is shown in this image?"
)

from transformers import AutoProcessor, Gemma3nForConditionalGeneration


model = Gemma3nForConditionalGeneration.from_pretrained(
    "google/gemma-3n-e4b-it",
    device_map="auto",
    attn_implementation="sdpa"
)
processor = AutoProcessor.from_pretrained(
    "google/gemma-3n-e4b-it",
    padding_side="left"
)

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/pipeline-cat-chonk.jpeg"},
            {"type": "text", "text": "What is shown in this image?"},
        ]
    },
]
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
).to(model.device)

output = model.generate(**inputs, max_new_tokens=50, cache_implementation="static")
print(processor.decode(output[0], skip_special_tokens=True))

Notes

• Use [Gemma3nForConditionalGeneration] for image-audio-and-text, image-and-text, image-and-audio, audio-and-text, image-only and audio-only inputs.

• Gemma 3n supports multiple images per input, but make sure the images are correctly batched before passing them to the processor. Each batch should be a list of one or more images.

  url_cow = "https://media.istockphoto.com/id/1192867753/photo/cow-in-berchida-beach-siniscola.jpg?s=612x612&w=0&k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4="
  url_cat = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
  
  messages =[
      {
          "role": "system",
          "content": [
              {"type": "text", "text": "You are a helpful assistant."}
          ]
      },
      {
          "role": "user",
          "content": [
              {"type": "image", "url": url_cow},
              {"type": "image", "url": url_cat},
              {"type": "text", "text": "Which image is cuter?"},
          ]
      },
  ]
  

• Text passed to the processor should have a <image_soft_token> token wherever an image should be inserted.

• Gemma 3n accept at most one target audio clip per input, though multiple audio clips can be provided in few-shot prompts, for example.

• Text passed to the processor should have a <audio_soft_token> token wherever an audio clip should be inserted.

• The processor has its own [~ProcessorMixin.apply_chat_template] method to convert chat messages to model inputs.

Gemma3nAudioFeatureExtractor

autodoc Gemma3nAudioFeatureExtractor

Gemma3nProcessor

autodoc Gemma3nProcessor - call

Gemma3nTextConfig

autodoc Gemma3nTextConfig

Gemma3nVisionConfig

autodoc Gemma3nVisionConfig

Gemma3nAudioConfig

autodoc Gemma3nAudioConfig

Gemma3nConfig

autodoc Gemma3nConfig

Gemma3nTextModel

autodoc Gemma3nTextModel - forward

Gemma3nModel

autodoc Gemma3nModel - forward - get_image_features - get_audio_features

Gemma3nForCausalLM

autodoc Gemma3nForCausalLM - forward

Gemma3nForConditionalGeneration

autodoc Gemma3nForConditionalGeneration - forward - get_image_features