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pipeline_tag: image-text-to-text
license: gemma
base_model: google/gemma-3n-E4B-it
library_name: kerasformers
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
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license_link: https://ai.google.dev/gemma/terms
language:
- en
tags:
- keras
- kerasformers
- gemma3n
- gemma-3n
- image-text-to-text
- audio-text-to-text
- multimodal
- pytorch
- jax
- tf
---
## ***See [our collection](https://huggingface.co/collections/kerasformers/gemma-3n-6a7a507adf78dde12680accf) for all versions of Gemma 3n.***
# Run Gemma 3n with Keras 3: JAX, PyTorch, or TensorFlow
[](https://github.com/IMvision12/KerasFormers) [](https://imvision12.github.io/KerasFormers/gemma3n/) [](https://huggingface.co/collections/kerasformers/gemma-3n-6a7a507adf78dde12680accf)
# kerasformers/gemma-3n-e4b-it
Gemma 3n is Google's on-device **multimodal** (image + audio + text) model. Its
decoder layers several on-device innovations on the Gemma shape: **AltUp**
(alternating updates over parallel hidden streams), **LAuReL** (learned augmented
residuals), **MatFormer** (nested per-layer widths), **Per-Layer Embeddings**, and
activation sparsity, with tail **KV-sharing** and a 5:1 sliding/global attention
schedule. Vision is a **MobileNet-V5** encoder and audio a **USM** conformer, both
feeding soft tokens into the decoder. Base checkpoints are for completion; `-it`
variants are instruction-tuned.
For more details, see Google's original [model card](https://huggingface.co/google/gemma-3n-E4B-it).
Pure-**Keras 3** conversion of [`google/gemma-3n-E4B-it`](https://huggingface.co/google/gemma-3n-E4B-it) for
[kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on
**TensorFlow / Torch / JAX**.
This is an **instruction-tuned** checkpoint: use the chat template via `Gemma3nProcessor`.
## ✨ Quick start
### Text-only
```python
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from kerasformers.models.gemma3n import Gemma3nTextGenerate, Gemma3nTokenizer
model = Gemma3nTextGenerate.from_weights("kerasformers/gemma-3n-e4b-it")
tokenizer = Gemma3nTokenizer.from_weights("kerasformers/gemma-3n-e4b-it")
inputs = tokenizer([{"role": "user", "content": "Hello, who are you?"}])
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))
```
### Image + text
```python
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from kerasformers.models.gemma3n import Gemma3nConditionalGenerate, Gemma3nProcessor
model = Gemma3nConditionalGenerate.from_weights("kerasformers/gemma-3n-e4b-it")
processor = Gemma3nProcessor.from_weights("kerasformers/gemma-3n-e4b-it")
inputs = processor(conversation=[
{"role": "user", "content": [
{"type": "image", "image": Image.open("cat.jpg")},
{"type": "text", "text": "Describe this image in one sentence."},
]}
])
outputs = model.generate(**inputs, max_new_tokens=64)
print(processor.decode(outputs[0]))
```
All Gemma 3n variants load the same way with `from_weights("kerasformers/<variant>")`:
| Variant | Hub | Type |
|---|---|---|
| `gemma-3n-e2b` | [`kerasformers/gemma-3n-e2b`](https://huggingface.co/kerasformers/gemma-3n-e2b) | multimodal / base |
| `gemma-3n-e2b-it` | [`kerasformers/gemma-3n-e2b-it`](https://huggingface.co/kerasformers/gemma-3n-e2b-it) | multimodal / instruct |
| `gemma-3n-e4b` | [`kerasformers/gemma-3n-e4b`](https://huggingface.co/kerasformers/gemma-3n-e4b) | multimodal / base |
| `gemma-3n-e4b-it` | [`kerasformers/gemma-3n-e4b-it`](https://huggingface.co/kerasformers/gemma-3n-e4b-it) | multimodal / instruct |
## Tips
- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
- Loads in **bfloat16** by default (the weights are bf16). Pass
`load_dtype="float32"` for full precision, or `quantization="int8"` to shrink further.
- Gemma 3n is **audio-capable** too: pass `audio` content items in the
conversation to transcribe / reason over speech.
- See [Gemma 3n docs](https://imvision12.github.io/KerasFormers/gemma3n/) and
[Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
- Upstream safetensors still work via the `hf:` prefix, e.g.
`Gemma3nConditionalGenerate.from_weights("hf:google/gemma-3n-E4B-it")`.
## Special Thanks
A huge thank you to the Google Gemma authors for creating and releasing these models.
License: Gemma (gated). Accept the license on the upstream Hub card before downloading.
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