README.md

metadata

pipeline_tag: image-text-to-text
license: gemma
base_model: google/gemma-3n-E2B-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
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
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 for all Gemma 3n sizes and variants.

Run Gemma 3n with Keras 3: JAX, PyTorch, or TensorFlow

kerasformers/gemma-3n-e2b-it

Pure-Keras 3 conversion of google/gemma-3n-E2B-it for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX. This is the instruction-tuned checkpoint, served here as image + audio + text -> text via Gemma3nConditionalGenerate; weights are stored in bfloat16.

For model details, license, and usage terms, see Google's model card.

✨ Quick start

Text-only

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from kerasformers.models.gemma3n import Gemma3nTextGenerate, Gemma3nTokenizer

model = Gemma3nTextGenerate.from_weights("kerasformers/gemma-3n-e2b-it")
tokenizer = Gemma3nTokenizer.from_weights("kerasformers/gemma-3n-e2b-it")

inputs = tokenizer([{"role": "user", "content": "Hello, who are you?"}])
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))

Image + audio + text

from kerasformers.models.gemma3n import Gemma3nConditionalGenerate, Gemma3nProcessor

model = Gemma3nConditionalGenerate.from_weights("kerasformers/gemma-3n-e2b-it")
processor = Gemma3nProcessor.from_weights("kerasformers/gemma-3n-e2b-it")

conversation = [
    {"role": "user", "content": [
        {"type": "image", "url": "https://.../image.jpg"},
        {"type": "text", "text": "Describe this image."},
    ]},
]
inputs = processor(conversation)
outputs = model.generate(**inputs, max_new_tokens=64)
print(processor.decode(outputs[0]))

Load any Gemma 3n variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
gemma-3n-e2b	kerasformers/gemma-3n-e2b
gemma-3n-e2b-it	kerasformers/gemma-3n-e2b-it
gemma-3n-e4b	kerasformers/gemma-3n-e4b
gemma-3n-e4b-it	kerasformers/gemma-3n-e4b-it

Tips

• Set KERAS_BACKEND before importing Keras / kerasformers.
• Loads in bfloat16 by default. Pass load_dtype="float32" for full precision, or quantization="int8" to shrink further.
• See the Gemma 3n docs.
• Community / upstream weights still work via the hf: prefix: Gemma3nConditionalGenerate.from_weights("hf:google/gemma-3n-E2B-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.