huggingface--transformers/docs/source/en/model_doc/t5gemma2.md

This model was contributed to Hugging Face Transformers on 2025-12-01.

T5Gemma 2

T5Gemma 2 is a family of pretrained encoder-decoder large language models with strong multilingual, multimodal and long-context capability, available in 270M-270M, 1B-1B and 4B-4B parameters. Following T5Gemma, it is built via model adaptation (based on Gemma 3) using UL2. The architecture is similar to T5Gemma and Gemma 3, enhanced with tied word embeddings and merged self- and cross-attention to save model parameters.

Tip

Click on the T5Gemma 2 models in the right sidebar for more examples of how to apply T5Gemma 2 to different language tasks.

The example below demonstrates how to chat with the model with [Pipeline] or the [AutoModel] class, and from the command line.

from transformers import pipeline


generator = pipeline(
    "image-text-to-text",
    model="google/t5gemma-2-270m-270m",
    device_map="auto",
)

generator(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
    text="<start_of_image> in this image, there is",
    generate_kwargs={"do_sample": False, "max_new_tokens": 50},
)

import requests
from PIL import Image

from transformers import AutoModelForSeq2SeqLM, AutoProcessor


processor = AutoProcessor.from_pretrained("google/t5gemma-2-270m-270m")
model = AutoModelForSeq2SeqLM.from_pretrained(
    "google/t5gemma-2-270m-270m",
    device_map="auto",
)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"
image = Image.open(requests.get(url, stream=True).raw)
prompt = "<start_of_image> in this image, there is"

model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
generation = model.generate(**model_inputs, max_new_tokens=20, do_sample=False)
print(processor.decode(generation[0]))

T5Gemma2Config

autodoc T5Gemma2Config

T5Gemma2TextConfig

autodoc T5Gemma2TextConfig

T5Gemma2EncoderConfig

autodoc T5Gemma2EncoderConfig

T5Gemma2DecoderConfig

autodoc T5Gemma2DecoderConfig

T5Gemma2Model

autodoc T5Gemma2Model - forward

T5Gemma2ForConditionalGeneration

autodoc T5Gemma2ForConditionalGeneration - forward - get_image_features

T5Gemma2ForSequenceClassification

autodoc T5Gemma2ForSequenceClassification - forward

T5Gemma2ForTokenClassification

autodoc T5Gemma2ForTokenClassification - forward