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 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.

Inputs and outputs

Input:
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size
Output:
- Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document
- Total output context of 8192 tokens

Usage

Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library with the version made for Gemma 3:

$ pip install git+https://github.com/huggingface/transformers@v4.49.0-Gemma3

Then, copy the snippet from the section that is relevant for your use case.

Running with the `pipeline` API

With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.

python

from transformers import pipeline

pipe = pipeline("text-generation", model="google/gemma-3-1b-it", device="cuda", torch_dtype=torch.bfloat16)

messages = [
    [
        {
            "role": "system",
            "content": [{"type": "text", "text": "You are a helpful assistant."},]
        },
        {
            "role": "user",
            "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
        },
    ],
]

output = pipe(messages, max_new_tokens=50)

Running the model on a single / multi GPU

python

from transformers import AutoTokenizer, BitsAndBytesConfig, Gemma3ForCausalLM
import torch

model_id = "google/gemma-3-1b-it"

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

model = Gemma3ForCausalLM.from_pretrained(
    model_id, quantization_config=quantization_config
).eval()

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    [
        {
            "role": "system",
            "content": [{"type": "text", "text": "You are a helpful assistant."},]
        },
        {
            "role": "user",
            "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
        },
    ],
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device).to(torch.bfloat16)


with torch.inference_mode():
    outputs = model.generate(**inputs, max_new_tokens=64)

outputs = tokenizer.batch_decode(outputs)

duyntnet/gemma-3-1b-it-imatrix-GGUF

Open source inference clients/UIs

Closed source inference clients/UIs

From original readme

Inputs and outputs

Usage

Running with the pipeline API

Running the model on a single / multi GPU

duyntnet/
gemma-3-1b-it-imatrix-GGUF

Running with the `pipeline` API