Add support for Gemma3 VLM architecture

[xenova]

Closes #1438

Example

import {
  AutoProcessor,
  AutoModelForImageTextToText,
  load_image,
  TextStreamer,
} from "@huggingface/transformers";

// Load model and processor
const model_id = "onnx-community/gemma-3-4b-it-ONNX";
const processor = await AutoProcessor.from_pretrained(model_id);
const model = await AutoModelForImageTextToText.from_pretrained(model_id, {
  device: "webgpu",
  dtype: "q4",
});

// Define the list of messages
const messages = [
  {
    role: "user",
    content: [
      { type: "image" },
      { type: "text", text: "Describe this image." },
    ],
  },
];

// Prepare inputs
const prompt = processor.apply_chat_template(messages, { add_generation_prompt: true });
const url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg";
const image = await load_image(url);
const inputs = await processor(prompt, image, { add_special_tokens: false });

const outputs = await model.generate({
  ...inputs,
  max_new_tokens: 2048,
  streamer: new TextStreamer(processor.tokenizer, {
    skip_prompt: true,
    // callback_function: (text) => { /* Do something with the streamed output */ },
  }),
});

// Decode output
const decoded = processor.batch_decode(
  outputs.slice(null, [inputs.input_ids.dims.at(-1), null]),
  { skip_special_tokens: true },
);
console.log(decoded[0]);

[HuggingFaceDocBuilderDev]

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