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Quick start with the Gemma-3 model

Gemma 3 introduces powerful vision-language capabilities across its 4B, 12B, and 27B models through a custom SigLIP vision encoder, enabling rich interpretation of visual input. It processes fixed-size 896x896 images using a “Pan&Scan” algorithm for adaptive cropping and resizing, balancing detail preservation with computational cost

Step 1: Install WasmEdge​

First off, you'll need WasmEdge, a high-performance, lightweight, and cross-platform LLM runtime.

curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install_v2.sh | bash -s

Step 2: Download the LLM model​

Next, you'll need to obtain model files: the Gemma-3 model and the mmproj model.

The Gemma-3-1B model is a small-scale language-only model and does not support vision-language capabilities

curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-Q5_K_M.gguf
curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-mmproj-f16.gguf

Step 3: Download a portable API server app​

Next, you need an application that can build and OpenAI-compatible API server for the Gemma-3 models The LlamaEdge api server app is a lightweight and cross-platform Wasm app that works on any device you might have. Just download the compiled binary app.

curl -LO https://github.com/second-state/LlamaEdge/releases/latest/download/llama-api-server.wasm

The version of the llama-api-server.wasm should be v0.18.5 and above.

Step 4: Chat with the chatbot UI​

The llama-api-server.wasm is a web server with an OpenAI-compatible API. You still need HTML files for the chatbot UI. It's optional and you can use curl to send an API request. Download and unzip the HTML UI files as follows.

curl -LO https://github.com/LlamaEdge/chatbot-ui/releases/latest/download/chatbot-ui.tar.gz
tar xzf chatbot-ui.tar.gz
rm chatbot-ui.tar.gz

Then, start the web server.

wasmedge --dir .:. --nn-preload default:GGML:AUTO:gemma-3-4b-it-Q5_K_M.gguf \
  llama-api-server.wasm \
  --prompt-template gemma-3 \
  --llava-mmproj gemma-3-4b-it-mmproj-f16.gguf \
  --ctx-size 4096 \
  --model-name gemma-3-4b

The above command line works on a Macbook with 16 GB memory.

Upon successful execution, you should see output similar to the following:

[2025-05-18 11:23:09.970] [info] llama_api_server in llama-api-server/src/main.rs:202: LOG LEVEL: info
[2025-05-18 11:23:09.973] [info] llama_api_server in llama-api-server/src/main.rs:205: SERVER VERSION: 0.18.5
[2025-05-18 11:23:09.976] [info] llama_api_server in llama-api-server/src/main.rs:544: model_name: Qwen2.5-VL-7B-Instruct

...

[2025-05-18 11:23:10.531] [info] llama_api_server in llama-api-server/src/main.rs:917: plugin_ggml_version: b5361 (commit cf0a43bb)
[2025-05-18 11:23:10.533] [info] llama_api_server in llama-api-server/src/main.rs:952: Listening on 0.0.0.0:8080

Then, go to http://localhost:8080 on your computer to access the chatbot UI on a web page! You can upload an imange and chat with the model based on the image.

Step 5: Send an API request​

You can send an API request to call the model, which is more universal. The following command demonstrates how to send a CURL request to llama-api-server. The request includes a base64-encoded string of an image in the image_url field. For demonstration purposes, only a portion of the base64 string is shown here. In practice, you should use the complete base64 string.

[!TIP] base64.guru provides a tool for encoding JPG to Base64.

curl --location 'http://localhost:8080/v1/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant that accurately describes the content of images provided by the user."
        },
        {
            "content": [
                {
                    "type": "text",
                    "text": "Describe the picture"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgFBgcGBQg......X/VaTer/ALzOU/Lg1XMiLMuR3EWMb77/AHsD/DNTIhXPnmvLmwj"
                    }
                }
            ],
            "role": "user"
        }
    ],
    "model": "gemma-3-4b"
}'

If the request is processed successfully, you will receive a response similar to the following:

{
    "id": "chatcmpl-e5f777db-c913-45ab-b37f-e2c499c8fa0b",
    "object": "chat.completion",
    "created": 1747652210,
    "model": "gemma-3-4b",
    "choices": [
        {
            "index": 0,
            "message": {
                "content": "mixed berries in a paper bowl",
                "role": "assistant"
            },
            "finish_reason": "stop",
            "logprobs": null
        }
    ],
    "usage": {
        "prompt_tokens": 27,
        "completion_tokens": 8,
        "total_tokens": 35
    }
}

Congratulations! You have now started an multimodal app on your own device.