Google Gemma

Run Google's Gemma model with vision capabilities via OpenRouter.

Ask a question to Google's Gemma model with vision capabilities.

You can specify arbitrary text prompts or predefined ones, the block supports the following types of prompt:

• Open Prompt (unconstrained) - Use any prompt to generate a raw response

• Text Recognition (OCR) (ocr) - Model recognizes text in the image

• Visual Question Answering (visual-question-answering) - Model answers the question you submit in the prompt

• Captioning (short) (caption) - Model provides a short description of the image

• Captioning (detailed-caption) - Model provides a long description of the image

• Single-Label Classification (classification) - Model classifies the image content as one of the provided classes

• Multi-Label Classification (multi-label-classification) - Model classifies the image content as one or more of the provided classes

• Unprompted Object Detection (object-detection) - Model detects and returns the bounding boxes for prominent objects in the image

• Structured Output Generation (structured-answering) - Model returns a JSON response with the specified fields

🛠️ API providers and model variants

Gemma is exposed via OpenRouter. By default this block uses the Roboflow-managed OpenRouter key and bills your Roboflow credits - no extra setup needed. To bypass Roboflow billing, paste your own sk-or-... key into the api_key field.

The privacy_level field controls which OpenRouter providers may serve the request:

• No data collection (default) – providers may not train on your inputs.

• Allow data collection – broader provider pool, including providers that train on inputs.

• Zero data retention – strictest, restricts to providers that retain nothing.

💡 Further reading and Acceptable Use Policy

Model license

Check the Gemma Terms of Use before use.

Type identifier

Use the following identifier in step "type" field: roboflow_core/google_gemma@v2 to add the block as a step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
api_key	str	OpenRouter API key. Defaults to Roboflow's managed key, billed in credits via Roboflow. Provide your own sk-or-... key to call OpenRouter directly without Roboflow billing..	✅
privacy_level	str	Provider privacy filter. Stricter levels reduce the pool of providers and may increase per-call cost on the managed key..	❌
max_tokens	int	Maximum number of tokens the model can generate in its response..	❌
temperature	float	Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..	✅
max_concurrent_requests	int	Number of concurrent requests for batches of images. If not given - block defaults to value configured globally in Workflows Execution Engine. Restrict if you hit rate limits..	❌
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to the Gemma model.	✅
output_structure	Dict[str, str]	Dictionary with structure of expected JSON response.	❌
classes	List[str]	List of classes to be used.	✅
model_version	str	Model to be used.	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Runtime compatibility

requires_internet - air-gapped / offline deployments : This block depends on a service that is not reachable from fully offline / air-gapped deployments.

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Google Gemma in version v2 has.

Input and output bindings

• input

  • api_key (Union[ROBOFLOW_MANAGED_KEY, secret, string]): OpenRouter API key. Defaults to Roboflow's managed key, billed in credits via Roboflow. Provide your own sk-or-... key to call OpenRouter directly without Roboflow billing..

  • temperature (float): Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..

  • images (image): The image to infer on..

  • prompt (string): Text prompt to the Gemma model.

  • classes (list_of_values): List of classes to be used.

  • model_version (string): Model to be used.

• output

  • output (Union[string, language_model_output]): String value if string or LLM / VLM output if language_model_output.

  • classes (list_of_values): List of values of any type.

Example JSON definition

{
	    "name": "<your_step_name_here>",
	    "type": "roboflow_core/google_gemma@v2",
	    "api_key": "rf_key:account",
	    "privacy_level": "<block_does_not_provide_example>",
	    "max_tokens": "<block_does_not_provide_example>",
	    "temperature": "<block_does_not_provide_example>",
	    "max_concurrent_requests": "<block_does_not_provide_example>",
	    "images": "$inputs.image",
	    "task_type": "<block_does_not_provide_example>",
	    "prompt": "my prompt",
	    "output_structure": {
	        "my_key": "description"
	    },
	    "classes": [
	        "class-a",
	        "class-b"
	    ],
	    "model_version": "Gemma 4 31B - OpenRouter"
	}