Llama 3.2 Vision¶

v2¶

Ask a question to Llama 3.2 Vision model.

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¶

Llama 3.2 Vision 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.
• Zero data retention – strictest, restricts to providers that retain nothing.

💡 Further reading and Acceptable Use Policy¶

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/llama_vision@v2to add the block as as step in your workflow.

Properties¶

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.

Available Connections¶

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Llama 3.2 Vision in version v2 has.

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/llama_vision@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": "11B - OpenRouter"
}

v1¶

Ask a question to Llama 3.2 Vision 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

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

🛠️ API providers and model variants¶

Llama Vision 3.2 model is exposed via OpenRouter API and we require passing OpenRouter API Key to run.

There are different versions of the model supported:

• smaller version (11B) is faster and cheaper, yet you can expect better quality of results using 90B version

• Regular version is paid (and usually faster) API, whereas Free is free for use for OpenRouter clients (state at 01.01.2025)

As for now, OpenRouter is the only provider for Llama 3.2 Vision model, but we will keep you posted if the state of the matter changes.

💡 Further reading and Acceptable Use Policy¶

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/llama_3_2_vision@v1to add the block as as step in your workflow.

Properties¶

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.

Available Connections¶

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Llama 3.2 Vision in version v1 has.

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/llama_3_2_vision@v1",
    "images": "$inputs.image",
    "task_type": "<block_does_not_provide_example>",
    "prompt": "my prompt",
    "output_structure": {
        "my_key": "description"
    },
    "classes": [
        "class-a",
        "class-b"
    ],
    "api_key": "xxx-xxx",
    "model_version": "11B (Free) - OpenRouter",
    "max_tokens": "<block_does_not_provide_example>",
    "temperature": "<block_does_not_provide_example>",
    "max_concurrent_requests": "<block_does_not_provide_example>"
}