Llama 3.2 Vision¶
Class: LlamaVisionBlockV1
Source: inference.core.workflows.core_steps.models.foundation.llama_vision.v1.LlamaVisionBlockV1
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
Issues with structured prompting
Model tends to be quite unpredictable when structured output (in our case JSON document) is expected.
That problems may impact tasks like structured-answering
, classification
or multi-label-classification
.
The cause seems to be quite sensitive "filters" of inappropriate content embedded in model.
🛠️ 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 using90B
version -
Regular
version is paid (and usually faster) API, whereasFree
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.
API Usage Charges
OpenRouter is external third party providing access to the model and incurring charges on the usage. Please check out pricing before use:
💡 Further reading and Acceptable Use Policy¶
Model license
Check out model license before use.
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@v1
to add the block as
as step in your workflow.
Properties¶
Name | Type | Description | Refs |
---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
task_type |
str |
Task type to be performed by model. Value determines required parameters and output response.. | ❌ |
prompt |
str |
Text prompt to the Llama model. | ✅ |
output_structure |
Dict[str, str] |
Dictionary with structure of expected JSON response. | ❌ |
classes |
List[str] |
List of classes to be used. | ✅ |
api_key |
str |
Your Llama Vision API key (dependent on provider, ex: OpenRouter API key). | ✅ |
model_version |
str |
Model to be used. | ✅ |
max_tokens |
int |
Maximum number of tokens the model can generate in it's 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 that can be executed by block when batch of input images provided. If not given - block defaults to value configured globally in Workflows Execution Engine. Please restrict if you hit limits.. | ❌ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow
runtime. See Bindings for more info.
Available Connections¶
Compatible Blocks
Check what blocks you can connect to Llama 3.2 Vision
in version v1
.
- inputs:
SIFT
,Image Blur
,Relative Static Crop
,Triangle Visualization
,OpenAI
,Polygon Zone Visualization
,Keypoint Visualization
,Dimension Collapse
,CSV Formatter
,Image Threshold
,Model Comparison Visualization
,Ellipse Visualization
,Anthropic Claude
,Multi-Label Classification Model
,Buffer
,Roboflow Dataset Upload
,Dot Visualization
,SIFT Comparison
,Stitch OCR Detections
,LMM
,Roboflow Dataset Upload
,Grid Visualization
,Clip Comparison
,Depth Estimation
,Color Visualization
,Email Notification
,Google Gemini
,VLM as Detector
,Image Slicer
,Bounding Box Visualization
,Classification Label Visualization
,Halo Visualization
,Google Vision OCR
,Stitch Images
,Clip Comparison
,LMM For Classification
,OpenAI
,Line Counter Visualization
,Roboflow Custom Metadata
,Blur Visualization
,Perspective Correction
,Model Monitoring Inference Aggregator
,Slack Notification
,Corner Visualization
,Image Contours
,Label Visualization
,Llama 3.2 Vision
,Dynamic Crop
,Circle Visualization
,Instance Segmentation Model
,Camera Calibration
,Stability AI Image Generation
,Single-Label Classification Model
,VLM as Classifier
,Florence-2 Model
,Webhook Sink
,OCR Model
,Local File Sink
,Image Slicer
,Camera Focus
,Stability AI Inpainting
,Identify Changes
,Florence-2 Model
,CogVLM
,Background Color Visualization
,Size Measurement
,Polygon Visualization
,Cosine Similarity
,Dynamic Zone
,Gaze Detection
,Trace Visualization
,Keypoint Detection Model
,Reference Path Visualization
,Image Preprocessing
,Image Convert Grayscale
,Object Detection Model
,Absolute Static Crop
,Crop Visualization
,Mask Visualization
,Twilio SMS Notification
,Pixelate Visualization
- outputs:
Image Blur
,Triangle Visualization
,OpenAI
,Keypoint Detection Model
,Polygon Zone Visualization
,Keypoint Visualization
,JSON Parser
,Detections Consensus
,Distance Measurement
,Line Counter
,Image Threshold
,Model Comparison Visualization
,Anthropic Claude
,Instance Segmentation Model
,YOLO-World Model
,Buffer
,Roboflow Dataset Upload
,Dot Visualization
,SIFT Comparison
,Roboflow Dataset Upload
,LMM
,Clip Comparison
,Path Deviation
,Grid Visualization
,Color Visualization
,Line Counter
,Email Notification
,Google Gemini
,Mask Visualization
,VLM as Detector
,Detections Stitch
,Cache Set
,Bounding Box Visualization
,Time in Zone
,Classification Label Visualization
,Halo Visualization
,Pixel Color Count
,Google Vision OCR
,Clip Comparison
,LMM For Classification
,Line Counter Visualization
,OpenAI
,Segment Anything 2 Model
,Roboflow Custom Metadata
,Perspective Correction
,Slack Notification
,Model Monitoring Inference Aggregator
,Corner Visualization
,Llama 3.2 Vision
,Label Visualization
,Dynamic Crop
,Circle Visualization
,Instance Segmentation Model
,Stability AI Image Generation
,VLM as Classifier
,Florence-2 Model
,Webhook Sink
,VLM as Detector
,Local File Sink
,Stability AI Inpainting
,Florence-2 Model
,Time in Zone
,CogVLM
,Object Detection Model
,Size Measurement
,Polygon Visualization
,VLM as Classifier
,Path Deviation
,Trace Visualization
,Keypoint Detection Model
,Reference Path Visualization
,Image Preprocessing
,CLIP Embedding Model
,Object Detection Model
,Crop Visualization
,Cache Get
,Ellipse Visualization
,Twilio SMS Notification
,Background Color Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Llama 3.2 Vision
in version v1
has.
Bindings
-
input
images
(image
): The image to infer on..prompt
(string
): Text prompt to the Llama model.classes
(list_of_values
): List of classes to be used.api_key
(string
): Your Llama Vision API key (dependent on provider, ex: OpenRouter API key).model_version
(string
): Model to be used.temperature
(float
): Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..
-
output
output
(Union[string
,language_model_output
]): String value ifstring
or LLM / VLM output iflanguage_model_output
.classes
(list_of_values
): List of values of any type.
Example JSON definition of step Llama 3.2 Vision
in version v1
{
"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>"
}