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:
LMM
,Depth Estimation
,Classification Label Visualization
,Camera Calibration
,Stitch OCR Detections
,LMM For Classification
,Clip Comparison
,CSV Formatter
,Instance Segmentation Model
,Stitch Images
,Cosine Similarity
,CogVLM
,Image Slicer
,Roboflow Dataset Upload
,Absolute Static Crop
,Twilio SMS Notification
,Florence-2 Model
,OpenAI
,Label Visualization
,Single-Label Classification Model
,Roboflow Dataset Upload
,Dimension Collapse
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Slack Notification
,Object Detection Model
,VLM as Classifier
,Grid Visualization
,Dynamic Zone
,Image Convert Grayscale
,Halo Visualization
,Buffer
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Keypoint Detection Model
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Camera Focus
,Identify Changes
,VLM as Detector
,Florence-2 Model
,Local File Sink
,OpenAI
,Google Vision OCR
,Stability AI Image Generation
,Ellipse Visualization
,Size Measurement
,SIFT
,Blur Visualization
,Circle Visualization
,Dot Visualization
,Image Blur
,Background Color Visualization
,Gaze Detection
,Multi-Label Classification Model
,Color Visualization
,Pixelate Visualization
,Clip Comparison
,Google Gemini
,Stability AI Inpainting
,Polygon Zone Visualization
,Relative Static Crop
,OCR Model
,Keypoint Visualization
,Mask Visualization
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,Trace Visualization
,Corner Visualization
,Polygon Visualization
,Crop Visualization
,Email Notification
,Image Contours
,Image Slicer
,Image Threshold
- outputs:
LMM
,Classification Label Visualization
,LMM For Classification
,Clip Comparison
,Line Counter
,Instance Segmentation Model
,CogVLM
,Roboflow Dataset Upload
,Distance Measurement
,Twilio SMS Notification
,Object Detection Model
,Florence-2 Model
,OpenAI
,Label Visualization
,Path Deviation
,Roboflow Dataset Upload
,Detections Stitch
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Slack Notification
,Pixel Color Count
,Object Detection Model
,VLM as Classifier
,Detections Consensus
,Grid Visualization
,Halo Visualization
,Buffer
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Time in Zone
,Keypoint Detection Model
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Time in Zone
,Cache Get
,VLM as Detector
,Florence-2 Model
,Cache Set
,Local File Sink
,Path Deviation
,OpenAI
,Google Vision OCR
,VLM as Detector
,Stability AI Image Generation
,Ellipse Visualization
,Size Measurement
,Circle Visualization
,Dot Visualization
,Image Blur
,CLIP Embedding Model
,Background Color Visualization
,Color Visualization
,Clip Comparison
,Google Gemini
,Stability AI Inpainting
,Keypoint Detection Model
,VLM as Classifier
,Polygon Zone Visualization
,Keypoint Visualization
,Mask Visualization
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,YOLO-World Model
,Trace Visualization
,JSON Parser
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Segment Anything 2 Model
,Crop Visualization
,Email Notification
,Line Counter
,Image Threshold
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>"
}