CogVLM¶
Class: CogVLMBlockV1
Source: inference.core.workflows.core_steps.models.foundation.cog_vlm.v1.CogVLMBlockV1
CogVLM reached End Of Life
Due to dependencies conflicts with newer models and security vulnerabilities discovered in transformers
library patched in the versions of library incompatible with the model we announced End Of Life for CogVLM
support in inference, effective since release 0.38.0.
We are leaving this block in ecosystem until release 0.42.0 for clients to get informed about change that
was introduced.
Starting as of now, all Workflows using the block stop being functional (runtime error will be raised),
after inference release 0.42.0 - this block will be removed and Execution Engine will raise compilation
error seeing the block in Workflow definition.
Ask a question to CogVLM, an open source vision-language model.
This model requires a GPU and can only be run on self-hosted devices, and is not available on the Roboflow Hosted API.
This model was previously part of the LMM block.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/cog_vlm@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
prompt |
str |
Text prompt to the CogVLM model. | ✅ |
json_output_format |
Dict[str, str] |
Holds dictionary that maps name of requested output field into its description. | ❌ |
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 CogVLM in version v1.
- inputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Bounding Box Visualization,Twilio SMS Notification,Object Detection Model,Line Counter Visualization,Image Preprocessing,Slack Notification,VLM as Detector,Trace Visualization,LMM,Label Visualization,Polygon Zone Visualization,Camera Focus,Local File Sink,Image Slicer,Image Slicer,Image Blur,Anthropic Claude,Crop Visualization,OCR Model,Dot Visualization,Google Gemini,Relative Static Crop,Model Comparison Visualization,Stability AI Inpainting,Roboflow Dataset Upload,Pixelate Visualization,Stitch OCR Detections,Perspective Correction,OpenAI,OpenAI,Image Convert Grayscale,Absolute Static Crop,Mask Visualization,Stability AI Image Generation,Webhook Sink,Color Visualization,Image Threshold,Clip Comparison,Dynamic Crop,Halo Visualization,Polygon Visualization,CSV Formatter,Florence-2 Model,VLM as Classifier,Instance Segmentation Model,Keypoint Detection Model,Image Contours,CogVLM,Camera Calibration,Email Notification,SIFT,Reference Path Visualization,Florence-2 Model,Classification Label Visualization,Triangle Visualization,Model Monitoring Inference Aggregator,Single-Label Classification Model,SIFT Comparison,Llama 3.2 Vision,Google Vision OCR,Roboflow Dataset Upload,Keypoint Visualization,Multi-Label Classification Model,LMM For Classification,Roboflow Custom Metadata,Grid Visualization,Ellipse Visualization,Stitch Images,Blur Visualization - outputs:
Corner Visualization,Slack Notification,VLM as Detector,VLM as Classifier,Polygon Zone Visualization,Image Slicer,Cache Set,Dot Visualization,Path Deviation,Roboflow Dataset Upload,Single-Label Classification Model,Dominant Color,Stability AI Image Generation,Image Convert Grayscale,Halo Visualization,Data Aggregator,Detections Classes Replacement,Dynamic Zone,CogVLM,Email Notification,Camera Calibration,Object Detection Model,Cosine Similarity,Single-Label Classification Model,Llama 3.2 Vision,Byte Tracker,Ellipse Visualization,Size Measurement,Pixel Color Count,Bounding Box Visualization,Line Counter Visualization,Image Preprocessing,Label Visualization,Local File Sink,Image Slicer,Detections Transformation,Anthropic Claude,Crop Visualization,Detections Stitch,OCR Model,Model Comparison Visualization,Relative Static Crop,QR Code Detection,Delta Filter,Path Deviation,Detections Filter,Clip Comparison,Time in Zone,CSV Formatter,Florence-2 Model,Keypoint Detection Model,Buffer,SIFT Comparison,Florence-2 Model,Multi-Label Classification Model,Rate Limiter,Keypoint Visualization,Identify Changes,Multi-Label Classification Model,Roboflow Custom Metadata,Line Counter,Stitch Images,Circle Visualization,Background Color Visualization,Twilio SMS Notification,Property Definition,LMM,Camera Focus,Image Blur,First Non Empty Or Default,Detections Merge,Google Gemini,Detection Offset,Stability AI Inpainting,Pixelate Visualization,Line Counter,OpenAI,Detections Consensus,Distance Measurement,Gaze Detection,Absolute Static Crop,Webhook Sink,Color Visualization,Image Threshold,Polygon Visualization,VLM as Classifier,Continue If,Instance Segmentation Model,Classification Label Visualization,Google Vision OCR,Roboflow Dataset Upload,Expression,Cache Get,JSON Parser,Object Detection Model,Keypoint Detection Model,Trace Visualization,Clip Comparison,Identify Outliers,YOLO-World Model,Stitch OCR Detections,Perspective Correction,OpenAI,Byte Tracker,Qwen2.5-VL,Mask Visualization,Time in Zone,Dynamic Crop,Template Matching,Byte Tracker,Barcode Detection,Instance Segmentation Model,Image Contours,SIFT,Reference Path Visualization,CLIP Embedding Model,Triangle Visualization,Model Monitoring Inference Aggregator,Bounding Rectangle,Velocity,SIFT Comparison,VLM as Detector,LMM For Classification,Grid Visualization,Segment Anything 2 Model,Detections Stabilizer,Dimension Collapse,Blur Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
CogVLM in version v1 has.
Bindings
-
input
-
output
parent_id(parent_id): Identifier of parent for step output.root_parent_id(parent_id): Identifier of parent for step output.image(image_metadata): Dictionary with image metadata required by supervision.structured_output(dictionary): Dictionary.raw_output(string): String value.*(*): Equivalent of any element.
Example JSON definition of step CogVLM in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/cog_vlm@v1",
"images": "$inputs.image",
"prompt": "my prompt",
"json_output_format": {
"count": "number of cats in the picture"
}
}