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:
Slack Notification,Image Slicer,Stitch OCR Detections,Stability AI Inpainting,Pixelate Visualization,Perspective Correction,Clip Comparison,Object Detection Model,OpenAI,Relative Static Crop,Roboflow Custom Metadata,Twilio SMS Notification,VLM as Classifier,SIFT Comparison,Roboflow Dataset Upload,Google Gemini,Grid Visualization,Ellipse Visualization,SIFT,Model Comparison Visualization,CogVLM,Halo Visualization,Image Contours,VLM as Detector,Crop Visualization,OpenAI,Absolute Static Crop,Camera Focus,Image Blur,Trace Visualization,Circle Visualization,Keypoint Detection Model,Image Preprocessing,Multi-Label Classification Model,Background Color Visualization,Dot Visualization,Google Vision OCR,Polygon Zone Visualization,Roboflow Dataset Upload,Florence-2 Model,Classification Label Visualization,Bounding Box Visualization,Corner Visualization,Llama 3.2 Vision,Image Slicer,Florence-2 Model,Local File Sink,Dynamic Crop,Reference Path Visualization,Label Visualization,LMM For Classification,Mask Visualization,Stitch Images,Triangle Visualization,Stability AI Image Generation,Image Threshold,Line Counter Visualization,OCR Model,LMM,Keypoint Visualization,Model Monitoring Inference Aggregator,Color Visualization,Email Notification,Single-Label Classification Model,Blur Visualization,Anthropic Claude,Webhook Sink,CSV Formatter,Image Convert Grayscale,Instance Segmentation Model,Polygon Visualization - outputs:
Cache Set,Object Detection Model,Object Detection Model,Detection Offset,CogVLM,Ellipse Visualization,Grid Visualization,SIFT,Camera Focus,Property Definition,CLIP Embedding Model,Dot Visualization,Google Vision OCR,Clip Comparison,Identify Changes,Polygon Zone Visualization,Gaze Detection,Classification Label Visualization,Corner Visualization,Dynamic Crop,Label Visualization,Detections Stabilizer,Triangle Visualization,Dynamic Zone,Dominant Color,Time in Zone,Barcode Detection,Blur Visualization,Line Counter,Instance Segmentation Model,Webhook Sink,Cosine Similarity,Path Deviation,Relative Static Crop,Detections Consensus,Twilio SMS Notification,Crop Visualization,Qwen2.5-VL,Distance Measurement,Circle Visualization,Velocity,Keypoint Detection Model,QR Code Detection,Size Measurement,Single-Label Classification Model,Bounding Box Visualization,LMM For Classification,Image Threshold,Detections Stitch,OCR Model,Keypoint Visualization,Single-Label Classification Model,Detections Classes Replacement,Polygon Visualization,Segment Anything 2 Model,Cache Get,Image Slicer,Stability AI Inpainting,Clip Comparison,Perspective Correction,Roboflow Custom Metadata,SIFT Comparison,VLM as Detector,Image Contours,Multi-Label Classification Model,OpenAI,Absolute Static Crop,Trace Visualization,Multi-Label Classification Model,VLM as Detector,Identify Outliers,Roboflow Dataset Upload,First Non Empty Or Default,VLM as Classifier,Llama 3.2 Vision,Byte Tracker,Line Counter,Reference Path Visualization,Mask Visualization,Line Counter Visualization,Template Matching,Detections Transformation,Model Monitoring Inference Aggregator,Anthropic Claude,SIFT Comparison,Time in Zone,Instance Segmentation Model,Slack Notification,Detections Filter,Stitch OCR Detections,Pixelate Visualization,Delta Filter,Dimension Collapse,VLM as Classifier,Roboflow Dataset Upload,Keypoint Detection Model,Google Gemini,Rate Limiter,Model Comparison Visualization,Halo Visualization,JSON Parser,Byte Tracker,Expression,Image Blur,Buffer,Image Preprocessing,Background Color Visualization,Continue If,Bounding Rectangle,Pixel Color Count,Florence-2 Model,Florence-2 Model,Local File Sink,Byte Tracker,Image Slicer,Stitch Images,Stability AI Image Generation,LMM,Email Notification,Color Visualization,Path Deviation,YOLO-World Model,Data Aggregator,CSV Formatter,Image Convert Grayscale,OpenAI
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"
}
}