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: Reference Path Visualization, Blur Visualization, Pixelate Visualization, Anthropic Claude, Multi-Label Classification Model, Single-Label Classification Model, LMM, Email Notification, Classification Label Visualization, Llama 3.2 Vision, CSV Formatter, Background Color Visualization, VLM as Detector, Dynamic Crop, Keypoint Visualization, Camera Focus, Mask Visualization, Webhook Sink, Clip Comparison, Image Slicer, Google Vision OCR, Twilio SMS Notification, Absolute Static Crop, Model Monitoring Inference Aggregator, Stability AI Image Generation, Florence-2 Model, Image Blur, LMM For Classification, Florence-2 Model, Roboflow Dataset Upload, CogVLM, Circle Visualization, OCR Model, Grid Visualization, Crop Visualization, Image Convert Grayscale, Image Threshold, Trace Visualization, Polygon Visualization, Triangle Visualization, Stability AI Inpainting, OpenAI, Stitch OCR Detections, Halo Visualization, Dot Visualization, Polygon Zone Visualization, Google Gemini, Local File Sink, Instance Segmentation Model, Roboflow Custom Metadata, OpenAI, VLM as Classifier, Camera Calibration, Slack Notification, Object Detection Model, SIFT, Corner Visualization, Image Contours, Model Comparison Visualization, Stitch Images, Bounding Box Visualization, Line Counter Visualization, Image Slicer, Roboflow Dataset Upload, Keypoint Detection Model, Perspective Correction, Image Preprocessing, SIFT Comparison, Label Visualization, Relative Static Crop, Color Visualization, Ellipse Visualization
• outputs: Multi-Label Classification Model, Classification Label Visualization, Dimension Collapse, Background Color Visualization, Dynamic Crop, Clip Comparison, Segment Anything 2 Model, Absolute Static Crop, LMM For Classification, Image Blur, Roboflow Dataset Upload, CogVLM, Circle Visualization, OCR Model, Clip Comparison, Template Matching, SIFT Comparison, Multi-Label Classification Model, Path Deviation, OpenAI, Stitch OCR Detections, Detections Stitch, QR Code Detection, Pixel Color Count, Detections Stabilizer, Path Deviation, Line Counter, VLM as Classifier, Time in Zone, Model Comparison Visualization, Stitch Images, Bounding Box Visualization, Keypoint Detection Model, Moondream2, Continue If, Slack Notification, Color Visualization, LMM, Llama 3.2 Vision, Instance Segmentation Model, VLM as Detector, Time in Zone, Byte Tracker, Detections Filter, YOLO-World Model, Barcode Detection, Grid Visualization, SmolVLM2, Stability AI Inpainting, Keypoint Detection Model, Cosine Similarity, Dot Visualization, Google Gemini, Detections Merge, CLIP Embedding Model, Dynamic Zone, Size Measurement, Property Definition, Roboflow Custom Metadata, Data Aggregator, VLM as Classifier, Detections Classes Replacement, Gaze Detection, Object Detection Model, Corner Visualization, Roboflow Dataset Upload, Image Preprocessing, Image Slicer, Byte Tracker, Line Counter, Label Visualization, Identify Outliers, Bounding Rectangle, Single-Label Classification Model, First Non Empty Or Default, Webhook Sink, Cache Get, Mask Visualization, Delta Filter, Twilio SMS Notification, Google Vision OCR, Buffer, Detection Offset, Model Monitoring Inference Aggregator, Stability AI Image Generation, Florence-2 Model, Cache Set, Identify Changes, Crop Visualization, VLM as Detector, JSON Parser, Velocity, OpenAI, Dominant Color, Perspective Correction, SIFT Comparison, Relative Static Crop, Ellipse Visualization, Reference Path Visualization, Anthropic Claude, Blur Visualization, Pixelate Visualization, Email Notification, Expression, CSV Formatter, Keypoint Visualization, Camera Focus, Single-Label Classification Model, Qwen2.5-VL, Florence-2 Model, Detections Transformation, Trace Visualization, Image Threshold, Image Convert Grayscale, Detections Consensus, Triangle Visualization, Polygon Visualization, Halo Visualization, Polygon Zone Visualization, Local File Sink, Rate Limiter, Instance Segmentation Model, Camera Calibration, SIFT, Image Contours, Line Counter Visualization, Image Slicer, Byte Tracker, Distance Measurement, Object Detection Model

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds CogVLM in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the CogVLM model.

• 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"
    }
}