Models APIRegister & Manage AI Models
Upload, manage, and execute machine learning models across the ML Bridge decentralized network.
6 Endpoints
IPFS Storage
Version Control
Endpoints
Complete list of Models API endpoints.
GET
List all models/api/modelsRetrieve a paginated list of all available models with metadata and status.
pagelimitcategoryverified
POST
Register model/api/modelsUpload and register a new ML model to the bridge network.
GET
Get model details/api/models/{id}Get detailed information about a specific model including performance metrics.
POST
Execute inference/api/models/{id}/executeExecute inference on a deployed model with provided input data.
PUT
Update model/api/models/{id}Update model metadata, configuration, or deploy a new version.
DELETE
Delete model/api/models/{id}Remove a model from the bridge network and clean up associated resources.
Example Request
Register a new model on the network.
Request
POST /api/models
Authorization: Bearer <token>
Content-Type: application/json
{
"name": "Sentiment Analyzer",
"description": "NLP model for sentiment analysis",
"category": "nlp",
"modelHash": "QmX1Y2Z3...",
"pricing": {
"inputTokens": 0.02,
"outputTokens": 0.04
},
"capabilities": ["sentiment-analysis"],
"metadata": {
"version": "1.0.0",
"framework": "pytorch"
}
}Response
{
"modelId": "model_456",
"status": "pending_verification",
"message": "Model submitted for verification",
"createdAt": "2025-01-15T10:30:00Z"
}Rate Limits
GET operations100/min
POST/PUT/DELETE10/min
Model executions50/hour
Error Codes
{
"error": {
"code": "MODEL_NOT_FOUND",
"message": "Model not found"
}
}