curl --request GET \
  --url https://api.tensormesh.ai/v1/models/{modelId} \
  --header 'Authorization: Bearer <token>'

import requests

url = "https://api.tensormesh.ai/v1/models/{modelId}"

headers = {"Authorization": "Bearer <token>"}

response = requests.get(url, headers=headers)

print(response.text)

const options = {method: 'GET', headers: {Authorization: 'Bearer <token>'}};

fetch('https://api.tensormesh.ai/v1/models/{modelId}', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

{
  "model": {
    "modelId": "<string>",
    "deploymentId": "<string>",
    "userId": "<string>",
    "description": "<string>",
    "modelPath": "<string>",
    "modelName": "<string>",
    "status": "MODEL_STATUS_UNSPECIFIED",
    "events": [
      {
        "createdAt": "2023-11-07T05:31:56Z",
        "log": "<string>",
        "eventType": "EVENT_TYPE_UNSPECIFIED"
      }
    ],
    "createdAt": "2023-11-07T05:31:56Z",
    "updatedAt": "2023-11-07T05:31:56Z",
    "modelSpec": {},
    "infra": {
      "cloudProvider": "CLOUD_PROVIDER_UNSPECIFIED",
      "nebiusRegion": "NEBIUS_REGION_UNSPECIFIED",
      "lambdaRegion": "LAMBDA_REGION_UNSPECIFIED",
      "onpremRegion": "<string>"
    },
    "gpuCount": 123,
    "gpuType": "GPU_TYPE_UNSPECIFIED",
    "replicas": 123,
    "endpoint": "<string>",
    "apiKey": "<string>"
  }
}

{
  "code": 123,
  "message": "<string>",
  "details": [
    {
      "@type": "<string>"
    }
  ]
}