Get a custom model

client.models.retrieve(, ?): InferenceModel { id, created_at, created_by_identity_type, 10 more }

GET/v5/models/{model_id}

Retrieve a single custom model record by its ID.

Returns the model record — including its vendor, configuration, and current deployment status — for a model managed through this API and owned by the caller’s account. This is distinct from GET /v5/chat/completions/models, which lists the models available to call for chat completions rather than returning a single managed record.

ParametersExpand Collapse

modelID: string

ReturnsExpand Collapse

InferenceModel { id, created_at, created_by_identity_type, 10 more }

id: string

The unique identifier of the entity.

created_at: string

The date and time when the entity was created in ISO format.

formatdate-time

created_by_identity_type: "user" | "service_account"

The type of identity that created the entity.

One of the following:

"user"

"service_account"

created_by_user_id: string

The user who originally created the entity.

model_type: InferenceModelType

One of the following:

"generic"

"completion"

"chat_completion"

model_vendor: InferenceModelVendor

One of the following:

"openai"

"cohere"

"vertex_ai"

"anthropic"

"azure"

"gemini"

"launch"

"llmengine"

"model_zoo"

"bedrock"

"xai"

"fireworks_ai"

name: string

status: "failed" | "ready" | "deploying" | "deployment_timeout"

One of the following:

"failed"

"ready"

"deploying"

"deployment_timeout"

model_availability?: InferenceModelAvailability

One of the following:

"unknown"

"available"

"unavailable"

model_metadata?: Record<string, unknown>

object?: "model"

status_reason?: string

vendor_configuration?: LaunchVendorConfiguration { model_image, model_infra } | LlmEngineVendorConfiguration { model, chat_template_override, checkpoint_path, 20 more }

One of the following:

LaunchVendorConfiguration { model_image, model_infra }

model_image: ModelImage { command, registry, repository, 9 more }

command: Array<string>

registry: string

repository: string

tag: string

env_vars?: Record<string, unknown>

healthcheck_route?: string

predict_route?: string

readiness_delay?: number

request_schema?: Record<string, unknown>

response_schema?: Record<string, unknown>

streaming_command?: Array<string>

streaming_predict_route?: string

model_infra: ModelInfra { cpus, endpoint_type, gpu_type, 9 more }

cpus?: string | number

One of the following:

string

number

endpoint_type?: "async" | "sync" | "streaming"

One of the following:

"async"

"sync"

"streaming"

gpu_type?: "nvidia-tesla-t4" | "nvidia-ampere-a10" | "nvidia-ampere-a100" | 4 more

One of the following:

"nvidia-tesla-t4"

"nvidia-ampere-a10"

"nvidia-ampere-a100"

"nvidia-ampere-a100e"

"nvidia-hopper-h100"

"nvidia-hopper-h100-1g20gb"

"nvidia-hopper-h100-3g40gb"

gpus?: number

high_priority?: boolean

labels?: Record<string, string>

max_workers?: number

memory?: string

min_workers?: number

per_worker?: number

public_inference?: boolean

storage?: string

LlmEngineVendorConfiguration { model, chat_template_override, checkpoint_path, 20 more }

model: string

chat_template_override?: string

checkpoint_path?: string

cpus?: number

default_callback_url?: string

endpoint_type?: string

gpu_type?: string

gpus?: number

high_priority?: boolean

inference_framework?: string

inference_framework_image_tag?: string

labels?: Record<string, string>

max_workers?: number

memory?: string

min_workers?: number

nodes_per_worker?: number

num_shards?: number

per_worker?: number

post_inference_hooks?: Array<string>

public_inference?: boolean

quantize?: string

source?: string

storage?: string

Get a custom model

import SGPClient from 'scale-gp';

const client = new SGPClient({
  accountID: 'My Account ID',
  apiKey: process.env['SGP_API_KEY'], // This is the default and can be omitted
});

const inferenceModel = await client.models.retrieve('model_id');

console.log(inferenceModel.id);

{
  "id": "id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by_identity_type": "user",
  "created_by_user_id": "created_by_user_id",
  "model_type": "generic",
  "model_vendor": "openai",
  "name": "name",
  "status": "failed",
  "model_availability": "unknown",
  "model_metadata": {
    "foo": "bar"
  },
  "object": "model",
  "status_reason": "status_reason",
  "vendor_configuration": {
    "model_image": {
      "command": [
        "string"
      ],
      "registry": "registry",
      "repository": "repository",
      "tag": "tag",
      "env_vars": {
        "foo": "bar"
      },
      "healthcheck_route": "healthcheck_route",
      "predict_route": "predict_route",
      "readiness_delay": 0,
      "request_schema": {
        "foo": "bar"
      },
      "response_schema": {
        "foo": "bar"
      },
      "streaming_command": [
        "string"
      ],
      "streaming_predict_route": "streaming_predict_route"
    },
    "model_infra": {
      "cpus": "string",
      "endpoint_type": "async",
      "gpu_type": "nvidia-tesla-t4",
      "gpus": 0,
      "high_priority": true,
      "labels": {
        "foo": "string"
      },
      "max_workers": 0,
      "memory": "memory",
      "min_workers": 0,
      "per_worker": 0,
      "public_inference": true,
      "storage": "storage"
    }
  }
}

Returns Examples

{
  "id": "id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by_identity_type": "user",
  "created_by_user_id": "created_by_user_id",
  "model_type": "generic",
  "model_vendor": "openai",
  "name": "name",
  "status": "failed",
  "model_availability": "unknown",
  "model_metadata": {
    "foo": "bar"
  },
  "object": "model",
  "status_reason": "status_reason",
  "vendor_configuration": {
    "model_image": {
      "command": [
        "string"
      ],
      "registry": "registry",
      "repository": "repository",
      "tag": "tag",
      "env_vars": {
        "foo": "bar"
      },
      "healthcheck_route": "healthcheck_route",
      "predict_route": "predict_route",
      "readiness_delay": 0,
      "request_schema": {
        "foo": "bar"
      },
      "response_schema": {
        "foo": "bar"
      },
      "streaming_command": [
        "string"
      ],
      "streaming_predict_route": "streaming_predict_route"
    },
    "model_infra": {
      "cpus": "string",
      "endpoint_type": "async",
      "gpu_type": "nvidia-tesla-t4",
      "gpus": 0,
      "high_priority": true,
      "labels": {
        "foo": "string"
      },
      "max_workers": 0,
      "memory": "memory",
      "min_workers": 0,
      "per_worker": 0,
      "public_inference": true,
      "storage": "storage"
    }
  }
}