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API Reference
Models

List custom models

models.list(ModelListParams**kwargs) -> SyncCursorPage[InferenceModel]
GET/v5/models

List the custom model records registered in your account.

Returns a paginated list of the model records managed through this API — models your account deploys through the launch or llmengine serving vendors — optionally filtered by name and by model vendor, and scoped to the caller’s account. This is different from GET /v5/chat/completions/models, which lists the models available to invoke for chat completions; this endpoint returns the managed records along with their deployment status, not the catalog of callable completion models.

ParametersExpand Collapse
ending_before: Optional[str]
limit: Optional[int]
maximum10000
minimum1
model_vendor: Optional[InferenceModelVendor]
One of the following:
"openai"
"cohere"
"vertex_ai"
"anthropic"
"azure"
"gemini"
"launch"
"llmengine"
"model_zoo"
"bedrock"
"xai"
"fireworks_ai"
name: Optional[str]
sort_by: Optional[str]
sort_order: Optional[SortOrder]
One of the following:
"asc"
"desc"
starting_after: Optional[str]
ReturnsExpand Collapse
class InferenceModel: …
id: str

The unique identifier of the entity.

created_at: datetime

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

formatdate-time
created_by_identity_type: Literal["user", "service_account"]

The type of identity that created the entity.

One of the following:
"user"
"service_account"
created_by_user_id: str

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: str
status: Literal["failed", "ready", "deploying", "deployment_timeout"]
One of the following:
"failed"
"ready"
"deploying"
"deployment_timeout"
model_availability: Optional[InferenceModelAvailability]
One of the following:
"unknown"
"available"
"unavailable"
model_metadata: Optional[Dict[str, object]]
object: Optional[Literal["model"]]
status_reason: Optional[str]
vendor_configuration: Optional[VendorConfiguration]
One of the following:
class LaunchVendorConfiguration: …
model_image: ModelImage
command: List[str]
registry: str
repository: str
tag: str
env_vars: Optional[Dict[str, object]]
healthcheck_route: Optional[str]
predict_route: Optional[str]
readiness_delay: Optional[int]
request_schema: Optional[Dict[str, object]]
response_schema: Optional[Dict[str, object]]
streaming_command: Optional[List[str]]
streaming_predict_route: Optional[str]
model_infra: ModelInfra
cpus: Optional[Union[str, int, null]]
One of the following:
str
int
endpoint_type: Optional[Literal["async", "sync", "streaming"]]
One of the following:
"async"
"sync"
"streaming"
gpu_type: Optional[Literal["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: Optional[int]
high_priority: Optional[bool]
labels: Optional[Dict[str, str]]
max_workers: Optional[int]
memory: Optional[str]
min_workers: Optional[int]
per_worker: Optional[int]
public_inference: Optional[bool]
storage: Optional[str]
class LlmEngineVendorConfiguration: …
model: str
chat_template_override: Optional[str]
checkpoint_path: Optional[str]
cpus: Optional[int]
default_callback_url: Optional[str]
endpoint_type: Optional[str]
gpu_type: Optional[str]
gpus: Optional[int]
high_priority: Optional[bool]
inference_framework: Optional[str]
inference_framework_image_tag: Optional[str]
labels: Optional[Dict[str, str]]
max_workers: Optional[int]
memory: Optional[str]
min_workers: Optional[int]
nodes_per_worker: Optional[int]
num_shards: Optional[int]
per_worker: Optional[int]
post_inference_hooks: Optional[List[str]]
public_inference: Optional[bool]
quantize: Optional[str]
source: Optional[str]
storage: Optional[str]

List custom models

import os
from scale_gp_beta import SGPClient

client = SGPClient(
    api_key=os.environ.get("SGP_API_KEY"),  # This is the default and can be omitted
)
page = client.models.list()
page = page.items[0]
print(page.id)
{
  "has_more": true,
  "items": [
    {
      "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"
        }
      }
    }
  ],
  "total": 0,
  "limit": 0,
  "object": "list"
}
Returns Examples
{
  "has_more": true,
  "items": [
    {
      "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"
        }
      }
    }
  ],
  "total": 0,
  "limit": 0,
  "object": "list"
}