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List Available Models
models.list(ModelListParams**kwargs) -> ModelListResponse
get/v2/gen-ai/models

To list all models, send a GET request to /v2/gen-ai/models.

ParametersExpand Collapse
page: Optional[int]

Page number.

per_page: Optional[int]

Items per page.

public_only: Optional[bool]

Only include models that are publicly available.

usecases: Optional[List[Literal["MODEL_USECASE_UNKNOWN", "MODEL_USECASE_AGENT", "MODEL_USECASE_FINETUNED", 4 more]]]

Include only models defined for the listed usecases.

  • MODEL_USECASE_UNKNOWN: The use case of the model is unknown
  • MODEL_USECASE_AGENT: The model maybe used in an agent
  • MODEL_USECASE_FINETUNED: The model maybe used for fine tuning
  • MODEL_USECASE_KNOWLEDGEBASE: The model maybe used for knowledge bases (embedding models)
  • MODEL_USECASE_GUARDRAIL: The model maybe used for guardrails
  • MODEL_USECASE_REASONING: The model usecase for reasoning
  • MODEL_USECASE_SERVERLESS: The model usecase for serverless inference
Accepts one of the following:
"MODEL_USECASE_UNKNOWN"
"MODEL_USECASE_AGENT"
"MODEL_USECASE_FINETUNED"
"MODEL_USECASE_KNOWLEDGEBASE"
"MODEL_USECASE_GUARDRAIL"
"MODEL_USECASE_REASONING"
"MODEL_USECASE_SERVERLESS"
ReturnsExpand Collapse
class ModelListResponse:

A list of models

Links to other pages

meta: Optional[APIMeta]

Meta information about the data set

models: Optional[List[APIModel]]

The models

id: Optional[str]

Human-readable model identifier

agreement: Optional[APIAgreement]

Agreement Description

created_at: Optional[datetime]

Creation date / time

formatdate-time
is_foundational: Optional[bool]

True if it is a foundational model provided by do

name: Optional[str]

Display name of the model

parent_uuid: Optional[str]

Unique id of the model, this model is based on

updated_at: Optional[datetime]

Last modified

formatdate-time
upload_complete: Optional[bool]

Model has been fully uploaded

url: Optional[str]

Download url

uuid: Optional[str]

Unique id

version: Optional[APIModelVersion]

Version Information about a Model

from gradient import Gradient

client = Gradient()
models = client.models.list()
print(models.links)
{
  "links": {
    "pages": {
      "first": "example string",
      "last": "example string",
      "next": "example string",
      "previous": "example string"
    }
  },
  "meta": {
    "page": 123,
    "pages": 123,
    "total": 123
  },
  "models": [
    {
      "id": "llama3.3-70b-instruct",
      "agreement": {
        "description": "example string",
        "name": "example name",
        "url": "example string",
        "uuid": "123e4567-e89b-12d3-a456-426614174000"
      },
      "created_at": "2021-01-01T00:00:00Z",
      "is_foundational": true,
      "name": "Llama 3.3 Instruct (70B)",
      "parent_uuid": "\"12345678-1234-1234-1234-123456789012\"",
      "updated_at": "2021-01-01T00:00:00Z",
      "upload_complete": true,
      "url": "https://example.com/model.zip",
      "uuid": "\"12345678-1234-1234-1234-123456789012\"",
      "version": {
        "major": 123,
        "minor": 123,
        "patch": 123
      }
    }
  ]
}
Returns Examples
{
  "links": {
    "pages": {
      "first": "example string",
      "last": "example string",
      "next": "example string",
      "previous": "example string"
    }
  },
  "meta": {
    "page": 123,
    "pages": 123,
    "total": 123
  },
  "models": [
    {
      "id": "llama3.3-70b-instruct",
      "agreement": {
        "description": "example string",
        "name": "example name",
        "url": "example string",
        "uuid": "123e4567-e89b-12d3-a456-426614174000"
      },
      "created_at": "2021-01-01T00:00:00Z",
      "is_foundational": true,
      "name": "Llama 3.3 Instruct (70B)",
      "parent_uuid": "\"12345678-1234-1234-1234-123456789012\"",
      "updated_at": "2021-01-01T00:00:00Z",
      "upload_complete": true,
      "url": "https://example.com/model.zip",
      "uuid": "\"12345678-1234-1234-1234-123456789012\"",
      "version": {
        "major": 123,
        "minor": 123,
        "patch": 123
      }
    }
  ]
}