Interface Model

A trained machine learning model.

interface Model {
    checkpoints?: Checkpoint[];
    defaultCheckpointId?: string;
    description?: string;
    displayName?: string;
    endpoints?: Endpoint[];
    inputTokenLimit?: number;
    labels?: Record<string, string>;
    maxTemperature?: number;
    name?: string;
    outputTokenLimit?: number;
    supportedActions?: string[];
    temperature?: number;
    thinking?: boolean;
    topK?: number;
    topP?: number;
    tunedModelInfo?: TunedModelInfo;
    version?: string;
}

Properties

checkpoints? defaultCheckpointId? description? displayName? endpoints? inputTokenLimit? labels? maxTemperature? name? outputTokenLimit? supportedActions? temperature? thinking? topK? topP? tunedModelInfo? version?

Properties

Optionalcheckpoints

checkpoints?: Checkpoint[]

The checkpoints of the model.

OptionaldefaultCheckpointId

defaultCheckpointId?: string

The default checkpoint id of a model version.

Optionaldescription

description?: string

Description of the model.

OptionaldisplayName

displayName?: string

Display name of the model.

Optionalendpoints

endpoints?: Endpoint[]

List of deployed models created from this base model. Note that a model could have been deployed to endpoints in different locations.

OptionalinputTokenLimit

inputTokenLimit?: number

The maximum number of input tokens that the model can handle.

Optionallabels

labels?: Record<string, string>

Labels with user-defined metadata to organize your models.

OptionalmaxTemperature

maxTemperature?: number

The maximum temperature value used for sampling set when the dataset was saved. This value is used to tune the degree of randomness.

Optionalname

name?: string

Resource name of the model.

OptionaloutputTokenLimit

outputTokenLimit?: number

The maximum number of output tokens that the model can generate.

OptionalsupportedActions

supportedActions?: string[]

List of actions that are supported by the model.

Optionaltemperature

temperature?: number

Temperature value used for sampling set when the dataset was saved. This value is used to tune the degree of randomness.

Optionalthinking

thinking?: boolean

Whether the model supports thinking features. If true, thoughts are returned only if the model supports thought and thoughts are available.

OptionaltopK

topK?: number

Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a top_k of 40 means the model will choose the next word from the 40 most likely words.

OptionaltopP

topP?: number

Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least top_p. This helps generate more diverse and less repetitive responses. For example, a top_p of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It's recommended to adjust either temperature or top_p, but not both.

OptionaltunedModelInfo

tunedModelInfo?: TunedModelInfo

Information about the tuned model from the base model.

Optionalversion

version?: string

Version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model ID. The version ID is an auto-incrementing decimal number in string representation.

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Properties