Package com.google.genai.types

Class Model

java.lang.Object
com.google.genai.JsonSerializable
com.google.genai.types.Model
public abstract class Model extends JsonSerializable
A trained machine learning model.

  • Constructor Details

    • Model

      public Model()

  • Method Details

    • name

      public abstract Optional<String> name()
      Resource name of the model.

    • displayName

      public abstract Optional<String> displayName()
      Display name of the model.

    • description

      public abstract Optional<String> description()
      Description of the model.

    • version

      public abstract Optional<String> version()
      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.

    • endpoints

      public abstract Optional<List<Endpoint>> endpoints()
      List of deployed models created from this base model. Note that a model could have been deployed to endpoints in different locations.

    • labels

      public abstract Optional<Map<String,String>> labels()
      Labels with user-defined metadata to organize your models.

    • tunedModelInfo

      public abstract Optional<TunedModelInfo> tunedModelInfo()
      Information about the tuned model from the base model.

    • inputTokenLimit

      public abstract Optional<Integer> inputTokenLimit()
      The maximum number of input tokens that the model can handle.

    • outputTokenLimit

      public abstract Optional<Integer> outputTokenLimit()
      The maximum number of output tokens that the model can generate.

    • supportedActions

      public abstract Optional<List<String>> supportedActions()
      List of actions that are supported by the model.

    • defaultCheckpointId

      public abstract Optional<String> defaultCheckpointId()
      The default checkpoint id of a model version.

    • checkpoints

      public abstract Optional<List<Checkpoint>> checkpoints()
      The checkpoints of the model.

    • temperature

      public abstract Optional<Float> temperature()
      Temperature value used for sampling set when the dataset was saved. This value is used to tune the degree of randomness.

    • maxTemperature

      public abstract Optional<Float> maxTemperature()
      The maximum temperature value used for sampling set when the dataset was saved. This value is used to tune the degree of randomness.

    • topP

      public abstract Optional<Float> topP()
      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.

    • topK

      public abstract Optional<Integer> topK()
      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.

    • thinking

      public abstract Optional<Boolean> thinking()
      Whether the model supports thinking features. If true, thoughts are returned only if the model supports thought and thoughts are available.

    • builder

      public static Model.Builder builder()
      Instantiates a builder for Model.

    • toBuilder

      public abstract Model.Builder toBuilder()
      Creates a builder with the same values as this instance.

    • fromJson

      public static Model fromJson(String jsonString)
      Deserializes a JSON string to a Model object.