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Understand and configure model parameters

Each call that you send to a model includes parameter values that control how the model generates a response. The model can generate different results for different parameter values. Experiment with different parameter values to get the best values for the task. The parameters available for different models may differ.

The configuration is maintained for the lifetime of the initialized Vertex AI service and model instance. To update model configuration, the model instance must be re-initialized.

Later on this page, you can learn how to configure model parameters.

Description of each parameter

The most common parameters are the following:

Max output tokens
Temperature
Top-K
Top-P

Learn about each of these parameters in the following sections of this page.

Max output tokens

Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 20 words.

Specify a lower value for shorter responses and a higher value for longer responses.

Temperature

The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a more deterministic and less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 is deterministic, meaning that the highest probability response is always selected.

For most use cases, try starting with a temperature of 0.2. If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature.

Top-K

Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-K of 3 means that the next token is selected from among the three most probable tokens by using temperature.

For each token selection step, the top-K tokens with the highest probabilities are sampled. Then tokens are further filtered based on top-P with the final token selected using temperature sampling.

Specify a lower value for less random responses and a higher value for more random responses. The default top-K is 40.

Top-P

Top-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate.

Specify a lower value for less random responses and a higher value for more random responses. The default top-P is 0.95.

Configure model parameters

You configure model parameters in the generationConfig during initialization of the model. Here's a basic example:

Kotlin

// ...

val config = generationConfig {
    maxOutputTokens = 200
    stopSequences = listOf("red")
    temperature = 0.9f
    topK = 16
    topP = 0.1f
}

val generativeModel = Firebase.vertexAI.generativeModel(
    modelName = "MODEL_NAME",
    generationConfig = config
)

// ...

Java

// ...

GenerationConfig.Builder configBuilder = new GenerationConfig.Builder();
configBuilder.maxOutputTokens = 200;
configBuilder.stopSequences = List.of("red");
configBuilder.temperature = 0.9f;
configBuilder.topK = 16;
configBuilder.topP = 0.1f;

GenerationConfig generationConfig = configBuilder.build();

GenerativeModel gm = FirebaseVertexAI.getInstance().generativeModel(
    "MODEL_NAME",
    generationConfig
);

GenerativeModelFutures model = GenerativeModelFutures.from(gm);

// ...