Interface InferenceParameterOrBuilder (4.59.0)

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public interface InferenceParameterOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getMaxOutputTokens()

public abstract int getMaxOutputTokens()

Optional. Maximum number of the output tokens for the generator.

optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
int	The maxOutputTokens.

getTemperature()

public abstract double getTemperature()

Optional. Controls the randomness of LLM predictions. Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.

optional double temperature = 2 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
double	The temperature.

getTopK()

public abstract int getTopK()

Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the 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 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.

optional int32 top_k = 3 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
int	The topK.

getTopP()

public abstract double getTopP()

Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least 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 (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.

optional double top_p = 4 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
double	The topP.

hasMaxOutputTokens()

public abstract boolean hasMaxOutputTokens()

Optional. Maximum number of the output tokens for the generator.

optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
boolean	Whether the maxOutputTokens field is set.

hasTemperature()

public abstract boolean hasTemperature()

Optional. Controls the randomness of LLM predictions. Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.

optional double temperature = 2 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
boolean	Whether the temperature field is set.

hasTopK()

public abstract boolean hasTopK()

Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the 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 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.

optional int32 top_k = 3 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
boolean	Whether the topK field is set.

hasTopP()

public abstract boolean hasTopP()

Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least 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 (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.

optional double top_p = 4 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
boolean	Whether the topP field is set.