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];
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. 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. 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];
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. 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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-01-28 UTC."],[],[]]
