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Toggles whether to use sampling. If not enabled, greedy decoding is used. When enabled, the platform randomly picks the next word according to its conditional probability distribution. Language generation using sampling does not remain deterministic. If you need to have deterministic results, set this as false, as the model is less likely to generate unexpected or unusual words. Setting it to true allows the model a better chance of generating a high quality response, even with inherent deficiencies. However, this is not desirable in an industrial pipeline as it can lead to more hallucinations and non-determinism.

The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. When using max tokens to generate, make sure your total tokens for the prompt plus the requested max tokens to generate are not more than the supported sequence length of the model. You can use this parameter to limit the response to a certain number of tokens. The generation will stop under the following conditions:

The repetition penalty, also known as frequency penalty, controls the model’s tendency to repeat predictions. The repetition penalty reduces the probability of words that have previously been generated. The penalty depends on how many times a word has previously occurred in the prediction. This parameter can be used to penalize words that were previously generated or belong to the context. It decreases the model’s likelihood to repeat the same line verbatim.

Shows the top <number> (the numerical value entered) of tokens by its probability to be generated. This indicates how likely a token was to be generated next. This helps debug a given generation and see alternative options to the generated token. The highlighted token is the one that the model predicted with the list sorted by probabilities from high to low, until the top <number> is reached. On the basis of tuning other parameters, you can use the logits feature to analyze how the predicted tokens by the model might change.

Figure 7. Return logits set to 4

Stop sequences are used to make the model stop generating text at a desired point, such as the end of a sentence or a list. It is an optional setting that tells the API when to stop generating tokens. The completion will not contain the stop sequence. If nothing is passed, it defaults to the token <|endoftext|>. This token represents a probable stopping point in the text.

The value used to modulate the next token probabilities. As the value decreases, the model becomes more deterministic and repetitive. With a temperature between 0 and 1, the randomness and creativity of the model’s predictions can be controlled. A temperature parameter close to 1 would mean that the logits are passed through the softmax function without modification. If the temperature is close to 0, the highest probable tokens will become very likely compared to the other tokens: the model becomes more deterministic and will always output the same set of tokens after a given sequence of words.

The number of highest probability vocabulary tokens to keep for top k filtering. Top k means allowing the model to choose randomly among the top k tokens by their respective probabilities. For example, choosing the top three tokens means setting the top k parameter to a value of 3. Changing the top k parameter sets the size of the shortlist the model samples from as it outputs each token. Setting top k to 1 gives us greedy decoding.

Top p sampling, sometimes called nucleus sampling, is a technique used to sample possible outcomes of the model. It controls diversity via nucleus sampling as well as the randomness and originality of the model. The top p parameter specifies a sampling threshold during inference time. Top p shortlists the top tokens whose sum of likelihoods does not exceed a certain value. If set to less than 1, only the smallest set of most probable tokens with probabilities that add up to top p or higher are kept for generation.