@@ -208,9 +208,9 @@ def create(
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tool. Use this to provide a list of functions the model may generate JSON inputs
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for.
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- top_logprobs: An integer between 0 and 5 specifying the number of most likely tokens to return
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- at each token position, each with an associated log probability. `logprobs` must
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- be set to `true` if this parameter is used.
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+ top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to
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+ return at each token position, each with an associated log probability.
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+ `logprobs` must be set to `true` if this parameter is used.
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top_p: An alternative to sampling with temperature, called nucleus sampling, where the
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model considers the results of the tokens with top_p probability mass. So 0.1
@@ -398,9 +398,9 @@ def create(
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tool. Use this to provide a list of functions the model may generate JSON inputs
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for.
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- top_logprobs: An integer between 0 and 5 specifying the number of most likely tokens to return
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- at each token position, each with an associated log probability. `logprobs` must
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- be set to `true` if this parameter is used.
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+ top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to
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+ return at each token position, each with an associated log probability.
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+ `logprobs` must be set to `true` if this parameter is used.
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top_p: An alternative to sampling with temperature, called nucleus sampling, where the
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model considers the results of the tokens with top_p probability mass. So 0.1
@@ -588,9 +588,9 @@ def create(
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tool. Use this to provide a list of functions the model may generate JSON inputs
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for.
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- top_logprobs: An integer between 0 and 5 specifying the number of most likely tokens to return
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- at each token position, each with an associated log probability. `logprobs` must
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- be set to `true` if this parameter is used.
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+ top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to
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+ return at each token position, each with an associated log probability.
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+ `logprobs` must be set to `true` if this parameter is used.
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top_p: An alternative to sampling with temperature, called nucleus sampling, where the
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model considers the results of the tokens with top_p probability mass. So 0.1
@@ -875,9 +875,9 @@ async def create(
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tool. Use this to provide a list of functions the model may generate JSON inputs
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for.
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- top_logprobs: An integer between 0 and 5 specifying the number of most likely tokens to return
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- at each token position, each with an associated log probability. `logprobs` must
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- be set to `true` if this parameter is used.
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+ top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to
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+ return at each token position, each with an associated log probability.
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+ `logprobs` must be set to `true` if this parameter is used.
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top_p: An alternative to sampling with temperature, called nucleus sampling, where the
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model considers the results of the tokens with top_p probability mass. So 0.1
@@ -1065,9 +1065,9 @@ async def create(
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tool. Use this to provide a list of functions the model may generate JSON inputs
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for.
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- top_logprobs: An integer between 0 and 5 specifying the number of most likely tokens to return
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- at each token position, each with an associated log probability. `logprobs` must
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- be set to `true` if this parameter is used.
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+ top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to
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+ return at each token position, each with an associated log probability.
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+ `logprobs` must be set to `true` if this parameter is used.
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top_p: An alternative to sampling with temperature, called nucleus sampling, where the
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model considers the results of the tokens with top_p probability mass. So 0.1
@@ -1255,9 +1255,9 @@ async def create(
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tool. Use this to provide a list of functions the model may generate JSON inputs
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for.
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- top_logprobs: An integer between 0 and 5 specifying the number of most likely tokens to return
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- at each token position, each with an associated log probability. `logprobs` must
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- be set to `true` if this parameter is used.
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+ top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to
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+ return at each token position, each with an associated log probability.
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+ `logprobs` must be set to `true` if this parameter is used.
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top_p: An alternative to sampling with temperature, called nucleus sampling, where the
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model considers the results of the tokens with top_p probability mass. So 0.1
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