refactor: cleanup
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@@ -534,16 +534,14 @@ class OpenAICompatibleProvider(ModelProvider):
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resolved_model = self._resolve_model_name(model_name)
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resolved_model = self._resolve_model_name(model_name)
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# Use the effective temperature we calculated earlier
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# Use the effective temperature we calculated earlier
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if effective_temperature is not None:
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supports_sampling = effective_temperature is not None
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if supports_sampling:
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completion_params["temperature"] = effective_temperature
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completion_params["temperature"] = effective_temperature
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supports_temperature = True
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else:
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# Model doesn't support temperature
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supports_temperature = False
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# Add max tokens if specified and model supports it
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# Add max tokens if specified and model supports it
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# O3/O4 models that don't support temperature also don't support max_tokens
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# O3/O4 models that don't support temperature also don't support max_tokens
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if max_output_tokens and supports_temperature:
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if max_output_tokens and supports_sampling:
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completion_params["max_tokens"] = max_output_tokens
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completion_params["max_tokens"] = max_output_tokens
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# Add any additional OpenAI-specific parameters
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# Add any additional OpenAI-specific parameters
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@@ -551,7 +549,7 @@ class OpenAICompatibleProvider(ModelProvider):
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for key, value in kwargs.items():
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for key, value in kwargs.items():
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if key in ["top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "stream"]:
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if key in ["top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "stream"]:
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# Reasoning models (those that don't support temperature) also don't support these parameters
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# Reasoning models (those that don't support temperature) also don't support these parameters
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if not supports_temperature and key in ["top_p", "frequency_penalty", "presence_penalty"]:
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if not supports_sampling and key in ["top_p", "frequency_penalty", "presence_penalty"]:
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continue # Skip unsupported parameters for reasoning models
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continue # Skip unsupported parameters for reasoning models
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completion_params[key] = value
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completion_params[key] = value
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