Merge branch 'main' into litellm_show_openai_params_model_hub

litellm/__init__.py

@@ -766,7 +766,12 @@ from .llms.bedrock import (
     AmazonMistralConfig,
     AmazonBedrockGlobalConfig,
 )
-from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig, MistralConfig
+from .llms.openai import (
+    OpenAIConfig,
+    OpenAITextCompletionConfig,
+    MistralConfig,
+    DeepInfraConfig,
+)
 from .llms.azure import AzureOpenAIConfig, AzureOpenAIError
 from .llms.watsonx import IBMWatsonXAIConfig
 from .main import *  # type: ignore

litellm/llms/openai.py

@@ -157,6 +157,101 @@ class MistralConfig:
                 )
             if param == "seed":
                 optional_params["extra_body"] = {"random_seed": value}
+            if param == "response_format":
+                optional_params["response_format"] = value
+        return optional_params
+
+
+class DeepInfraConfig:
+    """
+    Reference: https://deepinfra.com/docs/advanced/openai_api
+
+    The class `DeepInfra` provides configuration for the DeepInfra's Chat Completions API interface. Below are the parameters:
+    """
+
+    frequency_penalty: Optional[int] = None
+    function_call: Optional[Union[str, dict]] = None
+    functions: Optional[list] = None
+    logit_bias: Optional[dict] = None
+    max_tokens: Optional[int] = None
+    n: Optional[int] = None
+    presence_penalty: Optional[int] = None
+    stop: Optional[Union[str, list]] = None
+    temperature: Optional[int] = None
+    top_p: Optional[int] = None
+    response_format: Optional[dict] = None
+    tools: Optional[list] = None
+    tool_choice: Optional[Union[str, dict]] = None
+
+    def __init__(
+        self,
+        frequency_penalty: Optional[int] = None,
+        function_call: Optional[Union[str, dict]] = None,
+        functions: Optional[list] = None,
+        logit_bias: Optional[dict] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        presence_penalty: Optional[int] = None,
+        stop: Optional[Union[str, list]] = None,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+        response_format: Optional[dict] = None,
+        tools: Optional[list] = None,
+        tool_choice: Optional[Union[str, dict]] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "frequency_penalty",
+            "function_call",
+            "functions",
+            "logit_bias",
+            "max_tokens",
+            "n",
+            "presence_penalty",
+            "stop",
+            "temperature",
+            "top_p",
+            "response_format",
+            "tools",
+            "tool_choice",
+        ]
+
+    def map_openai_params(
+        self, non_default_params: dict, optional_params: dict, model: str
+    ):
+        supported_openai_params = self.get_supported_openai_params()
+        for param, value in non_default_params.items():
+            if (
+                param == "temperature"
+                and value == 0
+                and model == "mistralai/Mistral-7B-Instruct-v0.1"
+            ):  # this model does no support temperature == 0
+                value = 0.0001  # close to 0
+            if param in supported_openai_params:
+                optional_params[param] = value
         return optional_params
 
 
@@ -197,6 +292,7 @@ class OpenAIConfig:
     stop: Optional[Union[str, list]] = None
     temperature: Optional[int] = None
     top_p: Optional[int] = None
+    response_format: Optional[dict] = None
 
     def __init__(
         self,
@@ -210,6 +306,7 @@ class OpenAIConfig:
         stop: Optional[Union[str, list]] = None,
         temperature: Optional[int] = None,
         top_p: Optional[int] = None,
+        response_format: Optional[dict] = None,
     ) -> None:
         locals_ = locals().copy()
         for key, value in locals_.items():

litellm/utils.py

@@ -5797,30 +5797,11 @@ def get_optional_params(
             model=model, custom_llm_provider=custom_llm_provider
         )
         _check_valid_arg(supported_params=supported_params)
-        if temperature is not None:
+        optional_params = litellm.DeepInfraConfig().map_openai_params(
-            if (
+            non_default_params=non_default_params,
-                temperature == 0 and model == "mistralai/Mistral-7B-Instruct-v0.1"
+            optional_params=optional_params,
-            ):  # this model does no support temperature == 0
+            model=model,
-                temperature = 0.0001  # close to 0
+        )
-            optional_params["temperature"] = temperature
-        if top_p:
-            optional_params["top_p"] = top_p
-        if n:
-            optional_params["n"] = n
-        if stream:
-            optional_params["stream"] = stream
-        if stop:
-            optional_params["stop"] = stop
-        if max_tokens:
-            optional_params["max_tokens"] = max_tokens
-        if presence_penalty:
-            optional_params["presence_penalty"] = presence_penalty
-        if frequency_penalty:
-            optional_params["frequency_penalty"] = frequency_penalty
-        if logit_bias:
-            optional_params["logit_bias"] = logit_bias
-        if user:
-            optional_params["user"] = user
     elif custom_llm_provider == "perplexity":
         supported_params = get_supported_openai_params(
             model=model, custom_llm_provider=custom_llm_provider
@@ -6604,19 +6585,7 @@ def get_supported_openai_params(
     elif custom_llm_provider == "petals":
         return ["max_tokens", "temperature", "top_p", "stream"]
     elif custom_llm_provider == "deepinfra":
-        return [
+        return litellm.DeepInfraConfig().get_supported_openai_params()
-            "temperature",
-            "top_p",
-            "n",
-            "stream",
-            "stop",
-            "max_tokens",
-            "presence_penalty",
-            "frequency_penalty",
-            "logit_bias",
-            "user",
-            "response_format",
-        ]
     elif custom_llm_provider == "perplexity":
         return [
             "temperature",

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.38.10"
+version = "1.38.11"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -79,7 +79,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.38.10"
+version = "1.38.11"
 version_files = [
     "pyproject.toml:^version"
 ]

ui/litellm-dashboard/src/components/model_hub.tsx

@@ -39,6 +39,7 @@ interface ModelInfo {
     max_output_tokens?: number;
     supported_openai_params?: string[];
 
+
   // Add other properties if needed
 }
 
@@ -128,8 +129,6 @@ const ModelHub: React.FC<ModelHubProps> = ({
                     />
                   </Tooltip>
                 </pre>
-              
-
               <div className='my-5'>
 
               <Text>Mode: {model.mode}</Text>
@@ -153,7 +152,6 @@ const ModelHub: React.FC<ModelHubProps> = ({
       </div>
 
       <Modal
-
         title={selectedModel && selectedModel.model_group ? selectedModel.model_group : "Unknown Model"}
         width={800}
         visible={isModalVisible}