diff --git a/litellm/main.py b/litellm/main.py
index 45537eb49..0b58c0aab 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1139,26 +1139,20 @@ def batch_completion(
     messages: List = [],
     functions: List = [],
     function_call: str = "",  # optional params
-    temperature: float = 1,
-    top_p: float = 1,
-    n: int = 1,
-    stream: bool = False,
+    temperature: Optional[float] = None,
+    top_p: Optional[float] = None,
+    n: Optional[int] = None,
+    stream: Optional[bool] = None,
     stop=None,
-    max_tokens: float = float("inf"),
-    presence_penalty: float = 0,
-    frequency_penalty=0,
+    max_tokens: Optional[float] = None,
+    presence_penalty: Optional[float] = None,
+    frequency_penalty: Optional[float]=None,
     logit_bias: dict = {},
     user: str = "",
+    deployment_id = None,
+    request_timeout: Optional[int] = None,
     # Optional liteLLM function params
-    *,
-    return_async=False,
-    api_key: Optional[str] = None,
-    api_version: Optional[str] = None,
-    api_base: Optional[str] = None,
-    force_timeout=600,
-    # used by text-bison only
-    top_k=40,
-    custom_llm_provider=None,):
+    **kwargs):
     args = locals()
     batch_messages = messages
     completions = []
@@ -1183,10 +1177,10 @@ def batch_completion(
             user=user,
             # params to identify the model
             model=model,
-            custom_llm_provider=custom_llm_provider,
-            top_k=top_k,
+            custom_llm_provider=custom_llm_provider
         )
         results = vllm.batch_completions(model=model, messages=batch_messages, custom_prompt_dict=litellm.custom_prompt_dict, optional_params=optional_params)
+    # all non VLLM models for batch completion models
     else:
         def chunks(lst, n):
             """Yield successive n-sized chunks from lst."""
@@ -1195,9 +1189,12 @@ def batch_completion(
         with ThreadPoolExecutor(max_workers=100) as executor:
             for sub_batch in chunks(batch_messages, 100):
                 for message_list in sub_batch:
-                    kwargs_modified = args
+                    kwargs_modified = args.copy()
                     kwargs_modified["messages"] = message_list
-                    future = executor.submit(completion, **kwargs_modified)
+                    original_kwargs = {}
+                    if "kwargs" in kwargs_modified:
+                        original_kwargs = kwargs_modified.pop("kwargs")
+                    future = executor.submit(completion, **kwargs_modified, **original_kwargs)
                     completions.append(future)
 
         # Retrieve the results from the futures
diff --git a/pyproject.toml b/pyproject.toml
index ee80ec21c..bd85f7f7d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "0.2.6"
+version = "0.3.0"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT License"
@@ -25,7 +25,7 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "0.2.6"
+version = "0.3.0"
 version_files = [
     "pyproject.toml:^version"
 ]