diff --git a/litellm/main.py b/litellm/main.py
index 55918f3a12..9aed35f833 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1861,34 +1861,71 @@ def embedding(
 
 ###### Text Completion ################
 def text_completion(*args, **kwargs):
+    import copy
     """
     This maps to the Openai.Completion.create format, which has a different I/O (accepts prompt, returning ["choices"]["text"].
     """
     if "engine" in  kwargs:
         kwargs["model"] = kwargs["engine"]
         kwargs.pop("engine")
-    
-    # if echo == True, for TGI llms we need to set top_n_tokens to 3
-    if kwargs.get("echo", False) == True and (kwargs["model"] not in litellm.open_ai_text_completion_models):
-        # for tgi llms
-        if "top_n_tokens" not in kwargs:
-            kwargs["top_n_tokens"] = 3
-    if "prompt" in kwargs:
-        if type(kwargs["prompt"]) == list:
-            new_prompt = ""
-            for chunk in kwargs["prompt"]:
-                decoded = litellm.utils.decode(model="gpt2", tokens=chunk)
-                new_prompt+= decoded
-            kwargs["prompt"] = new_prompt
 
+    # input validation
+    if "prompt" not in kwargs:
+        raise ValueError("please pass prompt into the `text_completion` endpoint - `text_completion(model, prompt='hello world')`")
+
+    model = kwargs["model"]
+    prompt = kwargs["prompt"]
+    # get custom_llm_provider
+    _, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model)
+    
+    if custom_llm_provider == "text-completion-openai":
+        # text-davinci-003 and openai text completion models
+        messages = [{"role": "system", "content": kwargs["prompt"]}]
+        kwargs["messages"] = messages
+        kwargs.pop("prompt")
+        response = completion(*args, **kwargs) # assume the response is the openai response object 
+        # return raw response from openai
+        return response._hidden_params.get("original_response", None)
+
+    elif custom_llm_provider == "huggingface":
+        # if echo == True, for TGI llms we need to set top_n_tokens to 3
+        if kwargs.get("echo", False) == True:
+            # for tgi llms
+            if "top_n_tokens" not in kwargs:
+                kwargs["top_n_tokens"] = 3
+
+    # processing prompt - users can pass raw tokens to OpenAI Completion()
+    if type(prompt) == list:
+        tokenizer = tiktoken.encoding_for_model("text-davinci-003")
+        ## if it's a 2d list - each element in the list is a text_completion() request
+        if len(prompt) > 0 and type(prompt[0]) == list:
+            responses = [None for x in prompt] # init responses 
+            for i, request in enumerate(prompt):
+                decoded_prompt = tokenizer.decode(request)
+                # print("\ndecoded\n", decoded_prompt)
+                # print("type decoded", type(decoded_prompt))
+                new_kwargs = copy.deepcopy(kwargs)
+                new_kwargs["prompt"] = decoded_prompt
+                # print("making new individual request", new_kwargs)
+                response = text_completion(**new_kwargs)
+                # print("assigning for ", i)
+                responses[i] = response["choices"][0]
+            print(responses)
+            formatted_response_obj = {
+                "id": response["id"],
+                "object": "text_completion",
+                "created": response["created"],
+                "model": response["model"],
+                "choices": responses,
+                "usage": response["usage"]
+            }
+            return formatted_response_obj
+    else:
         messages = [{"role": "system", "content": kwargs["prompt"]}]
         kwargs["messages"] = messages
         kwargs.pop("prompt")
         response = completion(*args, **kwargs) # assume the response is the openai response object 
 
-        # if the model is text-davinci-003, return raw response from openai
-        if kwargs["model"] in litellm.open_ai_text_completion_models and response._hidden_params.get("original_response", None) != None:
-            return response._hidden_params.get("original_response", None)
         transformed_logprobs = None
         # only supported for TGI models
         try:
@@ -1912,8 +1949,6 @@ def text_completion(*args, **kwargs):
             "usage": response["usage"]
         }
         return formatted_response_obj
-    else:
-        raise ValueError("please pass prompt into the `text_completion` endpoint - `text_completion(model, prompt='hello world')`")
 
 ##### Moderation #######################
 def moderation(input: str, api_key: Optional[str]=None):