diff --git a/litellm/main.py b/litellm/main.py
index ab8548bc7b..3732d89f94 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1821,7 +1821,20 @@ def text_completion(*args, **kwargs):
     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
+
         messages = [{"role": "system", "content": kwargs["prompt"]}]
         kwargs["messages"] = messages
         kwargs.pop("prompt")
@@ -1834,19 +1847,7 @@ def text_completion(*args, **kwargs):
         # only supported for TGI models
         try:
             raw_response = response._hidden_params.get("original_response", None)
-            tokens = []
-            token_logprobs = []
-            if "prefill" in raw_response[0]["details"]:
-                prefills = raw_response[0]["details"]['prefill']
-                for prefill in prefills:
-                    tokens.append(prefill['text'])
-                    token_logprobs.append(prefill['logprob'])
-            new_tokens = [token['text'] for token in raw_response[0]['details']['tokens']]
-            new_token_logprobs = [token['logprob'] for token in raw_response[0]['details']['tokens']]         
-            transformed_logprobs = {
-                "tokens": tokens + new_tokens,
-                "token_logprobs": token_logprobs + new_token_logprobs
-            }
+            transformed_logprobs = litellm.utils.transform_logprobs(raw_response)
         except Exception as e:
             print("LiteLLM non blocking exception", e)
         formatted_response_obj = {