diff --git a/litellm/llms/sagemaker.py b/litellm/llms/sagemaker.py
index 3ddfb4c608a..e1c9ccdc8c7 100644
--- a/litellm/llms/sagemaker.py
+++ b/litellm/llms/sagemaker.py
@@ -63,6 +63,7 @@ def completion(
     encoding,
     logging_obj,
     custom_prompt_dict={},
+    hf_model_name=None,
     optional_params=None,
     litellm_params=None,
     logger_fn=None,
@@ -119,12 +120,7 @@ def completion(
             messages=messages
         )
     else:
-        hf_model_name = model
-        if "meta-textgeneration-llama-2" in model or "meta-textgenerationneuron-llama-2" in model: # llama2 model 
-            if model.endswith("-f") or "-f-" in model or "chat" in model: # sagemaker default for a chat model
-                hf_model_name = "meta-llama/Llama-2-7b-chat" # apply the prompt template for a llama2 chat model 
-            else:
-                hf_model_name = "meta-llama/Llama-2-7b" # apply the normal prompt template
+        hf_model_name = hf_model_name or model # pass in hf model name for pulling it's prompt template - (e.g. `hf_model_name="meta-llama/Llama-2-7b-chat-hf` applies the llama2 chat template to the prompt)
         prompt = prompt_factory(model=hf_model_name, messages=messages)
 
     data = json.dumps({
diff --git a/litellm/main.py b/litellm/main.py
index e0c7cf3b102..f265d4653be 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -341,11 +341,13 @@ def completion(
     final_prompt_value = kwargs.get("final_prompt_value", None)
     bos_token = kwargs.get("bos_token", None)
     eos_token = kwargs.get("eos_token", None)
+    hf_model_name = kwargs.get("hf_model_name", None)
+    ### ASYNC CALLS ### 
     acompletion = kwargs.get("acompletion", False)
     client = kwargs.get("client", None)
     ######## end of unpacking kwargs ###########
     openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries"]
-    litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token"]
+    litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name"]
     default_params = openai_params + litellm_params
     non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
     if mock_response:
@@ -1167,6 +1169,7 @@ def completion(
                 optional_params=optional_params,
                 litellm_params=litellm_params,
                 custom_prompt_dict=custom_prompt_dict,
+                hf_model_name=hf_model_name,
                 logger_fn=logger_fn,
                 encoding=encoding,
                 logging_obj=logging
diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py
index 3c4a9aa40c1..d0cda93353c 100644
--- a/litellm/tests/test_completion.py
+++ b/litellm/tests/test_completion.py
@@ -1039,6 +1039,7 @@ def test_completion_sagemaker():
             messages=messages,
             temperature=0.2,
             max_tokens=80,
+            hf_model_name="meta-llama/Llama-2-7b",
         )
         # Add any assertions here to check the response
         print(response)
@@ -1056,6 +1057,7 @@ def test_completion_chat_sagemaker():
             messages=messages,
             max_tokens=100,
             stream=True,
+            hf_model_name="meta-llama/Llama-2-7b-chat-hf",
         )
         # Add any assertions here to check the response
         complete_response = ""