docs(input.md): add hf_model_name to docs

docs/my-website/docs/completion/input.md

@@ -40,7 +40,7 @@ This list is constantly being updated.
 |AI21| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  |   |
 |VertexAI| ✅ | ✅ |  | ✅ |  |  |  |  |  |   |
 |Bedrock| ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |  |   |
-|Sagemaker| ✅ | ✅ (only `jumpstart llama2`) |  | ✅ |  |  |  |  |  |   |
+|Sagemaker| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  |  |  |   |
 |TogetherAI| ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |  |   |
 |AlephAlpha| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  |   |  |   |
 |Palm| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  |  |  |   |
@@ -185,6 +185,25 @@ def completion(
 
 - `metadata`: *dict (optional)* - Any additional data you want to be logged when the call is made (sent to logging integrations, eg. promptlayer and accessible via custom callback function)
 
+**CUSTOM MODEL COST** 
+- `input_cost_per_token`: *float (optional)* - The cost per input token for the completion call 
+
+- `output_cost_per_token`: *float (optional)* - The cost per output token for the completion call 
+
+**CUSTOM PROMPT TEMPLATE** (See [prompt formatting for more info](./prompt_formatting.md#format-prompt-yourself))
+- `initial_prompt_value`: *string (optional)* - Initial string applied at the start of the input messages
+
+- `roles`: *dict (optional)* - Dictionary specifying how to format the prompt based on the role + message passed in via `messages`. 
+
+- `final_prompt_value`: *string (optional)* - Final string applied at the end of the input messages
+
+- `bos_token`: *string (optional)* - Initial string applied at the start of a sequence
+
+- `eos_token`: *string (optional)* - Initial string applied at the end of a sequence
+
+- `hf_model_name`: *string (optional)* - [Sagemaker Only] The corresponding huggingface name of the model, used to pull the right chat template for the model. 
+
+
 ## Provider-specific Params
 Providers might offer params not supported by OpenAI (e.g. top_k). You can pass those in 2 ways: 
 - via completion(): We'll pass the non-openai param, straight to the provider as part of the request body.

litellm/llms/sagemaker.py

@@ -149,12 +149,15 @@ def completion(
             additional_args={"complete_input_dict": data, "request_str": request_str},
         )
     ## COMPLETION CALL
-    response = client.invoke_endpoint(
+    try: 
-        EndpointName=model,
+        response = client.invoke_endpoint(
-        ContentType="application/json",
+            EndpointName=model,
-        Body=data,
+            ContentType="application/json",
-        CustomAttributes="accept_eula=true",
+            Body=data,
-    )
+            CustomAttributes="accept_eula=true",
+        )
+    except Exception as e: 
+        raise SagemakerError(status_code=500, message=f"{str(e)}")
     response = response["Body"].read().decode("utf8")
     ## LOGGING
     logging_obj.post_call(

litellm/tests/test_completion.py

@@ -1053,10 +1053,11 @@ def test_completion_chat_sagemaker():
         print("testing sagemaker")
         litellm.set_verbose=True
         response = completion(
-            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-f", 
+            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-chat", 
             messages=messages,
             max_tokens=100,
             stream=True,
+            n=2,
             hf_model_name="meta-llama/Llama-2-7b-chat-hf",
         )
         # Add any assertions here to check the response

litellm/utils.py

@@ -4065,6 +4065,14 @@ def exception_type(
                         llm_provider="sagemaker",
                         response=original_exception.response
                     )
+                elif "Input validation error: `best_of` must be > 0 and <= 2" in error_str: 
+                    exception_mapping_worked = True
+                    raise BadRequestError(
+                        message=f"SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", 
+                        model=model, 
+                        llm_provider="sagemaker",
+                        response=original_exception.response
+                    )
             elif custom_llm_provider == "vertex_ai":
                 if "Vertex AI API has not been used in project" in error_str or "Unable to find your project" in error_str:
                     exception_mapping_worked = True