docs update

docs/my-website/docs/token_usage.md

@@ -1,4 +1,4 @@
-# Token Usage
+# Completion Token Usage & Cost
 By default LiteLLM returns token usage in all completion requests ([See here](https://litellm.readthedocs.io/en/latest/output/))
 
 However, we also expose 3 public helper functions to calculate token usage across providers:
@@ -33,13 +33,19 @@ print(prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar)
 ```
 
 3. `completion_cost`
+Accepts a `litellm.completion()` response and return a `float` of cost for the `completion` call 
 
 ```python
-from litellm import completion_cost
+from litellm import completion, completion_cost
 
-prompt = "Hey, how's it going"
-completion = "Hi, I'm gpt - I am doing well"
-cost_of_query = completion_cost(model="gpt-3.5-turbo", prompt=prompt, completion=completion))
-
-print(cost_of_query)
+response = completion(
+            model="together_ai/togethercomputer/llama-2-70b-chat",
+            messages=messages,
+            request_timeout=200,
+        )
+# pass your response from completion to completion_cost
+cost = completion_cost(completion_response=response)
+formatted_string = f"${float(cost):.10f}"
+print(formatted_string)
 ```
+

litellm/tests/test_completion_cost.py

@@ -28,11 +28,11 @@ def test_completion_togetherai_cost():
         )
         # Add any assertions here to check the response
         print(response)
-        print("Completion Cost:")
+        print("Completion Cost: for togethercomputer/llama-2-70b-chat")
         cost = completion_cost(completion_response=response)
         formatted_string = f"${float(cost):.10f}"
         print(formatted_string)
         
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-# test_completion_togetherai_cost()
+test_completion_togetherai_cost()

litellm/utils.py

@@ -578,9 +578,7 @@ def get_model_params_and_category(model_name):
     if params_match != None:
         params_match = params_match.group(1)
         params_match = params_match.replace("b", "")
-        print(params_match)
         params_billion = float(params_match)
-
         # Determine the category based on the number of parameters
         if params_billion <= 3.0:
             category = "together-ai-up-to-3b"
@@ -593,8 +591,8 @@ def get_model_params_and_category(model_name):
         elif params_billion <= 70.0:
             category = "together-ai-40.1b-70b"
         return category
-    
-    return "Model name not recognized or category not found."
+
+    return None
 
 
 def token_counter(model, text):