clean up logs

litellm/__init__.py

@@ -198,7 +198,6 @@ def get_model_cost_map(url: str):
  ):
  import importlib.resources
  import json
-
  with importlib.resources.open_text(
  "litellm", "model_prices_and_context_window_backup.json"
  ) as f:
@@ -308,7 +307,6 @@ def identify(event_details):
  elif value.get("litellm_provider") == "perplexity":
  perplexity_models.append(key)
 
-
 # known openai compatible endpoints - we'll eventually move this list to the model_prices_and_context_window.json dictionary
 openai_compatible_endpoints: List = [
  "api.perplexity.ai",

litellm/llms/together_ai.py

@@ -128,7 +128,6 @@ def completion(
  optional_params[k] = v
 
  print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}")
- print(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}")
  if model in custom_prompt_dict:
  # check if the model has a registered custom prompt
  model_prompt_details = custom_prompt_dict[model]

litellm/main.py

@@ -1546,7 +1546,6 @@ def completion(
  )
  return response
  response = model_response
- print('RESPONSE', response)
  elif custom_llm_provider == "palm":
  palm_api_key = api_key or get_secret("PALM_API_KEY") or litellm.api_key
 

litellm/utils.py

@@ -3201,14 +3201,12 @@ def get_model_params_and_category(model_name):
  params_match = re.search(
  r"(\d+b)", model_name
  ) # catch all decimals like 3b, 70b, etc
- print(f"model_name: {model_name} params_match: {params_match}")
  category = None
  if params_match != None:
  params_match = params_match.group(1)
  params_match = params_match.replace("b", "")
  params_billion = float(params_match)
 
- print (f"params_billion: {params_billion} MODEL NAME:{model_name.lower()} PARAMS MATCH: {params_match}")
  # Determine the category based on the number of parameters
  if params_billion <= 3.0:
  category = "together-ai-up-to-3b"