(v1.0+ breaking change) get_max_tokens -> return int

litellm/__init__.py

@@ -346,6 +346,7 @@ def identify(event_details):
     acreate,
     get_model_list,
     get_max_tokens,
+    get_model_info,
     register_prompt_template,
     validate_environment,
     check_valid_key,

litellm/tests/test_get_model_cost_map.py

@@ -9,20 +9,21 @@
 
 def test_get_gpt3_tokens():
     max_tokens = get_max_tokens("gpt-3.5-turbo")
-    results = max_tokens['max_tokens']
-    print(results)
-# test_get_gpt3_tokens()
+    print(max_tokens)
+    assert max_tokens==4097
+    # print(results)
+test_get_gpt3_tokens()
 
 def test_get_palm_tokens():
     # # 🦄🦄🦄🦄🦄🦄🦄🦄
     max_tokens = get_max_tokens("palm/chat-bison")
-    results = max_tokens['max_tokens']
-    print(results)
-# test_get_palm_tokens()
+    assert max_tokens == 4096
+    print(max_tokens)
+test_get_palm_tokens()
 
 def test_zephyr_hf_tokens():
     max_tokens = get_max_tokens("huggingface/HuggingFaceH4/zephyr-7b-beta")
-    results = max_tokens["max_tokens"]
-    print(results)
+    print(max_tokens)
+    assert max_tokens == 32768
 
 test_zephyr_hf_tokens()

litellm/utils.py

@@ -2358,6 +2358,58 @@ def get_api_key(llm_provider: str, dynamic_api_key: Optional[str]):
     return api_key
 
 def get_max_tokens(model: str):
+    """
+    Get the maximum number of tokens allowed for a given model.
+
+    Parameters:
+    model (str): The name of the model.
+
+    Returns:
+        int: The maximum number of tokens allowed for the given model.
+
+    Raises:
+        Exception: If the model is not mapped yet.
+
+    Example:
+        >>> get_max_tokens("gpt-4")
+        8192
+    """
+    def _get_max_position_embeddings(model_name):
+        # Construct the URL for the config.json file
+        config_url = f"https://huggingface.co/{model_name}/raw/main/config.json"
+
+        try:
+            # Make the HTTP request to get the raw JSON file
+            response = requests.get(config_url)
+            response.raise_for_status()  # Raise an exception for bad responses (4xx or 5xx)
+
+            # Parse the JSON response
+            config_json = response.json()
+
+            # Extract and return the max_position_embeddings
+            max_position_embeddings = config_json.get("max_position_embeddings")
+
+            if max_position_embeddings is not None:
+                return max_position_embeddings
+            else:
+                return None
+        except requests.exceptions.RequestException as e:
+            return None
+
+    try:
+        if model in litellm.model_cost:
+            return litellm.model_cost[model]["max_tokens"]
+        model, custom_llm_provider, _, _ =  get_llm_provider(model=model)
+        if custom_llm_provider == "huggingface": 
+            max_tokens = _get_max_position_embeddings(model_name=model)
+            return max_tokens
+        else: 
+            raise Exception()
+    except:
+        raise Exception("This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json")
+
+
+def get_model_info(model: str):
     """
     Get a dict for the maximum tokens (context window), 
     input_cost_per_token, output_cost_per_token  for a given model.
@@ -2377,7 +2429,7 @@ def get_max_tokens(model: str):
         Exception: If the model is not mapped yet.
 
     Example:
-        >>> get_max_tokens("gpt-4")
+        >>> get_model_info("gpt-4")
         {
             "max_tokens": 8192,
             "input_cost_per_token": 0.00003,