add petals to init and main

litellm/__init__.py

@@ -233,6 +233,10 @@ aleph_alpha_models: List = [
 
 baseten_models: List = ["qvv0xeq", "q841o8w", "31dxrj3"]  # FALCON 7B  # WizardLM  # Mosaic ML
 
+petals_models = [
+    "petals-team/StableBeluga2",
+]
+
 bedrock_models: List = [
     "amazon.titan-tg1-large",
     "ai21.j2-grande-instruct"
@@ -272,6 +276,7 @@ provider_list: List = [
     "vllm",
     "nlp_cloud",
     "bedrock",
+    "petals,"
     "custom", # custom apis
 ]
 
@@ -287,6 +292,7 @@ models_by_provider: dict = {
     "vertex_ai": vertex_chat_models + vertex_text_models,
     "ai21": ai21_models,
     "bedrock": bedrock_models,
+    "petals": petals_models,
 }
 
 ####### EMBEDDING MODELS ###################

litellm/main.py

@@ -34,6 +34,7 @@ from .llms import baseten
 from .llms import vllm
 from .llms import ollama
 from .llms import cohere
+from .llms import petals
 import tiktoken
 from concurrent.futures import ThreadPoolExecutor
 from typing import Callable, List, Optional, Dict
@@ -953,6 +954,32 @@ def completion(
                 )
                 return response
             response = model_response
+        elif (
+            custom_llm_provider == "petals"
+            or custom_llm_provider == "petals-team"
+            or model in litellm.petals_models
+        ):
+            custom_llm_provider = "baseten"
+
+            model_response = petals.completion(
+                model=model,
+                messages=messages,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                encoding=encoding, 
+                api_key=baseten_key, 
+                logging_obj=logging
+            )
+            if inspect.isgenerator(model_response) or (stream == True):
+                # don't try to access stream object,
+                response = CustomStreamWrapper(
+                    model_response, model, custom_llm_provider="petals", logging_obj=logging
+                )
+                return response
+            response = model_response
         elif (
             custom_llm_provider == "custom"
             ):