diff --git a/examples/pipelines/providers/cloudflare_ai_pipeline.py b/examples/pipelines/providers/cloudflare_ai_pipeline.py
index 5559e17..3bbcadc 100644
--- a/examples/pipelines/providers/cloudflare_ai_pipeline.py
+++ b/examples/pipelines/providers/cloudflare_ai_pipeline.py
@@ -1,7 +1,6 @@
 from typing import List, Union, Generator, Iterator
 from schemas import OpenAIChatMessage
 from pydantic import BaseModel
-
 import os
 import requests
 
@@ -10,40 +9,33 @@ class Pipeline:
     class Valves(BaseModel):
         CLOUDFLARE_ACCOUNT_ID: str = ""
         CLOUDFLARE_API_KEY: str = ""
-        CLOUDFLARE_MODELS: str = ""
+        CLOUDFLARE_MODEL: str = ""
         pass
 
     def __init__(self):
-        self.type = "manifold"
         # Optionally, you can set the id and name of the pipeline.
         # Best practice is to not specify the id so that it can be automatically inferred from the filename, so that users can install multiple versions of the same pipeline.
         # The identifier must be unique across all pipelines.
         # The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes.
         # self.id = "openai_pipeline"
-        self.name = "Cloudflare AI: "
-
+        self.name = "Cloudlfare AI"
         self.valves = self.Valves(
             **{
+                "CLOUDFLARE_ACCOUNT_ID": os.getenv(
+                    "CLOUDFLARE_ACCOUNT_ID",
+                    "your-account-id",
+                ),
                 "CLOUDFLARE_API_KEY": os.getenv(
-                    "CLOUDFLARE_API_KEY", "your-openai-api-key-here"
+                    "CLOUDFLARE_API_KEY", "your-cloudflare-api-key"
                 ),
-                "CLOUDFLARE_MODELS": os.getenv(
+                "CLOUDFLARE_MODEL": os.getenv(
                     "CLOUDFLARE_MODELS",
-                    "@cf/meta/llama-3.1-8,@cf/deepseek-ai/deepseek-math-7b-instruct",
+                    "@cf/meta/llama-3.1-8b-instruct",
                 ),
-            },
+            }
         )
-
-        self.pipelines = self.get_cloudflare_models()
         pass
 
-    def get_cloudflare_models(self):
-        models = [
-            {"id": model, "name": model}
-            for model in self.valves.CLOUDFLARE_MODELS.split(",")
-        ]
-        return models
-
     async def on_startup(self):
         # This function is called when the server is started.
         print(f"on_startup:{__name__}")
@@ -54,26 +46,17 @@ class Pipeline:
         print(f"on_shutdown:{__name__}")
         pass
 
-    async def on_valves_updated(self):
-        # This function is called when the valves are updated.
-        print(f"on_valves_updated:{__name__}")
-        self.pipelines = self.get_cloudflare_models()
-        pass
-
     def pipe(
         self, user_message: str, model_id: str, messages: List[dict], body: dict
     ) -> Union[str, Generator, Iterator]:
         # This is where you can add your custom pipelines like RAG.
         print(f"pipe:{__name__}")
 
-        print(messages)
-        print(user_message)
-
         headers = {}
         headers["Authorization"] = f"Bearer {self.valves.CLOUDFLARE_API_KEY}"
         headers["Content-Type"] = "application/json"
 
-        payload = {**body, "model": model_id}
+        payload = {**body, "model": self.valves.CLOUDFLARE_MODEL}
 
         if "user" in payload:
             del payload["user"]
@@ -82,8 +65,6 @@ class Pipeline:
         if "title" in payload:
             del payload["title"]
 
-        print(payload)
-
         try:
             r = requests.post(
                 url=f"https://api.cloudflare.com/client/v4/accounts/{self.valves.CLOUDFLARE_ACCOUNT_ID}/ai/v1/chat/completions",