fix: apply ruff fixes to ray_serve package

[ray-serve only]

- Fix whitespace in docstrings
- Add strict=True to zip() calls
- Use ternary operators where appropriate
- Rename unused loop variables

ray-serve/ray_serve/serve_llm.py

@@ -6,7 +6,7 @@ Runs on: khelben (Strix Halo 64GB, ROCm)
 import os
 import time
 import uuid
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 from ray import serve
 
@@ -15,15 +15,15 @@ from ray import serve
 class LLMDeployment:
     def __init__(self):
         from vllm import LLM, SamplingParams
-        
+
         self.model_id = os.environ.get("MODEL_ID", "meta-llama/Llama-3.1-70B-Instruct")
         self.max_model_len = int(os.environ.get("MAX_MODEL_LEN", "8192"))
         self.gpu_memory_utilization = float(os.environ.get("GPU_MEMORY_UTILIZATION", "0.9"))
-        
+
         print(f"Loading vLLM model: {self.model_id}")
         print(f"Max model length: {self.max_model_len}")
         print(f"GPU memory utilization: {self.gpu_memory_utilization}")
-        
+
         self.llm = LLM(
             model=self.model_id,
             max_model_len=self.max_model_len,
@@ -33,10 +33,10 @@ class LLMDeployment:
         self.SamplingParams = SamplingParams
         print(f"Model {self.model_id} loaded successfully")
 
-    async def __call__(self, request: Dict[str, Any]) -> Dict[str, Any]:
+    async def __call__(self, request: dict[str, Any]) -> dict[str, Any]:
         """
         Handle OpenAI-compatible chat completion requests.
-        
+
         Expected request format:
         {
             "model": "model-name",
@@ -51,21 +51,21 @@ class LLMDeployment:
         temperature = request.get("temperature", 0.7)
         max_tokens = request.get("max_tokens", 256)
         top_p = request.get("top_p", 1.0)
-        stop = request.get("stop", None)
-        
+        stop = request.get("stop")
+
         # Convert messages to prompt
         prompt = self._format_messages(messages)
-        
+
         sampling_params = self.SamplingParams(
             temperature=temperature,
             max_tokens=max_tokens,
             top_p=top_p,
             stop=stop,
         )
-        
+
         outputs = self.llm.generate([prompt], sampling_params)
         generated_text = outputs[0].outputs[0].text
-        
+
         # Return OpenAI-compatible response
         return {
             "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
@@ -89,7 +89,7 @@ class LLMDeployment:
             },
         }
 
-    def _format_messages(self, messages: List[Dict[str, str]]) -> str:
+    def _format_messages(self, messages: list[dict[str, str]]) -> str:
         """Format chat messages into a prompt string."""
         formatted = ""
         for msg in messages: