Update app.py

app.py

@@ -8,7 +8,6 @@ from llama_cpp import Llama
 
 app = FastAPI(title="Qwen 2.5 Coder OpenAI Server")
 
-# Enable CORS for local VS Code extension requests
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -17,19 +16,17 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Safe Hugging Face runtime model download path
 MODEL_REPO = "Qwen/Qwen2.5-Coder-7B-Instruct-GGUF"
 MODEL_FILE = "qwen2.5-coder-7b-instruct-q4_k_m.gguf"
 
 print("Checking and downloading Qwen 2.5 Coder 7B GGUF model...")
-# Downloads dynamically on container boot time via the HF library (cached across restarts)
 model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
 
 print(f"Loading model into memory from: {model_path}")
 llm = Llama(
     model_path=model_path,
-    n_ctx=4096,      # Expanded safely within 16GB memory bounds
-    n_threads=2,     # Perfectly matches your 2 vCPU resource cap
+    n_ctx=4096,      
+    n_threads=2,     
     n_batch=512,
     verbose=False
 )
@@ -42,7 +39,6 @@ async def chat_completions(request: Request):
     temperature = body.get("temperature", 0.2)
     max_tokens = body.get("max_tokens", 1024)
 
-    # Convert incoming standard OpenAI messages array to Qwen ChatML formatting
     formatted_prompt = ""
     for msg in messages:
         role = msg.get("role", "user")
@@ -50,7 +46,6 @@ async def chat_completions(request: Request):
         formatted_prompt += f"<|im_start|>{role}\n{content}<|im_end|>\n"
     formatted_prompt += "<|im_start|>assistant\n"
 
-    # Synchronous processing avoids lock stalls on CPU
     response = llm(
         formatted_prompt,
         max_tokens=max_tokens,