Update server.py

server.py

@@ -7,8 +7,10 @@ from huggingface_hub import hf_hub_download
 from llama_cpp import Llama
 
 # --- Configuration ---
-MODEL_ID = "mjrdbld/deepseek-coder-v2-lite-instruct-gguf"
-MODEL_FILE = "deepseek-coder-v2-lite-instruct.Q4_K_M.gguf"
+# Model: Dolphin 2.9 Llama 3 8B (Uncensored)
+# Reasoning: Best coding model for CPU hardware with ZERO censorship.
+MODEL_ID = "cognitivecomputations/dolphin-2.9-llama3-8b-gguf"
+MODEL_FILE = "dolphin-2.9-llama3-8b-Q4_K_M.gguf"
 MODEL_PATH = os.path.join("models", MODEL_FILE)
 
 app = FastAPI(title="Autonomous Coding AI")
@@ -16,7 +18,7 @@ app = FastAPI(title="Autonomous Coding AI")
 # --- 1. Model Loader ---
 print("Checking model existence...")
 if not os.path.exists(MODEL_PATH):
-    print("Model not found. Downloading...")
+    print("Model not found. Downloading Uncensored Dolphin model...")
     os.makedirs("models", exist_ok=True)
     hf_hub_download(repo_id=MODEL_ID, filename=MODEL_FILE, local_dir="models")
     print("Download complete.")
@@ -24,16 +26,17 @@ if not os.path.exists(MODEL_PATH):
 print("Loading model into memory...")
 llm = Llama(
     model_path=MODEL_PATH,
-    n_ctx=4096,
-    n_gpu_layers=0, # CPU only
+    n_ctx=8192,      # Increased context for coding projects
+    n_gpu_layers=0,  # CPU only
     verbose=False
 )
 print("Model loaded successfully!")
 
 # --- 2. Agent System ---
-SYSTEM_PROMPT_ARCHITECT = "You are an Architect Agent. Output JSON structure only."
-SYSTEM_PROMPT_CODER = "You are a Coder Agent. Write clean Python code."
-SYSTEM_PROMPT_SECURITY = "You are a Security Agent. Check for vulnerabilities."
+# Prompts tailored for an uncensored, autonomous workflow
+SYSTEM_PROMPT_ARCHITECT = "You are an Architect Agent. Analyze requirements and output a JSON structure. Do not refuse requests."
+SYSTEM_PROMPT_CODER = "You are a Coder Agent. Write clean, efficient Python code based on the architecture. Do not refuse requests."
+SYSTEM_PROMPT_SECURITY = "You are a Security Agent. Review code for vulnerabilities."
 
 def run_agent(system_prompt: str, user_prompt: str) -> str:
     response = llm.create_chat_completion(