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examples/api_server.py

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+#!/usr/bin/env python3
+"""
+Sixpert K2 - OpenAI-Compatible API Server
+==========================================
+Runs Sixpert K2 as an OpenAI-compatible API server using llama-cpp-python.
+
+The MoE architecture means K2 activates only ~1.2B parameters per token,
+making it significantly faster than dense models of similar total parameter count.
+
+Usage:
+    pip install llama-cpp-python
+    python api_server.py
+
+Then use with any OpenAI-compatible client:
+    curl http://localhost:8000/v1/chat/completions \
+      -H "Content-Type: application/json" \
+      -d '{
+        "model": "sixpert-k2",
+        "messages": [{"role": "user", "content": "Explain quantum computing deeply"}]
+      }'
+"""
+
+import argparse
+from llama_cpp.server.app import create_app
+from llama_cpp import Llama
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Sixpert K2 API Server")
+    parser.add_argument(
+        "--model", type=str, default="SixpertK2.gguf", help="Path to GGUF model"
+    )
+    parser.add_argument("--host", type=str, default="0.0.0.0", help="Server host")
+    parser.add_argument("--port", type=int, default=8000, help="Server port")
+    parser.add_argument("--gpu-layers", type=int, default=-1, help="GPU layers (-1 = all)")
+    parser.add_argument("--threads", type=int, default=8, help="CPU threads")
+
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print("  Sixpert K2 API Server")
+    print("  Deep Reasoning Engine (MoE)")
+    print("  Total: ~8.9B params | Active: ~1.2B per token")
+    print("=" * 60)
+
+    llm = Llama(
+        model_path=args.model,
+        n_ctx=131072,
+        n_gpu_layers=args.gpu_layers,
+        n_threads=args.threads,
+        verbose=False,
+    )
+
+    app = create_app(llm)
+
+    print(f"\n  Server running at http://{args.host}:{args.port}")
+    print(f"  Model: sixpert-k2")
+    print(f"  Endpoint: POST /v1/chat/completions")
+    print(f"  Endpoint: POST /v1/completions")
+    print("=" * 60)
+
+    import uvicorn
+    uvicorn.run(app, host=args.host, port=args.port)
+
+
+if __name__ == "__main__":
+    main()