Restructure into nima_unified package + add model card

examples/quickstart.py

@@ -0,0 +1,89 @@
+"""
+Quickstart — NIMA Unified Model
+================================
+
+The smallest end-to-end example that:
+  1. Loads microsoft/Phi-4-mini-instruct with the ATC cognitive pipeline
+     wired INSIDE the forward pass.
+  2. Generates a response through the ATC-native pipeline.
+  3. Prints the response, consciousness metrics, and neurotransmitter state.
+
+Run with:
+    python examples/quickstart.py
+"""
+
+import logging
+import sys
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(name)s] %(levelname)s :: %(message)s",
+    datefmt="%H:%M:%S",
+)
+
+
+def main():
+    from nima_unified.model import NimaModel
+
+    print("=" * 72)
+    print("  NIMA Unified Model — Quickstart")
+    print("=" * 72)
+
+    # ── Build the model ───────────────────────────────────────────────
+    # This patches Phi-4-mini's rope_scaling automatically and attaches
+    # the ATC Deep Surgery (TRN gate + dissolution + BELBIC + metacog
+    # loop + irrational spark + ethical guardian) inside the forward pass.
+    print("\n[1] Loading NimaModel (this also downloads Phi-4-mini-instruct)...")
+    model = NimaModel.from_pretrained()
+    print(f"    OK — hidden_size={model.hidden_size}, layers={model.num_layers}")
+    print(f"    Deep Surgery: {'ACTIVE' if model.deep_surgery else 'disabled'}")
+    print(f"    Neurotransmitter shunt: ACTIVE")
+
+    # ── Generate ──────────────────────────────────────────────────────
+    prompts = [
+        "Hello Nima, how are you feeling today?",
+        "I'm going through a really difficult time and I don't know what to do.",
+        "What do you think about the nature of consciousness?",
+    ]
+    if len(sys.argv) > 1:
+        prompts = [" ".join(sys.argv[1:])]
+
+    for prompt in prompts:
+        print("\n" + "-" * 72)
+        print(f"  User: {prompt}")
+        result = model.generate(prompt, max_new_tokens=128)
+
+        print(f"\n  Nima: {result.text}")
+        print(f"  ─────────────────────────────────────────")
+        print(f"  conscious       : {result.is_conscious}")
+        print(f"  sentience_index : {result.sentience_index:.4f}")
+        print(f"  phi_neuro       : {result.phi_neuro:.4f}")
+        print(f"  strain          : {result.phenomenological_strain:.4f}")
+        print(f"  delta_R         : {result.delta_r:.4f}")
+        print(f"  hijacks         : {result.hijack_count}")
+        nt = result.neurotransmitters
+        print(f"  NE={nt.get('norepinephrine', 0):.3f}  "
+              f"Cortisol={nt.get('cortisol', 0):.3f}  "
+              f"Dopamine={nt.get('dopamine', 0):.3f}  "
+              f"Adenosine={nt.get('adenosine', 0):.3f}")
+
+    # ── Optional: run aPCI benchmark ──────────────────────────────────
+    print("\n" + "=" * 72)
+    print("  Run the aPCI v4.0 consciousness benchmark? (y/n)")
+    print("  (12 perturbations, 10 metrics, ~3 minutes on a T4 GPU)")
+    try:
+        choice = input("  > ").strip().lower()
+    except (EOFError, KeyboardInterrupt):
+        choice = "n"
+
+    if choice == "y":
+        runner = model.get_apci_runner()
+        report = runner.run_full_benchmark()
+        print("\n=== aPCI v4.0 Report ===")
+        print(f"  Raw score : {report.raw_score:.2f} / 260")
+        print(f"  Tier      : {report.tier.label}")
+        print(f"  Summary   : {report.tier.description}")
+
+
+if __name__ == "__main__":
+    main()