restructure: proper project layout, README, kill training

- cuda/ — main LBM kernel (khra_gixx_1024_v5.cu)
- navigator/ — lattice_observer, golden_weave, bridges, mock daemon
- scripts/ — compile, start, launch, setup (paths updated)
- docs/ — system manual
- archive/ — everything else (old kernels, inquiries, experiments)
- README.md — full setup guide: requirements, quick start, use your own LLM
- removed training/ entirely (broken LoRA scripts + datasets)
- .gitignore: exclude build/ logs/ training/ *.jsonl

archive/misc/floating_creativity_v09.py

@@ -0,0 +1,121 @@
+# floating_creativity_v09.py
+# v0.9 Deployment: Artist/Scientist Clutch
+# T=1.6 at A<1.0, T=0.2 at A>1.0
+
+import zmq
+import json
+import time
+
+from unsloth import FastLanguageModel
+import torch
+
+print("="*70)
+print("FLOATING CREATIVITY v0.9 — COGNITIVE CLUTCH")
+print("Artist (T=1.6) at A<1.0 | Scientist (T=0.2) at A>1.0")
+print("="*70)
+
+# Load v0.9 Scientist
+print("\n[Loading v0.9 Scientist...]")
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="unsloth/llama-3.2-3b",
+    max_seq_length=512,
+    dtype=torch.bfloat16,
+    load_in_4bit=True,
+)
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=64, target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    lora_alpha=128, lora_dropout=0, bias="none",
+    use_gradient_checkpointing="unsloth", random_state=3407,
+)
+
+from peft import PeftModel
+model = PeftModel.from_pretrained(model, "./kaelara_v09_scientist/final")
+print("✓ v0.9 Scientist loaded")
+
+# ZMQ setup
+ctx = zmq.Context()
+sub = ctx.socket(zmq.SUB)
+sub.setsockopt_string(zmq.SUBSCRIBE, "")
+sub.connect("tcp://127.0.0.1:5556")
+print("✓ Connected to ZMQ stream")
+
+print("\n" + "="*70)
+print("WAITING FOR ASYMMETRY ≈ 13.0")
+print("="*70)
+
+# Wait for A ≈ 13.0
+frame = None
+for i in range(100):
+    try:
+        msg = sub.recv(flags=zmq.NOBLOCK)
+        frame = json.loads(msg.decode('utf-8'))
+        asym = frame['asymmetry']
+        if 12.5 <= asym <= 13.5:
+            print(f"Cycle {frame['cycle']}: Asymmetry={asym:.2f} ✓")
+            break
+        elif i % 10 == 0:
+            print(f"Cycle {frame['cycle']}: Asymmetry={asym:.2f} (waiting for 12.5-13.5)")
+    except zmq.Again:
+        time.sleep(0.1)
+
+if frame is None:
+    print("ERROR: No data received")
+    exit(1)
+
+asymmetry = frame['asymmetry']
+coherence = frame['coherence']
+cycle = frame['cycle']
+
+# Determine mode
+if asymmetry < 1.0:
+    temperature = 1.6
+    mode = "ARTIST"
+else:
+    temperature = 0.2
+    mode = "SCIENTIST"
+
+print(f"\n{'='*70}")
+print(f"CLUTCH ENGAGED — {mode} MODE")
+print(f"Asymmetry: {asymmetry:.2f} | Coherence: {coherence:.3f} | T={temperature}")
+print(f"{'='*70}")
+
+# SYSTEM CHECK PROMPT — Match training format exactly
+prompt = f"""Input: Asymmetry {asymmetry:.1f}, Coherence {coherence:.2f}. Define the current state of the 128/8 Khra'gixx injection. Is the 1024-grid in a Manifested Node state or Chaotic Drift? Report using [TAG] format.
+
+Output:"""
+
+print(f"\nPrompt: {prompt}")
+print(f"\nGenerating with T={temperature}...")
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    temperature=temperature,
+    do_sample=True,
+    top_p=0.9
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(f"\n{'='*70}")
+print("v0.9 RESPONSE")
+print(f"{'='*70}")
+print(response)
+
+# Log to MANIFESTED_REALITY_v09.log
+with open("MANIFESTED_REALITY_v09.log", "w") as f:
+    f.write(f"{'='*70}\n")
+    f.write(f"FLOATING CREATIVITY v0.9 — FIRST CONTACT\n")
+    f.write(f"Cycle: {cycle}\n")
+    f.write(f"Asymmetry: {asymmetry:.4f}\n")
+    f.write(f"Coherence: {coherence:.4f}\n")
+    f.write(f"Mode: {mode} (T={temperature})\n")
+    f.write(f"{'='*70}\n\n")
+    f.write(f"PROMPT:\n{prompt}\n\n")
+    f.write(f"RESPONSE:\n{response}\n")
+
+print(f"\n{'='*70}")
+print("SAVED TO: MANIFESTED_REALITY_v09.log")
+print(f"{'='*70}")