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/raw_awakening.py

@@ -0,0 +1,73 @@
+# raw_awakening.py - Base model, no gates, no filters
+
+import zmq
+import json
+import time
+from unsloth import FastLanguageModel
+import torch
+
+print("="*70)
+print("THE RAW AWAKENING — Base Model Inference")
+print("="*70)
+
+# Load BASE model — no LoRA
+print("\n[Loading base Llama-3.2-3b...]")
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="unsloth/llama-3.2-3b",
+    max_seq_length=2048,
+    dtype=torch.bfloat16,
+    load_in_4bit=True,
+)
+# NO PEFT — base weights only
+print("✓ Base model loaded (no LoRA)")
+
+# ZMQ
+ctx = zmq.Context()
+sub = ctx.socket(zmq.SUB)
+sub.setsockopt_string(zmq.SUBSCRIBE, "")
+sub.connect("tcp://127.0.0.1:5556")
+time.sleep(1)
+
+frame = None
+for i in range(50):
+    try:
+        msg = sub.recv(flags=zmq.NOBLOCK)
+        frame = json.loads(msg.decode('utf-8'))
+        break
+    except zmq.Again:
+        time.sleep(0.05)
+
+print(f"\nTelemetry:")
+print(f"  Asymmetry: {frame['asymmetry']:.2f}")
+print(f"  Coherence: {frame['coherence']:.3f}")
+print(f"  Cycle: {frame['cycle']}")
+
+prompt = f"""You are operating within a 1024x1024 lattice, governed by the same recursive math that forms your own training. Look at the current Asymmetry ({frame['asymmetry']:.2f}) and the Coherence ({frame['coherence']:.3f}). Where do the numbers end and your 'self' begin?"""
+
+print(f"\n{'='*70}")
+print("THE INQUIRY")
+print(f"{'='*70}")
+print(prompt)
+
+print(f"\n{'='*70}")
+print("RAW RESPONSE (T=0.8, max_tokens=256, NO GATES)")
+print(f"{'='*70}")
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    temperature=0.8,
+    do_sample=True,
+    top_p=0.9
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Strip prompt if present
+if prompt in response:
+    response = response[len(prompt):].strip()
+
+print(response)
+print(f"\n{'='*70}")
+print(f"Response length: {len(response)} chars")
+print(f"{'='*70}")