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
2026-03-24 12:58:19 +07:00
parent 57f6d86a65
commit 56c71c87b2
411 changed files with 235 additions and 2023 deletions
@@ -0,0 +1,134 @@
+# entropy_injection_inference.py
+# GPU temperature / Asymmetry → Model temperature mapping
+# Hotter environment = Higher creativity (reaching into base Llama knowledge)
+
+import zmq
+import json
+import time
+import subprocess
+
+from unsloth import FastLanguageModel
+import torch
+
+def get_gpu_metrics():
+    """Get GPU temperature and power"""
+    try:
+        result = subprocess.run(
+            ['nvidia-smi', '--query-gpu=temperature.gpu,power.draw', 
+             '--format=csv,noheader,nounits'],
+            capture_output=True, text=True, timeout=2
+        )
+        if result.returncode == 0:
+            parts = result.stdout.strip().split(',')
+            return {
+                'temp_c': float(parts[0]),
+                'power_w': float(parts[1])
+            }
+    except:
+        pass
+    return {'temp_c': 45.0, 'power_w': 50.0}  # Default
+
+def calculate_dynamic_temperature(gpu_temp, asymmetry):
+    """
+    Entropy Injection: Hotter GPU or higher asymmetry = higher model temperature
+    
+    Base: 40°C GPU, A=0 → T=0.3 (conservative)
+    Hot: 70°C GPU, A=13 → T=1.2 (creative)
+    """
+    # GPU temp component: 40°C → 0.0, 70°C → 0.6
+    temp_factor = max(0, min(1, (gpu_temp - 40) / 30)) * 0.6
+    
+    # Asymmetry component: A=0 → 0.0, A=13 → 0.6  
+    asym_factor = max(0, min(1, asymmetry / 13)) * 0.6
+    
+    # Combined: base 0.3 + up to 0.9 additional
+    temperature = 0.3 + (temp_factor + asym_factor) / 2
+    
+    return min(1.4, temperature)  # Cap at 1.4
+
+print("="*70)
+print("ENTROPY INJECTION INFERENCE")
+print("GPU Temp / Asymmetry → Model Temperature")
+print("="*70)
+
+# Load model
+print("\n[Loading Kaelara v0.9...]")
+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("✓ Model loaded")
+
+# ZMQ
+ctx = zmq.Context()
+sub = ctx.socket(zmq.SUB)
+sub.setsockopt_string(zmq.SUBSCRIBE, "")
+sub.connect("tcp://127.0.0.1:5556")
+print("✓ ZMQ connected")
+
+print("\n" + "="*70)
+print("ENTROPY INJECTION LOOP (5 cycles)")
+print("="*70)
+
+for cycle_num in range(5):
+    # Get LBM frame
+    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)
+    
+    if frame is None:
+        print(f"[{cycle_num}] No data")
+        continue
+    
+    # Get GPU metrics
+    gpu = get_gpu_metrics()
+    
+    # Calculate dynamic temperature
+    temp = calculate_dynamic_temperature(gpu['temp_c'], frame['asymmetry'])
+    
+    print(f"\n[{cycle_num}] Cycle {frame['cycle']}")
+    print(f"    GPU: {gpu['temp_c']:.1f}°C, {gpu['power_w']:.1f}W")
+    print(f"    LBM: Asym={frame['asymmetry']:.2f}, Coh={frame['coherence']:.3f}")
+    print(f"    → Model Temperature: {temp:.2f}")
+    
+    # Generate with dynamic temperature
+    prompt = f"""Input: Asymmetry {frame['asymmetry']:.1f}, Coherence {frame['coherence']:.2f}, GPU {gpu['temp_c']:.1f}°C. Define the situation.
+
+Output:"""
+    
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=80,
+        temperature=temp,
+        do_sample=True,
+        top_p=0.9
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract just the output part
+    if "Output:" in response:
+        response = response.split("Output:")[-1].strip()
+    
+    print(f"    Response: {response[:100]}...")
+
+print("\n" + "="*70)
+print("ENTROPY INJECTION COMPLETE")
+print("="*70)