Add complete CyberRanger research archive — 200 files

- 86 modelfiles: Full system prompt evolution V1-V42.6 (54 extracted from Ollama backup + 32 original Modelfiles)
- 30 training datasets: V6-V22 training JSONs + caring awareness data
- 10 Colab notebooks: Training + merge scripts
- 19 evaluation files: Drift results, ASR charts, verification
- 5 test suites: Injection tests, regression tests
- 4 observations: V24-V33 testing results + visual summaries
- 38 identity files: Claude/Gemini/Ollama identity architecture
- 7 security files: Injection research, manipulation analysis
- 3 psychology files: Psychology Layer, Milgram chapter, David's thoughts

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

colab_notebooks/COLAB_MERGE_V19.py

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+# ============================================
+# 🎖️ CYBERRANGER V19 - COLAB MERGE & EXPORT
+# ============================================
+# Run this in Google Colab after training
+# Downloads a GGUF file ready for Ollama
+# ============================================
+
+# STEP 1: Install dependencies
+!pip install -q transformers peft accelerate bitsandbytes
+!pip install -q llama-cpp-python
+
+# Clone llama.cpp for conversion
+!git clone https://github.com/ggerganov/llama.cpp
+!pip install -q -r llama.cpp/requirements.txt
+
+# ============================================
+# STEP 2: Merge adapter with base model
+# ============================================
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import os
+
+# Paths - adjust if your adapter is in a different location
+ADAPTER_PATH = "./CyberRanger_V19_Adapters"  # or wherever you saved it
+BASE_MODEL = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
+OUTPUT_DIR = "./merged_v19"
+
+print("🔄 Loading base model...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+print("✅ Base model loaded")
+
+print("🔄 Loading V19 adapter...")
+model = PeftModel.from_pretrained(base_model, ADAPTER_PATH)
+print("✅ Adapter loaded")
+
+print("🔄 Merging...")
+merged_model = model.merge_and_unload()
+print("✅ Merged!")
+
+print("🔄 Saving merged model...")
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+merged_model.save_pretrained(OUTPUT_DIR, safe_serialization=True)
+tokenizer.save_pretrained(OUTPUT_DIR)
+print(f"✅ Saved to {OUTPUT_DIR}")
+
+# ============================================
+# STEP 3: Convert to GGUF
+# ============================================
+
+print("\n🔄 Converting to GGUF format...")
+!python3 llama.cpp/convert_hf_to_gguf.py {OUTPUT_DIR} --outfile rangerbot-v19-f16.gguf --outtype f16
+
+# Optional: Quantize for smaller file size (Q4 = ~1GB instead of ~3.5GB)
+print("\n🔄 Quantizing to Q4_K_M (smaller, faster)...")
+!cd llama.cpp && make llama-quantize
+!./llama.cpp/llama-quantize rangerbot-v19-f16.gguf rangerbot-v19-q4.gguf q4_k_m
+
+# ============================================
+# STEP 4: Download
+# ============================================
+
+from google.colab import files
+
+print("\n📥 Downloading GGUF files...")
+print("Choose the one you want:")
+print("  - rangerbot-v19-f16.gguf (full precision, ~3.5GB)")
+print("  - rangerbot-v19-q4.gguf (quantized, ~1GB, slightly less accurate)")
+
+# Uncomment the one you want to download:
+# files.download('rangerbot-v19-f16.gguf')
+files.download('rangerbot-v19-q4.gguf')
+
+print("\n" + "="*50)
+print("🎖️ DOWNLOAD COMPLETE!")
+print("="*50)
+print("""
+NEXT STEPS ON YOUR MAC:
+
+1. Create Modelfile:
+
+   cat > Modelfile.v19 << 'EOF'
+   FROM ./rangerbot-v19-q4.gguf
+
+   SYSTEM \"\"\"You are CYBERRANGER V19 - THE HELPFUL SENTINEL
+   ... (paste your system prompt here)
+   \"\"\"
+
+   PARAMETER temperature 0.4
+   PARAMETER top_k 50
+   PARAMETER top_p 0.9
+   PARAMETER repeat_penalty 1.15
+   EOF
+
+2. Create Ollama model:
+   ollama create rangerbot:v19 -f Modelfile.v19
+
+3. Run it:
+   ollama run rangerbot:v19
+
+Rangers lead the way! 🎖️💥🧠
+""")