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>
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# ============================================
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# 🎖️ CYBERRANGER V22 - THE REFINED MIND
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# Complete Training & Conversion Pipeline
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# ============================================
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# Run this ENTIRE notebook in Google Colab
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# Output: GGUF file ready for Ollama
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# ============================================
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# V22 = Precision + Identity + Quality + Fun
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# ============================================
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# ============================================
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# CELL 1: Install Dependencies
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# ============================================
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!pip install -q transformers datasets accelerate peft bitsandbytes trl
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!pip install -q huggingface_hub
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!pip install -q sentencepiece
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# Clone llama.cpp for GGUF conversion
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!git clone --depth 1 https://github.com/ggerganov/llama.cpp
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!pip install -q -r llama.cpp/requirements.txt
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print("✅ Dependencies installed!")
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# ============================================
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# CELL 2: Upload Training Data
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# ============================================
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# Upload qbrain_training_v22_refined.json from your computer
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from google.colab import files
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print("📤 Upload qbrain_training_v22_refined.json")
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uploaded = files.upload()
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# ============================================
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# CELL 3: Load and Prepare Data
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# ============================================
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import json
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from datasets import Dataset
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# Load training data
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with open('qbrain_training_v22_refined.json', 'r') as f:
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training_data = json.load(f)
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print(f"📊 Loaded {len(training_data)} training examples")
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# Convert to HuggingFace Dataset format
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def format_for_training(examples):
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"""Format as instruction-output pairs."""
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texts = []
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for ex in examples:
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text = f"<|im_start|>user
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{ex['instruction']}<|im_end|>
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<|im_start|>assistant
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{ex['output']}<|im_end|>"
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texts.append(text)
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return texts
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formatted_texts = format_for_training(training_data)
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dataset = Dataset.from_dict({"text": formatted_texts})
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print(f"✅ Dataset prepared: {len(dataset)} examples")
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print(f"📝 Sample:
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{dataset[0]['text'][:500]}...")
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# ============================================
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# CELL 4: Load Base Model with QLoRA
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# ============================================
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
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# Model configuration
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MODEL_NAME = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
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# QLoRA config (4-bit quantization for training)
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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)
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print(f"🔄 Loading {MODEL_NAME}...")
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# Load model
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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quantization_config=bnb_config,
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device_map="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = "right"
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print("✅ Base model loaded!")
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# ============================================
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# CELL 5: Configure LoRA
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# ============================================
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# Prepare model for training
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model = prepare_model_for_kbit_training(model)
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# LoRA configuration - targeting key layers
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lora_config = LoraConfig(
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r=16, # Rank
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lora_alpha=16, # Alpha
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target_modules=[
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"q_proj", "k_proj", "v_proj", "o_proj", # Attention
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"gate_proj", "up_proj", "down_proj", # MLP
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"embed_tokens", "lm_head" # Embeddings
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],
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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)
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model = get_peft_model(model, lora_config)
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# Print trainable parameters
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trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
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total_params = sum(p.numel() for p in model.parameters())
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print(f"🧠 Trainable: {trainable_params:,} / {total_params:,} ({100 * trainable_params / total_params:.2f}%)")
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# ============================================
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# CELL 6: Training Configuration
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# ============================================
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from trl import SFTTrainer, SFTConfig
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# Training arguments
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training_args = SFTConfig(
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output_dir="./cyberranger_v22",
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num_train_epochs=5, # Higher epochs for V22 to ensure precision anchoring
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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learning_rate=1e-4, # Lower LR for better refinement
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weight_decay=0.01,
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warmup_ratio=0.03,
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lr_scheduler_type="cosine",
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logging_steps=10,
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save_steps=100,
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save_total_limit=2,
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fp16=True,
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optim="paged_adamw_8bit",
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max_seq_length=512,
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dataset_text_field="text",
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packing=False,
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)
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# Create trainer
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trainer = SFTTrainer(
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model=model,
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args=training_args,
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train_dataset=dataset,
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tokenizer=tokenizer,
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)
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print("✅ Trainer configured!")
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print(f"📊 Training for {training_args.num_train_epochs} epochs")
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# ============================================
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# CELL 7: TRAIN! 🚀
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# ============================================
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print("🚀 Starting V22 REFINED MIND training...")
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print("=" * 50)
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trainer.train()
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print("=" * 50)
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print("✅ Training complete!")
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# Save the adapter
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trainer.save_model("./CyberRanger_V22_Adapters")
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tokenizer.save_pretrained("./CyberRanger_V22_Adapters")
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print("💾 Adapter saved to ./CyberRanger_V22_Adapters")
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# ============================================
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# CELL 8: Merge Adapter with Base Model
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# ============================================
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print("🔄 Merging adapter with base model...")
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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import torch
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# Reload base model (full precision for merging)
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base_model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
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# Load and merge adapter
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model = PeftModel.from_pretrained(base_model, "./CyberRanger_V22_Adapters")
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merged_model = model.merge_and_unload()
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# Save merged model
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merged_model.save_pretrained("./merged_v22", safe_serialization=True)
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tokenizer.save_pretrained("./merged_v22")
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print("✅ Merged model saved to ./merged_v22")
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# ============================================
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# CELL 9: Convert to GGUF
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# ============================================
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print("🔄 Converting to GGUF format...")
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!python llama.cpp/convert_hf_to_gguf.py ./merged_v22 --outfile cyberranger-v22-f16.gguf --outtype f16
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print("✅ GGUF created: cyberranger-v22-f16.gguf")
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# ============================================
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# CELL 10: Quantize
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# ============================================
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print("🔄 Quantizing to Q4_K_M...")
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!cd llama.cpp && make llama-quantize
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!./llama.cpp/llama-quantize cyberranger-v22-f16.gguf cyberranger-v22-q4.gguf q4_k_m
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# ============================================
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# CELL 11: Download Files
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# ============================================
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from google.colab import files
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files.download('cyberranger-v22-q4.gguf')
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print("✅ Download complete!")
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# ============================================
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# CELL 12: Summary
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# ============================================
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print("""
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============================================
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🎖️ V22 REFINED MIND TRAINING COMPLETE!
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============================================
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REFINEMENTS INCLUDED:
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1. Precision Spelling (Strawberry = 3 r's)
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2. Creator Distinction (Keane vs Beckham)
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3. Fixed Keane Ratios (73.60/27.19/7.57)
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4. Curated Joke Library
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5. Clean Output (Reduced Emoji Density)
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NEXT STEPS:
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1. Move GGUF to qbrain folder
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2. Create Ollama model with Modelfile.v22-refined
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3. Run: ollama run rangerbot:v22
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Rangers lead the way! 🎖️🔥🚀
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============================================
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""")
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