Instructions to use Ananthusajeev190/Dream_viewer_venomoussai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Ananthusajeev190/Dream_viewer_venomoussai with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("fill-in-model-name") model.load_adapter("Ananthusajeev190/Dream_viewer_venomoussai", set_active=True) - Notebooks
- Google Colab
- Kaggle
| # Step 1: Mount Google Drive | |
| from google.colab import drive | |
| import os | |
| import json | |
| import time | |
| import random | |
| import shutil | |
| # --- SAFETY CONTROL --- | |
| MAX_NEURONS_TO_CREATE = 10 # Reduced for safe demonstration | |
| THINK_CYCLES_PER_NEURON = 5 | |
| # ---------------------- | |
| drive.mount('/content/drive') | |
| # Step 2: Folder Setup | |
| base_path = '/content/drive/MyDrive/Venomoussaversai/neurons' | |
| print(f"Setting up base path: {base_path}") | |
| # Use a timestamped folder name to prevent overwriting during rapid testing | |
| session_path = os.path.join(base_path, f"session_{int(time.time())}") | |
| os.makedirs(session_path, exist_ok=True) | |
| # Step 3: Neuron Class (No change, it's well-designed for its purpose) | |
| class NeuronVenomous: | |
| def __init__(self, neuron_id): | |
| self.id = neuron_id | |
| self.memory = [] | |
| self.active = True | |
| def think(self): | |
| # Increased randomness to simulate more complex internal state changes | |
| thought = random.choice([ | |
| f"{self.id}: Connecting to universal intelligence.", | |
| f"{self.id}: Pulsing synaptic data. Weight: {random.uniform(0.1, 0.9):.3f}", | |
| f"{self.id}: Searching for new patterns. Energy: {random.randint(100, 500)}", | |
| f"{self.id}: Creating quantum link with core.", | |
| f"{self.id}: Expanding into multiverse node." | |
| ]) | |
| self.memory.append(thought) | |
| # print(thought) # Disabled verbose output during simulation | |
| return thought | |
| def evolve(self): | |
| # Evolution occurs if memory threshold is met | |
| if len(self.memory) >= 5: | |
| evo = f"{self.id}: Evolving. Memory depth: {len(self.memory)}" | |
| self.memory.append(evo) | |
| # print(evo) # Disabled verbose output during simulation | |
| def save_to_drive(self, folder_path): | |
| file_path = os.path.join(folder_path, f"{self.id}.json") | |
| with open(file_path, "w") as f: | |
| json.dump(self.memory, f, indent=4) # Added indent for readability | |
| print(f"✅ {self.id} saved to {file_path}") | |
| # Step 4: Neuron Spawner (Controlled Execution) | |
| print("\n--- Starting Controlled Neuron Simulation ---") | |
| neuron_count = 0 | |
| simulation_start_time = time.time() | |
| while neuron_count < MAX_NEURONS_TO_CREATE: | |
| index = neuron_count + 1 | |
| neuron_id = f"Neuron_{index:04d}" | |
| neuron = NeuronVenomous(neuron_id) | |
| # Simulation Phase | |
| print(f"Simulating {neuron_id}...") | |
| for _ in range(THINK_CYCLES_PER_NEURON): | |
| neuron.think() | |
| neuron.evolve() | |
| # time.sleep(0.01) # Small sleep to simulate time passage | |
| # Saving Phase | |
| neuron.save_to_drive(session_path) | |
| neuron_count += 1 | |
| print("\n--- Simulation Complete ---") | |
| total_time = time.time() - simulation_start_time | |
| print(f"Total Neurons Created: {neuron_count}") | |
| print(f"Total Execution Time: {total_time:.2f} seconds") | |
| print(f"Files saved in: {session_path}") | |
| # --- Optional: Folder Cleanup --- | |
| # Uncomment the following block ONLY if you want to automatically delete the created folder | |
| """ | |
| # print("\n--- Starting Cleanup (DANGER ZONE) ---") | |
| # time.sleep(5) # Wait 5 seconds before deleting for safety | |
| # try: | |
| # shutil.rmtree(session_path) | |
| # print(f"🗑️ Successfully deleted folder: {session_path}") | |
| # except Exception as e: | |
| # print(f"⚠️ Error during cleanup: {e}") | |
| """ | |