Core stucture

The structure of our model.

1. Input Orchestrator

• Purpose: Analyzes user inputs to determine the best task routing and splits complex tasks into sub-tasks for specialized models.

• Key Features:

  • Uses a pre-trained model (e.g., BERT) to classify input prompts.

  • Routes tasks to the appropriate model based on the classification (e.g., code generation or text generation).

• Example:

  import un1ty
  
  # Load pre-trained model for input classification
  tokenizer = un1ty.load_tokenizer("bert-base-uncased")
  model = un1ty.load_model("bert-base-uncased")
  
  # Analyze input prompt
  input_prompt = "Write a Python script to analyze data and summarize the results."
  inputs = tokenizer(input_prompt, return_tensors="pt")
  
  # Classify input for task routing
  outputs = model(**inputs)
  task_type = "code_generation" if outputs.logits[0][0] > 0.5 else "text_generation"
  print(f"Task Type: {task_type}")

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2. Model Integration Layer

• Purpose: Connects Hybr1d to external AI models like Shapesh1ft, gh0st, and N3O and ensures seamless communication between them.

• Key Features:

  • Optimizes API calls for speed and efficiency.

  • Routes tasks to the most suitable model (e.g., Shapesh1ft for creative writing, gh0st for summarization).

• Example:

  import un1ty
  
  # Route input to Shapesh1ft for creative writing
  def generate_with_shapesh1ft(prompt):
      response = un1ty.shapesh1ft.generate(
          prompt=prompt,
          max_tokens=500
      )
      return response.text
  
  # Route input to gh0st for summarization
  def summarize_with_gh0st(text):
      response = un1ty.gh0st.summarize(
          text=text,
          max_tokens=100
      )
      return response.summary
  
  # Example usage
  creative_text = generate_with_shapesh1ft("Write a story about a futuristic city.")
  summary = summarize_with_gh0st(creative_text)
  print(summary)

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3. Collaboration Engine

• Purpose: Combines outputs from multiple AI models into a single, cohesive result.

• Key Features:

  • Resolves conflicts between model outputs.

  • Enhances outputs for consistency and quality.

• Example:

  import un1ty
  
  # Combine outputs from Shapesh1ft and gh0st
  def combine_outputs(shapesh1ft_output, gh0st_output):
      combined_output = un1ty.collaborate(
          creative_text=shapesh1ft_output,
          summary=gh0st_output
      )
      return combined_output
  
  # Example usage
  shapesh1ft_output = un1ty.shapesh1ft.generate("Explain the concept of quantum computing.")
  gh0st_output = un1ty.gh0st.summarize(shapesh1ft_output)
  final_output = combine_outputs(shapesh1ft_output, gh0st_output)
  print(final_output)

---

4. Output Synthesizer

• Purpose: Refines and formats the final output for usability and quality.

• Key Features:

  • Adds metadata, translations, or documentation as needed.

  • Ensures outputs are polished and ready for use.

• Example:

  import un1ty
  
  # Translate output to Spanish
  def translate_to_spanish(text):
      translation = un1ty.translate(
          text=text,
          target_language="es"
      )
      return translation
  
  # Example usage
  translated_output = translate_to_spanish("This is a sample output from Hybr1d.")
  print(translated_output)

---

5. Feedback Loop

• Purpose: Collects user feedback to improve future outputs and adapts to user preferences over time.

• Key Features:

  • Ensures continuous improvement of the platform.

  • Records user ratings and adjusts outputs accordingly.

• Example:

  import un1ty
  
  # Collect user feedback and improve future outputs
  def collect_feedback(output, user_rating):
      un1ty.feedback.learn(
          output=output,
          rating=user_rating
      )
      return "Feedback recorded. Thank you!"
  
  # Example usage
  feedback = collect_feedback("Sample output from Hybr1d.", user_rating=4.5)
  print(feedback)

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6. Scalability and Modularity

• Purpose: Designed to be scalable and modular for easy integration of new AI models.

• Key Features:

  • Ensures the platform remains cutting-edge as new technologies emerge.

  • Supports future expansion and customization.

• Example:

  import un1ty
  # Add a new AI model to the framework
  def add_new_model(model_name, api_key):
      un1ty.integrate_model(
          name=model_name,
          api_key=api_key
      )
      return f"Model {model_name} integrated successfully."
  
  # Example usage
  add_new_model("N3O", "your-api-key-here")

---

7. User-Centric Design

• Purpose: Prioritizes intuitive and easy-to-use interfaces and adapts to user preferences for personalized outputs.

• Key Features:

  • Ensures a seamless and enjoyable user experience.

  • Personalizes outputs based on user preferences (e.g., tone, language).

• Example:

  import un1ty
  
  # Personalize output based on user preferences
  def personalize_output(output, user_preferences):
      personalized_output = un1ty.personalize(
          output=output,
          preferences=user_preferences
      )
      return personalized_output
  
  # Example usage
  user_preferences = {"tone": "formal", "language": "French"}
  output = "This is a sample output from Hybr1d."
  personalized_output = personalize_output(output, user_preferences)
  print(personalized_output)