LLM Supercharger! Introducing Recursive Unified Validators (rUv MoE Toolkit)

What's the future of software look like?

Introducing rUv MoE Toolkit: Powering software with self-learning & auto-enhancement. Supercharging older models, drastically boosting Ai performance and significantly reducing costs.

Imagine if you could give super powers to older, less capable Ai models. The difference in performance, cost and capabilities is drastically different between the newest models and smaller or older models.

What if there was a way to automatically & easily tune older, cheaper, less capable models to greatly improve them?

My approach uses what I'm calling Recursive Unified Validators (rUv). It's an AI optimization framework that leverages a Mixture of Experts (MoE) with a self-optimization & training methodology. It reimagines AI optimization by combining reinforcement learning, self-optimization/hyper-tuning, and an autonomous self evolving architecture.

Built using DSPy, rUv allows for seamless integration of expert modules and facilitates the creation of powerful autonomous AI systems.

Core Benefits

• It evolves as it learns, auto-optimizing itself: Using an internal teleprompter, it can create its own internal prompts on the fly, learning new things based any information / data / requests made to it.
• Efficiency through Resource Optimization: rUv optimizes computational resources by dynamically selecting the most relevant & intelligent expert models for each task.
• Hyper-Tuning: Each model is hyper optimized for a specific topic or domain using a automatic fine tuning based on internal reward system (reinforcement learning with human feedback).
• Accuracy via Tailored Outputs: The framework generates tailored outputs by leveraging the specialized knowledge of multiple expert models. Automatically selects the best expert by testing for the best results.
• Flexibility with Versatile Application: rUv can be applied to a wide range of domains and tasks, making it a versatile tool for various AI applications.
• Automation: rUv is great for automating various actions or task that require the application to learn and adjust. Think self driving software.
• Insight Generation through Continuous Learning: The self-learning capabilities of rUv enable it to continuously generate valuable insights and improve its performance over time.

Novel Features

• Reinforcement Learning and Self-Optimization & Self-Learning capabilities: rUv continuously learns and improves its performance through reinforcement learning techniques.
• Self-Optimizing Architecture: The framework dynamically adjusts its architecture to adapt to different tasks and optimize its performance.
• Dynamic Expert Model Selection Mixture of Experts (MoE) approach: rUv employs a MoE approach, where multiple expert models are trained to specialize in different domains or tasks.
• Context-aware selection: The gating model dynamically selects the most relevant expert model based on the input context.
• Enhanced Performance through Hyper-Tuning rUv allows for fine-grained control over various hyperparameters, enabling users to tune the system for optimal performance based on their specific requirements.
• Adaptable Architecture for Output Generation The framework generates comprehensive outputs by combining the knowledge and capabilities of multiple expert models, resulting in more accurate and diverse results.
• Auto Completion of Content or Code: The rUv MoE Toolkit supports output continuation to generate more comprehensive responses. It recursively prompts the expert models to extend their outputs until a satisfactory level of completeness is achieved, as determined by checking for proper conclusion markers like context, grammar or code syntax, periods, exclamation points, or question marks at the end of the generated text.

Use Cases

rUv finds applications in various domains, including:

• Business Analysis: Analyzing market trends, customer behavior, and competitive landscapes to support data-driven decision-making.
• Code Development: Assisting in code generation, optimization, and debugging for software development projects.
• Creative Writing: Generating creative content, such as stories, articles, and scripts, based on user prompts and guidelines.
• Academic Research: Supporting research activities by providing insights, generating hypotheses, and assisting in data analysis.

Technical Configuration Overview

rUv allows users to configure various technical parameters to customize the system's behavior and performance.

Some key configuration options include:

?? Number of Expert Models

• Purpose: Determines the range and specialization of the expert models within the system.
• Impact: More experts increase topic coverage but require additional computational resources.
• Configurable Range: Typically between 3 to 12, with higher values offering greater diversity and specialization.

?? Minimum Number of Iterations

• Purpose: Ensures a meaningful exploration and refinement process by running the system for a sufficient number of iterations.
• Impact: Higher iteration counts allow for more thorough output refinement and system adaptation.
• Configurable Range: Common settings range from 3 for quick tasks to 15 for in-depth refinement.

?? Learning Rate

• Purpose: Adjusts the speed at which the system adapts by controlling the step size of expert value updates.
• Impact: Balances between fast adaptation and stability. Higher rates increase speed but may lead to instability.
• Configurable Range: Varied from 0.05 for slow, stable learning to 0.5 for rapid adaptation.

?? Discount Factor

• Purpose: Weighs the importance of future rewards in the system's decision-making process.
• Impact: Higher factors prioritize long-term success, while lower factors focus on immediate outcomes.
• Configurable Range: From 0.8, emphasizing short-term gains, to 0.99, focusing on long-term rewards.

?? Exploration Rate

• Purpose: Manages the exploration-exploitation trade-off by varying the system's willingness to try different experts.
• Impact: Higher exploration rates foster diversity and adaptability, whereas lower rates optimize for current knowledge.
• Configurable Range: Ranges from 0.05 for minimal exploration to 0.5 for aggressive exploration of new strategies.

You can edit all these values further, I set these as the ranges since they appear to work the best.

Getting Started

Visit the Google Colab: https://colab.research.google.com/gist/ruvnet/6fde27b943cb539f6001201a6a5240bf/ruv-final.ipynb

To get started with rUv, follow these steps:

1. Set up the Language Model (LM) and Retrieval Model (RM) by configuring the appropriate APIs and credentials.
2. Configure the OpenAI API key or use an alternative language model provider. Make sure to add it the Google Colab secrets area.
3. Click ??to install the required dependencies, such as DSPy and OpenAI, using the provided installation commands.
4. Go one by the ?? next to each code block.

Configuring Experts

rUv allows you to configure the expert models based on your specific requirements. The key configuration options for experts include:

• Number of Expert Models: Specify the desired number of expert models to be used by rUv.

• Minimum Number of Iterations: Set the minimum number of iterations the system should run to generate meaningful outputs.

• Learning Rate: Adjust the learning rate to control the step size of the value updates during the learning process.

• Discount Factor: Determine the importance of future rewards in the reinforcement learning algorithm.

• Exploration Rate: Balance the trade-off between exploiting the current best expert and exploring potentially better experts.

Prompts

rUv provides a user-friendly interface for generating prompts and guiding the system's output generation. You edit any of my examples.

The interface includes:

• Dropdown for selecting predefined templates: Choose from a range of predefined templates for common tasks, such as business analysis, application planning, source code generation, SQL generation, story creation, and TV/movie scripts.
• Customizing Context, Prompt, and Guidance: Tailor the input context, prompt, and guidance to align with your specific requirements and desired output.
• Text Max Tokens set the lenght of each output when generating it's thought processes. Smaller is faster, larger is better for more verbose outputs like code, long form books etc.

Using the rUv UI

Click ?? Recursive Unified Validators (rUv) MoE Toolkit Source Code

So What's happening?

The expert reward is an external evaluation of the quality and relevance of the output generated by the selected expert model during each iteration of the Recursive Unified Validators (rUv) process.

The reward value is a numeric score that represents the level of satisfaction or effectiveness of the output. It's important to note that the expert reward is specific to each iteration and expert model.

It allows for fine-grained feedback and adaptation, enabling the system to continuously improve its performance and generate more relevant and coherent outputs over time.

It plays a crucial role in guiding the learning and adaptation of the expert models over time. Here's how the expert reward affects the output:

1. Feedback mechanism: The expert reward serves as a feedback signal that indicates how well the selected expert model performed in generating a relevant and high-quality output for the given context and prompt. It allows the system to assess the effectiveness of each expert model based on external evaluation.
2. Updating expert values: The expert reward is used to update the value estimate of the selected expert model. The update_expert_values method in the MixtureOfExperts class adjusts the value of the selected expert based on the received reward, the learning rate, and the discount factor. This update helps the system learn which experts are more reliable and valuable for specific contexts over time.
3. Reinforcement learning: The expert reward is combined with the intrinsic reward (generated by the IntrinsicRewardModel) to calculate the total reward for each iteration. This total reward is used to guide the reinforcement learning process, where the system learns to select the most appropriate expert models based on their historical performance and the current context.
4. Balancing exploration and exploitation: The expert reward influences the balance between exploration and exploitation in the expert selection process. If an expert consistently receives high rewards, it is more likely to be selected in future iterations (exploitation). However, the system also maintains an exploration rate to occasionally select random experts and explore potentially better options (exploration).
5. Termination condition: The expert reward contributes to the total reward, which is used to check the termination condition for the rUv process. If the total reward exceeds a certain threshold and the minimum number of iterations is reached, the process may terminate early, indicating that a satisfactory output has been generated.

By providing an external evaluation of the generated outputs, the expert reward helps the rUv system learn and adapt over time. It guides the selection and improvement of expert models, ensuring that the most relevant and high-quality outputs are generated for the given context and prompt.

The expert reward is typically provided by a human evaluator or a separate evaluation model that assesses the quality and relevance of the generated outputs.

rUv MoE Toolkit Source Code Walkthrough

The provided source code demonstrates the implementation of the rUv MoE Toolkit using the DSPy framework.

Let's walk through the key components:

Initializing DSPy: The code initializes the DSPy framework and sets up the necessary models, such as the OpenAI language model and the ColBERTv2 retrieval model.

• Configuring Logging: The logging module is configured to capture relevant information during the execution of the code.
• Defining Signatures for Experts, Gating Model, and Intrinsic Reward Model: The code defines the input and output fields for the expert models, gating model, and intrinsic reward model using DSPy's Signature class.

MixtureOfExperts Class

• Initialization: The MixtureOfExperts class is initialized with default values for the number of experts, minimum iterations, learning rate, discount factor, and exploration rate.
• Expert Architecture Setup: The code initializes the architecture of each expert model randomly.
• Gating Architecture Setup: The code initializes the architecture of the gating model randomly.
• Generating Expert Outputs: The generate_expert_outputs method generates outputs from each expert model based on the given context and prompt.
• Selecting Relevant Expert: The code selects the most relevant expert model based on the input context using the gating model.
• Updating Expert Values and Architectures: The update_expert_values and update_expert_architecture methods update the value estimates and architectures of the expert models based on the received rewards and self-improvement logic.

Example Usage:

• The code demonstrates an example usage of the rUv MoE Toolkit, where the user can input the desired configuration values through widgets, and the system generates outputs based on the provided context and prompt.

Customization and Applications

Customization and Applications rUv's design emphasizes adaptability for bespoke configurations and uses across various fields. Essential aspects for effective utilization include:

• Domain-Specific Customization: Adjust rUv's expert models and datasets to meet the unique requirements of your target domain or sector.
• Integration of Bespoke Expert Models: Enhance rUv by adding your specialized expert models, tapping into unique insights and expertise specific to your field.
• Operational Deployment: When deploying rUv in live settings, prioritize considerations like system scalability, operational efficiency, and data security.

The Recursive Unified Validators (rUv) MoE Toolkit is a powerful and innovative framework for AI optimization.

By leveraging reinforcement learning, self-optimization, and a modular architecture, rUv enables the dynamic selection and integration of specialized expert models to solve complex problems.

With its novel features, core benefits, and versatile applications, rUv offers a compelling solution for businesses, researchers, and developers seeking to harness the power of AI optimization.

As rUv continues to evolve, future enhancements and extensions will further expand its capabilities and potential applications. We encourage you to explore the rUv MoE Toolkit, experiment with its features, and contribute to its development.

Start optimizing your AI systems with rUv today and unlock new possibilities in AI optimization!

Visit the Google Colab to try it.

https://colab.research.google.com/gist/ruvnet/6fde27b943cb539f6001201a6a5240bf/ruv-final.ipynb

GitHub GIST