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Getting Started

Welcome to the Model Train Protocol (MTP) documentation. This section will help you understand the system architecture and get started with the API.

What is MTP?​

The Model Train Protocol (MTP) is an open-source framework for creating and training custom Language Models on Databiomes. MTP provides a structured approach to defining all the data, patterns, and behaviors that your model will learn.

System Architecture​

The MTP system is built on a hierarchical structure of five main components that work together to create comprehensive training protocols for language models.

Core Components​

  1. Context - Background information and domain knowledge for the model
  2. Tokens - The fundamental building blocks
  3. TokenSets - Combinations of tokens that define input patterns
  4. Instructions - Training patterns that inform the model what to do
  5. Guardrails - Safety mechanisms for bad user prompts

Component Hierarchy​

Context​

Context provides the foundational background information and domain knowledge that the model needs to understand the training data and respond appropriately.

  • Context establishes the domain, setting, and background information
  • It helps the model understand the context in which tokens, instructions, and responses should be interpreted
  • Context is added to the protocol using the add_context() method

Tokens​

Tokens are the base building blocks of the MTP system. They represent words, symbols, concepts, or actions that the model will understand and use.

  • Basic Token: Standard tokens for concepts, actions, or entities
  • NumToken: Tokens associated with numerical values
  • NumListToken: Tokens for lists of numerical values

TokenSets​

TokenSets group multiple Tokens together to define specific input patterns. They represent the structure of data that will be fed to the model.

  • TokenSets are the basic building blocks of instructions
  • They can contain any combination of token types
  • Snippets are created on TokenSets to provide training examples

Instructions​

Instructions define how the model should respond to different input patterns. There are two main types:

  • Instruction: For scenarios where the model responds without user input
  • ExtendedInstruction: For scenarios where the model responds to user prompts with extended context

Guardrails​

Guardrails provide safety mechanisms for user interactions by defining what constitutes good vs. bad user prompts and how the model should respond to inappropriate inputs.

Data Flow​

  1. Context Establishment: Add background information and domain knowledge
  2. Token Creation: Define the basic building blocks
  3. TokenSet Assembly: Combine tokens into meaningful patterns
  4. Snippet Generation: Create training examples from TokenSets
  5. Instruction Definition: Specify how the model should respond to TokenSet patterns
  6. Guardrail Application: Add safety mechanisms

Best Practices​

  • Start with a clear understanding of your model's purpose
  • Establish comprehensive context to provide domain knowledge and background information
  • Define tokens that represent the core concepts in your domain
  • Create TokenSets that capture meaningful input patterns
  • Use instructions to teach the model appropriate responses
  • Always include guardrails for user-facing applications
  • Test your protocol with various examples before deployment

Next Steps​