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metadata 
library_name: transformers
license: mit
base_model: openai-community/gpt2
language:
  - en
tags:
  - modular-intelligence
  - text-generation
  - structured-reasoning
  - experimental

Modular Intelligence (GPT-2 baseline)

This repository is an experimental baseline for Modular Intelligence built on top of openai-community/gpt2.

The goal is not to claim that GPT-2 is “intelligent”, but to show how a small, simple model can be wrapped inside a modular reasoning architecture:

  • Modules: small, single-purpose “skills” (e.g. analysis note, strategy memo).
  • Checkers: strict reviewers that check the output of a module.
  • Structured outputs: fixed sections like CONTEXT / OPTIONS / RISKS / NEXT STEPS.

Later, this same architecture can be reused with much stronger models.

What this model is

  • A GPT-2 checkpoint configured as the engine behind a Modular Intelligence framework.
  • It is not heavily fine-tuned; it is used mainly to demonstrate:
    • Structured prompts
    • Module definitions
    • Checker patterns
    • Deterministic, repeatable formats

Think of this repo as:

“The engine inside a modular reasoning system, using GPT-2 for a minimal, low-cost demo.”

What’s different from base GPT-2?

Base GPT-2 is a generic text generator.

Here, GPT-2 is wrapped in a specific contract:

  1. Fixed module types
    For example:

    • analysis_note_v1
    • document_explainer_v1
    • strategy_memo_v1
    • message_reply_v1
    • profile_application_v1
    • system_blueprint_v1
    • modular_brainstorm_v1

  2. Fixed output sections
    Each module must respond in a strict, labelled format. Example (Strategy Memo):

    • CONTEXT
    • OBJECTIVE
    • CONSTRAINTS
    • OPTIONS
    • RECOMMENDATION
    • RISKS
    • NEXT_ACTIONS

  3. Paired checkers
    Certain modules have a checker module that:

    • Re-reads the original task
    • Reviews the draft output
    • Returns a verdict + issues + suggested fixes

  4. Use pattern
    Instead of “just generating text”, you:

    • Call a module with structured inputs
    • Get a structured output
    • Optionally call a checker on that output

So the “intelligence” here is in the architecture and prompts, not in new weights.

Dataset

This repository does not introduce a new training dataset and does not re-train GPT-2.

  • Base model: openai-community/gpt2
  • Training objective: next-token prediction (causal language modeling)
  • Original GPT-2 pretraining data (by OpenAI, not included here):
    • Large, general-domain English web corpus (“WebText”)
    • ~40 GB of text from web pages linked from Reddit posts with score ≥ 3
    • Mixed content (news, blogs, forums, technical/non-technical)

In this repository:

  • GPT-2 is used as-is as the language engine.
  • The Modular Intelligence behaviour comes from:
    • The module prompts (how we talk to the model)
    • The checker prompts (how we review its answers)
    • The fixed output formats

No new datasets are uploaded or used for further fine-tuning inside this repo.

Modular Intelligence: modules and checkers (simple view)

Generator modules

Each generator is a “skill” with a fixed format.

  1. Analysis Note (analysis_note_v1)

    • Inputs:

      • context – short description of the situation or text
      • questions – what you want to understand
      • constraints – any limits (time, style, scope)

    • Outputs (sections):

      • CONTEXT
      • QUESTIONS
      • FRAMEWORK
      • ANALYSIS
      • CONCLUSION
      • NEXT_STEPS

  2. Document Explainer (document_explainer_v1)

    • Inputs:

      • document_text
      • focus
      • audience

    • Outputs:

      • SNAPSHOT
      • KEY_POINTS
      • STRUCTURE
      • DETAILED_EXPLANATION
      • IMPLICATIONS
      • ACTIONS

  3. Strategy Memo (strategy_memo_v1)

    • Inputs:

      • context
      • objective
      • constraints

    • Outputs:

      • CONTEXT
      • OBJECTIVE
      • CONSTRAINTS
      • OPTIONS
      • RECOMMENDATION
      • RISKS
      • NEXT_ACTIONS

  4. Message / Post Reply (message_reply_v1)

    • Inputs:

      • source_text
      • your_angle
      • tone_notes

    • Outputs:

      • DRAFT_REPLY

  5. Profile / Application Draft (profile_application_v1)

    • Inputs:

      • target_role_or_goal
      • your_background
      • audience

    • Outputs:

      • POSITIONING
      • KEY_POINTS
      • FULL_DRAFT

  6. System / Architecture Blueprint (system_blueprint_v1)

    • Inputs:

      • objective
      • current_state
      • constraints

    • Outputs:

      • OBJECTIVE
      • CURRENT_STATE
      • COMPONENTS
      • FLOWS
      • RISKS
      • IMPROVEMENTS
      • NEXT_STEPS

  7. Modular Brainstorm (modular_brainstorm_v1)

    • Inputs:

      • problem_or_domain
      • goal

    • Outputs:

      • OBJECTIVE
      • CURRENT
      • MODULES
      • CHECKERS
      • DATA_NEEDS
      • NEXT_STEPS

Checker modules

Checkers are “reviewers” that inspect generated outputs.

Examples:

  1. Analysis Note Checker (analysis_note_checker_v1)

    • Inputs:

      • original_task
      • draft_output

    • Outputs:

      • VERDICT
      • STRUCTURE
      • CLARITY
      • ALIGNMENT
      • GAPS
      • FIXES

  2. Document Explainer Checker (document_explainer_checker_v1)

    • VERDICT
    • ACCURACY
    • STRUCTURE
    • AUDIENCE_FIT
    • MISSING
    • FIXES

  3. Strategy Memo Checker (strategy_memo_checker_v1)

    • VERDICT
    • OBJECTIVE_ALIGNMENT
    • CONSTRAINT_HANDLING
    • OPTION_QUALITY
    • RISKS
    • FIXES

  4. Style & Voice Checker (style_voice_checker_v1)

    • VERDICT
    • STYLE_MATCH
    • TONE
    • REDUNDANCY
    • SUGGESTIONS

  5. Profile Checker (profile_checker_v1)

    • VERDICT
    • ALIGNMENT
    • SIGNAL
    • CLARITY
    • FIXES

  6. System Checker (system_blueprint_checker_v1)

    • VERDICT
    • COHERENCE
    • GAPS
    • FLOW_ISSUES
    • RISKS
    • FIXES

How to use this model (simple)

You can treat this model like any GPT-2 text generator, but if you want Modular Intelligence behaviour:

  1. Pick a module (e.g. strategy_memo_v1).

  2. Build a prompt that:

    • States the module name
    • Lists the inputs clearly
    • Lists the required output sections

  3. Ask the model to fill in each section in order.

  4. Optionally call the corresponding checker with:

    • Original task
    • Draft output

A reference implementation and UI are provided in the accompanying Hugging Face Space (if linked), but the pattern can be re-implemented in any environment.

Limitations

  • GPT-2 is small and outdated by modern standards.
  • It will:
    • Hallucinate
    • Get facts wrong
    • Sometimes ignore structure
    • Struggle with long contexts

The goal is to demonstrate the architecture, not to claim state-of-the-art performance.

Do not use this model for high-stakes decisions or any application where mistakes could cause real harm.

License and IP

  • Code and configuration: MIT License.
  • The Modular Intelligence architecture, module definitions, and checker patterns are a conceptual layer that can be reused and extended, but the name and approach may be treated as separate intellectual property by the author.

Always review the base model’s license (openai-community/gpt2) for any additional constraints.