README.md

---
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.