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README.md

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 ---
+library_name: transformers
 license: mit
-base_model:
-- openai-community/gpt2-large
-- openai-community/gpt2
----
+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.