---
license: mit
language: en
library_name: transformers
tags:
- modular-intelligence
- structured-reasoning
- modular-system
- system-level-ai
- gpt2
- reasoning-scaffolds
- auto-routing
- gradio
pipeline_tag: text-generation
base_model: openai-community/gpt2
model_type: gpt2
datasets: []
widget:
- text: "Write a strategy memo: Should we expand into a new city?"
---
# Modular Intelligence Demo — Model Card

## Overview

This Space demonstrates a **Modular Intelligence** architecture built on top of a small, open text-generation model (default: `gpt2` from Hugging Face Transformers).

The focus is on:

- Structured, modular reasoning patterns
- Separation of **generators** (modules) and **checkers** (verifiers)
- Deterministic output formats
- Domain-agnostic usage

The underlying model is intentionally small and generic so the architecture can run on free CPU tiers and be easily swapped for stronger models.

---

## Model Details

### Base Model

- **Name:** `gpt2`
- **Type:** Causal language model (decoder-only Transformer)
- **Provider:** Hugging Face (OpenAI GPT-2 weights via HF Hub)
- **Task:** Text generation

### Intended Use in This Space

The model is used as a **generic language engine** behind:

- Generator modules:  
  - Analysis Note  
  - Document Explainer  
  - Strategy Memo  
  - Message/Post Reply  
  - Profile/Application Draft  
  - System/Architecture Blueprint  
  - Modular Brainstorm  

- Checker modules:  
  - Analysis Note Checker  
  - Document Explainer Checker  
  - Strategy Memo Checker  
  - Style & Voice Checker  
  - Profile Checker  
  - System Checker  

The intelligence comes from the **module specifications and checker prompts**, not from the raw model alone.

---

## Intended Use Cases

This demo is intended for:

- Exploring **Modular Intelligence** as an architecture:
  - Module contracts (inputs → structured outputs)
  - Paired checkers for verification
  - Stable output formats
- Educational and experimental use:
  - Showing how to structure reasoning tasks
  - Demonstrating generators vs checkers
  - Prototyping new modules for any domain

It is **not** intended as a production-grade reasoning system in its current form.

---

## Out-of-Scope / Misuse

This setup and base model **should not** be relied on for:

- High-stakes decisions (law, medicine, finance, safety)
- Factual claims where accuracy is critical
- Personal advice with real-world consequences
- Any use requiring guarantees of truth, completeness, or legal/compliance correctness

All outputs must be **reviewed by a human** before use.

---

## Limitations

### Model-Level Limitations

- `gpt2` is:
  - Small by modern standards
  - Trained on older, general web data
  - Not tuned for instruction-following
  - Not tuned for safety or domain-specific reasoning

Expect:

- Hallucinations / fabricated details
- Incomplete or shallow analysis
- Inconsistent adherence to strict formats
- Limited context length

### Architecture-Level Limitations

Even with Modular Intelligence patterns:

- Checkers are still language-model-based
- Verification is heuristic, not formal proof
- Complex domains require domain experts to design the modules/checkers
- This Space does not store memory, logs, or regression tests

---

## Ethical and Safety Considerations

- Do not treat outputs as professional advice.  
- Do not use for:
  - Discriminatory or harmful content
  - Harassment
  - Misinformation campaigns
- Make sure users know:
  - This is an **architecture demo**, not a final product.
  - All content is generated by a language model and may be wrong.

If you adapt this to high-stakes domains, you must:

- Swap in stronger, more aligned models
- Add strict validation layers
- Add logging, monitoring, and human review
- Perform domain-specific evaluations and audits

---

## How to Swap Models

You can replace `gpt2` with any compatible text-generation model:

1. Edit `app.py`:

   ```python
   from transformers import pipeline

   llm = pipeline("text-generation", model="gpt2", max_new_tokens=512)