model_card.md

metadata

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.

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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.

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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

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How to Swap Models

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

1. Edit app.py:

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