| --- |
| license: mit |
| tags: |
| - modular-intelligence |
| - reasoning |
| - structure |
| - transformers |
| - experimental |
| base_model: openai-community/gpt2 |
| pipeline_tag: text-generation |
| language: en |
| --- |
| |
| # Modular Intelligence |
|
|
| Modular Intelligence is a lightweight reasoning framework built on top of a language model. |
| It provides **Modules** (task-specific lenses), **Checkers** (second-pass reviewers), **Contracts** (structured output sections), and optional **Routing** (automatic module selection). |
|
|
| The base model is GPT-2, but the architecture is model-agnostic—any LLM can be plugged in. |
|
|
| --- |
|
|
| ## Features |
|
|
| ### Modules |
| Task-specific reasoning modes. |
| Examples: |
| - **Analysis Note** – explanation and breakdown of concepts |
| - **Document Explainer** – summaries of contracts, policies, articles |
| - **Strategy Memo** – Options → Recommendation → Risks → Next Steps |
| - **System Blueprint** – workflow / system design |
| - **Brainstorm** – structured idea generation |
| - **Message Reply** – concise responses for emails, posts, chats |
|
|
| ### Checkers |
| A second pass that evaluates: |
| - correctness |
| - clarity |
| - missing pieces |
| - contradictions |
|
|
| ### Contracts |
| Every module produces a fixed output template. |
| This ensures reproducible structure and reduces variance. |
|
|
| ### Router |
| Optional automatic module selection based on prompt classification. |
|
|
| --- |
|
|
| ## Usage |
|
|
| ### Python |
|
|
| ```python |
| from app import run_module |
| |
| result = run_module( |
| module="StrategyMemo", |
| prompt="Should we expand operations to Region X next quarter?" |
| ) |
| |
| print(result) |
