Update README.md

README.md

@@ -1,146 +1,63 @@
 ---
 license: mit
-language:
-- en
-pipeline_tag: text-generation
-library_name: transformers
 tags:
-- Modular
-- Intelligence
----
-# Modular Intelligence  
-A Small but Complete Reasoning System Built on Top of a Language Model
-
-## System-Level View (60 Seconds)
-
-This repository is not a standard GPT-2 model.  
-It is a compact reasoning system built on top of an LLM, using:
-
-- Modules – structured reasoning lenses  
-- Checkers – strict second-pass reviewers  
-- Contracts – fixed output sections for every module  
-- Optional Router – automatic module selection from free-form tasks  
-
-Pipeline:
-
-**Task → Lens (Module) → Structured Output → Checker (Optional)**
-
-The LLM engine is replaceable; the architecture is the system.
-
----
-
-## Architecture
-
-+—————————————————————+
-|                       MODULAR INTELLIGENCE                    |
-+—————————————————————+
-|   User Task                                                   |
-|        |                                                      |
-|        v                                                      |
-|   [Auto-router or manual module selection]                    |
-|        |                                                      |
-|        v                                                      |
-|   +———————–+       +————————+  |
-|   |    GENERATOR MODULE   | ––> |   CHECKER MODULE       |  |
-|   |  (structured output)  |       | (optional verification)|  |
-|   +———————–+       +————————+  |
-|                 |                            ^               |
-|                 v                            |               |
-|         Structured Output           Feedback / Fix Ideas      |
-+—————————————————————+
-
----
-
-## Repository Contents
-
-- `modules.json` — all modules, inputs, sections, checkers  
-- `app.py` — Gradio UI with optional auto-routing  
-- Model card  
-- GPT-2 base model (fully swappable)
-
+  - modular-intelligence
+  - reasoning
+  - structure
+  - transformers
+  - experimental
+base_model: openai-community/gpt2
+pipeline_tag: text-generation
+language: en
 ---
 
-## Problem This Solves
-
-Standard LLMs: unstructured text, drift, no audit trail.  
-Modular Intelligence: task decomposition, structured reasoning, verification, consistency, interpretability.
-
----
+# Modular Intelligence
 
-## Modules (Lenses)
+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).
 
-| Module | Use |
-|--------|-----|
-| Analysis Note | Explain or break down text/situations |
-| Document Explainer | Summaries of contracts/policies/articles |
-| Strategy Memo | Options → Recommendation → Risks → Next Steps |
-| Message/Post Reply | Structured replies |
-| Profile/Application | Bios, cover letters, statements |
-| System Blueprint | Design or improve systems and workflows |
-| Modular Brainstorm | Decompose problems into modules/checkers |
+The base model is GPT-2, but the architecture is model-agnostic—any LLM can be plugged in.
 
 ---
 
-## Checker Layer
-
-Checkers produce:
+## Features
 
-- VERDICT  
-- ALIGNMENT  
-- STRUCTURE  
-- GAPS  
-- FIXES  
+### 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  
 
-Implements: **Reason → Critique → Refine**.
-
----
+### Checkers
+A second pass that evaluates:
+- correctness  
+- clarity  
+- missing pieces  
+- contradictions  
 
-## End-to-End Example
+### Contracts
+Every module produces a fixed output template.  
+This ensures reproducible structure and reduces variance.
 
-Task: “Should we expand into a new city in 6 months?”
-
-1. Select `strategy_memo_v1`  
-2. Fill: context, objective, constraints  
-3. Run → structured memo with required sections  
-4. Run checker → verdict + issues + fixes  
+### Router
+Optional automatic module selection based on prompt classification.
 
 ---
 
-## Auto-Routing (Optional)
-
-The provided `app.py` includes an **Auto-Route** tab:
+## Usage
 
-1. Paste free-form task  
-2. Zero-shot classifier ranks modules  
-3. Jump directly to best lens
-
----
-
-## Code Usage
+### Python
 
 ```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tok = AutoTokenizer.from_pretrained("botbottingbot/Modular_Intelligence")
-model = AutoModelForCausalLM.from_pretrained("botbottingbot/Modular_Intelligence")
-
-prompt = """
-MODULE: Strategy Memo
-INPUTS:
-CONTEXT: We are expanding to City X.
-OBJECTIVE: Decide whether to enter within 6 months.
-CONSTRAINTS: Budget limits; regulatory uncertainty.
+from app import run_module
 
-OUTPUT SECTIONS:
-- CONTEXT
-- OBJECTIVE
-- CONSTRAINTS
-- OPTIONS
-- RECOMMENDATION
-- RISKS
-- NEXT_ACTIONS
-"""
+result = run_module(
+    module="StrategyMemo",
+    prompt="Should we expand operations to Region X next quarter?"
+)
 
-inp = tok(prompt, return_tensors="pt")
-out = model.generate(**inp, max_new_tokens=250)
-print(tok.decode(out[0], skip_special_tokens=
+print(result)