botbottingbot
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Modular_Intelligence

@@ -1,334 +1,136 @@
-## System-level view (60 seconds)
-This is **not** just a GPT-2 model.
-It is a small, self-contained **reasoning system** with:
-- **Modules**: task-specific lenses (Analysis Note, Strategy Memo, Document Explainer, etc.).
-- **Checkers**: second-pass reviewers that audit a module’s output.
-- **Contracts**: every module must answer in fixed sections (e.g. CONTEXT / OPTIONS / RISKS / NEXT_STEPS).
-Under the hood it still uses next-token prediction, but the **system behaviour** is:
-> free-form task → choose lens (module) → generate structured output → optional checker review.
-You can swap the engine (`gpt2`) for any stronger model. The **architecture stays the same**.
----
-library_name: transformers
-license: mit
-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.

+# 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)
 ---
+## Problem This Solves
+Standard LLMs: unstructured text, drift, no audit trail.
+Modular Intelligence: task decomposition, structured reasoning, verification, consistency, interpretability.
 ---
+## Modules (Lenses)
+| 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 |
+---
+## Checker Layer
+Checkers produce:
+- VERDICT
+- ALIGNMENT
+- STRUCTURE
+- GAPS
+- FIXES
+Implements: **Reason → Critique → Refine**.
 ---
+## End-to-End Example
+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
 ---
+## Auto-Routing (Optional)
+The provided `app.py` includes an **Auto-Route** tab:
+1. Paste free-form task
+2. Zero-shot classifier ranks modules
+3. Jump directly to best lens
 ---
+## Code Usage
+```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.
+OUTPUT SECTIONS:
+- CONTEXT
+- OBJECTIVE
+- CONSTRAINTS
+- OPTIONS
+- RECOMMENDATION
+- RISKS
+- NEXT_ACTIONS
+"""
+inp = tok(prompt, return_tensors="pt")
+out = model.generate(**inp, max_new_tokens=250)
+print(tok.decode(out[0], skip_special_tokens=