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+# 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)