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README.md

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-title: README
+title: Complexity Deep
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-Edit this `README.md` markdown file to author your organization card.
+# Complexity Deep
+
+**Next-generation LLM architecture with INL Dynamics and Token-Routed MLP**
+
+## What is Complexity Deep?
+
+Complexity Deep is a novel transformer architecture designed for **stability** and **efficiency**. It combines:
+
+- **INL Dynamics** - Robotics-grade control system for training stability
+- **Token-Routed MLP** - Deterministic MoE without routing overhead
+- **GQA (Grouped Query Attention)** - 4x faster inference, 4x smaller KV cache
+- **QK Norm** - Attention stability for deep models
+
+## Key Innovation: INL Dynamics
+
+INL (Inertial Navigation Layer) Dynamics brings robotics control theory to LLM training:
+
+```
+Standard Transformer:  hidden → LayerNorm → Attention → MLP → output
+                       (can diverge on bad data)
+
+Complexity Deep:       hidden → INL Controller → Attention → MLP → output
+                       (self-stabilizing, recovers from spikes)
+```
+
+**Real-world proof**: Our 150M model survived a loss spike of **4000x** and auto-recovered in 45 minutes without any intervention.
+
+## Token-Routed MLP
+
+Unlike learned MoE (Mixtral, etc.), Token-Routed MLP routes by token ID:
+
+| Aspect | Learned MoE | Token-Routed (Ours) |
+|--------|-------------|---------------------|
+| Routing | Neural network | `token_id % num_experts` |
+| Latency | 5-10ms | **<0.1ms** |
+| Deterministic | No | **Yes** |
+| Load balancing needed | Yes | **No** |
+
+**Why it works**: BPE tokenizers sort by frequency. Token ID = frequency category = natural expert specialization.
+
+## Models
+
+| Model | Params | Status | Link |
+|-------|--------|--------|------|
+| pacific-prime | 150M | Training (100K+ steps) | [HuggingFace](https://huggingface.co/Pacific-Prime/pacific-prime) |
+| complexity-tiny | 15M | Available | [HuggingFace](https://huggingface.co/Pacific-Prime/complexity-tiny) |
+
+## Installation
+
+```bash
+pip install complexity-deep
+```
+
+## Quick Start
+
+```python
+from complexity_deep import DeepConfig, DeepForCausalLM, create_deep_model
+
+# Create a model
+model = create_deep_model(size="tiny", vocab_size=100000)
+
+# Or use presets
+config = DeepConfig.complexity_150m()  # 150M params
+config = DeepConfig.complexity_3_8b()  # 3.8B params
+config = DeepConfig.complexity_7b()    # 7B params
+```
+
+## Architecture Comparison
+
+| Feature | LLaMA | Mistral | Complexity Deep |
+|---------|-------|---------|-----------------|
+| Attention | GQA | GQA + Sliding | GQA + QK Norm |
+| MLP | Dense | MoE (learned) | Token-Routed MoE |
+| Stability | Gradient clip | Gradient clip | **INL Dynamics** |
+| Recovery from spike | Manual rollback | Manual rollback | **Auto-recovery** |
+
+## Training Stability Demo
+
+**Real training run - Loss spike of 4000x with auto-recovery:**
+
+![INL Dynamics Recovery](https://cdn-uploads.huggingface.co/production/uploads/643222d9f76c34519e96a299/8j1GHX24MV3-sv-4zl7ZB.png)
+
+```
+Loss during training with bad batch:
+
+Standard:     5.6 → 4000 → NaN → DEAD
+Complexity:   5.6 → 4000 → 46 → 16 → 8 → 5.6 (auto-recovered!)
+```
+
+The spike visible in the graph shows INL Dynamics absorbing a corrupted batch from FineWeb-Edu and automatically recovering without any manual intervention.
+
+## Available Configurations
+
+```python
+# Small models (for testing)
+DeepConfig.complexity_tiny()   # ~15M
+DeepConfig.complexity_20m()    # ~20M
+DeepConfig.complexity_small()  # ~50M
+
+# Medium models
+DeepConfig.complexity_150m()   # ~150M (default)
+DeepConfig.complexity_base()   # ~125M
+DeepConfig.complexity_medium() # ~350M
+
+# Large models
+DeepConfig.complexity_1b()     # ~1B
+DeepConfig.complexity_3b()     # ~3B
+DeepConfig.complexity_3_8b()   # ~3.8B
+DeepConfig.complexity_7b()     # ~7B
+```
+
+## INL Dynamics Parameters
+
+```python
+config = DeepConfig(
+    dynamics_alpha=0.9,    # Inertia (momentum)
+    dynamics_beta=0.1,     # Correction strength
+    dynamics_gate=0.5,     # Amplitude control
+    dynamics_dt=0.1,       # Integration timestep
+)
+```
+
+## Use Cases
+
+### 1. Training on Noisy Data
+INL Dynamics absorbs bad batches without killing your training run.
+
+### 2. Budget-Constrained Training
+No need for expensive rollbacks - the model self-heals.
+
+### 3. Robotics Applications
+Deterministic Token-Routed MLP = predictable, certifiable behavior.
+
+### 4. Edge Deployment
+GQA + Token-Routed = fast inference with small KV cache.
+
+## Research
+
+Complexity Deep introduces two novel concepts:
+
+1. **INL Dynamics**: First application of robotics control theory (PID-like) to transformer hidden states for training stability.
+
+2. **Deterministic Token-Routed MoE**: First MoE that routes by token ID instead of learned routing, leveraging BPE frequency ordering.
+
+## Links
+
+- [PyPI Package](https://pypi.org/project/complexity-deep/)
+- [GitHub](https://github.com/Web3-League/complexity-deep)
+- [Pacific-Prime Organization](https://huggingface.co/Pacific-Prime)
+
+## License
+
+CC-BY-4.0
+
+## Citation
+
+```bibtex
+@software{complexity_deep_2024,
+  title={Complexity Deep: INL Dynamics and Token-Routed MLP for Stable LLM Training},
+  author={Pacific Prime},
+  year={2024},
+  url={https://huggingface.co/Pacific-Prime}
+}
+```
+
+---
+
+**Built with stability in mind. Train with confidence.**