Update README.md

README.md

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----
-license: mit
----
+---
+language:
+- en
+- es
+tags:
+- pytorch
+- custom-code
+- text-generation
+- conversational
+- moire-attention
+- biological-ai
+license: mit
+---
+
+# MoireFormer (104.9M Proof-of-Concept)
+
+This repository hosts the PyTorch weights (`moire_phase2_weights_final.pt`) for **MoireFormer**,
+a fundamentally new neural network architecture that replaces standard scalar dot-product attention with **Moiré phase-interference wave mechanics**.
+
+Instead of computing attention via `Q · K^T`, this model splits token embeddings into amplitude and phase
+(`q_amp`, `q_phase`) and computes attention through geometric wave resonance (`q_real * k_real + q_imag * k_imag`). 
+This proves that artificial intelligence can be trained using the continuous, biological wave-geometry observed
+in human EEGs.
+
+🔗 **GitHub Repository (Code & Inference):** [anttiluode/MoireFormer](https://github.com/anttiluode/MoireFormer)
+🔗 **Theory & Clinical Proof:** [anttiluode/Geometric-Neuron](https://github.com/anttiluode/Geometric-Neuron)
+
+## Model Details
+* **Architecture:** MoireGPT (Custom Transformer Bolt-on)
+* **Size:** 104.9M Parameters
+* **Structure:** 8 Layers, 8 Heads, 768 Embedding Dimension
+* **Capabilities:** Coherent bilingual (English/Spanish) grammar, persona adoption (Assistant), structural instruction following.
+* **Disclaimer:** At ~100M parameters, this is a proof-of-substrate, not a knowledge oracle. It demonstrates that wave
+fields can learn discrete human syntax, but it will hallucinate factual data due to its small parameter count.
+
+## ⚠️ How to Use (Read Before Downloading)
+Because this is a novel mathematical architecture, **you cannot load this model using the standard Hugging Face `AutoModel` pipeline.**
+
+To run inference, you must download these weights and run them through the custom Moiré architecture provided in the
+GitHub repository.
+
+### Step-by-Step Instructions:
+
+**1. Clone the GitHub Repository:**
+```bash
+git clone [https://github.com/anttiluode/MoireFormer.git](https://github.com/anttiluode/MoireFormer.git)
+cd MoireFormer
+2. Download the Weights:
+Download moire_phase2_weights_final.pt from the Files and versions tab of this Hugging Face repository and place
+it in your cloned MoireFormer folder.
+
+3. Run the Chat Interface:
+
+Bash
+pip install torch transformers datasets
+python moire_chat.py --weights moire_phase2_weights_final.pt --size large
+Training Curriculum
+The model was trained in two continuous phases to demonstrate that wave-fields avoid catastrophic forgetting via
+phase-locking (destructive and constructive interference):
+
+Phase 1 (Base Geometry): 15 Epochs on a mixed dataset of Databricks Dolly-15k, WikiText-2, and OpenAssistant.
+This established the foundational phase-space for English and conversational structure.
+
+Phase 2 (Phase-Space Expansion): 5 Epochs finetuning on the Guanaco dataset to refine logical geometry
+and instruction-following, organically expanding the model's topological complexity without overwriting previous data.