Update README.md

README.md

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 - ai
 - llm
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+---
+# SymbioticLM-1B
+
+**Author**: Roy S. Colca Jr.  
+**Model Type**: Hybrid Symbolic–Transformer  
+**Base Model**: Qwen-1B  
+**License**: MIT  
+**Framework**: PyTorch + HuggingFace Transformers  
+**Purpose**: Lightweight, memory-augmented reasoning model for CPU and embedded inference
+
+---
+
+## Overview
+
+SymbioticLM-1B is the compact version of the SymbioticAI architecture. It fuses Qwen’s rotary transformer design with a symbolic processing pipeline and a persistent episodic memory. Though smaller in parameter count, it retains the full cognitive engine: symbolic memory, dynamic thought evolution, and entropy-gated control.
+
+This model is ideal for symbolic reasoning in constrained environments — like research agents, lightweight assistants, and memory-efficient logical processing.
+
+---
+
+## Architecture Highlights
+
+- **Backbone**: Qwen-1B rotary transformer
+- **Symbolic Dim**: 1024
+- **Symbolic Modules**:
+  - ThoughtDynamicsLNN
+  - CrystallineProcessor (DNAConv GNN)
+  - LiquidThoughtProcessor
+  - HelicalDNAProcessor
+- **Memory**: 2048 symbolic vectors with entropic and contextual retrieval
+- **Dream Mode**: Symbolic simulation with ThoughtGenerator
+
+---
+
+## Files Included
+
+| File                     | Description                                           |
+|--------------------------|-------------------------------------------------------|
+| `model.bin`              | PyTorch model weights                                 |
+| `model.safetensors`      | SafeTensor weights                                    |
+| `memory.pt`              | Serialized symbolic memory vectors                    |
+| `config.json`            | Model architecture config                             |
+| `generation_config.json` | Generation strategy configuration                     |
+| `tokenizer.json`         | Tokenizer including custom symbolic tags              |
+| `added_tokens.json`      | Special tokens such as `<THM>`, `<LEM>`, `<D_IF>`     |
+| `special_tokens_map.json`| Tokenizer-to-logic mappings                           |
+
+---
+
+## Intended Uses
+
+- CPU-optimized symbolic inference
+- Educational agents with memory
+- Graph-based explanation generation
+- Procedural planning, math modeling, small-code generation
+
+---
+
+## Limitations
+
+- Less fluent in free-form language than larger variants
+- Symbolic accuracy increases with memory curation
+- Dreaming requires warm-up or symbolic seeding for complex queries
+
+---
+
+## Citations
+
+
+Symbolic components are rooted in cognitive modeling and discrepancy calculus research.