Update README.md

README.md

@@ -14,4 +14,74 @@ tags:
 - symbioticai
 - llm
 - Symbols
----
+---
+
+# SymbioticLM-14B
+
+
+**Model Type**: Hybrid Symbolic–Transformer with Persistent Memory  
+**Base Model**: Qwen-14B  
+**Framework**: PyTorch + HuggingFace Transformers  
+**Purpose**: Full-scale cognitive reasoning model with self-organizing memory and generative symbolic evolution
+
+---
+
+## Overview
+
+SymbioticLM-14B is a state-of-the-art 17.8 billion parameter symbolic–transformer hybrid model that tightly couples high-capacity neural representation with structured symbolic cognition. Designed to match or exceed performance of top-tier LLMs in symbolic domains, it supports persistent memory, entropic recall, multi-stage symbolic routing, and self-organizing knowledge structures.
+
+This model is ideal for advanced reasoning agents, research assistants, and symbolic math/code generation systems.
+
+---
+
+## Architecture Highlights
+
+- **Backbone**: Qwen-14B transformer with rotary embeddings + FlashAttention
+- **Symbolic Dim**: 8192
+- **Symbolic Modules**:
+  - ThoughtDynamicsLNN (multi-head LSTM attention)
+  - LiquidThoughtProcessor
+  - CrystallineProcessor (DNAConv GNN)
+  - HelicalDNAProcessor (linear helical encoding)
+- **Memory**: 4096 symbolic states in FP32, retrieved using entropy + contextual similarity
+- **Dream Mode**: Background symbolic simulation for open-ended cognition
+- **Router**: Intent classifier + entropy gating for processor path selection
+
+---
+
+## Files Included
+
+| File                     | Description                                              |
+|--------------------------|----------------------------------------------------------|
+| `model.bin`              | Transformer weights (LFS)                                |
+| `model.safetensors`      | Memory-safe weights, optimized for loading               |
+| `memory.pt`              | 4096-symbolic vector bank                                |
+| `config.json`            | Model and architectural metadata                         |
+| `generation_config.json` | Top-p, temperature, decoding settings                    |
+| `tokenizer.json`         | Full tokenizer with symbolic tag support                 |
+| `added_tokens.json`      | Tags like `<D_LIM>`, `<PROOF>`, `<BY_MEASURE>`, etc.     |
+| `special_tokens_map.json`| Special token mapping for tokenizer                      |
+
+---
+
+## Intended Uses
+
+- Multi-step conversational agents with true memory
+- Long-form symbolic theorem generation and proof planning
+- Scientific dialogue, symbolic simulations, math/code synthesis
+- Reasoning in fuzzy, discontinuous, or non-smooth problem domains
+
+---
+
+## Limitations
+
+- Memory requires curation and seeding for maximum utility
+- Symbolic cognition is not instruction-tuned for general QA
+- FlashAttention and symbolic modules increase VRAM usage during generation
+
+---
+
+## Citations
+
+
+Please cite "SymbioticLM" when using symbolic memory components in research or applications.