reaperdoesntknow / Symbiotic-1B

SymbioticLM-1B

Model Type : Hybrid Symbolic–Transformer
Base Model : Qwen-1B
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

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.