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

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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.

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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

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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

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Intended Uses

• CPU-optimized symbolic inference
• Educational agents with memory
• Graph-based explanation generation
• Procedural planning, math modeling, small-code generation

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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

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Citations

Symbolic components are rooted in cognitive modeling and discrepancy calculus research.