reaperdoesntknow/Symbiotic-1B
reaperdoesntknow/Symbiotic-1B is a 1 billion parameter hybrid symbolic-transformer model built on Qwen-1B, developed by Convergent Intelligence LLC: Research Division. It integrates a rotary transformer with a symbolic processing pipeline and persistent episodic memory for enhanced reasoning. This model is optimized for lightweight, memory-augmented symbolic inference on CPU and embedded systems. It excels at tasks requiring logical processing, procedural planning, and graph-based explanation generation in resource-constrained environments.
Loading preview...
SymbioticLM-1B: Hybrid Symbolic-Transformer for Constrained Environments
SymbioticLM-1B, developed by Convergent Intelligence LLC: Research Division, is a 1 billion parameter model that uniquely combines a Qwen-1B rotary transformer backbone with a sophisticated symbolic processing pipeline and persistent episodic memory. This architecture allows it to perform advanced reasoning tasks even in resource-limited settings, such as CPU and embedded inference.
Key Capabilities & Architecture Highlights
- Hybrid Design: Fuses a Qwen-1B transformer with a symbolic cognitive engine, including symbolic memory and dynamic thought evolution.
- Memory-Augmented Reasoning: Features 2048 symbolic vectors with entropic and contextual retrieval, enabling persistent learning and recall.
- Symbolic Processing: Incorporates advanced symbolic modules like ThoughtDynamicsLNN, CrystallineProcessor (DNAConv GNN), LiquidThoughtProcessor, and HelicalDNAProcessor.
- "Dream Mode": Supports symbolic simulation with a ThoughtGenerator for complex problem-solving.
- Discrepancy Calculus Foundation: Developed under the DISC framework, which treats training singularities as structural signals for understanding learning geometry.
Ideal Use Cases
- CPU-optimized symbolic inference: Designed for efficient operation on standard CPUs.
- Educational agents with memory: Suitable for applications requiring persistent knowledge and logical processing.
- Graph-based explanation generation: Excels at creating structured, logical explanations.
- Procedural planning, math modeling, and small-code generation: Effective for tasks demanding precise, step-by-step reasoning.
Limitations
- Less fluent in free-form language compared to larger, purely neural models.
- Symbolic accuracy improves with careful memory curation.
- Complex queries in "Dream Mode" may require warm-up or symbolic seeding.