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
| 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
Discrepancy Calculus Foundation
This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces.
DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the learning problem. Key concepts:
- Discrepancy Operator (D): Measures the gap between expected and observed behavior at each training step
- Jump Sets: Boundaries where model behavior changes discontinuously — these are features, not bugs
- Ghost Imprinting: Teacher knowledge that transfers to student models through weight-space topology rather than explicit distillation signal
For the full mathematical treatment, see
Discrepancy Calculus: Foundations and Core Theory (DOI: 10.57967/hf/8194).
Citation chain: Structure Over Scale (DOI: 10.57967/hf/8165) →
Three Teachers to Dual Cognition (DOI: 10.57967/hf/8184) →
Discrepancy Calculus (DOI: 10.57967/hf/8194)
Citations
Symbolic components are rooted in cognitive modeling and discrepancy calculus research.
Convergent Intelligence Portfolio
Part of the Symbiotic AI Series by Convergent Intelligence LLC: Research Division
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Total Portfolio: 41 models | 2,781 total downloads
Last updated: 2026-03-28 12:57 UTC
From the Convergent Intelligence Portfolio
DistilQwen Collection — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware.
Convergent Intelligence LLC: Research Division
