SymbioticLM-8B
Model Type: Hybrid Symbolic–Transformer
Base Model: Qwen-8B
Framework: PyTorch + Transformers-compatible
Purpose: Long-memory symbolic reasoning + high-fidelity language generation
Overview
SymbioticLM-8B is a state-of-the-art hybrid transformer model with built-in symbolic cognition. It combines an 8B Qwen-based transformer with modular symbolic processors and a persistent memory buffer. The model supports both general conversation and deep symbolic tasks such as theorem generation, logical chaining, and structured reasoning with retained memory across turns.
Architecture Highlights
- Backbone: Qwen-8B rotary transformer
- Symbolic Dim: 4096
- Symbolic Modules:
- ThoughtDynamicsLNN (multi-head LSTM attention)
- CrystallineProcessor (DNAConv GNN)
- LiquidThoughtProcessor (recurrent symbol folding)
- HelicalDNAProcessor (helical linear projection)
- Memory: 2048 symbolic vectors (float32) with entropy-aware retrieval and contextual recall
- Dream Mode: Self-generates symbolic cognition offline
Files Included
| File | Description |
|---|
model.bin | PyTorch weights (LFS tracked) |
model.safetensors | Same weights in safetensors format (recommended) |
memory.pt | Symbolic memory snapshot (entropic, pretrained) |
config.json | Base model configuration |
generation_config.json | Sampling and decoding config (temperature, top_p, etc.) |
tokenizer.json | Tokenizer data with custom tags and structure |
added_tokens.json | Extra tokens like <THM>, <PROOF>, <D_EPS> |
special_tokens_map.json | Maps for special tokens used during generation |
Intended Uses
- General symbolic reasoning and logical conversation
- Memory-aware tutoring, research assistants
- Code + math proof modeling
- Context-persistent dialogue systems
Limitations
- Not instruction-tuned (e.g., chat-style inputs may require prompt engineering)
- Larger memory buffer may increase CPU load slightly
- Symbolic inference is offline-evolved; memory must be actively seeded
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
This model was designed and built from Discrepancy Analysis, paper to be published soon!
Convergent Intelligence Portfolio
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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
