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

Part of the Symbiotic AI Series by Convergent Intelligence LLC: Research Division

Related Models

Model	Downloads	Format
Symbiotic-1B	4	HF
Symiotic-14B	3	HF
Symbiotic-Beta	3	HF

Top Models from Our Lab

Model	Downloads
Qwen3-1.7B-Thinking-Distil	501
LFM2.5-1.2B-Distilled-SFT	342
Qwen3-1.7B-Coder-Distilled-SFT	302
Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF	203
Qwen3-1.7B-Coder-Distilled-SFT-GGUF	194

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.

Top model: Qwen3-1.7B-Coder-Distilled-SFT — 508 downloads

Full methodology: Structure Over Scale (DOI: 10.57967/hf/8165)

Convergent Intelligence LLC: Research Division