---
license: afl-3.0
datasets:
- 0xZee/dataset-CoT-Advanced-Calculus-268
language:
- en
base_model:
- Qwen/Qwen3-14B
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3
- symbiotic
- symbioticai
- llm
- Symbols
- convergentintel
---

# SymbioticLM-14B


**Model Type**: Hybrid Symbolic–Transformer with Persistent Memory  
**Base Model**: Qwen-14B  
**Framework**: PyTorch + HuggingFace Transformers  
**Purpose**: Full-scale cognitive reasoning model with self-organizing memory and generative symbolic evolution

---

## Overview

SymbioticLM-14B is a state-of-the-art 17.8 billion parameter symbolic–transformer hybrid model that tightly couples high-capacity neural representation with structured symbolic cognition. Designed to match or exceed performance of top-tier LLMs in symbolic domains, it supports persistent memory, entropic recall, multi-stage symbolic routing, and self-organizing knowledge structures.

This model is ideal for advanced reasoning agents, research assistants, and symbolic math/code generation systems.

---

## Architecture Highlights

- **Backbone**: Qwen-14B transformer with rotary embeddings + FlashAttention
- **Symbolic Dim**: 8192
- **Symbolic Modules**:
  - ThoughtDynamicsLNN (multi-head LSTM attention)
  - LiquidThoughtProcessor
  - CrystallineProcessor (DNAConv GNN)
  - HelicalDNAProcessor (linear helical encoding)
- **Memory**: 4096 symbolic states in FP32, retrieved using entropy + contextual similarity
- **Dream Mode**: Background symbolic simulation for open-ended cognition
- **Router**: Intent classifier + entropy gating for processor path selection

---

## Files Included

| File                     | Description                                              |
|--------------------------|----------------------------------------------------------|
| `model.bin`              | Transformer weights (LFS)                                |
| `model.safetensors`      | Memory-safe weights, optimized for loading               |
| `memory.pt`              | 4096-symbolic vector bank                                |
| `config.json`            | Model and architectural metadata                         |
| `generation_config.json` | Top-p, temperature, decoding settings                    |
| `tokenizer.json`         | Full tokenizer with symbolic tag support                 |
| `added_tokens.json`      | Tags like `<D_LIM>`, `<PROOF>`, `<BY_MEASURE>`, etc.     |
| `special_tokens_map.json`| Special token mapping for tokenizer                      |

---

## Intended Uses

- Multi-step conversational agents with true memory
- Long-form symbolic theorem generation and proof planning
- Scientific dialogue, symbolic simulations, math/code synthesis
- Reasoning in fuzzy, discontinuous, or non-smooth problem domains

---

## Limitations

- Memory requires curation and seeding for maximum utility
- Symbolic cognition is not instruction-tuned for general QA
- FlashAttention and symbolic modules increase VRAM usage during generation

---

## Citations


Please cite "SymbioticLM" when using symbolic memory components in research or applications.

---

## Convergent Intelligence Portfolio

*Part of the [Symbiotic AI Series](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)*


#
## Mathematical Foundations: Discrepancy Calculus (DISC)

SymbioticLM's persistent memory and symbolic evolution connect to Discrepancy Calculus through **self-generating completeness** (Ch. 3 of the DISC monograph) and **symbolic-root domains**. The discrepancy operator:

$$Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|}\, dt$$

quantifies local mismatch between integration and differentiation. In the symbolic-transformer context, $D$ measures the gap between what the symbolic system encodes (discrete structure) and what the transformer integrates (continuous representation). The self-generating completeness theorem establishes that completeness emerges dynamically via energy computation on symbolic-root domains — the mathematical foundation for why symbolic-neural hybrids can produce structure that neither component generates alone.

The **discrepancy energy** $E_{\text{disc}}[f] = \frac{1}{2}\int w(x)(Df(x))^2 d\mu(x)$ provides a natural stability criterion for the memory consolidation process: memory states with bounded discrepancy energy are stable; those with divergent energy indicate structural transitions requiring reorganization.

Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division).

## Related Models

| Model | Downloads | Format |
|-------|-----------|--------|
| [Symbiotic-1B](https://huggingface.co/reaperdoesntknow/Symbiotic-1B) | 4 | HF |
| [Symbiotic-8B](https://huggingface.co/reaperdoesntknow/Symbiotic-8B) | 4 | HF |
| [Symbiotic-Beta](https://huggingface.co/reaperdoesntknow/Symbiotic-Beta) | 3 | HF |

### Top Models from Our Lab

| Model | Downloads |
|-------|-----------|
| [Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil) | 501 |
| [LFM2.5-1.2B-Distilled-SFT](https://huggingface.co/reaperdoesntknow/LFM2.5-1.2B-Distilled-SFT) | 342 |
| [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | 302 |
| [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 |
| [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 |

**Total Portfolio: 49 models, 22,598 total downloads**


*Last updated: 2026-03-28 12:57 UTC*

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---

## From the Convergent Intelligence Portfolio

**[DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)** — 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](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) — 508 downloads

Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165)

*Convergent Intelligence LLC: Research Division*

<!-- CIX-CROSSLINK-END -->