---
license: afl-3.0
datasets:
- 0xZee/dataset-CoT-Advanced-Calculus-268
language:
- en
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3
- symbioticai
- symbioticllm
- discrepancy_calculus
- ai
- llm
- text
- convergentintel
---
# 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](https://huggingface.co/reaperdoesntknow) 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](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) (DOI: 10.57967/hf/8194).

**Citation chain:** [Structure Over Scale](https://huggingface.co/reaperdoesntknow/Structure-Over-Scale) (DOI: 10.57967/hf/8165) → [Three Teachers to Dual Cognition](https://huggingface.co/reaperdoesntknow/DualMind_Methodolgy) (DOI: 10.57967/hf/8184) → [Discrepancy Calculus](https://huggingface.co/reaperdoesntknow/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](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)*


### Related Models

| Model | Downloads | Format |
|-------|-----------|--------|
| [Symbiotic-8B](https://huggingface.co/reaperdoesntknow/Symbiotic-8B) | 4 | HF |
| [Symiotic-14B](https://huggingface.co/reaperdoesntknow/Symiotic-14B) | 3 | 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: 41 models | 2,781 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*

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