---
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
---
# 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

---

## Citations


Symbolic components are rooted in cognitive modeling and discrepancy calculus research.