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---
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.
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