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---
license: afl-3.0
datasets:
- 0xZee/dataset-CoT-Advanced-Calculus-268
language:
- en
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3
- 8b
- qwen3-8b
- symbiotic
- symbtioicai
---
# 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
---
## Citations
This model was designed and built from Discrepancy Analysis, paper to be published soon! |