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---
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
---
# 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. |