reaperdoesntknow / Symiotic-14B

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

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.