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

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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.

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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

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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

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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

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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

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Citations

Please cite "SymbioticLM" when using symbolic memory components in research or applications.