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.

Convergent Intelligence Portfolio

Part of the Symbiotic AI Series by Convergent Intelligence LLC: Research Division

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