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license: afl-3.0 |
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datasets: |
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- 0xZee/dataset-CoT-Advanced-Calculus-268 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-14B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- qwen3 |
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- symbiotic |
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- symbioticai |
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- llm |
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- Symbols |
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--- |
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# SymbioticLM-14B |
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**Model Type**: Hybrid Symbolic–Transformer with Persistent Memory |
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**Base Model**: Qwen-14B |
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**Framework**: PyTorch + HuggingFace Transformers |
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**Purpose**: Full-scale cognitive reasoning model with self-organizing memory and generative symbolic evolution |
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--- |
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## Overview |
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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. |
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This model is ideal for advanced reasoning agents, research assistants, and symbolic math/code generation systems. |
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## Architecture Highlights |
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- **Backbone**: Qwen-14B transformer with rotary embeddings + FlashAttention |
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- **Symbolic Dim**: 8192 |
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- **Symbolic Modules**: |
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- ThoughtDynamicsLNN (multi-head LSTM attention) |
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- LiquidThoughtProcessor |
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- CrystallineProcessor (DNAConv GNN) |
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- HelicalDNAProcessor (linear helical encoding) |
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- **Memory**: 4096 symbolic states in FP32, retrieved using entropy + contextual similarity |
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- **Dream Mode**: Background symbolic simulation for open-ended cognition |
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- **Router**: Intent classifier + entropy gating for processor path selection |
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--- |
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## Files Included |
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| File | Description | |
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|--------------------------|----------------------------------------------------------| |
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| `model.bin` | Transformer weights (LFS) | |
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| `model.safetensors` | Memory-safe weights, optimized for loading | |
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| `memory.pt` | 4096-symbolic vector bank | |
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| `config.json` | Model and architectural metadata | |
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| `generation_config.json` | Top-p, temperature, decoding settings | |
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| `tokenizer.json` | Full tokenizer with symbolic tag support | |
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| `added_tokens.json` | Tags like `<D_LIM>`, `<PROOF>`, `<BY_MEASURE>`, etc. | |
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| `special_tokens_map.json`| Special token mapping for tokenizer | |
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## Intended Uses |
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- Multi-step conversational agents with true memory |
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- Long-form symbolic theorem generation and proof planning |
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- Scientific dialogue, symbolic simulations, math/code synthesis |
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- Reasoning in fuzzy, discontinuous, or non-smooth problem domains |
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## Limitations |
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- Memory requires curation and seeding for maximum utility |
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- Symbolic cognition is not instruction-tuned for general QA |
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- FlashAttention and symbolic modules increase VRAM usage during generation |
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## Citations |
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Please cite "SymbioticLM" when using symbolic memory components in research or applications. |
