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  - qwen3-8b
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  - symbiotic
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  - symbtioicai
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
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  - qwen3-8b
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  - symbiotic
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  - symbtioicai
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+ ---
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+
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+ # SymbioticLM-8B
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+ **Model Type**: Hybrid Symbolic–Transformer
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+ **Base Model**: Qwen-8B
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+ **Framework**: PyTorch + Transformers-compatible
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+ **Purpose**: Long-memory symbolic reasoning + high-fidelity language generation
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+
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+ ---
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+
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+ ## Overview
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+
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+ SymbioticLM-8B is a state-of-the-art hybrid transformer model with built-in symbolic cognition. It combines an 8B Qwen-based transformer with modular symbolic processors and a persistent memory buffer. The model supports both general conversation and deep symbolic tasks such as theorem generation, logical chaining, and structured reasoning with retained memory across turns.
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+ ---
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+
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+ ## Architecture Highlights
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+
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+ - **Backbone**: Qwen-8B rotary transformer
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+ - **Symbolic Dim**: 4096
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+ - **Symbolic Modules**:
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+ - ThoughtDynamicsLNN (multi-head LSTM attention)
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+ - CrystallineProcessor (DNAConv GNN)
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+ - LiquidThoughtProcessor (recurrent symbol folding)
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+ - HelicalDNAProcessor (helical linear projection)
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+ - **Memory**: 2048 symbolic vectors (float32) with entropy-aware retrieval and contextual recall
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+ - **Dream Mode**: Self-generates symbolic cognition offline
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+
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+ ---
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+
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+ ## Files Included
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+
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+ | File | Description |
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+ |--------------------------|-------------------------------------------------------|
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+ | `model.bin` | PyTorch weights (LFS tracked) |
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+ | `model.safetensors` | Same weights in `safetensors` format (recommended) |
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+ | `memory.pt` | Symbolic memory snapshot (entropic, pretrained) |
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+ | `config.json` | Base model configuration |
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+ | `generation_config.json` | Sampling and decoding config (temperature, top_p, etc.)|
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+ | `tokenizer.json` | Tokenizer data with custom tags and structure |
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+ | `added_tokens.json` | Extra tokens like `<THM>`, `<PROOF>`, `<D_EPS>` |
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+ | `special_tokens_map.json`| Maps for special tokens used during generation |
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+
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+ ---
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+
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+ ## Intended Uses
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+
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+ - General symbolic reasoning and logical conversation
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+ - Memory-aware tutoring, research assistants
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+ - Code + math proof modeling
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+ - Context-persistent dialogue systems
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+
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+ ---
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+
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+ ## Limitations
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+ - Not instruction-tuned (e.g., chat-style inputs may require prompt engineering)
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+ - Larger memory buffer may increase CPU load slightly
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+ - Symbolic inference is offline-evolved; memory must be actively seeded
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+
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+ ---
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+
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+ ## Citations
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+ This model was independently developed by Roy S. Colca Jr.
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+ Please credit "SymbioticLM" if using symbolic components in downstream applications.
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  ---