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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/Qwen3-8B |
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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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- 8b |
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- qwen3-8b |
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- symbiotic |
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- symbtioicai |
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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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## Overview |
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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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## Architecture Highlights |
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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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## Files Included |
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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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## Intended Uses |
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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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## 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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## Citations |
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This model was designed and built from Discrepancy Analysis, paper to be published soon! |
