reaperdoesntknow/Symbiotic-8B

TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:May 6, 2025License:afl-3.0Architecture:Transformer0.0K Featherless Exclusive Cold

SymbioticLM-8B by reaperdoesntknow is an 8 billion parameter hybrid symbolic-transformer model built on a Qwen-8B backbone with a 32768 token context length. It integrates modular symbolic processors and a persistent memory buffer for long-memory symbolic reasoning and high-fidelity language generation. This model excels at deep symbolic tasks like theorem generation, logical chaining, and structured reasoning with retained memory, making it suitable for memory-aware tutoring, research assistants, and context-persistent dialogue systems.

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Overview

SymbioticLM-8B is a unique 8 billion parameter hybrid model developed by Convergent Intelligence LLC: Research Division, combining a Qwen-8B transformer with advanced symbolic cognition capabilities. Unlike traditional LLMs, it features modular symbolic processors and a persistent memory buffer, enabling it to perform long-memory symbolic reasoning and generate high-fidelity language. The model's architecture includes a Qwen-8B rotary transformer backbone, a 4096 symbolic dimension, and specialized symbolic modules like ThoughtDynamicsLNN and CrystallineProcessor. It also incorporates a 2048 symbolic vector memory with entropy-aware retrieval and contextual recall, allowing for sustained, memory-aware interactions.

Key Capabilities

  • Hybrid Symbolic-Transformer Architecture: Integrates an 8B Qwen-based transformer with dedicated symbolic processors for enhanced reasoning.
  • Persistent Memory: Features a 2048 symbolic vector memory for long-term contextual recall across turns.
  • Deep Symbolic Reasoning: Excels at tasks such as theorem generation, logical chaining, and structured reasoning.
  • "Dream Mode": Capable of self-generating symbolic cognition offline.
  • Discrepancy Calculus Foundation: Developed under a unique measure-theoretic framework that treats training singularities as structural signals.

Good For

  • General Symbolic Reasoning: Ideal for tasks requiring logical inference and structured thought.
  • Memory-Aware Tutoring & Research Assistants: Supports applications needing to retain and recall information over extended interactions.
  • Code & Math Proof Modeling: Specialized for generating and validating mathematical and programming proofs.
  • Context-Persistent Dialogue Systems: Enables more coherent and informed conversations by maintaining long-term context.