Quark-AI is a proprietary, ground-up Large Language Model (LLM) architecture designed to move beyond the limitations of standard Transformers. It features a unique 24-layer hybrid backbone specifically engineered for stateful reasoning and dynamic memory retention.
Unlike traditional models that treat every token in isolation, Quark utilizes a specialized Recurrent Memory Slot (RMS) system, allowing it to maintain a "thread of thought" across complex prompts.
Note: To protect the intellectual property of the Quark architecture, the core source code is currently Private. Documentation below highlights high-level capabilities.
Quark doesn't just attend to context; it stores compressed reasoning states in dedicated slots, reducing hallucination in long-form logic tasks.
Equipped with Neural Tangent Kernel (NTK) aware Rotary Positional Embeddings, Quark is designed to scale its context window dynamically without losing precision.
Each of the 24 layers participates in a "Chain-of-Thought" process before producing the final output vector, ensuring deeper semantic understanding.
| Attribute | Specification |
|---|---|
| Model Family | Quark-V1 (Base) |
| Layer Depth | 24 Transformer-Recurrent Blocks |
| Attention Mechanism | Confidence Gated Multi-Head Attention |
| Training Phase | Phase 2: Instruction & Logic Injection |
| Dataset | Curated SlimOrca & Synthetic Reasoning Pairs |
- Inception: Architecture design & Weight Initialization.
- Early Training: Pattern recognition & Base language modeling.
- Mid-Training: Instruction following (Current Stage).
- Optimization: Quantization to 4-bit/8-bit for edge deployment.
- Public Weights: Deployment of the first Quark-Base-V1 weights.
The source code and training scripts for Quark-AI are Closed Source to maintain the integrity of the architecture. For collaborations or research inquiries, stay tuned for the official API documentation.
[Ghosthets] - AI Research & Development