QuantaSparkLabs/Quantum-X
Quantum-X is a compact 0.5 billion parameter conversational AI developed by QuantaSparkLabs, fine-tuned from Qwen 2.5 0.5B. Optimized for fast inference and edge devices, it provides natural, warm dialogue and factual Q&A capabilities. This model is designed for efficient, real-time conversational applications on resource-constrained hardware.
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Quantum-X: Compact, High-Speed Conversational AI
Quantum-X is a 0.5 billion parameter language model developed by QuantaSparkLabs, built upon the Qwen 2.5 0.5B base model. It has been fine-tuned using QLoRA with Unsloth on a combination of OpenHermes-2.5 conversations and custom identity data, resulting in a model capable of warm, direct conversational abilities.
Key Capabilities
- Natural Conversational AI: Excels at engaging in warm, natural dialogues with a distinct identity.
- Factual Q&A: Capable of answering general knowledge questions accurately.
- Blazing Fast Inference: Its compact size (0.5B parameters) allows for near-instant responses on both CPU and GPU.
- Edge-Friendly: Designed to run comfortably on devices with as little as 2 GB RAM, making it suitable for embedded applications and mobile inference.
Hardware Requirements
Quantum-X is highly efficient, requiring approximately 2 GB RAM for CPU-based testing and embedded applications, and 1-2 GB VRAM for GPU-based development and serving. It is particularly well-suited for on-device inference on mobile platforms with over 1 GB RAM.
Limitations
While efficient, Quantum-X has limitations in complex reasoning and advanced mathematical tasks, where consistency may vary. It can also occasionally produce outdated or incorrect factual information and is not recommended for high-stakes applications such as medical, legal, or safety-critical decisions.