QuZO introduces a groundbreaking approach to fine-tune large language models after quantization, solving critical memory and performance issues in low-precision environments.

• Eliminates error-prone backpropagation in quantized settings
• Reduces memory requirements during fine-tuning
• Maintains model performance despite lower precision
• Offers practical efficiency for real-world deployment

This engineering advancement enables more efficient LLM deployment on resource-constrained devices, making powerful AI more accessible and cost-effective for business applications.

QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models