SSH (Sparse Spectrum Adaptation) offers a breakthrough approach for fine-tuning large language models with significantly fewer parameters and better performance.

• Transforms adaptation to the frequency domain using Discrete Hartley Transformation
• Achieves up to 25% parameter reduction compared to LoRA while maintaining performance
• Enables faster convergence and better generalization across diverse language tasks
• Particularly valuable for resource-constrained environments and larger model deployments

This engineering innovation addresses critical scaling challenges for LLM adaptation in production environments, making fine-tuning more accessible and cost-effective.

SSH: Sparse Spectrum Adaptation via Discrete Hartley Transformation