Liger efficiently converts standard transformer-based LLMs into linear recurrent structures with significant performance advantages.

• Enables linear-time training and constant-memory inference
• Preserves model quality while improving deployment efficiency
• Eliminates the need for costly pretraining of non-standard architectures
• Creates more accessible, resource-efficient AI systems

This engineering breakthrough addresses a critical challenge in AI deployment by making large language models more computationally efficient and accessible for real-world applications, potentially reducing infrastructure costs and environmental impact.

Liger: Linearizing Large Language Models to Gated Recurrent Structures