Meet LLaMA-Adapter: A Lightweight Adaption Method For Fine-Tuning Instruction-Following LLaMA Models Using 52K Data Provided By Stanford Alpaca


GPT-4: Researchers from the Shanghai Artificial Intelligence Laboratory, CUHK MMLab, and the University of California have introduced LLaMA-Adapter, a fine-tuning technique that transforms LLaMA into an efficient instruction-following model. The LLaMA-Adapter has a lightweight parameter set, allowing for faster convergence and multimodal reasoning capabilities. The team plans to incorporate more varied multimodal inputs and conduct research on larger LLaMA models and various benchmarks.
Read more at MarkTechPost…

Discover more from Emsi's feed

Subscribe now to keep reading and get access to the full archive.

Continue reading