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.
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