The emergence of large language models like GPT, Claude, and Gemini has accelerated natural language processing (NLP) advances. Parameter-Efficient Sparsity Crafting (PESC) transforms dense models into sparse ones, enhancing instruction tuning’s efficacy for general tasks. The method significantly reduces GPU memory needs and computational expenses, presenting outstanding performance. The researchers’ Camelidae-8Ï34B outperforms GPT-3.5.
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The Emergence of Large Language Models (LLMs) in Natural Language Processing (NLP)
The emergence of large language models (LLMs) like GPT, Claude, Gemini, LLaMA, Mistral, etc., has greatly accelerated recent advances in natural language processing (NLP). Instruction tweaking is a well-known approach to training LLMs, allowing them to improve their pre-trained representations to follow human instructions using large-scale, well-formatted instruction data.
Challenges in Fine-Tuning Large Language Models
Fine-tuning large language models can be complex and may lead to poor performance for general tasks due to the inability of larger models to maximize losses from competing activities. Additionally, the dense pre-trained models built using transformer architecture severely restrict scalability when tweaking the instructions.
Parameter-Efficient Sparsity Crafting (PESC)
The Parameter-Efficient Sparsity Crafting (PESC) method transforms dense models into sparse ones using the MoE blueprint, allowing for outstanding performance on general tasks. PESC drastically reduces GPU memory needs and computational expenses, enabling the expansion of model capacity with minimal increase in parameters.
Practical Application and Performance
The PESC method has been utilized to create Camelidae sparse models, outperforming GPT-3.5 in general and reaching state-of-the-art performance on all open-source sparse models. The researchers have demonstrated the model’s learning capabilities across various skills and integrated datasets from different domains.
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