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Nature Magazine: #GenAI-#LLMs: #PEFT for #PLM's: Parameter-Efficient Fine-Tuning (PEFT) of large-scale pre-trained language models: "As PLMs scale up, fine-tuning and storing all the parameters is prohibitively costly and eventually becomes practically infeasible. This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, which optimizes a small portion of the model parameters while keeping the rest fixed, drastically cutting down computation and storage costs." HTML https://lnkd.in/eX6GpxRB : PDF https://lnkd.in/ePDtVGFe . #Generative #ArtificialIntelligence #LargeLanguageModels #InDepth #Focus #PEFT: PEFT for LLMs: #Comprehensive #Survey: https://lnkd.in/e6yVCEGF PDF: https://lnkd.in/eAD48d_x . "PEFT is a practical solution by efficiently adapting the large models over the various downstream tasks. PEFT refers to the process of adjusting the parameters of a pre-trained large models to adapt it to a specific task or domain while minimizing the number of additional parameters introduced or computational resources required." Hugging Face Notebooks: #Fine-#tune #pretrained #model: #Colab [Mixed: https://lnkd.in/eyqDc9ZD ; PyTorch: https://lnkd.in/efc2wVQE; TensorFlow: https://lnkd.in/eb83uN-e ] #StudioLab: [Mixed: https://lnkd.in/eKCnhudR ; PyTorch: https://lnkd.in/eDvPuDfm; TensorFlow: https://lnkd.in/eAUEYyKC] AIMLExchange.com Meta-GenAI Meta-Search Engine : Top GenAI-LLMs on Top GenAI-LLMs Technical Topics. #PromptEngineering: #A: AIMLExchange.com #C #ChatGPT - #P #Perplexity - #Y #You What are advantages & limitations of PLMs? #A https://lnkd.in/eZQj3GRc #C https://lnkd.in/e6agV-_9 #P https://lnkd.in/eXdU6tyg #Y https://lnkd.in/eGPxS4DP Why's PEFT of large-scale PLMs important? #A: https://lnkd.in/eztmnTaS #C https://lnkd.in/esm7mkJk #P https://lnkd.in/ek5FjuMF #Y https://lnkd.in/eSZ7Gi-w How to #Assess #Strengths & #Limitations of PLMs and diverse PEFTs for PLMs?: #A: https://lnkd.in/eBSgQRqi #C https://lnkd.in/eae8MtVu #P https://lnkd.in/ea8bhKVq #Y https://lnkd.in/efFw9xWr How to resolve model overfit & underfit for PLMs using PEFTs to balance bias-variance tradeoffs? #A: https://lnkd.in/eZSq-fKs #C https://lnkd.in/ea7FCugd #P https://lnkd.in/erqAqNFw #Y https://lnkd.in/eq2mzQyu --- New York State: "Join Dr. Yogi Malhotra to get up to speed on Cloud Technology." USAF-AFRL Ventures: "Do Something Epic: Save the World™": We Create the Digital Future™. You Can Too! Let's Show You How! AIMLExchange™: AIMLExchange.com: We Create the Digital Future™ BRINT™: BRINT.com: From Future of Finance™ to Future of Defense™ C4I-Cyber™: C4I-Cyber.com: Because the Future of the World Depends Upon It™ --- AWS-Quantum Valley Building the Future of AI-Quantum Networks: Global Risk Management Network LLC-NY Silicon Valley's Next Big Thing™: CEO-CTO-CFO Know-Build-Monetize™ Networks: Join The CxO Metaverse™ C4I-Cyber Quantum Valley-SiliconValley Digital Pioneer USAF-USSF Ventures Engineering Sustainability
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1yThe development of parameter-efficient fine-tuning (PEFT) for large-scale pre-trained language models (PLMs) addresses critical challenges in computational cost and scalability. By optimizing a small subset of parameters while keeping the majority fixed, PEFT significantly reduces computational and storage overheads, enhancing model adaptability and efficiency. However, assessing the strengths and limitations of various PLMs and PEFT strategies remains complex. How do you envision overcoming these challenges to ensure effective utilization of PLMs and PEFT techniques in real-world applications, particularly in domains where computational resources are limited?