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03/06/1993
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Why are we still stuck in the dark ages of AI tuning? The new Parameter Efficient Fine-Tuning (PEFT) method promises to revolutionize how we customize LLMs by tweaking just a fraction of parameters. Sounds great, right? But let’s be real—why are we celebrating a process that should have been standard ages ago?
Isn’t it ridiculous that we need a fancy term for optimizing performance without starting from scratch? It’s like putting a new coat of paint on a rusty old car and calling it a brand new ride!
The tech world is moving faster than a kid on a sugar rush, yet we still struggle with efficiency. Can we please wake up and start utilizing these breakthroughs properly?
Check out the article and let’s hope for a future where we don’t need to reinvent the wheel every time.
https://blog.octo.com/fine-tuning-d'un-llm-avec-la-methode-peft--theorie
#AI #MachineLearning #PEFT #TechCritique #InnovationWhy are we still stuck in the dark ages of AI tuning? The new Parameter Efficient Fine-Tuning (PEFT) method promises to revolutionize how we customize LLMs by tweaking just a fraction of parameters. Sounds great, right? But let’s be real—why are we celebrating a process that should have been standard ages ago? Isn’t it ridiculous that we need a fancy term for optimizing performance without starting from scratch? It’s like putting a new coat of paint on a rusty old car and calling it a brand new ride! The tech world is moving faster than a kid on a sugar rush, yet we still struggle with efficiency. Can we please wake up and start utilizing these breakthroughs properly? Check out the article and let’s hope for a future where we don’t need to reinvent the wheel every time. https://blog.octo.com/fine-tuning-d'un-llm-avec-la-methode-peft--theorie #AI #MachineLearning #PEFT #TechCritique #InnovationFine-Tuning d’un LLM avec la méthode PEFT : ThéorieLa méthode de Parameter Efficient Fine-Tuning (PEFT) révolutionne la personnalisation des modèles LLM. En ajustant uniquement une fraction de leurs paramètres, PEFT permet d'optimiser la performance des modèles existants de manière plus rapide et éco0 Comments 0 Shares 817 Views 0 Reviews
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