What is parameter efficient tuning? #GenerativeAI

Google Cloud Tech · Beginner ·🧠 Large Language Models ·2y ago

Key Takeaways

Parameter efficient tuning is a method for retraining large language models on new domain-specific datasets, allowing for customization without requiring extensive ML expertise, using techniques such as fine-tuning and other parameter-efficient methods.

Full Transcript

prompt design allows for fast experimentation and customization and because you're not writing any complicated code you don't need to be an ml expert to get started but coming up with prompts can be tricky and you can't really fit all that many examples into a prompt so what's tuning you ask well one version you might be familiar with is fine tuning in this scenario we take a model that has been pre-trained on a generic data set we make a copy of this model and then using those learned weights as a starting point we retrain the model on a new domain-specific data set if you want to learn more about parameter efficient tuning and some of the different methods There's a summary paper linked below for those who are extra curious

Original Description

Large language models such as Bard or Chat GPT can help you increase productivity. Watch along and learn what parameter efficient tuning is and how it can retrain a model on a new domain specific dataset. A guide to parameter-efficient fine-tuning → https://goo.gle/3NwFoPn
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Parameter efficient tuning allows for customization of large language models without requiring extensive ML expertise. This method involves retraining a pre-trained model on a new domain-specific dataset, enabling fast experimentation and customization.

Key Takeaways
  1. Identify a pre-trained model to fine-tune
  2. Prepare a domain-specific dataset
  3. Make a copy of the pre-trained model
  4. Retrain the model on the new dataset
  5. Test and evaluate the fine-tuned model
💡 Parameter efficient tuning enables customization of large language models without requiring extensive ML expertise, allowing for fast experimentation and customization.

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