Module recap
1. Module recap
Congratulations! Let's go over what you've covered in this module. We looked at what fine-tuning is and how Cortex fine-tune function uses parameter-efficient fine-tuning technique to fine-tune an LLM. We explored how the general-purpose models were not efficient in generating response in the desired style for our support tickets. We learned when we should fine-tune a general-purpose LLM. We looked at preparing our data, splitting it into training and evaluation datasets, and the specifics of making sure we have the correct columns passed to the fine-tuned function for completions. We started our fine-tuned job and managed it by using show and describe. We used our fine-tuned model for inference. We created a streamlined user interface for the AI application we built. The app takes customer request, contact preference, and an LLM model as input. The model classifies the support request into one of five categories listed by our business teams. It also custom generates a response to the support ticket based on the customer request and their contact preference. Gen AI allows us to do incredible things. We have only started to scratch the surface of the abilities Gen AI offers in the Snowflake environment. Well done! You have completed a lot and increased your skills and knowledge. Coming up, we will talk about next steps in your journey. See you then!2. Let's practice!
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