LoRA Land for Customer Support
Learn how to fine-tune Mistral-7B for specialized customer support tasks
In this example, we will show you how to use the Predibase SDK to fine tune Mistral-7B for customer support for 3 different types of customers. We’ll give a detailed walkthrough of the steps from data preparation to training and running inference.
What is LoRA Land?
LoRA Land is a collection of 25 fine-tuned Mistral-7b models that consistently outperform base models by 70% and GPT-4 by 4-15%, depending on the task, all fine-tuned on Predibase for an average cost of $8.00. This collection of specialized fine-tuned models–all trained with the same base model – offers a blueprint for teams seeking to efficiently and cost-effectively deploy highly performant AI systems.
Task Description
To assist with customer support requests, we have decided to use an LLM to power a chat bot to assist us. In particular, we want it to take a chat/input message from a customer, and return a JSON response with two keys:
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intent: Classify the input query into a fixed list of intents (for e.g., “get_refund” or “get_invoice” or “cancel_order”)
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response: Write a free-form response back to the user indicating that we’ve understood what the user wants to do and we’ll be happy to assist them with the task at hand, as well as apologize for difficulties if at all required.
For example, for an input:
We want to get the LLM to return: