Prompting Llama-2 at Scale with Gretel

In this blog, the author demonstrates how to prompt Meta's Llama-2 chat model with a series of 250 questions from GSM8k dataset using Gretel's platform. Batch inference is introduced as an efficient method for handling large sets of prompts, allowing organizations to leverage LLMs more effectively across extensive datasets. The tutorial includes setting up the development environment, loading and preparing the dataset, initializing the Llama-2 model, and batch prompting the model. Gretel's platform handles scaling out requests, monitoring progress, assessing synthetic text quality, and fine-tuning models like Llama 2 on custom data. The full code for this tutorial is available on Colab and GitHub.

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