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Data is the heart of AI and while it is a valuable asset, we know how challenging and costly it is to develop high-quality datasets. A well-curated and filtered dataset can make up for a lack of complexity in a model. This is also the case with Large Language Models where smaller-sized models have shown to outperform bigger LLMs by leveraging good data.
In this article, we will explore how to use Llama 3.1 405B to create a synthetic dataset of git commands in natural language. I will show how you can use this 405B beast without running tens of GPUs in parallel. After having an initial dataset of instructions and responses, we will use Nvidia’s Nemotron 4 as a reward model to filter out any bad prompt/response pairs. Finally, we will push this dataset to HuggingFace for later fine-tuning of our LLM.
This will be fast, free, and will leave you much in control.
I will keep this post concise and knowledge-packed, so make sure to read through the end and familiarize yourself with…