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Private GPT: Fine-Tune LLM on Enterprise Data

Doing cool things with data

Priya Dwivedi
Towards Data Science
9 min readJul 5, 2023

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Photo by Robynne Hu on Unsplash

Introduction

In the era of big data and advanced artificial intelligence, language models have emerged as formidable tools capable of processing and generating human-like text. Large Language Models like ChatGPT are general-purpose bots capable of having conversations on many topics. However, LLMs can also be fine-tuned on domain-specific data making them more accurate and on-point on domain-specific enterprise questions.

Many industries and applications will require a fine-tuned LLMs. Reasons include:

  • Better performance from a chatbot trained on specific data
  • OpenAI models like chatgpt are a black box and companies may be hesitant to share their confidential data over an API
  • ChatGPT API costs may be prohibitive for large applications

The challenge with fine-tuning an LLM is that the process is unknown and the computational resources required to train a billion-parameter model without optimizations can be prohibitive.

Fortunately, a lot of research has been done on training techniques that allow us now to fine-tune LLMs on smaller GPUs.

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Priya Dwivedi
Priya Dwivedi

Written by Priya Dwivedi

CEO of Deep Learning Analytics, a AI/ML consultancy. We builds custom models for different use cases. Check us at: https://deeplearninganalytics.org

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