Private GPT: Fine-Tune LLM on Enterprise Data

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: <ul> <li>Better performance from a chatbot trained on specific data</li> <li>OpenAI models like chatgpt are a black box and companies may be hesitant to share their confidential data over an API</li> <li>ChatGPT API costs may be prohibitive for large applications</li> </ul> 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. In this blog, we will cover some of the techniques used for fine-tuning LLMs. We will train <a href="https://huggingface.co/blog/falcon" rel="noopener ugc nofollow" target="_blank">Falcon 7B model</a> on finance data on a Colab GPU! The techniques used here are general and can be applied to other bigger models like MPT-7B and MPT-30B. At <a href="https://deeplearninganalytics.org/" rel="noopener ugc nofollow" target="_blank">Deep Learning Analytics</a>, we have been building custom machine-learning models for the last 6 years. <a href="https://deeplearninganalytics.org/contact-us/" rel="noopener ugc nofollow" target="_blank">Reach out to us</a> if you are interested in fine-tuning a LLM for your application. <a href="https://towardsdatascience.com/private-gpt-fine-tune-llm-on-enterprise-data-7e663d808e6a">Website</a>