Private GPT: Fine-Tune LLM on Enterprise Data | by Priya Dwivedi | Jul, 2023

Doing cool things with data

Photo by Robynne Hu on Unsplash

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.

In this blog, we will cover some of the techniques used for fine-tuning LLMs. We will train Falcon 7B model 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 Deep Learning Analytics, we have been building custom machine-learning models for the last 6 years. Reach out to us if you are interested in fine-tuning a LLM for your application.

QLoRA, which stands for “Quantized Low-Rank Adaptation,” presents an approach that combines quantization and low-rank adaptation to achieve efficient fine-tuning of AI models. Both these terms are explained in more detail below.

QLoRA reduces the memory required for fine-tuning LLM, without any drop in performance with respect to a standard 16-bit model fine-tuned model. This method enables a 7 billion parameter model to be fine-tuned on a 16GB GPU, a 33 billion parameter model to be fine-tuned on a single 24GB GPU and a 65…

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