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.
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…