Five months ago, we wrote a blog on when fine tuning may be a good idea for your LLM application - there were clear cost and latency benefits for specialized tasks. However, 5 months is a long time in the world of LLMs! Since then, retrieval augmented generation has been far more popular and fine-tuning isn’t supported on the latest instruction tuned models from OpenAI or Anthropic either. More recently though, fine tuning has started to make a comeback coinciding with the rise of open source models. New open source models are being released quickly, with the hotly anticipated Llama 2 coming out yesterday (other top models are Falcon-40b, MPT-30b). And these models are very well suited for fine-tuning.
"Prompt and prosper" may seem like the ideal mantra for working with LLMs, but eventually you'll find that relying exclusively on prompts can paint you into a corner. The initial ease of using prompts often gives way to challenges that become more pronounced over time. High costs, sub-optimal handling of edge cases, limited personalization, high latency, a tendency towards hallucination, and the gradual erosion of your competitive advantage are all potential issues that can take the sheen off your LLM deployment.
Enter fine-tuning: a method that enables you to optimize your LLMs for specific tasks, resulting in lower costs, improved accuracy, and lower latency. In the following sections, we'll explore fine tuning more, demonstrating how this approach is likely to be an important approach moving forward.
In the realm of AI (not just LLMs), fine-tuning involves training a pre-existing model on a smaller, task-specific dataset to adapt it to a particular task or domain.
The foundation model, a pre-trained LLM, serves as the initial starting point. The weights of this network are then further optimized based on the data specific to the task at hand. This process allows the model to develop a nuanced understanding of the particular context and language patterns it's being fine-tuned for.
The result is a model that uses its pre-trained proficiency in general language to become an expert in your specific application, thanks to the additional layer of learning imparted through fine-tuning. In essence, fine-tuning is a process of specialization that enhances the general skills of a language model to perform better on task-specific applications.
The AI industry is moving fast, and new developments constantly make us rethink our strategies. Recently released high quality open source models are doing just that.
The reason for this renewed interest lies in their performance. Open source models are showing potential that can be harnessed using fine-tuning, making them an attractive choice for LLM applications. By employing your own data, you can tune these models to align better with your specific needs. This move not only adds an extra layer of specialization to the model but also empowers you to maintain control of your AI strategy.
Before we get too deep into fine-tuning, it's crucial to understand its benefits and potential drawbacks. Later we’ll share a step by step guide to fine tuning.
However, there are also some challenges to keep in mind.
Embarking on the fine-tuning journey might seem daunting, but it doesn't have to be. Here's a straightforward guide to set you on the right path:
Fine-tuning is a potent tool, but like any tool, its effectiveness depends on how well you wield it. Here are some considerations to keep in mind:
Fine-tuning models can provide significant benefits and solve many of the challenges associated with using large language models. Despite some potential pitfalls, with the right approach and considerations, fine-tuning can be a robust tool in your AI arsenal.
To delve even deeper into fine-tuning, consider exploring more resources on the topic, such as online courses, tutorials, and research papers. And remember, you're not alone on this journey. Need help getting started or fine-tuning your model? Feel free to reach out to me at firstname.lastname@example.org