Mastering AI: The Power of Fine-Tuning Large Language Models
Pushing the boundaries of what AI can achieve.
This is what the art and science of fine-tuning large language models (LLMs) is all about.
But pulling this off and mastering AI is a complex process fraught with challenges like overcoming built-in biases, overspecializing a model, and more, which we will demystify while presenting one tool making the task easier.
What in the World is AI Fine-Tuning?
Keeping your car running smoothly entails a routine battery test, checking fluids, and so forth.
Even the most well-designed and cared for vehicle requires regular check-ups and the occasional fine-tuning.
Artificial intelligence is no different.
Large language models (LLMs), are the literal engine of generative AI applications. They are built by compiling large datasets, defining millions and even billions of data parameters.
For example, Open AI’s ChatGPT-4 reportedly contains as many as 1.8 trillion parameters, and training the dataset until it achieves optimal performance, along with the guiding hand of researchers carefully supervise the entire process from beginning to end, is no easy feat.
On top of that, the multi-faceted process of data collection, training, and supervised learning can cost hundreds of millions of dollars and take months or even years, depending on the infrastructure being used.
After all this time, effort, and expenditure, we get fine-tuning, which is simply the act of training an existing LLM on new data.
Easy in theory, not so much in practice.
This is why fine-tuning is now at the forefront of AI research and development with three major challenges to overcome.
LLM Inadaptability
It turns out that training a large dataset and then trying to re-train it on brand new parameters is akin to banging on a brick wall with a plastic hammer…hard, brittle, and ineffective.
As others have pointed out, this is due to the phenomenon known as “catastrophic forgetting”, whereby an LLM loses previously acquired knowledge as it learns new information.
Although LLMs may be good at performing general tasks, enterprise-grade production requires finetuning.
Not even popular generative AI applications are able to retain all of the initial factual data they are trained on, with many infamously “hallucinating” when prompted about current events.
However, this is far from the worst fine-tuning challenge.
Unmitigated Biases
One person who has pointed out and famously railed against biased AI, is OpenAI co-founder and x.AI founder Elon Musk.
Saying that x.AI’s latest LLM, Grok 2.0 defies censorship.
But because biases are inevitably found in the initial training data, which is 99% scrubbed from the web, fine-tuning is the way to mitigate them.
This is where the art and science of overweighting some pre-existing parameters, while underweighting others to create more logical outputs comes in.
If a light touch isn’t applied and the pendulum swings too far in either direction, it can create a whole host of new problems.
Overfitting
When the fine-tuning process is overdone, an LLM can struggle to adapt to new data.
For example, if your model is trained to identify information pertaining to dogs and you fine-tune the model to include more and more identifying information, it can then have trouble recognizing more general identifying info over time.
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There are several reasons why this can happen:
Mitigating this has typically meant more data analysis, further tuning of parameters, and regulating models…until now.
Fine-Tuning Made Easy
Anyone deploying a Large Language Model will need to continually fine-tune it for optimal performance and public consumption.
Not only this, but general LLMs also need to be finetuned for industry specific applications, as such models are usually too broad for specialized use cases.
Naturally, all of this takes time, money, and effort, but one tool is making the fine-tuning process easier for everyone.
Built on an open-source architecture with a no-frills file configuration interface, Cadenza-backed Axolotl is an easy-to-use, low-code tool for fine-tuning LLMs.
As of this publication date, the tool is still in stealth mode, with a sizeable waiting list, but here is what we do know about it:
First, it supports a growing number of open-source LLMs such as Hugging Face, Mistral, llama, and others, so training and full fine-tuning of any public models is possible.
However, it is not limited to just these LLMs.
Axolotl is adaptable to different dataset formats and even custom datasets, so long as they are in jsonl file format.
Second, the tool integrates with performance-boosting LLM technologies like Xformer, flash attention, RoPe scaling, and it is one of the few that also supports the newer GaLore training method.
Finally, Axolotl’s customizable configurations & parameters using just a simple yaml file make it an ideal tool for regularly-tuned models.
This isn’t the simplest stuff to wrap one’s head around if you haven’t been in the trenches working with LLMs, but that’s what platforms like Hugging Face, Mistral, and tools such as Axolotl are aiming to change by making AI more accessible.
If AI is going to equal or even exceed the impact of the Internet, both economically and in terms of the societal change it ushers in, it needs to be easy to use and affordable…for developers.
Think about this, had only a select few large, moneyed corporations been able to build websites and apps, the Internet would have never proliferated like it did to more than five billion users and counting, while contributing over $2 Trillion to the U.S. economy, annually.
This is exactly what is happening today.
Artificial intelligence is currently concentrated at the very top, with the largest corporations utilizing it on the broadest scale.
However, as AI becomes smarter and more sophisticated, it is increasingly being applied to more uses, such as specialized chatbots, task automation, and robotics.
Fine-tuning tools such as Axolotl are the modern day equivalent of the JavaScript and PHP programming languages that helped individuals and companies alike create and maintain their own websites.
And just like these programming tools did in the past, this is unleashing a wave of creativity and innovation that will push the boundaries of what AI can achieve.
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If you would like more information on our thesis surrounding AI Fine-Tuning or other transformative technologies, please email info@cadenza.vc