It’s not about the heaviest anchor. It’s about the right placement. Equally important is testing. Benchmarks are your lighthouses. Ignore them, and you’re sailing blind.

But when you embrace them you discover the route to content that lands. Content that resonates. Content that holds.

Are you ready to elevate your AI’s language to art? Are you set to turn readers into followers? Followers into advocates?

Let’s not just sail.

Let’s lead the fleet.

But, before we get started. Get a deeper dive into rapid AI software launch strategies that rejuvenate your business model, stop by my LinkedIn.

Prepare Your Data

Garbage in, garbage out is a common mantra for data scientists and AI engineers alike. That’s because the process of fine tuning an AI model requires consistent data that is properly scoped to a specific niche or category.

One way to think about this is if you were playing a game of rock, paper, scissors and someone made the shape of a Vulcan Salute you would be pretty confused. For following games you would not know if they would play it again, if the rules of the game had changed, or if they were just crazy.

This example shows why it is important to remove unnecessary data from your model training set while simultaneously formatting the data in a predictable way so it can be recognized every time it is seen.

Choose Your Model

We’re entering an interesting time in AI model development. The open source community now has access to many highly capable models that have been tuned and trained already.

One needs to choose their model depending on a criteria of their own choosing that best meets their particular use case. Depending on the parameters size and the category of the training data functionality and output can vary wildly.

Start with models that already exists on hosted platforms to simplify the process so you can focus on getting the desired output from these models. Once you are feeling more comfortable it’s time to go to Hugging Face and research what it takes to host a model for fine tuning.

Train Your Model

Fine-tuning in this context refers to training of only a small set of the model. A small subset of the training layers, you can think of these as a set of weights, are able to be modified with new weights when you submit data for the process of fine-tuning.

Depending on your technical ability what is possible with the model the impact of the training can have varying affects. For most models, fine-tuning will influence the last layer of output. Language abilities will remain the same but the structure of the sentences and paragraphs will change.

A great example of fine-tuning is in ChatGPT itself. It was nearly useless until they fine-tuned the earlier GPT model to respond to questions and give answers. Outputting ChatGPT.

Test Your Model

Finally making it to testing for your models you need to start with setting benchmarks and measuring metrics. This will ensure that the capabilities of the model are specialized enough to meet your expected outputs while simultaneously being general enough to handle unexpected situations.

Metrics benchmarks are well studied and uniform in the field of artificial intelligence. In addition to accuracy of the prediction you should measure:

1. Precision & Recall: Measure of false positives and actual positives.
2. F1 Score: A simplified version of precision and recall. The mean.
3. Perplexity: specific to language models, this measures the output’s similarity to human language patterns.

Ideally, these metrics should be automated.

Improve Your Model

In order to improve our model we need to develop an infrastructure around it to enhance its robustness and generalization. We call this a retraining pipeline. As we start to see test results and receive feedback. We start to collect more data.

Our data then needs to go through a process of augmentation and regularization to have it ready to fine-tune our model again. As part of the field of MLOps this code is written ahead of time and automated within cloud infrastructure.

Processes like this explain the improving capabilities of different models like GPT-4 as they are being used by users. The more data these models are regularly being fed the more they improve. Something improvements can be seen on a daily basis.

While fine-tuning can provide some pretty incredible results the main benefit is the savings on cost and resources. Training a model from scratch often takes millions of dollars and thousands of hours of coding time to get the exact result that you want.

With fine-tuning one is able to “stand on the shoulders of giants”. Using existing models that are often times quite advanced. Tweaking only a few non-frozen weights to influence the behaviour of the entire model.

Want a free guide to nail product-market fit and launch your own AI software in just 8 weeks? Head over to my LinkedIn and grab a free copy

All the best,

-Adrian