In the world of AI, there’s been a big shift. For the first time, open-source models are catching up with closed-source models. This means we can now do more with smaller models. A great example is the Lama 3 model, which, with just 18 billion parameters, is almost as capable as the older Lama model with 70 billion parameters. This change is crucial because it suggests that smaller models are better candidates for fine-tuning, especially for those with limited resources like VRAM. In this blog, we’ll explore how to fine-tune a Lama 3.1 model using a tool called Unslot, focusing on how this tool can help you achieve better results with less hardware.
What is Fine-Tuning?
Fine-tuning is the process of taking a pre-trained model and making it more specialized by training it on a smaller, more specific dataset. There are usually three stages in the training process:
- Pre-training: This is where the model learns from a large amount of raw text data. It learns to predict the next word or token in a sentence, acquiring a broad understanding of language.
- Supervised Fine-Tuning: In this stage, the model…
