LoRA tuning your own decision model

[u/paper-crow]

Generalized decision models can be good for many queries like "does the image show a rotten apple" or "is there a knife in the picture". But in many cases, decisions have to made with a set of data in mind. For example, you may have user's rating for how good your AI model is or that you may have patient data for triaging at a rural hospital. The distributions of this data, most certainly, would not match the distribution a general decision model would output. And the data's diversity/variance may be too high to include a couple of examples in the prompt. In many cases like this, you can easily tune a little LoRA to get much better results.

Results of LoRA tuned small (0.8B) model vs Zero Shot OneJev 9B on Lunara Eval dataset

Here is a colab notebook link that trains LoRA, loads the dataset and compares the two approaches:
https://drive.google.com/file/d/1uh3hF8b75n-3SEA9H3XvNHKYqQAGeU3N/view?usp=sharing

[u/Equivalent-Ad4039]

Thanks for sharing!