Fine-tuning Llama 3.2 on Your Data with torchtune
Modern open LLMs are really getting close to their closed counterparts, but still require a lot of compute to do inference (get predictions). Luckily, we have smaller (0.5B - 3B) LLMs that are very capable and can be fine-tuned on your custom data. In this tutorial, we'll fine-tune Llama 3.2 1B on a mental health sentiment dataset using torchtune.
Tutorial Goals
In this tutorial, you will:
- Prepare a custom dataset for training
- Evaluate the base (untrained) model
- Train the model on the custom dataset
- Upload and evaluate the trained model
Will the fine-tuned model outperform the base model? Let's find out!