Best ways to Mastering Large Language Model Fine-tuning in 2025
The field of natural language processing (NLP) has undergone a notable transformation in the last 18 months.
The process of selecting a pre-trained model and refining it through additional training on a domain-specific dataset is known as fine-tuning.
What is Fine-tuning
Having our concrete objective clear
Imagine we want to infer the sentiment of any text and decide to try GPT-2 for such a task.
Choose a pre-trained model and a dataset
The second step is to pick what model to take as a base model. In our case, we already picked the model: GPT-2.
Load the data to use
Now that we have both our model and our main task, we need some data to work with.
Tokenizer
We now have the dataset to refine our model as well as our model itself. Therefore, loading a tokenizer is the next logical step.
Initialize our base model
Once we have the dataset to be used, we load our model and specify the number of expected labels.
Evaluate method
The Transformers library provides a class called “Trainer” that optimizes both the training and the evaluation of our model.
Fine-tune using the Trainer Method
The final step is fine-tuning the model. To do so, we set up the training arguments together with the evaluation strategy and execute the Trainer object.