Fine-Tuning a GPT Model for Text Classification

A saved model can be resumed: load the model and optimizer in a new Python session or notebook, then continue pretraining for one more epoch with train_model_simple.

Programming

Why a Saved Model Is Not Yet Finished

Training a model is slow. Nobody wants to repeat hours of work just because a session ended. A saved model is what turns training into something you can pause and resume. The key idea in this article is that a saved checkpoint is not a finished product by default. It is a snapshot of a training run that can be restored later and pushed further along.

What do you think happens?

Once a training run has been saved, is the checkpoint a finished product that can only be used for predictions?

Yes, a saved model is always a finished product
No, it is a snapshot that can be restored and trained further

Reveal answer

Answer:

No, it is a snapshot that can be restored and trained further

The source describes a saved checkpoint as a snapshot of a training run. It can be loaded in a new session and continued with more pretraining, so it is not a finished product by default.

This article follows one workflow from start to finish. You save the weights, load the model and optimizer again, possibly in a completely new Python session or a Jupyter notebook, and then continue pretraining for one more epoch with the train_model_simple function. Along the way you will also see where the weights live inside the model, how pretrained OpenAI GPT-2 weights are brought in, and how the same machinery is repurposed for a text classification task.

Tracing the Restore Sequence

Read the restore sequence from left to right. Each stage depends on the one before it. The saved weights have no effect until they are loaded back into a model, and training can only continue once the model and the optimizer are both in place. The final stage is the same train_model_simple call used for the original training, now applied for one additional epoch.

In what order do the saved weights, the model, the optimizer, and train_model_simple come together, and what happens if a piece is missing?

Notice that the saved weights are only the start of the chain. The sequence needs the model to hold those values and the optimizer to drive further updates. If the saved weights are loaded but the optimizer is not restored, the run is no longer a single continuous training process. The source describes restoring both so that training continues as one run rather than as a fresh start with a partly trained model.

What Counts as a Weight

Throughout this chapter the word weights has a specific meaning. Weights are the weight parameters stored in the .weight attributes of PyTorch's Linear and Embedding layers. These are the values that save and load operations carry from one session to the next. When this article says a model has been saved, the thing being saved is the collection of these values.

A GPTModel is built out of Linear and Embedding layers, and each of those layers exposes its weights through its .weight attribute. You can reach all of these parameters together through model.parameters(), the same method the source notes was used earlier to inspect the model during training.

Which kinds of layers inside a GPTModel hold weights, and where each layer exposes those weights?

python

for name_param in model.parameters():
    print(name_param.shape)

for name_param in model.parameters():
    print(name_param.shape)

Output (expected)

The shape of each weight tensor is printed, one line per parameter tensor in the model.

Notes on the code

model.parameters() returns the weight parameters of the model
Each item is a tensor holding the values stored in a .weight attribute
This loop only inspects the parameters; it does not change them

Loading OpenAI's Pretrained GPT-2 Weights

The earlier training in this chapter used a small GPT-2 style model on a limited dataset, a short story book. That kept the focus on fundamentals, but it also meant the model had seen very little text. The source points out that OpenAI openly shared the weights of its GPT-2 models. Using those shared weights means you do not have to retrain a model on a large corpus yourself, which would require a very large investment of money and computing resources.

The plan is to load these pretrained weights into the GPTModel class you built and then use that model for text generation. Chapter 6 reuses the same pretrained weights to fine-tune the model for a text classification task and to follow instructions in a way similar to ChatGPT. So the loading step is not a one-off; it is the foundation for the classification work that follows.

Best practice

Treat the pretrained weights as a starting point you reuse rather than a model you rebuild from scratch. Loading shared weights avoids the cost of pretraining on a large corpus, and the same weights can then serve more than one downstream task.

Fine-Tuning and the Idea of a Specific Task

Definition

Fine-tuning is the process of adjusting a pre-trained LLM to perform a specific task.

A Large Language Model that has been pre-trained is a general-purpose starting point. It has learned from a broad body of language, but it has not been set up for any one job. Fine-tuning is the step that connects that general model to a specific task. The word that matters in the definition is adjusting. Fine-tuning starts from a model that already exists and changes it so that it performs a particular task. The starting point is general and the ending point is specialized.

Text classification is the task of categorizing text into predefined categories. The categories are fixed in advance, and the model's output is a choice among them. The source uses identifying spam messages as its example and states that this is a text classification task. For a spam filter, the predefined categories are spam and not spam.

Following a Spam Message Through the Model

The source gives the example of fine-tuning an LLM to identify spam messages. The worked example below follows that goal step by step, using a generated scenario with a single incoming message. The question to keep in mind is what happens to the message between the moment it arrives and the moment a label comes out.

When a message is fed to the fine-tuned model, what happens to it at each step, from input through the GPT layers to the final label?

Worked example

Classifying one incoming message

A fine-tuned model receives the single message: 'You have won a free prize, click here to claim it.' Trace how the model turns this message into one predefined category.

1
Start with the input
The message is raw text. It has no category attached yet.
2
Convert the text for the model
The text is prepared so the GPT layers can process it. The pipeline above shows this as tokenization.
3
Process through the fine-tuned layers
The GPT layers carry the weights that fine-tuning adjusted, so the model now responds to the spam-identification task rather than to general text generation.
4
Choose among fixed categories
The output is not new prose. It is a choice between the predefined categories, spam and not spam.
5
Read the label
For this message the output category is spam.

Result

The message is assigned to the spam category. The model produced a category from a fixed set, not a sequence of newly written words.

Classification Output Versus Text Generation

A classification task produces a choice among fixed categories rather than newly generated text. This is the key difference from the text generation the model was originally trained and used for. In generation, the model produces a sequence of new tokens. In classification, the model returns one category from a set that was fixed before the task began.

Aspect	Classification output	Open-ended text generation
What the model returns	One category from a fixed set	A sequence of newly produced text
Set of possible answers	Fixed in advance, such as spam and not spam	Not limited to a predefined list
Example from this article	A message labeled spam	Continuing a passage with new words
Role in this chapter	The target of fine-tuning for spam identification	The use of pretrained weights for text generation

Classification and open-ended generation use the same kind of model but produce different kinds of output.

Mistakes Learners Make With Resumed Training

Common mistake
Assuming the saved model alone is enough to resume training
Why it fails
The source says that continuing pretraining means loading both the model and the optimizer, so training continues as one run.
Do this instead
Restore the model and the optimizer, then call train_model_simple for one more epoch.
Common mistake
Treating a checkpoint as a finished product
Why it fails
A saved checkpoint is a snapshot of a training run that can be pushed further along.
Do this instead
Think of a checkpoint as a resumable starting point.
Common mistake
Using the word weights loosely
Why it fails
In this chapter weights specifically means the parameters in the .weight attributes of Linear and Embedding layers.
Do this instead
Access them through model.parameters() when you need to inspect them.
Common mistake
Expecting a classifier to write a sentence
Why it fails
A classification task returns a choice among fixed categories, not newly generated text.
Do this instead
Expect a single category such as spam or not spam as the output.

Practice Tracing the Workflow

MEDIUM

A colleague saved a model after training and now opens a new Jupyter notebook. They load only the saved model weights and immediately call train_model_simple for one more epoch. Explain what is missing from this workflow and describe the correct order of steps.

Hints

Recall which two components the source says must be loaded when training continues.
Think about what the optimizer does during training.
The correct order is: load the model, load the optimizer, then run train_model_simple for one additional epoch.

Your attempt (not saved)

EASY

Given a message in a spam filter, state the two predefined categories the output must come from, and explain in one sentence why the output is not open-ended text generation.