Pretraining a GPT Model
A model adapted this way is meant to handle a wide range of requests, and the quality of its responses to those requests is the main target.
Starting From the Goal
Once a GPT model has been pretrained, a team that wants to adapt it has to make a decision before any training starts. The useful first question is not which training recipe to use. It is what the adapted model must do. Some projects need one model that can take many different kinds of requests and respond sensibly to each. Other projects need a model that sorts incoming text into a fixed set of labels and does nothing else. The two fine-tuning approaches covered here are built around those two goals, and the choice between them shapes the data you collect, the hardware you need, and the evaluation you run.
What do you think happens?
A company wants a model that reads each incoming customer message and labels it as spam or not spam. Which approach fits best: instruction fine-tuning or classification fine-tuning?
Reveal answer
Classification fine-tuning
The source describes classification fine-tuning as suited to precise categorization into predefined classes, such as spam detection. Instruction fine-tuning is built for flexibility across many different instructions, which this project does not need.
Two Goals for an Adapted Model
Instruction fine-tuning aims at flexibility and interaction quality across many complex user instructions. A model adapted this way is meant to handle a wide range of requests, and the quality of its responses to those requests is the main target. Classification fine-tuning has a narrower aim. It is suited to precise categorization into predefined classes, such as sentiment analysis or spam detection. The model is not asked to write open-ended responses. It is asked to decide which of a known set of labels a piece of text belongs to.
| Question | Instruction fine-tuning | Classification fine-tuning |
|---|---|---|
| Main goal | Flexibility and interaction quality across many complex instructions | Precise categorization into predefined classes |
| Typical examples | Responding to many different kinds of user requests | Sentiment analysis and spam detection |
| Data demand | Larger datasets | Less data |
| Compute demand | Greater computational resources | Less compute |
| Scope of use | Broad, across many tasks | Confined to the specific classes it was trained on |
The two approaches are built around different goals, which determines the data, hardware, and evaluation a project needs.
Trading Data and Compute for Scope
The two approaches sit on opposite sides of a trade-off. Instruction fine-tuning is the more versatile of the two, but it demands larger datasets and greater computational resources in order to produce a model that is proficient across many tasks. Classification fine-tuning asks less of both data and compute. The cost of that economy is scope. A classification model's use is confined to the specific classes it was trained on.
Put simply, the trade-off is between breadth and economy. Choosing instruction fine-tuning buys flexibility at the price of more data and more compute. Choosing classification fine-tuning keeps data and compute demands lower, and accepts that the model will only ever answer within the label set it learned.
Three Stages From Pretrained Model to Classifier
Once classification fine-tuning has been chosen, the work proceeds in three stages. The diagram below shows how the stages hand off to one another. Each stage must finish before the next one begins, so the order matters. A later stage can reveal a problem that was introduced by an earlier choice.
The three stages are detailed in the subsections below. Read them as a sequence: the data prepared in Stage 1 feeds the setup in Stage 2, and the trained model from Stage 3 is only as sound as the choices made before it.
Worked Project: Spam Detection Setup
The worked example used throughout this stage is a classification fine-tuning project on the GPT model that was implemented and pretrained earlier. The task is to modify that model and fine-tune it for classification, using a text message dataset that contains both spam and non-spam messages. The text message setting makes the goal intuitive, because each message needs one of two labels.
Deciding how to adapt the model for spam labeling
A team has a pretrained GPT model and wants it to decide, for each text message, whether the message is spam or non-spam. Which approach should they choose, and what should they do first?
- 1Identify the goal
Each message needs one of two labels. The model must categorize text into predefined classes rather than respond flexibly to many kinds of requests.
- 2Choose the approach
Because the goal is precise categorization, classification fine-tuning is the matching approach. It needs less data and compute than instruction fine-tuning, and its limit, a confinement to the trained classes, is acceptable when the label set is just spam and non-spam.
- 3Begin Stage 1
The first practical work is dataset preparation: download the labeled text message dataset, inspect it, and preprocess it before creating data loaders.
- 4Plan for the later stages
Model setup and fine-tuning follow. If evaluation later reveals a problem, the team returns to the earlier choices instead of patching the final model.
The team chooses classification fine-tuning and starts with dataset preparation on the labeled spam and non-spam messages, keeping the three-stage order in mind.
First Steps With the Text Message Dataset
Preparing the text message dataset is the first stage of the project, and its opening steps follow a clear order. The raw labeled messages are downloaded first. They are then inspected so that you understand what the data contains. After inspection, the data is preprocessed, and only then are data loaders created to feed it into training. The flow below shows that order.
- 1Download the labeled text message dataset that contains both spam and non-spam messages.
- 2Inspect the dataset to understand its contents before changing anything.
- 3Preprocess the dataset so that the messages are ready for training.
- 4Create data loaders that supply the prepared data to the training process.
Mistakes When Choosing and Preparing
The mistakes below cluster around the two decisions this article has focused on: which approach matches the goal, and how the stages fit together.
Common mistake Choosing instruction fine-tuning for a fixed set of labels
What it looks likeA team needs only spam or non-spam decisions, but adopts the approach built for many complex instructions.
Why it failsInstruction fine-tuning is built for flexibility across many tasks and demands larger datasets and more compute. A project that only needs precise categorization does not use that flexibility.
Do this insteadMatch the approach to the goal. For precise categorization into predefined classes, classification fine-tuning is the suited choice.
Common mistake Assuming a classifier can handle a category it never saw
What it looks likeA spam classifier is trained on spam and non-spam, then is expected to recognize a third kind of message without any retraining.
Why it failsA classification model's use is confined to the specific classes it was trained on. A new category it has never seen is not something it can handle by design.
Do this insteadDecide up front whether the label set is stable. If new categories are expected, plan for that limit rather than assuming the model will cope.
Common mistake Patching evaluation problems only at the end
Why it failsThe stages are sequential. A problem found in evaluation usually traces back to an earlier choice, so fixing only the final output leaves the cause in place.
Do this insteadWhen evaluation reveals a problem, revisit the earlier stages and their choices.
Common mistake Skipping inspection and moving straight to data loaders
Tracing the Path for a New Project
A team wants a pretrained GPT model to label customer feedback as positive, negative, or neutral, and it has no plans to add new labels later. Trace the path. Which approach should they choose, what is the first stage, and what are the first two steps inside that stage? Then name one limit they should accept and explain why.
Hints
- Ask whether the goal is flexible response quality or precise categorization into predefined classes.
- Stage 1 is dataset preparation. Its opening steps are downloading and inspecting the dataset.
- Think about what the model can and cannot do with a label it was never trained on.
Key Takeaways for Choosing a Path
- 1Instruction fine-tuning aims at flexibility and interaction quality across many complex user instructions, and it demands larger datasets and more computational resources.
- 2Classification fine-tuning suits precise categorization into predefined classes, needs less data and compute, and is limited to the classes it was trained on.
- 3Classification fine-tuning proceeds in three stages: dataset preparation, model setup, and fine-tuning with evaluation. Because the stages are sequential, evaluation problems send you back to earlier choices.
- 4Dataset preparation begins with downloading the dataset, followed by inspecting and preprocessing it before creating data loaders.
At a glance
- Instruction fine-tuning aims at flexibility and interaction quality across many complex user instructions, and it needs larger datasets and more compute.
- Classification fine-tuning suits precise categorization into predefined classes and needs less data and compute, but it is limited to the classes it was trained on.
- Classification fine-tuning runs in three stages: dataset preparation, model setup, and fine-tuning with evaluation, and problems found late send you back to earlier choices.
- Dataset preparation starts with downloading the labeled text message dataset, then inspecting and preprocessing it before creating data loaders.