How do you fine-tune a pre-trained model for a custom dataset?

Q: How do you fine-tune a pre-trained model for a custom dataset?

Learn the answer to "How do you fine-tune a pre-trained model for a custom dataset?" with detailed explanations, code examples, and best practices on DeployU.

The Scenario

You are an ML engineer at a large electronics company. The company wants to build a chatbot that can answer customer questions about its products. You have been given a dataset of 10,000 question-answer pairs that have been manually created by the customer support team.

Your task is to fine-tune a pre-trained language model to create a chatbot that can answer customer questions accurately and efficiently. The chatbot must be able to handle a wide range of questions, from simple “what is” questions to more complex “how to” questions. The target is to achieve a customer satisfaction score of at least 90%.

The Challenge

Explain your strategy for fine-tuning a pre-trained model for this task. What are the key steps you would take, from data preparation to model evaluation? What are some of the challenges you might face, and how would you address them?

Wrong Approach

A junior engineer might just jump straight into fine-tuning a model without a clear strategy. They might not consider the different fine-tuning strategies that are available, or they might not know how to properly prepare the data for this task. They might also not have a clear plan for evaluating the model's performance.

Addresses symptoms, not root cause

Right Approach

A senior engineer would start by carefully analyzing the requirements of the task and the characteristics of the dataset. They would then develop a clear strategy for fine-tuning the model, including a plan for data preparation, model selection, training, and evaluation. They would also be aware of the potential challenges and would have a plan for addressing them.

Step 1: Data Preparation and Pre-processing

The first step is to prepare the data for fine-tuning. This involves:

Cleaning the data: Remove any noise from the data, such as HTML tags, special characters, and duplicate examples.
Formatting the data: The data needs to be formatted in a way that is suitable for training a question-answering model. A common approach is to concatenate the question and answer into a single string, separated by a special token.
Splitting the data: Split the data into a training set, a validation set, and a test set.

Step 2: Model Selection

The next step is to choose a pre-trained model to fine-tune. For this task, a generative model like GPT-2 or a sequence-to-sequence model like T5 would be a good choice. We’ll start with distilgpt2, a smaller and faster version of GPT-2.

Step 3: Fine-tuning Strategy

There are two main strategies for fine-tuning a model:

Strategy	Description	Pros	Cons
Full Fine-tuning	Update all the weights of the pre-trained model.	Can lead to better performance if you have a large amount of data.	Can be computationally expensive and prone to catastrophic forgetting.
Parameter-Efficient Fine-tuning (PEFT)	Freeze the weights of the pre-trained model and only train a small number of additional parameters (e.g., LoRA, adapters).	Much more memory-efficient and faster to train. Less prone to catastrophic forgetting.	Might not perform as well as full fine-tuning if you have a very large dataset.

Given our dataset size (10,000 examples), we’ll start with PEFT using LoRA (Low-Rank Adaptation) as it’s a good balance between performance and efficiency.

Step 4: Training and Evaluation

We will use the Trainer API from the transformers library to fine-tune the model. We will use a custom training loop to have more control over the training process.

Code Example:

from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from datasets import load_dataset
from peft import get_peft_model, LoraConfig

# 1. Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")

# 2. Configure LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)

# 3. Load and prepare dataset
# ... (code to load and process the dataset)

# 4. Set up training arguments
training_args = TrainingArguments(
    output_dir="chatbot_model",
    num_train_epochs=3,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    evaluation_strategy="epoch",
    logging_dir="logs",
)

# 5. Create Trainer and train
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()

We will evaluate the model using metrics like BLEU and ROUGE, as well as human evaluation to assess the quality of the chatbot’s responses.

Systematic, production-ready debugging

Practice Question

You are fine-tuning a large language model and want to minimize the risk of catastrophic forgetting. Which fine-tuning strategy would be the most suitable?

Questions