Introduction

In this post, we will walk through the process of fine-tuning a pre-trained language model (LLM) on a custom dataset. We will be using the HuggingFace Transformers library for this purpose.

Step 1: Install Dependencies

First, we need to install the required dependencies. We will be using the PyTorch backend for this tutorial, but you can also use TensorFlow if you prefer.

pip install torch torchvision torchaudio transformers datasets

Step 2: Import Libraries

Next, we import the required libraries. We will be using the Trainer class from the transformers library to fine-tune our model. We will also be using the datasets library to load our custom dataset.

from transformers import Trainer
from datasets import load_dataset

Step 3: Load Dataset

Now, we load our custom dataset. We will be using the Wikitext-2 dataset for this tutorial.

dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")

Step 4: Load Model

Next, we load the pre-trained model that we want to fine-tune. We will be using the GPT2 model for this tutorial.

from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

Step 5: Tokenize Dataset

Now, we tokenize our dataset using the tokenizer that we loaded in the previous step.

def tokenize_function(examples):
    return tokenizer(examples["text"])

tokenized_dataset = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])

Step 6: Define Training Arguments

Next, we define the training arguments for our model. We will be using the Trainer class from the transformers library to fine-tune our model.

from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    overwrite_output_dir=True,
    num_train_epochs=1,
    per_device_train_batch_size=16,
    save_steps=10_000,
    save_total_limit=2,
    prediction_loss_only=True,
)

Step 7: Define Training Function

Now, we define the training function for our model. We will be using the Trainer class from the transformers library to fine-tune our model.

def model_init():
    return GPT2LMHeadModel.from_pretrained("gpt2")

trainer = Trainer(
    model_init=model_init,
    args=training_args,
    train_dataset=tokenized_dataset,
)

Step 8: Train Model

Finally, we train our model using the Trainer class from the transformers library.

trainer.train()

Conclusion

In this post, we walked through the process of fine-tuning a pre-trained language model (LLM) on a custom dataset. We used the HuggingFace Transformers library for this purpose.

References