Fine-tune GPT-2 using code.

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Can you show me (with code) how to fine-tune GPT

Sure! Here's a basic example of how you can fine-tune the GPT-2 model using the Hugging Face Transformers library. This example assumes that you have already downloaded the pre-trained GPT-2 model and have a fine-tuning dataset ready.

First, you'll need to install the Transformers library if you haven't already. You can do this with pip:

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pip install transformers
Next, let's import the necessary modules:

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from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW
import torch
from torch.utils.data import Dataset, DataLoader
Now, let's define our custom dataset class, which will load our fine-tuning data:

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class FineTuningDataset(Dataset):
    def __init__(self, encodings, labels=None):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        if self.labels:
            item["labels"] = torch.tensor(self.labels[idx])
        return item

    def __len__(self):
        return len(self.encodings.input_ids)
Now, let's load the pre-trained GPT-2 model and tokenizer:

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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
Next, let's tokenize our fine-tuning dataset and create our DataLoader:

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encoded_dataset = tokenizer(dataset, padding=True, truncation=True, max_length=512)
fine_tuning_dataset = FineTuningDataset(encoded_dataset)

dataloader = DataLoader(fine_tuning_dataset, batch_size=8, shuffle=True)
Now, let's define our training function:

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def train(model, dataloader, optimizer, device, scheduler, max_length=512):
    model.train()
    total_loss = 0
    for batch in dataloader:
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device) if 'labels' in batch else None

        optimizer.zero_grad()
        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        total_loss += loss.item()
        loss.backward()
        optimizer.step()
        scheduler.step()
    avg_loss = total_loss / len(dataloader)
    return avg_loss
Finally, let's train the model for a few epochs:

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device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)

optimizer = AdamW(model.parameters(), lr=1e-5)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=1000, num_training_steps=10000)

num_epochs = 3
for epoch in range(num_epochs):
    print(f"Epoch {epoch + 1}/{num_epochs}")
    avg_loss = train(model, dataloader, optimizer, device, scheduler)
    print(f"Average loss: {avg_loss:.4f}")

This is just a basic example to get you started. You may need to modify this code to fit your specific use case. For example, you might want to use a different batch size, learning rate, or number of epochs. You might also want to use a different tokenizer or model architecture. But hopefully, this gives you a good starting point for fine-tuning GPT-2!

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