Write a training loop in Hugging Face s Trainer API to fine-tune GPT-4 on a custom dataset

Question

Can you tell me Write a training loop in Hugging Face’s Trainer API to fine-tune GPT-4 on a custom dataset.

Answer 1

You can fine-tune GPT-4 on a custom dataset using Hugging Face’s Trainer API by configuring the dataset, model, tokenizer, and training arguments.
Here is the code snippet below:

In the above code we are using the following key points:

• GPT2Tokenizer and GPT2LMHeadModel to simulate a GPT-like setup (can be swapped for GPT-4 if available through Hugging Face or API).

• TextDataset to load plain text data and prepare it for causal language modeling.

• DataCollatorForLanguageModeling to handle dynamic padding and label shifting.

• TrainingArguments to control training hyperparameters like batch size and number of epochs.

• Trainer API to abstract away the training loop with all required components.

Hence, this setup enables a clean and modular approach to fine-tune large language models efficiently using Hugging Face's Trainer API.