Why is checkpointing important?
Checkpointing is a common practice in LLM training due to the long duration and resource-intensive nature of the LLM training process.
Let’s implement a basic checkpointing system:
import torch
from transformers import GPT2LMHeadModel, GPT2Config
import os
class LLMTrainer:
    def __init__(
        self, model, optimizer, checkpoint_dir='checkpoints'
    ):
        self.model = model
        self.optimizer = optimizer
        self.checkpoint_dir = checkpoint_dir
        os.makedirs(checkpoint_dir, exist_ok=True)
    def save_checkpoint(self, epoch, step, loss):
        checkpoint = {
        ...
