Provide the validation loop needed in this code and fixes for any possible issues that you see:

import os
import yaml
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from transformers import AutoTokenizer, get_cosine_schedule_with_warmup, AdamW
from transformers import AutoModelForCausalLM
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
import wandb
from datasets import load_from_disk

def main():
    # Initialize distributed training
    dist.init_process_group('nccl')
    local_rank = int(os.environ["LOCAL_RANK"])
    torch.cuda.set_device(local_rank)
    device = torch.device("cuda", local_rank)

    # Load configuration
    with open('configs/text_training_config.yaml') as f:
        config = yaml.safe_load(f)

    # Initialize W&B
    if local_rank == 0:
        wandb.init(project='multi-modal-llm', name='llama3-2-1b-oasst1-finetune', config=config)

    # Load tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-3.2-1B')
    model = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-3.2-1B')
    if tokenizer.pad_token is None:
        tokenizer.add_special_tokens({'pad_token': '[PAD]'})
        model.resize_token_embeddings(len(tokenizer))
    model = model.to(device)
    model = nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank)

    # Load dataset
    dataset = load_from_disk('data/processed/oasst1_tokenized')
    train_dataset = dataset['train']
    val_dataset = dataset['validation']

    # Create DataLoaders
    train_sampler = DistributedSampler(train_dataset)
    val_sampler = DistributedSampler(val_dataset)
    train_loader = DataLoader(train_dataset, batch_size=config['batch_size'], sampler=train_sampler)
    val_loader = DataLoader(val_dataset, batch_size=config['batch_size'], sampler=val_sampler)

    # Define optimizer and scheduler
    optimizer = AdamW(model.parameters(), lr=config['learning_rate'])
    total_steps = len(train_loader) * config['num_epochs']
    scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=config['warmup_steps'], num_training_steps=total_steps)

    # Mixed precision
    scaler = torch.cuda.amp.GradScaler(enabled=config['fp16'])

    # Training loop
    for epoch in range(config['num_epochs']):
        model.train()
        train_sampler.set_epoch(epoch)
        pbar = tqdm(train_loader) if local_rank == 0 else train_loader

        for batch in pbar:
            optimizer.zero_grad()
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            labels = batch['labels'].to(device)

            with torch.cuda.amp.autocast(enabled=config['fp16']):
                outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
                loss = outputs.loss

            scaler.scale(loss).backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), config['max_grad_norm'])
            scaler.step(optimizer)
            scaler.update()
            scheduler.step()

            if local_rank == 0:
                pbar.set_postfix({'loss': loss.item()})
                wandb.log({'loss': loss.item()})

        # Validation loop can be added here

        # Save checkpoint
        if local_rank == 0:
            model.module.save_pretrained(f'models/checkpoints/epoch_{epoch}')

if __name__ == "__main__":
    main()