# Standard Hugging Face training setup
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from datasets import load_dataset
import torch

# Load model and tokenizer
model_name = "meta-llama/Llama-2-7b-hf"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load dataset (example)
dataset = load_dataset("your_dataset_name")

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=8,
    learning_rate=2e-5,
    num_train_epochs=3,
    # ... other args
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    tokenizer=tokenizer,
)

# Train the model
trainer.train()