# Unsloth enhanced training setup
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from datasets import load_dataset
import torch
from unsloth import FastLanguageModel # Import Unsloth

# Load model and tokenizer with Unsloth's FastLanguageModel
# This implicitly applies optimizations like QAT and FlashAttention patches
model, tokenizer = FastLanguageModel.from_pretrained(
    "unsloth/llama-2-7b-hf", # Using a pre-quantized Unsloth model variant can be even faster
    model_name="meta-llama/Llama-2-7b-hf", # Or specify the base model and let FastLanguageModel quantize
    load_in_4bit=True, # Enable 4-bit quantization
    # Other potential Unsloth specific args like use_flash_attention_2=True
)

# Configure LoRA if needed (Unsloth also optimizes LoRA)
# model = FastLanguageModel.getlora_model(model, lora_r=8, lora_alpha=16, lora_dropout=0.05)

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

# Define training arguments (largely the same)
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()