from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from huggingface_hub import login
token1="hf_"
token2="rPlNHzkJScHYmtGSaQPcaoKcjJGYQEpjLu"
login(token=token1+token2)
# Load pre-trained model and tokenizer (replace with desired model name)
model_name = "meta-llama/Llama-2-7b-chat-hf"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define training arguments (hyperparameters)
training_args = TrainingArguments(
    output_dir='output',  # Output directory for checkpoints etc.
    per_device_train_batch_size=8,  # Adjust based on your hardware
    save_steps=10_000,
    num_train_epochs=3,  # Adjust training epochs as needed
)

# Load your training and validation data (specific to your chosen library)
train_dataset = "data/train.csv"
val_dataset = "data/val.csv"
# Create a Trainer object for fine-tuning
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,  # Replace with your training data loader
    eval_dataset=val_dataset,  # Replace with your validation data loader
)

# Start fine-tuning
trainer.train()