from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
import torch

# Load pre-trained model and tokenizer
model_name = "facebook/llama-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Load and preprocess your chat data
# This is a simplified example; you'll need to adapt it to your data format
train_data = ["Hello, how are you?", "I'm fine, thank you."]
train_encodings = tokenizer(train_data, padding=True, truncation=True, return_tensors="pt")

# Define a custom dataset
class ChatDataset(torch.utils.data.Dataset):
    def __init__(self, encodings):
        self.encodings = encodings

    def __len__(self):
        return len(self.encodings["input_ids"])

    def __getitem__(self, idx):
        return {key: val[idx] for key, val in self.encodings.items()}

train_dataset = ChatDataset(train_encodings)

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    logging_dir="./logs",
)

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Start training
trainer.train()