train.py

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments

# Load dataset from Hugging Face Hub
dataset = load_dataset("pathii/css_design_snippets")

# Load pre-trained model and tokenizer
model_name = "TinyLlama/TinyLlama_v1.1"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Tokenize dataset
def tokenize_function(example):
    return tokenizer(example["input"], truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Define training arguments
training_args = TrainingArguments(
    output_dir="./model",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01,
    save_total_limit=2,
    save_strategy="epoch"
)

# Create Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["validation"],
)

# Start training
trainer.train()