fine_tune_model.py

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

# Load custom dataset
dataset = load_dataset('json', data_files='path_to_your/shell_commands_mock_data.json')

# Load tokenizer and model for Repl.it LLM
model_name = "Repl.it/llama-2-13b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Tokenization function
def tokenize_function(examples):
    return tokenizer(examples['prompt'], padding="max_length", truncation=True)

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

# Training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=1,
    per_device_eval_batch_size=1,
    num_train_epochs=3,
    weight_decay=0.01,
    logging_dir="./logs",
    logging_steps=10,
    save_steps=100,
)

# Trainer setup
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['test'] if 'test' in tokenized_datasets else None,
)

# Start training
trainer.train()

# Save fine-tuned model
trainer.save_model("./fine_tuned_model")

# Evaluate the model
trainer.evaluate()