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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() |