train_and_save_model.py

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

# Load datasets
dataset_pentesting = load_dataset("canstralian/pentesting-ai")
dataset_redpajama = load_dataset("togethercomputer/RedPajama-Data-1T")

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained("canstralian/rabbitredeux")

def tokenize_function(examples):
    return tokenizer(examples['text'], padding="max_length", truncation=True)

# Tokenize datasets
tokenized_dataset_pentesting = dataset_pentesting.map(tokenize_function, batched=True)
tokenized_dataset_redpajama = dataset_redpajama.map(tokenize_function, batched=True)

# Prepare datasets
train_dataset_pentesting = tokenized_dataset_pentesting["train"]
validation_dataset_pentesting = tokenized_dataset_pentesting["validation"]

# Load model and adapter
model = AutoAdapterModel.from_pretrained("canstralian/rabbitredeux")
model.load_adapter("Canstralian/RabbitRedux", set_active=True)

# Training arguments
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir="./logs",
    logging_steps=10,
    evaluation_strategy="epoch",
)

# Trainer setup
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset_pentesting,
    eval_dataset=validation_dataset_pentesting,
)

# Training
trainer.train()

# Evaluate model
trainer.evaluate()

# Save the fine-tuned model
model.save_pretrained("./fine_tuned_model")