Guide to Auto-Training LLaMA 3 Model with Data Cleanup

Table of Contents

1. Preparing for Auto-Training LLaMA 3
2. Setting Up the Training Script
3. Automating Data Cleanup After Training
4. Running the Auto-Training Process

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Preparing for Auto-Training LLaMA 3

LLaMA 3 is a highly efficient and powerful language model. To start auto-training with data stored in a specific folder (e.g., train_data), we will automate the process and ensure that any used training data is cleaned up after the training process is complete. Below is a step-by-step guide.

Step 1: Install Required Libraries

First, ensure that you have the necessary libraries installed. This includes transformers for the model, torch for training, and datasets for loading the data.

pip install transformers torch datasets

Step 2: Preparing the Dataset

For this example, we assume that your training data is stored in a folder named train_data. The data should be in text format, with each file containing training examples.

Step 3: Write the Data Preprocessing Script

Before we train the model, we need to preprocess the data from the train_data folder. We’ll read all the text files in this folder and concatenate them into a single dataset.

import os
from datasets import Dataset
import glob

def load_data_from_folder(folder_path):
    # Get all text files from the folder
    files = glob.glob(os.path.join(folder_path, "*.txt"))
    all_texts = []

    # Read each file and append its content to all_texts
    for file in files:
        with open(file, 'r') as f:
            all_texts.append(f.read())

    # Create a dataset
    dataset = Dataset.from_dict({"text": all_texts})
    return dataset

# Load data
train_data = load_data_from_folder('train_data')

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Setting Up the Training Script

Now that we have the dataset, we need to set up the training script. We’ll use Hugging Face’s transformers library for model training.

Step 1: Load the Pretrained LLaMA 3 Model

We’ll assume that LLaMA 3 is available on Hugging Face’s Model Hub or that you have a pretrained version.

from transformers import LlamaForCausalLM, LlamaTokenizer, Trainer, TrainingArguments

# Load tokenizer and model
tokenizer = LlamaTokenizer.from_pretrained('facebook/llama-3')
model = LlamaForCausalLM.from_pretrained('facebook/llama-3')

Step 2: Tokenize the Dataset

To prepare the data for training, we need to tokenize the text data.

def tokenize_data(dataset):
    # Tokenize the dataset
    def tokenize_function(examples):
        return tokenizer(examples['text'], return_tensors='pt', truncation=True, padding=True)

    tokenized_data = dataset.map(tokenize_function, batched=True)
    return tokenized_data

# Tokenize the training data
train_data = tokenize_data(train_data)

Step 3: Set Up Training Arguments

Define the training arguments such as the number of epochs, batch size, and output directory.

training_args = TrainingArguments(
    output_dir="./llama3_model",          # Directory to save model
    num_train_epochs=3,                   # Number of training epochs
    per_device_train_batch_size=8,        # Batch size per device
    save_steps=10_000,                    # Save checkpoint every 10,000 steps
    logging_dir="./logs",                 # Directory for logs
    logging_steps=200,                    # Log every 200 steps
    remove_unused_columns=False          # Prevent errors related to unused columns
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_data,
)

Step 4: Train the Model

Train the model using the trainer.

# Start training
trainer.train()

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Automating Data Cleanup After Training

After the model is trained, we will automatically delete the used training data files from the train_data folder to free up space.

Step 1: Delete Used Files

To automatically delete the files after training, you can add the following function to clean up the train_data folder.

import shutil

def cleanup_data(folder_path):
    # Delete all files in the 'train_data' folder after training
    for file in os.listdir(folder_path):
        file_path = os.path.join(folder_path, file)
        if os.path.isfile(file_path):
            os.remove(file_path)
        elif os.path.isdir(file_path):
            shutil.rmtree(file_path)

# Cleanup after training
cleanup_data('train_data')

Step 2: Integrate Cleanup with Training Script

Finally, we will integrate the cleanup process into the training script to ensure that the files are deleted once the training completes.

# Train the model and clean up data afterward
try:
    trainer.train()
finally:
    # Clean up the training data
    cleanup_data('train_data')
    print("Training data has been cleaned up.")

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Running the Auto-Training Process

Step 1: Run the Script

To start the auto-training process, simply run your Python script:

python train_llama3.py

This will automatically:

• Load the training data from the train_data folder.
• Tokenize and preprocess the data.
• Train the LLaMA 3 model.
• Clean up the used data files after training.

Step 2: Monitor the Training Progress

The script will log training progress. You can adjust logging intervals or monitor the model’s performance as needed.