A flexible and efficient framework for training and fine-tuning Large Language Models (LLMs) using PyTorch and Hugging Face Transformers.

• Support for various pre-trained models from Hugging Face
• Efficient data processing and tokenization
• Checkpoint saving and model versioning
• GPU acceleration support
• Customizable training parameters
• Progress tracking with tqdm

xl/
├── model.py        # Model architecture definition
├── dataset.py      # Data loading and processing
├── train.py        # Training loop implementation
├── requirements.txt # Project dependencies

• Python 3.8+
• PyTorch 2.1.0+
• Transformers 4.35.0+
• CUDA-capable GPU (recommended)

1. Clone the repository:

git clone [your-repository-url]
cd xl

2. Install dependencies:

pip install -r requirements.txt

1. Prepare your training data as a list of text strings.

2. Run the training script:

from train import train

texts = [
    "Your training text 1",
    "Your training text 2",
    # ... more training texts
]

model = train(
    texts,
    model_name="gpt2",  # or any other model from Hugging Face
    num_epochs=3,
    batch_size=8
)

The training function accepts several parameters:

• model_name: Name of the pre-trained model (default: "gpt2")
• output_dir: Directory for saving checkpoints (default: "checkpoints")
• num_epochs: Number of training epochs (default: 3)
• batch_size: Batch size for training (default: 8)
• learning_rate: Learning rate (default: 5e-5)
• max_length: Maximum sequence length (default: 512)
• device: Training device ("cuda" or "cpu")

You can customize the training process by:

1. Modifying the model architecture in model.py
2. Adjusting data processing in dataset.py
3. Changing training parameters in train.py

During training, the model saves checkpoints at:

• Each epoch: checkpoints/checkpoint-epoch-{epoch_number}
• Final model: checkpoints/final-model

1. Use GPU acceleration when possible
2. Adjust batch size based on available memory
3. Monitor training loss for optimal learning rate
4. Use gradient accumulation for larger effective batch sizes

Contributions are welcome! Please feel free to submit pull requests.

This project is licensed under the MIT License - see the LICENSE file for details.

• Hugging Face Transformers library
• PyTorch team
• Open source AI community

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