diffusionLM 0.1.9

A diffusion-based language model implementation

DiffusionLM: Large Language Models with Diffusion

DiffusionLM is a novel approach to language modeling that combines transformer architectures with diffusion processes for high-quality text generation. This package provides a flexible and efficient implementation of diffusion-based language models.

Features

• Advanced Architecture

  • Transformer-based backbone with diffusion capabilities
  • Configurable model sizes (small, medium, large)
  • Time step conditioning
  • Attention mechanisms optimized for text

• Multiple Generation Strategies

  • Auto-regressive generation
  • Parallel generation
  • Confidence-based masking
  • Semi-autoregressive generation
  • Top-p (nucleus) sampling
  • Beam search

• Training Features

  • Distributed training support
  • Mixed precision training
  • Gradient checkpointing
  • Early stopping
  • Model checkpointing
  • Learning rate scheduling

• Utilities

  • Real-time token generation streaming
  • Model saving and loading
  • HuggingFace Hub integration
  • Comprehensive logging
  • Error handling

Installation

pip install diffusionLM

For development installation:

git clone https://github.com/codewithdark-git/DiffusionLM.git
cd DiffusionLM
pip install -e .

Quick Start

from diffusionLM.utils import prepare_dataset
from diffusionLM.model import DiffusionConfig, DiffusionLLM
from transformers import AutoTokenizer

# Load tokenizer and prepare dataset
tokenizer = AutoTokenizer.from_pretrained("gpt2")
train_dataset, val_dataset, _ = prepare_dataset(
    dataset_name="wikitext/wikitext-103-v1",
    tokenizer_name="gpt2"
)

# Initialize model
config = DiffusionConfig(
        vocab_size=len(tokenizer),
        max_position_embeddings=256,
        num_timesteps=50,
        pad_token_id=tokenizer.pad_token_id,
        mask_token_id=tokenizer.mask_token_id,
        # **config_kwargs
    )

model = DiffusionLLM(config)

Training

Basic Training

from diffusionLM import trainer

train_model = trainer(
        model=model,
        train_dataset=train_dataset,
        val_dataset=val_dataset,
        batch_size=batch_size,
        num_epochs=num_epochs,
        learning_rate=learning_rate,
        num_timesteps=num_timesteps,
        save_path=save_dir,
        device=device,
    )

Model Registry

from diffusionLM import registerANDpush

registerANDpush(
    model=trained_model,
    tokenizer=tokenizer,
    model_type="diffusionLM",
    repo_id="your-username/model-name"
)

Error Handling

The package includes comprehensive error handling:

from diffusionLM import DiffusionLMError, handle_errors

@handle_errors()
def your_function():
    # Your code here
    pass

Sponsorship

If you find DiffusionLM useful for your project or research, please consider supporting its development through GitHub Sponsors. Your sponsorship helps maintain the project and develop new features.

Why Sponsor?

• Support ongoing development and maintenance
• Priority bug fixes and feature requests
• Recognition in our documentation
• Help make DiffusionLM better for everyone

How to Sponsor

Click the "Sponsor" button at the top of the repository or visit our GitHub Sponsors page.

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Requirements

• Python ≥ 3.8
• PyTorch ≥ 1.9.0
• Transformers ≥ 4.21.0
• For full requirements, see requirements.txt

License

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

Citation

@article{diffusionllm2025,
  title={DiffusionLM: Large Language Models with Diffusion},
  author={Dark Coder},
  journal={GitHub Repository},
  year={2025},
  publisher={GitHub},
  url={https://github.com/codewithdark-git/DiffusionLM}
}

Contact

• GitHub: @codewithdark-git
• Email: [e-mail]