EMMAT: Efficient Multi-Modal Language Model Auto Trainer

EMMAT is a powerful and efficient framework for training large-scale language models that can leverage multi-modal data, adapt to new tasks quickly, and continuously improve their performance. It combines state-of-the-art techniques such as Dynamic Knowledge Distillation and Pruning (DKDP), Multi-Modal Adversarial Transfer Learning (MMATL), and AXOLOTL (A fleXible cOmpositional neural architecture for Language mOdel Transfer Learning) to create a comprehensive and adaptable language model training pipeline.

Features

• Multi-Modal Pre-training: EMMAT can leverage large-scale multi-modal datasets, including text, images, audio, and other relevant modalities, to pre-train the language model and learn rich representations.
• Compositional Architecture: EMMAT employs a modular and compositional architecture that allows for the flexible combination of different neural components, such as transformers, RNNs, and CNNs, to capture various aspects of language.
• Task-Specific Adapters: EMMAT introduces task-specific adapters that can be attached to the pre-trained language model for efficient transfer learning and adaptation to new tasks.
• Dynamic Knowledge Distillation and Pruning: EMMAT incorporates DKDP techniques to dynamically optimize the model size and performance by distilling knowledge from a larger teacher model to a smaller student model and pruning less important parameters.
• Adversarial Training: EMMAT utilizes adversarial training techniques from MMATL to improve the robustness and generalization of the language model by encouraging it to generate outputs that are indistinguishable from human-generated text.
• Curriculum Learning: EMMAT supports curriculum learning strategies to gradually increase the complexity of the training data and tasks, enabling the model to learn more efficiently and effectively.
• Multi-Task Learning: EMMAT allows for simultaneous training of the language model on multiple related tasks using task-specific adapters, facilitating knowledge sharing and improving overall performance.
• Continual Learning: EMMAT implements continual learning mechanisms to enable the model to adapt to new data and tasks without forgetting previously acquired knowledge.
• Hyperparameter Optimization: EMMAT includes automated hyperparameter optimization techniques to find the optimal configuration for the language model and training process.
• Model Compression: EMMAT applies model compression techniques, such as quantization, pruning, and knowledge distillation, to reduce the size and computational requirements of the language model.

Getting Started

To get started with EMMAT, follow these steps:

1. Install the required dependencies.

2. Prepare your multi-modal dataset:

   • Collect and preprocess large-scale multi-modal datasets, including text, images, audio, and other relevant modalities.
   • Ensure that the samples from each modality are properly aligned and synchronized.

3. Configure the EMMAT settings:

   • Modify the configuration file (config.py) to specify the desired model architecture, hyperparameters, and training settings.

4. Train the EMMAT model:

   python train.py --config config.py
   

5. Evaluate the trained model:

   python evaluate.py --model path/to/trained/model
   

6. Fine-tune the model for specific tasks:

   • Use the task-specific adapters to fine-tune the pre-trained EMMAT model for your desired downstream tasks.

Contributing

Contributions to EMMAT are welcome! If you encounter any issues, have suggestions for improvements, or want to contribute new features, please open an issue or submit a pull request on the GitHub repository.

License

EMMAT is released under the APACHE License.

Acknowledgments

EMMAT builds upon the following research papers and open-source projects:

• Dynamic Knowledge Distillation and Pruning (DKDP)
• Multi-Modal Adversarial Transfer Learning (MMATL)
• AXOLOTL: A fleXible cOmpositional neural architecture for Language mOdel Transfer Learning
• Hugging Face Transformers
• PyTorch

We would like to thank the authors and contributors of these works for their valuable research and contributions to the field of natural language processing and machine learning.