History of language models: Evolution, Frameworks, Deployment

Welcome to the DevConf.CZ 2024 tutorial on the history of language models! In this tutorial, we will explore the evolution of language models, the frameworks that have been developed to create and train them, and the various methods of deploying language models in real-world applications.

Evolution of Language Models

Language models have been around for decades, but it wasn’t until recent advancements in deep learning that they really began to take off. Early language models, such as n-gram models, were limited in their ability to understand the nuances and complexities of human language. However, with the introduction of neural networks and transformer architectures, language models have become far more powerful and capable of generating realistic and coherent text.

One of the key milestones in the evolution of language models was the release of OpenAI’s GPT-3 model in 2020. GPT-3 was one of the largest language models ever created, with 175 billion parameters, and it demonstrated the potential of large-scale language models for a wide range of applications.

Frameworks for Language Models

There are several popular frameworks that are commonly used for building and training language models. Some of the most widely used frameworks include:

• PyTorch
• TensorFlow
• Hugging Face Transformers

Each of these frameworks has its own strengths and weaknesses, and the choice of framework will often depend on the specific requirements of the project.

Deployment of Language Models

Once a language model has been trained, it can be deployed in a variety of ways to enable real-world applications. Some common methods of deploying language models include:

• API endpoints: Language models can be deployed as API endpoints that can be accessed by other applications.
• Embedded in applications: Language models can be embedded directly into applications to provide on-device functionality.
• Cloud services: Many cloud providers offer pre-trained language models that can be easily deployed and scaled as needed.

When deploying language models, it’s important to consider factors such as latency, scalability, and security to ensure that the model performs well in production environments.

Conclusion

In this tutorial, we’ve explored the history of language models, the frameworks that are commonly used to create them, and the various methods of deploying language models in real-world applications. By understanding the evolution of language models and knowing how to effectively deploy them, developers can harness the power of language models to create innovative and impactful applications.

Thank you for joining us at DevConf.CZ 2024!