Date : June 5
Time : 1:00 PM ET/17:00 UTC
Presenters : Sebastian Raschka , Marlene Mhangami
Introduction: Join us for an insightful talk that will walk you through the crucial stages involved in developing large language models (LLMs). Whether you’re a seasoned developer, a data scientist, or simply curious about how these powerful AI models are created, this session will provide a detailed and practical overview from initial coding to deployment. We’ll delve into the technicalities, share real-world examples, and encourage an interactive learning environment with plenty of opportunities for questions.
Building Large Language Models: Coding and Architecture: Our journey begins with the foundational step of building LLMs: coding their architectures. We’ll explore the core components that make up these models, such as neural network structures, layers, and parameters. You’ll learn about the different architectures used in LLMs, like transformers, and how these architectures are coded to enable models to understand and generate human-like text. We’ll cover:
- Understanding Model Architectures: An in-depth look at various LLM architectures, including transformers, and how they function.
- Coding the Model: Step-by-step guidance on writing the code for these architectures, highlighting key considerations and best practices.
Pre-training: Setting the Stage for Learning: Once the model architecture is in place, the next step is pre-training. This stage involves training the model on vast amounts of data to help it understand the nuances of language. We’ll explain:
- What is Pre-training? The concept of pre-training and why it’s a critical step in developing LLMs.
- Data and Resources: The types of data used for pre-training and the computational resources required.
- Process and Techniques: The methods used during pre-training, such as unsupervised learning, and the challenges faced.
Fine-tuning: Tailoring the Model to Specific Tasks: After pre-training, the model undergoes fine-tuning, where it is adjusted to perform specific tasks more accurately. This stage fine-tunes the model’s capabilities and adapts it to particular domains or applications. We’ll discuss:
- The Importance of Fine-tuning: How fine-tuning enhances the model’s performance and accuracy.
- Techniques and Approaches: Various techniques used in fine-tuning, such as supervised learning and transfer learning.
- Real-world Applications: Examples of how fine-tuning is applied in different industries and use cases.
Deployment: Bringing LLMs to Life: The final stage is deploying the model, making it accessible and usable in real-world applications. We’ll cover the practical aspects of deployment, including:
- Deployment Strategies: Different methods for deploying LLMs, from cloud services to on-premises solutions.
- Challenges and Solutions: Common challenges faced during deployment and how to overcome them.
- Monitoring and Maintenance: Best practices for monitoring the model’s performance and maintaining its efficiency post-deployment.
Interactive Learning and Real Examples: Throughout the talk, we’ll provide real examples of LLM development and deployment, illustrating the concepts discussed with tangible cases. Attendees are encouraged to ask questions, share their experiences, and engage in discussions, making this session a dynamic and interactive learning experience.
Conclusion: By the end of this talk, you’ll have a comprehensive understanding of the key stages involved in developing large language models, from initial coding to deployment. You’ll gain practical insights and learn best practices that you can apply to your own projects. Join us to explore the fascinating world of LLMs and unlock the potential of these advanced AI models in your work.
Call to Action: Don’t miss this opportunity to deepen your knowledge of large language models and enhance your technical skills. Register now and join us for a detailed and interactive session on the development and deployment of LLMs!
Register here : Webinar
Reference to the Article : TECHTalks
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