Optimizing Large Language Models (LLM) Performance-Fine-tuning, Testing, and Deployment
Training Provider: Republic Polytechnic
Course Reference: TGS-2024050519
S$90
Original: S$300
Save S$210
About This Course
In this hands-on workshop, you'll learn how to fine-tune Large Language Models (LLMs) to significantly improve their performance for specific tasks—without any coding required. Explore the fundamentals of LLMs, master Retrieval Augmented Generation (RAG), and develop practical skills in dataset preparation and model configuration. You'll also learn how to evaluate and deploy fine-tuned models effectively across various platforms. By the end, you'll be equipped to apply fine-tuning techniques to enhance LLMs for real-world, industry-specific applications.
What You'll Learn
Understand the Fundamentals of Large Language Models (LLMs): Gain a foundational understanding of Large Language Models, their architecture, and key concepts.
Explore Retrieval Augmented Generation (RAG): Understand how to integrate RAG with LLMs to enhance their capabilities.
Develop Skills in Fine-Tuning LLMs: Learn the complete process of fine-tuning, from selecting and configuring pre-trained models to adjusting hyperparameters.
Prepare and Optimize Datasets: Acquire the skills to clean, annotate, and prepare datasets specifically for LLM fine-tuning.
Evaluate Fine-Tuned Models: Learn techniques for testing and evaluating the performance of fine-tuned models using relevant metrics.
Deploy Fine-Tuned Models: Understand the best practices for deploying fine-tuned models in various environments, including local, cloud, and API-based setups.
Apply Fine-Tuning to Real-World Scenarios: Equip yourself with the knowledge to apply fine-tuning techniques to practical, industry-relevant applications.
Explore Retrieval Augmented Generation (RAG): Understand how to integrate RAG with LLMs to enhance their capabilities.
Develop Skills in Fine-Tuning LLMs: Learn the complete process of fine-tuning, from selecting and configuring pre-trained models to adjusting hyperparameters.
Prepare and Optimize Datasets: Acquire the skills to clean, annotate, and prepare datasets specifically for LLM fine-tuning.
Evaluate Fine-Tuned Models: Learn techniques for testing and evaluating the performance of fine-tuned models using relevant metrics.
Deploy Fine-Tuned Models: Understand the best practices for deploying fine-tuned models in various environments, including local, cloud, and API-based setups.
Apply Fine-Tuning to Real-World Scenarios: Equip yourself with the knowledge to apply fine-tuning techniques to practical, industry-relevant applications.
Entry Requirements
Proficiency in English
Proficiency in use of computer and internet
Course Details
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