Deploying LLMs
Goals: Familiarize students with efficient LLM deployment techniques, emphasizing quantization and pruning. Offer hands-on experiences with deployments on platforms like GCP and Intel® CPUs.
This module dives into deploying Large Language Models. Model quantization and pruning are central to these strategies, each serving as an effective tool to optimize model performance without compromising efficiency. With a foundation in research articles and real-world applications, this module also introduces participants to deployment on cloud platforms.
- Challenges of LLM deployment: The lesson covers challenges during LLM deployment such as the sheer size of models, associated costs, and potential latency issues. We also provide a survey on optimizations, rooted in a research article on Transformer inference and a deepened perspective on potential solutions in addressing these challenges.
- Model Quantization: This lesson centers on Quantization, highlighting its role in streamlining LLM deployments. We research the balance between model performance and efficiency by understanding its usefulness and various techniques.
- Model Pruning: This module discusses model pruning, showcasing its place in LLM optimization. We will introduce various pruning techniques backed by recent research.
- Deploying an LLM on a Cloud CPU: This module uncovers the advantages, considerations, and challenges of deploying large language models on cloud-based CPUs. This lesson needs a server instance equipped with an Intel® Xeon® processor.
By the end of this module, students have gained a robust understanding of the intricacies involved in LLM deployment. The exploration of model quantization, pruning, and practical deployment strategies has provided them with the tools necessary to navigate real-world challenges. Moving beyond foundational concepts, the next section offers a deep dive into the advanced topics and future directions in the realm of LLMs.
After navigating the diverse terrain of Transformers and LLMs, participants now deeply understand significant architectures like GPT and BERT. The sessions shed light on model evaluation metrics, advanced control techniques for optimal outputs, and the roles of pretraining and finetuning. The upcoming module dives into the complexities of deciding when to train an LLM from scratch, the operational necessities of LLMs, and the sequential steps crucial for the training process.
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