A comprehensive guide for engineers on constructing, training, and optimizing large language models. This course covers the essential components, from data preparation and architectural design using Transformers to advanced distributed training techniques and efficient deployment strategies. Gain the practical knowledge required to develop sophisticated language models.
Prerequisites Programming and Deep Learning
Level:
LLM Architecture Design
Implement and customize Transformer-based architectures for large-scale language modeling.
Data Management for LLMs
Develop pipelines for acquiring, cleaning, and managing massive text datasets suitable for LLM pre-training.
Distributed Training Implementation
Configure and execute distributed training jobs for LLMs using various parallelism strategies and frameworks.
Model Training and Optimization
Apply advanced optimization techniques, learning rate schedules, and regularization methods specific to LLM training.
LLM Evaluation Techniques
Evaluate model performance using intrinsic metrics and downstream task benchmarks.
Inference Optimization
Implement methods for model compression and efficient inference serving.
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