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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch - Second Edition

By : Joseph Babcock, Raghav Bali
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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch

5 (1)
By: Joseph Babcock, Raghav Bali

Overview of this book

Become an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
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Index

Inference time improvements

We covered a number of important techniques to bring in efficiencies during the overall training workflow. However, a major part of an LLM’s lifecycle is the inference aspect (i.e., the actual utilization of such models for different real-world use cases). Due to their immense size, the infrastructure requirements are very large and expensive. To improve upon this and bring down associated operational costs, the following techniques prove quite beneficial:

  • Offloading is a smart way of leveraging compute and data storage responsibilities across hardware devices effectively. The most widely used techniques involve moving parts of the model (layers/blocks) to secondary memory or NVMe when not actively used. This reduces GPU memory usage and allows for larger models to fit within limited resources. Microsoft’s DeepSpeed and Hugging Face’s bitsandbytes are two popular libraries that provide interfaces to handle such capabilities...
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Generative AI with Python and PyTorch
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