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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
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18
Index

Updated training setup

In the previous section, we touched on the issue of fine-tuned language models going off-context after generating a few tokens, a problem often referred to as the alignment issue. This challenge restricts the model’s ability to maintain consistent output context, affecting task performance. While fine-tuned models improved at few-shot and zero-shot tasks (refer to the sections on GPT-2 and GPT-3 in Chapter 4), they didn’t always reliably produce the desired results. For instance, a model might handle sentiment analysis well in a few-shot setting but struggle with a task like translation in a similar setup.

To address this limitation, Ouyang et al. proposed InstructGPT7 in early 2022. Although similar in architecture to previous GPT models, InstructGPT was significantly smaller, with just 1.3 billion parameters compared to GPT-3’s 175 billion. The key innovation lay in two additional training steps: instruction fine-tuning and reinforcement...

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