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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 (18 chapters)
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16
Other Books You May Enjoy
17
Index

Grok-1

The last open-source model we’ll discuss in this section is Grok-1, which was released by Xai in early 202421. Like Mixtral, it uses a mixture of expert architecture and is not purpose-built for a particular product domain. It was inspired by the science fiction classic “The Hitchhiker’s Guide to the Galaxy,” and is intended to have a humorous personality relative to other models22.

Unlike the other models in this chapter, we cannot directly load Grok in the pipelines modules. Instead, we can use the following code to load the weights and execute the model23:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_dtype(torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("hpcai-tech/grok-1",
    trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "hpcai-tech/grok-1",
    trust_remote_code=True,
    device_map="auto",
    torch_dtype=torch.bfloat16...
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