Preeti Sharma – författare

The rise of the Auntie Sewing Squad, a massive mutual-aid network of volunteers who provided free masks in the wake of US government failures during the COVID-19 pandemic.  In March 2020, when the US government failed to provide personal protective gear during the COVID-19 pandemic, the Auntie Sewing Squad emerged. Founded by performance artist Kristina Wong, the mutual-aid group sewed face masks with a bold social justice mission: to protect the most vulnerable and most neglected.  Written and edited by Aunties themselves, The Auntie Sewing Squad Guide to Mask Making, Radical Care, and Racial Justice tells a powerful story. As the pandemic unfolded, hate crimes against Asian Americans spiked. In this climate of fear and despair, a team of mostly Asian American women using the familial label "Auntie" formed online, gathered momentum, and sewed masks at home by the thousands. The Aunties nimbly made and funneled masks to asylum seekers, Indigenous communities, incarcerated people, farmworkers, and others disproportionately impacted by COVID-19. When anti-lockdown agitators descended on state capitals—and, eventually, the US Capitol—the Aunties dug in. And as the nation erupted in rebellion over police violence against Black people, the Aunties supported and supplied Black Lives Matter protesters and organizations serving Black communities. Providing hundreds of thousands of homemade masks met an urgent public health need and expressed solidarity, care, and political action in a moment of social upheaval.  The Auntie Sewing Squad is a quirky, fast-moving, and adaptive mutual-aid group that showed up to meet a critical need. Led primarily by women of color, the group includes some who learned to sew from mothers and grandmothers working for sweatshops or as a survival skill passed down by refugee relatives. The Auntie Sewing Squad speaks back to the history of exploited immigrant labor as it enacts an intersectional commitment to public health for all. This collection of essays and ephemera is a community document of the labor and care of the Auntie Sewing Squad.

The book is a must-have resource for anyone looking to understand the complexities of generative AI, offering comprehensive insights into LLMs, effective training strategies, and practical applications. Textual Intelligence: Large Language Models and Their Real-World Applications provides an overview of generative AI and its multifaceted applications, as well as the significance and potential of Large Language Models (LLMs), including GPT and LLaMA. It addresses the generative AI project lifecycle, challenges in existing data architectures, proposed use case planning and scope definition, model deployment, and application integration. Training LLMs, data requirements for effective LLM training, pre-training and fine-tuning processes, and navigating computational resources and infrastructure are also discussed. The volume delves into in-context learning and prompt engineering, offering strategies for crafting effective prompts, techniques for controlling model behavior and output quality, and best practices for prompt engineering. Textual Intelligence: Large Language Models and Their Real-World Applications also discusses cost optimization strategies for LLM training, aligning models to human values, optimizing model architectures, the power of transfer learning and fine-tuning, instruction fine-tuning for precision, and parameter-efficient fine-tuning (PEFT) with adapters such as LoRA, QLoRA, and soft prompts, making it an essential guide for both beginners and industry veterans. Readers will find this book: Explores the real-world potential of large language models;Introduces industry-changing AI solutions;Provides advanced insights on AI and its models.Audience Industry professionals, academics, graduate students, and researchers seeking real-world solutions using generative AI.

The book is a must-have resource for anyone looking to understand the complexities of generative AI, offering comprehensive insights into LLMs, effective training strategies, and practical applications.

Textual Intelligence: Large Language Models and Their Real-World Applications provides an overview of generative AI and its multifaceted applications, as well as the significance and potential of Large Language Models (LLMs), including GPT and LLaMA. It addresses the generative AI project lifecycle, challenges in existing data architectures, proposed use case planning and scope definition, model deployment, and application integration. Training LLMs, data requirements for effective LLM training, pre-training and fine-tuning processes, and navigating computational resources and infrastructure are also discussed. The volume delves into in-context learning and prompt engineering, offering strategies for crafting effective prompts, techniques for controlling model behavior and output quality, and best practices for prompt engineering.

Textual Intelligence: Large Language Models and Their Real-World Applications also discusses cost optimization strategies for LLM training, aligning models to human values, optimizing model architectures, the power of transfer learning and fine-tuning, instruction fine-tuning for precision, and parameter-efficient fine-tuning (PEFT) with adapters such as LoRA, QLoRA, and soft prompts, making it an essential guide for both beginners and industry veterans.

Readers will find this book:

Explores the real-world potential of large language models; Introduces industry-changing AI solutions; Provides advanced insights on AI and its models.

Audience

Industry professionals, academics, graduate students, and researchers seeking real-world solutions using generative AI.

The book is a must-have resource for anyone looking to understand the complexities of generative AI, offering comprehensive insights into LLMs, effective training strategies, and practical applications.

Textual Intelligence: Large Language Models and Their Real-World Applications provides an overview of generative AI and its multifaceted applications, as well as the significance and potential of Large Language Models (LLMs), including GPT and LLaMA. It addresses the generative AI project lifecycle, challenges in existing data architectures, proposed use case planning and scope definition, model deployment, and application integration. Training LLMs, data requirements for effective LLM training, pre-training and fine-tuning processes, and navigating computational resources and infrastructure are also discussed. The volume delves into in-context learning and prompt engineering, offering strategies for crafting effective prompts, techniques for controlling model behavior and output quality, and best practices for prompt engineering.

Textual Intelligence: Large Language Models and Their Real-World Applications also discusses cost optimization strategies for LLM training, aligning models to human values, optimizing model architectures, the power of transfer learning and fine-tuning, instruction fine-tuning for precision, and parameter-efficient fine-tuning (PEFT) with adapters such as LoRA, QLoRA, and soft prompts, making it an essential guide for both beginners and industry veterans.

Readers will find this book:

Explores the real-world potential of large language models; Introduces industry-changing AI solutions; Provides advanced insights on AI and its models.

Audience

Industry professionals, academics, graduate students, and researchers seeking real-world solutions using generative AI.