Introduction to LLM

Total of 9 articles available. Currently on page 1 of 1.

7.0 Future Outlook and Challenges

A preview from Chapter 7: Explore the future of large language models—ethics, efficiency, multimodal AI, and responsible governance beyond scaling.

2024-10-06

6.2 Simple Python Experiments with LLMs

A preview from Chapter 6.2: Learn how to run large language models with Hugging Face, OpenAI, Google Cloud, and Azure using just Python and a few lines of code.

2024-10-05

6.1 Introducing Open-Source Tools and APIs

A preview from Chapter 6.1: Explore Hugging Face, OpenAI, Google Cloud Vertex AI, and Azure Cognitive Services—leading tools to bring LLMs into your projects.

2024-10-04

6.0 Hands-On with LLMs

A preview from Chapter 6: Learn how to run large language models yourself with open-source libraries, cloud APIs, and Python—making LLMs accessible to everyone.

2024-10-02

5.3 Real-Time Deployment Challenges

A preview from Chapter 5.3: Explore latency, scalability, and optimization techniques for deploying large language models in real-time applications.

2024-10-01

5.2 Compute Resources and Cost

A preview from Chapter 5.2: Learn why LLMs demand massive compute power, what drives cost, and practical strategies to optimize performance and sustainability.

2024-09-30

5.1 Bias & Ethical Considerations

A preview from Chapter 5.1 of our book: uncover how large language models inherit bias and learn strategies to build fair, trustworthy AI.

2024-09-29

4.2 Enhancing Customer Support with LLM-Based Question Answering Systems

Discover how Question Answering Systems powered by Large Language Models (LLMs) are transforming customer support, search engines, and specialized fields with high accuracy and flexibility.

2024-09-17

A Guide to LLMs (Large Language Models): Understanding the Foundations of Generative AI

Learn about large language models (LLMs), including GPT, BERT, and T5, their functionality, training processes, and practical applications in NLP. This guide provides insights for engineers interested in leveraging LLMs in various fields.

2024-09-01

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Authors

SHO

CTO of Receipt Roller Inc., he builds innovative AI solutions and writes to make large language models more understandable, sharing both practical uses and behind-the-scenes insights.