Introduction to LLM

Total of 7 articles available. Currently on page 1 of 1.

7.3 Integrating Multimodal Models

A preview from Chapter 7.3: Discover how multimodal models fuse text, images, audio, and video to unlock richer AI capabilities beyond text-only LLMs.

2024-10-09

7.2 Resource-Efficient Training

A preview from Chapter 7.2: Learn how techniques like distillation, quantization, distributed training, and data efficiency make LLMs faster, cheaper, and greener.

2024-10-08

7.1 The Evolution of Large-Scale Models

A preview from Chapter 7.1: Explore how LLMs have scaled from billions to trillions of parameters, the gains in performance, and the rising technical and ethical challenges.

2024-10-07

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

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

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.