| When you need a language model to respond accurately and quickly about a specific field of knowledge, the sprawling capacity of a LLM may hurt more than it helps. In this book Guglielmo Iozzia teaches how to build generative AI models optimized for specific fields. Perfect for cost- or hardware-constrained environments, Small Language Models (SLMs) train on domain specific data for high-quality results in specific tasks. In this book you’ll develop SLMs that can generate everything from Python code to protein structures and antibody sequences, all on commodity hardware. <ASIN:1633436705 > Author: Guglielmo Iozzia Publisher: Manning Date: May 2026 Pages: 376 ISBN: 978-1633436701 Print: 1633436705 Kindle: B0FSSMWPF8 Audience: Developers interested in machine learning Level: Intermediate Category: Artificial Intelligence Topics include:
- Model sizing best practices
- Open source libraries, frameworks, utilities and runtimes
- Fine-tuning techniques for custom datasets
- Hugging Face’s libraries for SLMs
- Running SLMs on commodity hardware
- Model optimization or quantization
- ONNX and other quantization methods
- Integrate SLMs into end-to-end applications
- Deploy SLMs on laptops, smartphones, and other devices
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Bare Metal C Author: Steve Oualline Publisher: No Starch Press Date: August 2022 Pages: 304 ISBN: 978-1718501621 Print: 1718501625 Kindle: B08YJB9BCF Audience: C programmers Rating: 3 Reviewer: Harry Fairhead Bare metal C sounds exciting and very basic. Time to find out how the machine really works. | Algorithmic Thinking, 2nd Ed (No Starch Press) Author: Dr. Daniel Zingaro Publisher: No Starch Date: January 2024 Pages: 480 ISBN: 978-1718503229 Print: 1718503229 Kindle: B0BZGZHK3B Audience: C programmers Rating: 4 Reviewer: Mike James What exactly is algorithmic thinking? | | More Reviews | |