The author recommends using Llama.cpp and llama-cpp-python for a more hands-on approach to learning about LLMs. The guide also breaks down the jargon associated with choosing a model, such as parameters, quantization, base/chat/instruct models, fine-tuning, LoRA/QLoRA/PEFT, Mixture-Of-Experts (MoE), and model formats. The author emphasizes the importance of understanding these terms to effectively use and develop applications with LLMs.
Key takeaways:
- The article provides a comprehensive guide to developing applications with Large Language Models (LLMs), including setting up the project, choosing a model, and understanding various terminologies.
- The author recommends using llama.cpp and llama-cpp-python for working directly with LLMs, as they allow for a closer look at the underlying details and can be run on any hardware.
- Various terms related to LLMs, such as Parameters, Quantized, Base/Chat/Instruct Models, Fine-Tuning, LoRA, Mixture-Of-Experts, and Model Formats, are explained in detail to help beginners understand the jargon.
- The guide also includes resources for learning more about Machine Learning and Transformers, as well as instructions for setting up the project on Ubuntu 24.04.