llama.cpp guide - Running LLMs locally, on any hardware, from scratch

The article is a detailed guide on how to use Language Learning Models (LLMs) for AI applications. The author shares their experience of using LLMs, specifically the ChatGPT model, and discusses their initial skepticism about the AI/LLM boom. They also talk about their concerns regarding data privacy and the cost of using AI. The author then explores open-source models and discusses the process of setting up an LLM on a GPU. They also provide a step-by-step guide on how to build and run an LLM, how to get a model, and how to convert a HuggingFace model to GGUF.

The author emphasizes that one does not need a high-end GPU to run LLMs and that performance can vary based on hardware, memory bandwidth, and model size. They also discuss the quality of responses one can expect from different models. The author recommends LM Studio for those who want to easily run popular models on most modern hardware for non-commercial purposes. However, for those who want to learn more about LLMs, use them for commercial purposes, or run them on exotic hardware, the author suggests reading the rest of the post for more detailed information.

Key takeaways:

The author discusses their experience with Language Learning Models (LLMs), specifically focusing on the open-source model, llama.cpp.
LLMs can be run on various hardware, including GPUs and CPUs, and even on devices with limited resources like RaspberryPi, thanks to quantization.
LLMs can be used for non-commercial purposes using software like LM Studio, but for commercial use or for more control over the model, one can build and host their own LLM.
The author provides a detailed guide on how to build and host an LLM, including how to convert a model from HuggingFace to a format compatible with llama.cpp.

llama.cpp guide - Running LLMs locally, on any hardware, from scratch

Key takeaways:

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