Everything I've learned so far about running local LLMs

The article discusses the author's exploration of Large Language Models (LLMs) and their increasing accessibility and potential for use on personal hardware, such as a Raspberry Pi or a modest desktop. The author notes the rapid evolution of this technology, highlighting its potential for private, offline, unlimited, and registration-free use. The author also shares their experiences with running LLMs on their own hardware, focusing on text-based models and the software and models needed for this.

The author provides a detailed account of their favorite models, their strengths and weaknesses, and their specific uses. They also discuss the user interfaces they have used and their creation of their own interface, Illume, for more efficient use of LLMs. The article also delves into Fill-in-the-Middle (FIM) tokens, explaining how they work and their role in training LLMs. The author concludes by sharing their experiences and challenges with FIM.

Key takeaways:

The author has been exploring the world of Large Language Models (LLMs), which have evolved to the point where they can be run on a Raspberry Pi and are smarter than the original ChatGPT.
LLMs are improving rapidly, with new developments each week, and the author recommends using r/LocalLLaMa to keep up with the latest information.
The author has used a software called llama.cpp to run LLMs and has found it to be effective and efficient, requiring nothing beyond a C++ toolchain.
The author has also built their own user interface, Illume, to support their exploration of the LLM ecosystem and to better integrate with their text editor.

Everything I've learned so far about running local LLMs

Key takeaways:

Comments (0)

Newsletter