Where to Find LLM Models: Hugging Face Hub

To operate a Large Language Model locally, the primary step is to obtain the model files. Think of an LLM as the engine for generating text; without it, the tools we'll discuss later have nothing to operate on. But where do you find these engines? Fortunately, the machine learning community collaborates extensively, and there are central places online where models are shared.

The most prominent and widely used platform for finding AI models, including LLMs suitable for local use, is the Hugging Face Hub.

Imagine an expansive digital library or a workshop filled with tools and components, but specifically for artificial intelligence. That's similar to what Hugging Face offers. It serves as a central meeting point for researchers, developers, and organizations to share and discover AI models, datasets, and related software tools. While it hosts many types of AI resources, it has become the primary repository for finding Large Language Models.

What is the Hugging Face Hub?

The Hugging Face Hub (often just called "Hugging Face") is an online platform accessible through your web browser. Its core function is hosting:

• Models: Pre-trained models for various tasks, including text generation (LLMs), image recognition, translation, and more.
• Datasets: Collections of data used to train or evaluate models.
• Spaces: Demonstrations and applications built using models from the Hub.
• Libraries: Software tools (like transformers, diffusers) that help developers work with these models and datasets.

For our purpose of running LLMs locally, we are mainly interested in the Models section of the Hub. This section contains thousands of models contributed by individuals, research groups, and companies like Meta, Google, and Mistral AI.

Finding LLMs on the Hub

When you visit the Hugging Face website (huggingface.co), you'll find a search bar and options to browse models. Here’s how you can typically look for LLMs:

1. Use the Search Bar: You can search for specific model names if you know them (e.g., "Llama 2", "Mistral 7B"). You can also search for terms related to model formats suitable for local use, like "GGUF", which we will discuss shortly.
2. Filter by Task: Hugging Face categorizes models by the task they perform. For LLMs, the relevant task is usually "Text Generation" or sometimes "Conversational". Filtering by these tasks helps narrow down the options.
3. Explore Collections: Sometimes, models are grouped into collections, which can be helpful for finding related models or specific types.

For example, searching for "Mistral 7B GGUF" would likely show you versions of the popular Mistral 7B model that are packaged in the GGUF format, a format often used for running models efficiently on consumer hardware.

What to Expect on a Model Page

When you click on a model on the Hub, you'll land on its dedicated page, often called a "model card". This page acts like an instruction manual or specification sheet for the model. It typically includes:

• Model Description: Information about the model, its capabilities, how it was trained, and its intended uses.
• Files and Versions: A list of the actual model files available for download. You might see different versions or formats here. This is where you'll look for files like .gguf.
• Usage Examples: Code snippets or instructions on how to use the model (often geared towards developers using Hugging Face libraries, but still informative).
• License Information: Details about how the model can be used (e.g., for research only, commercially). We'll cover licenses later in this chapter.
• Community Tab: Discussions, questions, and contributions from users.

Think of the Hugging Face Hub as your primary starting point for browsing and discovering the LLMs you might want to run locally. Its centralized nature makes it much easier than searching scattered websites. In the following sections, we'll discuss the characteristics you should look for when examining models on the Hub, such as their size, format, and license, to help you choose one that fits your needs and your hardware.