The Open Model Ecosystem: Hugging Face, Qwen, DeepSeek and More

Open source AI models, more precisely called open-weight models, are AI models whose trained files are published so anyone can download and run them. The ecosystem around them includes Hugging Face, the main hub for sharing models, and model families such as Qwen, DeepSeek, Gemma, Llama and Mistral’s open models. With tools like Ollama, you can run many of them on your own computer, with no data leaving your machine.

This guide maps the main players and shows how to use open models in your own projects and AI agents.

Open source vs open-weight

People say “open source AI” all the time, but most models are better described as open-weight.

• Open-weight means the trained model weights, the large set of numbers that make up the model, are published for download.
• Fully open source would also include the training code and, ideally, the training data, so anyone could reproduce the model.

Most popular open models publish weights but not the full training data. Licenses also vary: some are very permissive, while others add conditions, for example on commercial use or attribution. Always read the license of the specific model you plan to use. For the bigger trade-offs, see open vs closed AI models.

Hugging Face: the hub

Hugging Face is the central place where companies, research labs and individuals share models. Think of it as a mix of a library and a code-hosting site, but for AI.

On Hugging Face you’ll find:

• Models, each with a page describing what it does, its license and how to use it.
• Datasets for training and evaluating models.
• Spaces, small demo apps where you can try models in the browser.
• Open-source libraries that developers use to load, run and fine-tune models.

You’ll also find many community variants: models fine-tuned for a language or task, and compressed versions that run on smaller hardware. Model pages are the best place to check the license and intended use.

The major open model families

Llama (Meta)

Llama is Meta’s family of open-weight models. It became a common starting point for many community fine-tunes and helped make running models locally mainstream. It is released under Meta’s own license, which has conditions. More in Meta AI and Llama.

Gemma (Google)

Gemma is Google’s family of open-weight models, separate from its closed Gemini models. Gemma models are designed to be small enough to run on ordinary hardware, which makes them popular for local use.

Qwen (Alibaba)

Qwen is a family of models from Alibaba. It includes general chat models and variants specialized for tasks such as coding, in a wide range of sizes. Qwen models are widely used locally and are known for good multilingual support.

DeepSeek

DeepSeek is an AI company whose openly released models drew a lot of attention, including models focused on step-by-step reasoning. Its larger models need serious hardware, but smaller versions and community adaptations can run on more modest machines.

Mistral

Mistral AI, a Paris-based company, offers both open-weight and commercial models. Its open models are a popular choice for local and self-hosted use.

And many more

These are just some of the best-known families. New open models appear regularly from companies, universities and independent researchers, so the landscape keeps shifting.

Running open models locally with Ollama

You don’t need to be a machine learning engineer to use open models. Ollama is a free tool that downloads and runs open-weight models on your own computer with a single command. Once a model is downloaded, you can chat with it in the terminal or connect other apps and agents to it.

How to get started:

1. Install Ollama on your computer.
2. Pick a small model first, such as a compact Gemma, Qwen or Llama model.
3. Run it and try a few tasks you actually do, like summaries or small code changes.
4. Move up in size only if your computer handles it comfortably.

Full instructions are in how to run AI models locally with Ollama. Under the hood, many local tools use compressed (quantized) versions of models, which trade a little quality for much lower memory use.

What open models are good for

Open-weight models shine when control matters more than raw power:

• Privacy. Prompts, code and documents never leave your machine.
• Offline work. Once downloaded, no internet connection is needed.
• Cost control. No per-request fees, only your own hardware and electricity.
• Customization. You can fine-tune a model on your own examples.
• Learning. Running models yourself is a great way to understand how they behave.

The trade-offs are real too. On typical laptops, local models are slower than cloud services, and the largest closed models usually perform better on complex, multi-step work. Local vs cloud AI models walks through when to use which.

Open models in AI agents

AI agents plan and carry out multi-step tasks, such as reading a project, editing files and running tests. Open models can power agents through Ollama or through open-source agents such as OpenCode, which can use many model providers.

A sensible way to use them:

• Give local models routine steps: formatting, comments, small fixes, summaries.
• Keep sensitive work local when code or data shouldn’t leave your machine.
• Send hard planning and final review to a strong cloud model when quality matters most.
• Expect slower progress on modest hardware, and size tasks accordingly.

BotQuarters supports exactly this kind of mix: a team of AI coding agents on your Mac where some teammates run local models through Ollama while others use Claude Code, Codex, Gemini or OpenCode.

Common misconceptions

• “Open source models are always free to use for anything.” Downloads are free, but licenses can include conditions.
• “Open models are toys.” Many are very capable, especially for focused tasks, though the largest closed models usually lead on the hardest work.
• “You need a powerful GPU.” Small models run on ordinary laptops; bigger ones benefit from more memory and a strong graphics chip.
• “Hugging Face makes all these models.” Hugging Face hosts them; the models come from many different companies and researchers.

FAQ

What are open source AI models?

They are AI models whose trained weights are published so anyone can download and run them. Most are more accurately called open-weight, because the training data usually isn’t released, and each model comes with its own license.

What is Hugging Face used for?

Hugging Face is the main hub for sharing AI models, datasets and demo apps. Developers use it to find models, check their licenses, try them in the browser and download them for their own projects.

Are Qwen and DeepSeek models safe to run locally?

Running an open-weight model locally keeps your prompts on your own machine, since nothing is sent to the model maker. As with any download, get models from trusted sources, check the license, and review the output of any model before relying on it.

What is the best open source AI model to run locally?

It depends on your hardware and tasks. A good approach is to try a small model from two or three families, such as Gemma, Qwen and Llama, on your real work with Ollama, then keep the one that gives the best results at a speed you can live with.