- The Hugging Face Hub is Git repositories of models, datasets and apps; every model has a card in a README.md file.
- You can download a model with the hf download command or by cloning the repository.
- Before use, check the card, the licence and the file format, and run the model on your own cases.
The Hugging Face Hub is often called GitHub for machine learning models, which is a good first approximation. You will find repositories with models, datasets and demo applications there, and each model is a Git repository with files, change history and a description. For somebody just starting with open models it is the central place, but also a place where it is easy to download something you do not understand. Below is a simple process: find, assess, download, check.
How to find a model
The Model Hub documentation describes the models page with filters. Start with the task you want to solve, for example text generation, text embedding, speech recognition or image generation, and only within it look at popularity, the number of downloads and the date of the last change. Popularity is a hint, not a guarantee: what counts is whether the model fits your language, hardware size and licence. If you are building something in Polish, look for models whose card mentions Polish, and verify it with your own examples.
Mind the size as well. Models differ in the number of parameters, and that determines how much memory is needed to run them. In the article What fits on your own computer I show how to estimate it.
How to assess a model page
A model page consists of the card (README.md) and a tab with files. The model card is a Markdown file with metadata that, according to the documentation, should describe the model, its uses and limitations, the training parameters, the datasets used and the evaluation results. The metadata let you filter the list and display the licence. Read the card before you download anything.
| Good sign | Warning | |
|---|---|---|
| Card | Description of uses, limits and data | No card or just a welcome sentence |
| Licence | Clear, matching your use | No licence or unclear terms |
| Files | safetensors format, readable names | Unknown pickle files with no description |
| Author | Known publisher, active repository | Anonymous account with no history |
| Results | A described evaluation method | Slogans with no method |
How to download
The download documentation points to the client library: the hf download command with a repository identifier fetches a model from the command line, and the hf_hub_download function does it from Python. Because models are Git repositories, they can also be cloned after installing the git-xet extension. In both cases many libraries, such as Transformers, download and cache files automatically on first use. Remember disk space: weights of large models take many gigabytes, and the local cache grows with each model you try.
Safety when downloading
The warning in the pickle format documentation concerns every beginner: loading a file in that format can execute arbitrary code. The documentation advises using models from trusted users and organizations, relying on signed commits and preferring the newer safetensors format. So download models from known publishers, ideally in safetensors, do not run scripts attached to somebody else’s repositories without reading them and keep experiments on a machine with no access to production keys.
- 01TaskChoose the kind of task and the language.
- 02FilterNarrow the list, compare 3–5 candidates.
- 03Card and licenceRead before you download anything.
- 04Downloadhf download or a library.
- 05TestA dozen of your own cases.
- 06DecisionRecord the result and the licence terms.
Your first evening with a model
A good plan for a first contact looks like this. Pick one concrete task from your own work, for example summarizing notes or classifying tickets. Choose three candidates of a size that fits in your memory and read their cards. Download the smallest and run it on ten of your own examples, recording the answers. Repeat for the others and compare results and running time in a single table. Only then decide whether you need a local model at all. An evening like that teaches more than a week of reading leaderboards, because it shows what working with a model on your own data, not somebody else’s, looks like.
What next
If you want to learn systematically, Hugging Face Learn offers courses for beginners and advanced users. The first real test is running a chosen model locally and comparing it with an API model on your examples; how to organize that is in the text on evaluation. The concept of a vector, which appears with embedding models, is explained by the glossary entry on embedding.
Last checked: 20 October 2026. The site’s interface and command names change; confirm them in the documentation.