Hugging Face Hub

What you will find there

• Models: from small text classifiers to large language, vision, speech and image generation models. Filters cover the task (text generation, image to text, text to speech and dozens more), the library (Transformers, Diffusers, PyTorch, GGUF, MLX and others), language and licence.
• Datasets: public datasets with viewers so you can inspect rows before downloading.
• Spaces: demo apps, often the quickest way to try a model without writing code.
• Run-it options: many model pages show how to load the model with local tools such as Ollama, llama.cpp, LM Studio or vLLM, or through third-party inference providers.

When you need it

The Hub becomes relevant when a single hosted API stops being the right answer. Common reasons: cost at high volume, data that cannot leave your network, latency, or a narrow task where a small specialised model beats a large general one. It is also the place to find embedding models, rerankers and classifiers that sit around an LLM in a real application.

How to judge a model before using it

Three million models means most of them are experiments. Before you commit:

• Read the licence first. Licences vary widely and some forbid commercial use or specific applications. This is the check people skip and regret.
• Read the model card. Look for training data, intended use, known limitations and evaluation results. A missing card is a warning sign.
• Check who published it. Official organisation accounts and well known community fine-tuners are safer starting points than anonymous uploads.
• Look at downloads, likes and recent activity as a rough signal, not proof of quality.
• Test on your own examples. Benchmark numbers rarely match your data. Try a Space or a small local run with twenty real inputs.
• Prefer safetensors files over older pickle-based weights where there is a choice, since they cannot run arbitrary code when loaded.

Free and paid

Browsing, downloading public models and hosting public repositories are free. The pricing page lists a PRO account at $9 a month with more storage, inference credits and GPU quota, a Team plan at $20 per user a month, and Enterprise above that. Paid GPU hardware for Spaces and dedicated Inference Endpoints are billed by the hour. Most learners never need to pay.

Common questions

Do I need an account? Not to browse or download most public models. You need a free account for gated models (which require accepting terms), to like or follow, and to publish your own work.

Can I run these models on a laptop? Small and quantised models, yes, through tools such as Ollama or llama.cpp. Large models need a GPU or a hosted provider; model pages often list options.

How do I pick among thousands of similar models? Filter by task and licence, sort by downloads or trending, then shortlist two or three from reputable publishers and test them on your own examples.

Is a model on the Hub safe to use commercially? Only if its licence allows it. Read it every time.

Best for: engineers evaluating or running open models, and anyone whose requirements rule out sending data to a third-party API.