How to use Hugging Face: the center of the AI ​​open source ecosystem

Hugging Face is a tool focusing on AI data processing scenarios. Hugging Face is the center of the AI ​​open source ecosystem, hosting models, data sets, Spaces demonstrations and Inference services. It is the GitHub of machine learning. This article is based on the official product documentation to sort out its capabilities and suitable usage methods.

Market signals

• Hugging Face is the birthplace of the Hugging Face Transformers library - a library that has long been ranked Top 1 in the AI category on GitHub.
• Customers include Meta, Google, Amazon, Microsoft, IBM, Intel, NVIDIA and almost all mainstream AI companies, and major LLM companies publish weights in HF.
• The 2023 Series D financing will be participated by Salesforce, Google, Amazon, Nvidia, Sound Ventures, etc., with a valuation of approximately US$4.5 billion.
• Leading multiple open source projects: BLOOM multi-language large model, SmolLM series, IDEFICS multi-modal, Distilabel/Argilla data stack, etc.

Product capabilities

• Models Hub: millions of public models, including weights/cards/inference examples.
• Datasets Hub: Large-scale data set hosting + online preview and streaming loading.
• Spaces: Hosted Gradio/Streamlit/Docker demos with ZeroGPU elastic GPU.
• Inference Endpoints: Deploy any model into a production-grade inference API.
• Inference Providers: Unified calls Together / Replicate / Fal / Cerebras, etc.
• AutoTrain: code-free training of classification, question answering, image, and tabular models.

Judgment in one sentence: If your workflow is indeed stuck in the above links, Hugging Face is worthy of being put on the candidate list for small-scale verification first; otherwise, there is no need to introduce it for functionality.