What is Hugging Face?

Andrew Andreev

Author at ApiX-Drive

Reading time: ~7 min

How the Hugging Face Project Emerged and Developed

Key Tools

• Model Hub. The service library contains more than 300,000 models with the ability to sort by type and other parameters. This makes it the largest database of AI/ML models on the internet. The most in demand are Hugging Face Transformers, trained and optimized to perform text generation, analysis, translation, and editing tasks.
• Tokenizers. This tool is designed to convert text into a format readable by ML models. It helps to process text information in different languages and with different structures. Tokenizers break text into tokens (words, subwords, and characters). This allows neural networks to understand human language.
  
• Datasets. The library of NLP datasets used in training, testing, and analyzing language models includes a solid collection of datasets. You can view and work with them in the Hugging Face Hub. Additionally, users can easily add any of the Huggingface datasets to their code.
  
• Spaces. The platform provides a convenient interface for working with models that does not require special technical knowledge and skills. In the “Spaces” section, there is an impressive list of ready-made solutions of different types: generators of text, images, music, and so on.
  

Objectives and Areas of Application

• Implementation of ML models into user programs. The platform contains several hundred thousand open-source models for various purposes: for natural language processing (NLP), image generation, audio and video, computer vision, and more. Developers can freely use them in their services and applications.
• Training and distribution of ML models. Hugging Face offers the ability to train and tune deep learning models using API tools. Ready-made neural networks can be shared with other community members by adding them to the Spaces and Transformers libraries.
  
• Exchange of datasets. The platform helps users exchange data with each other to train language models. They can freely publish them and download them from the dataset library hosted on the site.
  
• Hosting demo versions. Hugging Face is the largest host of interactive language model demos. It allows you to develop a demo version of the program using your computing resources and then test and run it in a browser.
  
• Analysis of ML models and datasets. The service gives you access to your own library of code for analyzing and evaluating machine learning models and datasets.
  
• Development of business applications. The enterprise version of Hugging Face Enterprise Hub provides SaaS services to companies with model deployment in a private environment.
  
• Scientific research. The project community participates in joint research activities. The most famous among them is the BigScience seminar on the development of NLP technologies. The result of this collaboration was the BLOOM language model, released in 2022.
  

How to Use Hugging Face

1. Registration. Go to huggingface.co and create an account by clicking on the "Sign up" button. Fill in the required information and confirm registration via your email.
2. Studying models. Use the "Models" section to view the available models. For convenience, they are divided into categories: text, sound, images. For example, BERT and GPT may be suitable for working with text.
3. Testing. After selecting a model, go to its page and use the “Try it out” interactive window to test. Paste text or download a data file to test how your model performs.
4. Model integration. To implement the model into your project, use the instructions in the "Use in your project" section. This could be a link to a Python library or an API for working over the internet.
5. Communication with the community. If you have questions or need assistance, please visit the Discussions forum. There you can get answers and exchange experiences with other users and developers.

Advantages and Limitations

• Availability. The platform makes artificial intelligence and machine learning technologies available not only to large corporations but also to individual developers. It provides hundreds of thousands of ready-made open-source ML models, computing resources for training them, fine-grained development scripts, and an API for deploying neural networks.
• Prototyping. The service contains professional tools for the rapid development of prototypes and the deployment of NLP and ML applications.
  
• Cost optimization and scalability. Hugging Face empowers individuals and enterprises to launch cost-effective AI/ML products with flexible scalability. By using predefined models and other solutions instead of developing an LLM from scratch, businesses significantly optimize their budget.
  
• Integration. Users can integrate different ML frameworks from the platform database. For example, the Huggingface Transformers library supports integration with the TensorFlow and PyTorch frameworks.

• Lack of resources. The computing resources of the platform do not allow the use of all ML models available in its library. Often, they are only enough for a demonstration run but not for the full-scale deployment of neural networks. Accordingly, developers have to rent resources in other places.
• Search system. The search function in the large database of Huggingface does not always work perfectly. This makes it difficult to find specific models, libraries, or tools.
  
• Content bias. The majority of models available on the platform are created and trained by third-party developers. This creates the risk that they will generate inaccurate, illegal, or inappropriate information.
  
• Data security. Corporate users should make sure that their data is reliably protected by the security measures offered by the service.
  

The Implications of Hugging Face for the AI and ML Ecosystem

Conclusion