Hugging Face

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Hugging Face, Inc 🤗 ( is a New York City based French-American company that develops tools for building applications using machine learning. It is a company known for its work in the field of AI, particularly in natural language processing (NLP). Founded in 2016, Hugging Face has become notable for its open-source contributions and the development of Transformer-based models that have significantly advanced the capabilities of NLP applications.

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Key Aspects of Hugging Face

  1. Transformer Models: Hugging Face is most famous for its library called 'Transformers'. This library provides thousands of pre-trained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, and text generation. These models include BERT, GPT, T5, and DistilBERT, among others.
  2. Open-Source Approach: One of the reasons for Hugging Face's popularity is its commitment to open-source. The company actively maintains and updates its libraries, making them accessible to researchers, developers, and businesses.
  3. Community and Collaboration: Hugging Face has fostered a strong community around its tools, encouraging collaboration, sharing of models, and discussion on best practices in NLP. This has made it a go-to resource for many working in AI and NLP.
  4. Easy Integration: The Transformers library is designed for ease of use, allowing for the integration of state-of-the-art NLP models with minimal effort. It supports several programming languages, primarily Python, and can be integrated with other machine learning frameworks like TensorFlow and PyTorch.
  5. Hugging Face Hub: This is a platform where the community can share and collaborate on models. Users can upload their own models, download others, and contribute to the ongoing improvement of NLP technologies.
  6. Commercial Services: Beyond its open-source contributions, Hugging Face also offers commercial services. This includes enterprise solutions for companies looking to leverage advanced NLP models in their products and services.
  7. Research Contributions: Hugging Face is actively involved in AI research, often publishing papers and contributing to the advancement of NLP technologies. They collaborate with academic institutions and other research organizations.
  8. Tokenizers and Datasets: Alongside the Transformers library, Hugging Face also develops and maintains libraries for efficient tokenization (necessary for preparing text for processing by models) and a vast collection of datasets, which are crucial for training and evaluating NLP models.

Hugging Face has become a pivotal player in the AI field, especially in democratizing access to cutting-edge NLP technologies. Their tools are used by a wide range of users, from independent developers to large corporations, making significant contributions to the advancement and accessibility of NLP.

Useful Articles

Here are some helpful articles:

  • RLHF (conceptual overview): [1]
  • RLHF with Meta's Llama models (conceptual + code): [2]
  • Reward modelling (code): [3]
  • Quantisation deep-dive: [4]
  • Quantisation in practice: [5]
  • Qlora: [6]

Models being used:

  • LLM for fine-tuning: [7]
  • LLM for reward modelling (or unstructured regression modelling / URM as we can refer to it): [8] ([9] is another contender here)


See Also