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Hugging Face, Inc. is an American company that develops computation tools for building applications using machine learning. It is incorporated under the Delaware General Corporation Law [1] and based in New York City. It is known for its transformers library built for natural language processing applications.
Transformers were first developed as an improvement over previous architectures for machine translation, [4] [5] but have found many applications since. They are used in large-scale natural language processing , computer vision ( vision transformers ), reinforcement learning , [ 6 ] [ 7 ] audio , [ 8 ] multimodal learning , robotics , [ 9 ] and ...
BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [3]
Gschwind led hardware and software architecture for the first general-purpose programmable accelerator Accelerators and is widely recognized for his contributionsHeterogeneous computing as architect of the Cell Broadband Engine processor used in the Sony PlayStation 3, [2] [3] and RoadRunner, the first supercomputer to reach sustained Petaflop operation.
Watsonx.ai is a platform that allows AI developers to leverage a wide range of LLMs under IBM's own Granite series and others such as Facebook's LLaMA-2, free and open-source model Mistral and many others present in Hugging Face community for a diverse set of AI development tasks.
Large collaboration led by Hugging Face: 175 [50] 350 billion tokens (1.6TB) [51] Responsible AI Essentially GPT-3 but trained on a multi-lingual corpus (30% English excluding programming languages) Galactica: November 2022: Meta: 120: 106 billion tokens [52] unknown: CC-BY-NC-4.0 Trained on scientific text and modalities. AlexaTM (Teacher ...
Operating on byte-sized tokens, transformers scale poorly as every token must "attend" to every other token leading to O(n 2) scaling laws, as a result, Transformers opt to use subword tokenization to reduce the number of tokens in text, however, this leads to very large vocabulary tables and word embeddings.
Hugging Face's MarianMT is a prominent example, providing support for a wide range of language pairs, becoming a valuable tool for translation and global communication. [64] Another notable model, OpenNMT, offers a comprehensive toolkit for building high-quality, customized translation models, which are used in both academic research and ...