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The Transformers library is a Python package that contains open-source implementations of transformer models for text, image, and audio tasks. It is compatible with the PyTorch , TensorFlow and JAX deep learning libraries and includes implementations of notable models like BERT and GPT-2 . [ 16 ]
GPT-J is a GPT-3-like model with 6 billion parameters. [4] Like GPT-3, it is an autoregressive, decoder-only transformer model designed to solve natural language processing (NLP) tasks by predicting how a piece of text will continue. [1] Its architecture differs from GPT-3 in three main ways. [1]
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
BLOOM is the main outcome of the BigScience collaborative initiative, [6] a one-year-long research workshop that took place between May 2021 and May 2022. BigScience was led by HuggingFace and involved several hundreds of researchers and engineers from France and abroad representing both the academia and the private sector.
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 ...
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.
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 even playing ...
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]