Search results
Results from the WOW.Com Content Network
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 . [ 17 ]
The following list of C++ template libraries details the various libraries of templates available for the C++ programming language.. The choice of a typical library depends on a diverse range of requirements such as: desired features (e.g.: large dimensional linear algebra, parallel computation, partial differential equations), commercial/opensource nature, readability of API, portability or ...
[[Category:Programming language templates]] to the <includeonly> section at the bottom of that page. Otherwise, add <noinclude>[[Category:Programming language templates]]</noinclude> to the end of the template code, making sure it starts on the same line as the code's last character.
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.
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]
GPT-J or GPT-J-6B is an open-source large language model (LLM) developed by EleutherAI in 2021. [1] As the name suggests, it is a generative pre-trained transformer model designed to produce human-like text that continues from a prompt.
Code Llama is a fine-tune of LLaMa 2 with code specific datasets. 7B, 13B, and 34B versions were released on August 24, 2023, with the 70B releasing on the January 29, 2024. [29] Starting with the foundation models from LLaMa 2, Meta AI would train an additional 500B tokens of code datasets, before an additional 20B token of long-context data ...
To change this template's initial visibility, the |state= parameter may be used: {{Transformers | state = collapsed}} will show the template collapsed, i.e. hidden apart from its title bar. {{Transformers | state = expanded}} will show the template expanded, i.e. fully visible.