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DBRX is an open-sourced large language model (LLM) developed by Mosaic ML team at Databricks, released on March 27, 2024. [1] [2] [3] It is a mixture-of-experts transformer model, with 132 billion parameters in total. 36 billion parameters (4 out of 16 experts) are active for each token. [4]
The organization provides tools that utilize artificial intelligence algorithms, machine learning, natural language processing, and language models for the purpose of researching, processing, and analyzing Hebrew texts and creating Hebrew content. These tools are available for free use and open source for the benefit of the public. [2]
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. The optional "6B" in the name refers to the fact that it has 6 billion parameters. [2]
Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. [2]It is capable of transcribing speech in English and several other languages, and is also capable of translating several non-English languages into English. [1]
LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, [3] with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London.
Moses is a statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language, developed by the University of Edinburgh. [2] Moses then allows new source-language text to be decoded using these models to produce automatic translations in the target
PaLM is pre-trained on a high-quality corpus of 780 billion tokens that comprise various natural language tasks and use cases. This dataset includes filtered webpages, books, Wikipedia articles, news articles, source code obtained from open source repositories on GitHub, and social media conversations.