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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 ]
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI . As with other text-to-image models, Flux generates images from natural language descriptions, called prompts .
The Hugging Face Hub is a platform (centralized web service) for hosting: [20] Git-based code repositories, including discussions and pull requests for projects. models, also with Git-based version control; datasets, mainly in text, images, and audio;
Download QR code; Print/export ... Pages in category "Hugging Face people" ... Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; ...
GPT-2 completion using the Hugging Face Write With Transformer website, prompted with text from this article (All highlighted text after the initial prompt is machine-generated from the first suggested completion, without further editing.)
The Stanford University Institute for Human-Centered Artificial Intelligence (HAI) Center for Research on Foundation Models (CRFM) released Alpaca, a training recipe based on the LLaMA 7B model that uses the "Self-Instruct" method of instruction tuning to acquire capabilities comparable to the OpenAI GPT-3 series text-davinci-003 model at a ...
In October 2024, Anthropic released an improved version of Claude 3.5, along with a beta feature called "Computer use", which enables Claude to take screenshots, click, and type text. [ 52 ] In November 2024, Palantir announced a partnership with Anthropic and Amazon Web Services to provide U.S. intelligence and defense agencies access to ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.