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Hugging Face, Inc. is a Franco-American company that develops computation tools for building applications using machine learning. It is known for its transformers library built for natural language processing applications.
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.
The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. [26] The accompanying preprint [26] also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets. LLaMa 2 includes foundation models and models fine-tuned for ...
Valued at $4.5 billion, Hugging Face has become a key platform for AI researchers and developers to share chatbots and other AI software. Also Read: Amazon Heats Up AI B
It is the first Grok model with image generation capabilities. [44] On October 21, 2024, xAI released an applications programming interface (API). [45] On December 9, 2024, xAI released a text-to-image model named Aurora. [46] On February 15, Elon Musk, the founder of xAI, announced the release of Grok 3, and Grok 3 currently tops LLM leaderboards.
Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5]
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 ]
Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. Since 2017, several consumer grade CPUs and SoCs have on-die NPUs. As of 2023, the market for AI hardware is dominated by GPUs. [1]