Search results
Results from the WOW.Com Content Network
DeepSeek-V2 was released in May 2024. In June 2024, the DeepSeek-Coder V2 series was released. [32] The DeepSeek login page shortly after a cyberattack that occurred following its January 20 launch. DeepSeek V2.5 was released in September and updated in December 2024. [33] On 20 November 2024, DeepSeek-R1-Lite-Preview became accessible via API ...
Granite Code Models: May 2024: IBM: Unknown Unknown Unknown: Apache 2.0 Qwen2 June 2024: Alibaba Cloud: 72 [93] 3T Tokens Unknown Qwen License Multiple sizes, the smallest being 0.5B. DeepSeek-V2: June 2024: DeepSeek 236 8.1T tokens 28,000: DeepSeek License 1.4M hours on H800. [94] Nemotron-4 June 2024: Nvidia: 340: 9T Tokens 200,000: NVIDIA ...
The newly released open source code will provide infrastructure to support the AI models that DeepSeek has already publicly shared, building on top of those existing open source model frameworks.
huggingface.co 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.
In January 2025, DeepSeek released DeepSeek R1, a 671-billion-parameter open-weight model that performs comparably to OpenAI o1 but at a much lower cost. [19] Since 2023, many LLMs have been trained to be multimodal, having the ability to also process or generate other types of data, such as images or audio. These LLMs are also called large ...
Mistral AI was established in April 2023 by three French AI researchers, Arthur Mensch, Guillaume Lample and Timothée Lacroix. [5]Mensch, an expert in advanced AI systems, is a former employee of Google DeepMind; Lample and Lacroix, meanwhile, are large-scale AI models specialists who had worked for Meta Platforms.
Unlike most Oscar categories, best picture isn't determined solely by which movie gets the most No. 1 votes. Let's break it down for you.
llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project.