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Apache 2.0 [84] DeepSeek-LLM: November 29, 2023: DeepSeek 67 2T tokens [85]: table 2 12,000: DeepSeek License Trained on English and Chinese text. 1e24 FLOPs for 67B. 1e23 FLOPs for 7B [85]: figure 5 Phi-2: December 2023: Microsoft 2.7 1.4T tokens 419 [86] MIT Trained on real and synthetic "textbook-quality" data, for 14 days on 96 A100 GPUs. [86]
The new version called Phi-3-mini is the first of the three small language models (SLM) to be released by the company, as it stakes its future on a technology that is expected to have a wide ...
On May 5, 2022, the company announced its Series C funding round led by Coatue and Sequoia. [8] The company received a $2 billion valuation. On August 3, 2022, the company announced the Private Hub, an enterprise version of its public Hugging Face Hub that supports SaaS or on-premises deployment. [9]
On July 18, 2023, in partnership with Microsoft, Meta announced LLaMa 2, the next generation of Llama. Meta trained and released Llama 2 in three model sizes: 7, 13, and 70 billion parameters. [7] The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. [26]
Hugging Face on Wednesday said it is releasing a new open-source software offering with Amazon.com, Alphabet's Google and others aimed at lowering the costs for building chatbots and other AI systems.
Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0: Yes Apache Spark Scala Scala, Python No No Yes Yes Yes Yes Caffe: Berkeley Vision and Learning Center 2013 BSD: Yes Linux, macOS, Windows [3] C++: Python ...
U.S. President Joe Biden on Wednesday announced plans by Microsoft Corp to build a $3.3 billion data center in southeastern Wisconsin that will create thousands of jobs in the presidential ...
Margaret Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning.She is most well known for her work on automatically removing undesired biases concerning demographic groups from machine learning models, [2] as well as more transparent reporting of their intended use.