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The largest and most capable LLMs, as of June 2024, are built with a decoder-only transformer-based architecture, which enables efficient processing and generation of large-scale text data. Historically, up to 2020, fine-tuning was the primary method used to adapt a model for specific tasks.
MMLU. Measuring Massive Multitask Language Understanding ( MMLU) is a benchmark for evaluating the capabilities of language models. It consists of about 16,000 multiple-choice questions spanning 57 academic subjects including mathematics, philosophy, law, and medicine. It is one of the most commonly used benchmarks for comparing the ...
BLOOM (language model) 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] BLOOM was trained on approximately 366 ...
Foundation model. License. Meta Llama 3 Community License [ 1] Website. llama .meta .com. Llama (acronym for Large Language Model Meta AI, and formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023. [ 2][ 3] The latest version is Llama 3.1, released in July 2024. [ 4]
Machine learningand data mining. These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning ), computer hardware, and, less ...
Vicuna LLM is an omnibus Large Language Model used in AI research. [1] Its methodology is to enable the public at large to contrast and compare the accuracy of LLMs "in the wild" (an example of citizen science) and to vote on their output; a question-and-answer chat format is used. At the beginning of each round two LLM chatbots from a diverse ...
Low-rank adaptation (LoRA) is an adapter-based technique for efficiently fine-tuning models. The basic idea is to design a low- rank matrix that is then added to the original matrix. [ 13 ] An adapter, in this context, is a collection of low-rank matrices which, when added to a base model, produces a fine-tuned model.
The cloud computing arm of Alphabet Inc said on Thursday it had formed a partnership with startup Hugging Face to ease artificial intelligence (AI) software development in the company's Google Cloud.