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A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. This page lists notable large language models.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
ArangoDB is a transactional native multi-model database supporting two major NoSQL data models (graph and document [1]) with one query language. Written in C++ and optimized for in-memory computing. In addition ArangoDB integrated RocksDB for persistent storage. ArangoDB supports Java, JavaScript, Python, PHP, NodeJS, C++ and Elixir.
Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of large language models (LLMs) released by Meta AI starting in February 2023. [2] [3] The latest version is Llama 3.3, released in December 2024. [4] Llama models are trained at different parameter sizes, ranging between 1B and 405B. [5]
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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 ]
Gensim is an open-source library for unsupervised topic modeling, document indexing, retrieval by similarity, and other natural language processing functionalities, using modern statistical machine learning. Gensim is implemented in Python and Cython for performance. Gensim is designed to handle large text collections using data streaming and ...
It is notable for its dramatic improvement over previous state-of-the-art models, and as an early example of a large language model. As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. [3] BERT is trained by masked token prediction and next sentence prediction.