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- Large language model - Wikipedia
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation
- List of large language models - Wikipedia
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
- Language model - Wikipedia
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation
- Large language model - Simple English Wikipedia, the free encyclopedia
A large language model (LLM) is a type of artificial intelligence that can understand and create human language These models learn by studying huge amounts of text from books, websites, and other sources
- Category:Large language models - Wikipedia
Pages in category "Large language models" The following 73 pages are in this category, out of 73 total This list may not reflect recent changes
- Reasoning language model - Wikipedia
A large language model (LLM) can be fine-tuned on a dataset of reasoning tasks paired with example solutions and step-by-step (reasoning) traces The fine-tuned model can then produce its own reasoning traces for new problems [22][23]
- Llama (language model) - Wikipedia
Llama (Large Language Model Meta AI) [a] is a family of large language models (LLMs) released by Meta AI starting in February 2023 [3] The latest version is Llama 4, released in April 2025 [4] Llama models come in different sizes, ranging from 1 billion to 2 trillion parameters Initially only a foundation model, [5] starting with Llama 2, Meta AI released instruction fine-tuned versions
- Wikipedia:Large language models - Wikipedia
While large language models (colloquially termed "AI chatbots" in some contexts) can be very useful, machine-generated text—much like human-created text—can contain errors or flaws, or be outright useless
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