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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]
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.
The SLP prompting procedure uses and removes prompts by moving through a hierarchy from less to more restrictive prompts. [2] [3] [4] If the student emits the correct behavior at any point during this instructional trial [5] (with or without prompts), reinforcement is provided. The system of least prompts gives the learner the opportunity to ...
Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence ...
Data scientist Teresa Kubacka has recounted deliberately making up the phrase "cycloidal inverted electromagnon" and testing ChatGPT by asking it about the (nonexistent) phenomenon. ChatGPT invented a plausible-sounding answer backed with plausible-looking citations that compelled her to double-check whether she had accidentally typed in the ...
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence.The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [1]
When compared to concept mapping alone, retrieval practice is more beneficial, [104] despite students not seeing retrieval practice as a useful learning tool. [105] When combined, learner performance was increased, suggesting concept mapping is a tool that should be combined with retrieval practice [ 106 ] alongside other non-verbal responses ...
The motivation for mastery learning comes from trying to reduce achievement gaps for students in average school classrooms. During the 1960s John B. Carroll and Benjamin S. Bloom pointed out that, if students are normally distributed with respect to aptitude for a subject and if they are provided uniform instruction (in terms of quality and learning time), then achievement level at completion ...