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Most capable GPT-3 model. Can do any task the other models can do, often with higher quality. Base GPT-3 text-ada-001 350 M Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost. InstructGPT text-babbage-001 1.3B Capable of straightforward tasks, very fast, and lower cost. InstructGPT text-curie-001 6.7B
The first iteration, GPT-1, [18] was scaled up to produce GPT-2 in 2019; [19] in 2020, it was scaled up again to produce GPT-3, with 175 billion parameters. [ 20 ] [ 5 ] [ 21 ] DALL·E's model is a multimodal implementation of GPT-3 [ 22 ] with 12 billion parameters [ 5 ] which "swaps text for pixels," trained on text–image pairs from the ...
QBasic is an integrated development environment (IDE) and interpreter for a variety of dialects of BASIC which are based on QuickBASIC.Code entered into the IDE is compiled to an intermediate representation (IR), and this IR is immediately executed on demand within the IDE.
Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing , machine translation , and natural language generation and can be used as foundation models for other tasks. [ 43 ]
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A large language model (LLM) is a computational model capable of language generation or other natural language processing tasks. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.
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Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...
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