When.com Web Search

Search results

1. Results From The WOW.Com Content Network

2. Large language model - Wikipedia

   en.wikipedia.org/wiki/Large_language_model

   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.

3. llama.cpp - Wikipedia

   en.wikipedia.org/wiki/Llama.cpp

   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.

4. Llama (language model) - Wikipedia

   en.wikipedia.org/wiki/Llama_(language_model)

   Llama was trained on only publicly available information, and was trained at various model sizes, with the intention to make it more accessible to different hardware. The model was exclusively a foundation model , [ 6 ] although the paper contained examples of instruction fine-tuned versions of the model.

5. Wikipedia : Using neural network language models on Wikipedia

   en.wikipedia.org/wiki/Wikipedia:Using_neural...

   As stated above, LLM outputs should not be used verbatim to expand an article. Asking an LLM for feedback on an existing article. Such feedback should never be taken at face value. Just because an LLM says something, does not make it true. But such feedback may be helpful if you apply your own judgment to each suggestion.

6. DOGE will use AI to assess responses from federal workers who ...

   www.aol.com/federal-workers-agencies-push-back...

   The information will go into an LLM (Large Language Model), an advanced AI system that looks at huge amounts of text data to understand, generate and process human language, the sources said ...

7. Generative pre-trained transformer - Wikipedia

   en.wikipedia.org/wiki/Generative_pre-trained...

   Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.