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llama.cpp is an open source software library that performs inference on various large language models such as Llama. [3] It is co-developed alongside the GGML project, a general-purpose tensor library. [4] Command-line tools are included with the library, [5] alongside a server with a simple web interface. [6] [7]
Code Llama is a fine-tune of LLaMa 2 with code specific datasets. 7B, 13B, and 34B versions were released on August 24, 2023, with the 70B releasing on the January 29, 2024. [29] Starting with the foundation models from LLaMa 2, Meta AI would train an additional 500B tokens of code datasets, before an additional 20B token of long-context data ...
Free software by library used (2 C) Free software by license (28 C) ... Llama.cpp; M. Managed Extensibility Framework; Mastodon (social network) MicMac (software)
llama.cpp: An open source software library that performs inference on various Large Language Models such as Llama. [33] Automotive Industry. ISO 26262: The international standard for functional safety of automotive electrical and electronic systems. SYCL is used in automotive applications to accelerate safety-critical computations and ...
In line with the general notability guidelines and the recommendation in Wikipedia:Multiple_sources that "it seems that challenges to notability are successfully rebuffed when there are three good in-depth references in reliable sources that are independent of each other" I have four sources that are mainly about llama.cpp that explain what the ...
Syft, free and open-source software bill-of-materials command-line tool and Go library: A cute cartoon owl [62] Tux: Linux kernel, a free and open-source monolithic Unix-like computer operating system kernel that has been included in many OS distributions: A cartoon anthropomorphic penguin [63] [1] Tizen Genie
Hennepin County issued more library cards last year than it had in years. Library visits have been declining nationwide for the past decade, so any reversal of that trend — even a 6% uptick in ...
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.