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There will be two or more parent classes which are used to be inherited. There will be sub-classes each of which is derived from one of the super-classes. The sub-classes are mutually linked via fields, and each sub-class may override the methods inherited from the super-class. New methods and fields are usually declared in one sub-class. [1]
Java class name« extends parentclass»« implements interfaces» { members} interface name« extends parentinterfaces» {members } package name; members: PHP namespace name; members: Objective-C @interface name« : parentclass» [8] «< protocols >» { instance_fields} method_and_property_declarations @end @implementation name method ...
The Computer Language Benchmarks Game site warns against over-generalizing from benchmark data, but contains a large number of micro-benchmarks of reader-contributed code snippets, with an interface that generates various charts and tables comparing specific programming languages and types of tests. [56]
Concretely, one can construct an LLM that can understand images as follows: take a trained LLM, and take a trained image encoder . Make a small multilayered perceptron f {\displaystyle f} , so that for any image y {\displaystyle y} , the post-processed vector f ( E ( y ) ) {\displaystyle f(E(y))} has the same dimensions as an encoded token.
Vicuna LLM is an omnibus Large Language Model used in AI research. [1] Its methodology is to enable the public at large to contrast and compare the accuracy of LLMs "in the wild" (an example of citizen science ) and to vote on their output; a question-and-answer chat format is used.
The llama.cpp project introduced the GGUF file format, a binary format that stores both tensors and metadata. [62] The format focuses on supporting different quantization types, which can reduce memory usage, and increase speed at the expense of lower model precision.
The GGUF (GGML Universal File) [26] file format is a binary format that stores both tensors and metadata in a single file, and is designed for fast saving, and loading of model data. [27] It was introduced in August 2023 by the llama.cpp project to better maintain backwards compatibility as support was added for other model architectures.
Each file represents a single experiment and contains a single anomaly. The dataset represents a multivariate time series collected from the sensors installed on the testbed. There are two markups for Outlier detection (point anomalies) and Changepoint detection (collective anomalies) problems 30+ files (v0.9) CSV Anomaly detection