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Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.
The Pile is an 886.03 GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. [1] [2] It is composed of 22 smaller datasets, including 14 new ones. [1]
BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [3]
A disk image is a snapshot of a storage device's structure and data typically stored in one or more computer files on another storage device. [1] [2]Traditionally, disk images were bit-by-bit copies of every sector on a hard disk often created for digital forensic purposes, but it is now common to only copy allocated data to reduce storage space.
Disk cloning is the process of duplicating all data on a digital storage drive, such as a hard disk or solid state drive, using hardware or software techniques. [1] Unlike file copying, disk cloning also duplicates the filesystems , partitions , drive meta data and slack space on the drive. [ 2 ]
Database tables and indexes may be stored on disk in one of a number of forms, including ordered/unordered flat files, ISAM, heap files, hash buckets, or B+ trees. Each form has its own particular advantages and disadvantages. The most commonly used forms are B-trees and ISAM.
On a modern filesystem with sparse file support, this helps minimise actual disk usage. The file format of LMDB is, unlike that of Berkeley DB , architecture-dependent. This means that a conversion must be done before moving a database from a 32-bit machine to a 64-bit machine, [ 8 ] or between computers of differing endianness .
In-memory databases are faster than disk-optimized databases because disk access is slower than memory access and the internal optimization algorithms are simpler and execute fewer CPU instructions. Accessing data in memory eliminates seek time when querying the data, which provides faster and more predictable performance than disk. [1] [2]