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We present TinyLlama, a compact 1.1B language model pretrained on around 1 trillion tokens for approximately 3 epochs. Building on the architecture and tokenizer of Llama 2, TinyLlama leverages...

The TinyLlama project is an open endeavor to train a compact 1.1B Llama model on 3 trillion tokens.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Training models are part of instructional design. It’s the process of creating instruction material for different training needs. Get to know about the different types of training models from Harappa to transform your employees and faculty into confidants and coaches.

House officers detect many AEs among inpatients. Confidential peer interviews of front-line providers is a promising method for identifying medical errors and substandard quality.

Europe PMC is an archive of life sciences journal literature.

In this prospective study, investigators used post-discharge interviews and medical record review to capture the frequency and types of adverse events as reported by patients. The authors enrolled 228 patients hospitalized on an inpatient medical service and discovered that 8% reported suffering an adverse event. Discussion includes detailed analysis of the reported events and comparison to those noted in the medical record and from hospital incident reports. The authors conclude that engaging patients in the identification of medical errors may offer an additional and equally important approach to improving patient safety.

Reduction of adverse events depends on accurate detection. The utility of a Trigger Tool to detect and classify severity of adverse events in an intensive care ...

In this article we cover Confidential interviews find more adverse events - Healthcare Risk Management – October 1, 2000. Stay up to date on the latest in healthcare news and online CME/CE.