Large language model (LLM)
PMD NowSolutions | ServiceNow & Ivanti Partner
IT Services Company with focus on bringing best-in-industry ServiceNow competency to customers.
Large language models (LLMs) are statistical language models trained on a massive amount of data that can be used to generate and translate text and other content and perform other natural language processing tasks. LLMs are large language models trained on a vast and diverse range of text data, enabling them to perform a wide variety of language tasks without being specifically optimized for any single application.
Training
·??LLMs are trained on a huge amount of data, making them super flexible. They can do all sorts of things like generate text, summarize it, and even translate languages. And the best part? They can be easily tweaked to do specific tasks, which makes them even better at those things.
·??Now, training these models takes a lot of time and resources. The size of the language model plays a big role in how long it takes to train. Smaller models can be trained faster, but as they get bigger, things get a bit more complicated.
·? When models are around 5 billion parameters, the amount of memory they need becomes a problem. This memory is stored in the graphics processing unit (GPU), and it can get full. So, to make training faster, we need to optimize how we use this memory. This means we need to find ways to store and use the data in a more efficient way.
Memory efficiency
A rapid and efficient approach to memory management for large language models involves meticulous optimization of memory usage from all sources, including the training state, activations, and gradients. Additionally, a comprehensive strategy is employed to effectively eliminate memory fragmentation.
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LLM in Healthcare:
? Rapidly analyze vast amounts of medical literature, offering potential diagnoses based on symptoms and suggesting treatment options.
? Create tailored health plans by comprehending patients’ medical histories, lifestyles, and genetic factors.
? Accelerate research by analyzing large datasets, identifying potential connections between studies, and expediting drug discovery processes.
? Predict disease outbreaks, identify vulnerable patients, and suggest preventive measures.
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