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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning Blog

Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLMs capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk.

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The Vulnerabilities and Security Threats Facing Large Language Models

Unite.AI

Large language models (LLMs) like GPT-4, DALL-E have captivated the public imagination and demonstrated immense potential across a variety of applications. In this post, we will explore the attack vectors threat actors could leverage to compromise LLMs and propose countermeasures to bolster their security.

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Top Large Language Models LLMs Courses

Marktechpost

Large Language Models (LLMs) have revolutionized AI with their ability to understand and generate human-like text. Learning about LLMs is essential to harness their potential for solving complex language tasks and staying ahead in the evolving AI landscape.

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Techniques and approaches for monitoring large language models on AWS

AWS Machine Learning Blog

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis. Monitoring the performance and behavior of LLMs is a critical task for ensuring their safety and effectiveness.

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LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

AWS Machine Learning Blog

Fine-tuning a pre-trained large language model (LLM) allows users to customize the model to perform better on domain-specific tasks or align more closely with human preferences. Continuous fine-tuning also enables models to integrate human feedback, address errors, and tailor to real-world applications.

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Achieve ~2x speed-up in LLM inference with Medusa-1 on Amazon SageMaker AI

AWS Machine Learning Blog

Large language models (LLMs) have revolutionized the field of natural language processing with their ability to understand and generate humanlike text. Researchers developed Medusa , a framework to speed up LLM inference by adding extra heads to predict multiple tokens simultaneously.

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Yandex Introduces YaFSDP: An Open-Source AI Tool that Promises to Revolutionize LLM Training by Cutting GPU Usage by 20%

Marktechpost

Developing large language models requires substantial investments in time and GPU resources, translating directly into high costs. The larger the model, the more pronounced these challenges become. ML engineers can leverage this tool to enhance the efficiency of their LLM training processes.

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