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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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5 Tools to Help Build Your LLM Apps

Flipboard

Whether you're a seasoned ML engineer or a new LLM developer, these tools will help you get more productive and accelerate the development and deployment of your AI projects.

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Go from Engineer to ML Engineer with Declarative ML

Flipboard

Learn how to easily build any AI model and customize your own LLM in just a few lines of code with a declarative approach to machine learning.

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

AWS Machine Learning Blog

Researchers developed Medusa , a framework to speed up LLM inference by adding extra heads to predict multiple tokens simultaneously. This post demonstrates how to use Medusa-1, the first version of the framework, to speed up an LLM by fine-tuning it on Amazon SageMaker AI and confirms the speed up with deployment and a simple load test.

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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. You can use supervised fine-tuning (SFT) and instruction tuning to train the LLM to perform better on specific tasks using human-annotated datasets and instructions.

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Future AGI Secures $1.6M to Launch the World’s Most Accurate AI Evaluation Platform

Unite.AI

” Transforming AI Performance Across Industries Future AGI is already delivering impactful results across industries: A Series E sales-tech company used Future AGIs LLM Experimentation Hub to achieve 99% accuracy in its agentic pipeline, compressing weeks of work into just hours.

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LLM experimentation at scale using Amazon SageMaker Pipelines and MLflow

AWS Machine Learning Blog

Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You may need to customize an LLM to adapt to your unique use case, improving its performance on your specific dataset or task.

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