Remove APIs Remove Benchmark Remove Calibration
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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

Refer to the appendix for instance details and benchmark data. Import intel extensions for PyTorch to help with quantization and optimization and import torch for array manipulations: import intel_extension_for_pytorch as ipex import torch Apply model calibration for 100 iterations. times greater with INT8 quantization.

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Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning

In this post, we explore the latest features introduced in this release, examine performance benchmarks, and provide a detailed guide on deploying new LLMs with LMI DLCs at high performance. Be mindful that LLM token probabilities are generally overconfident without calibration.

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Improve factual consistency with LLM Debates

AWS Machine Learning

Dataset The dataset for this post is manually distilled from the Amazon Science evaluation benchmark dataset called TofuEval. LLM debates need to be calibrated and aligned with human preference for the task and dataset. For this post, 10 meeting transcripts have been curated from the MediaSum repository inside the TofuEval dataset.

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Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

We explored nearest neighbors, decision trees, neural networks, and also collaborative filtering in terms of algorithms, while trying different sampling strategies (filtering, random, stratified, and time-based sampling) and evaluated performance on Area Under the Curve (AUC) and calibration distribution along with Brier score loss.

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Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning

Each trained model needs to be benchmarked against many tasks not only to assess its performances but also to compare it with other existing models, to identify areas that needs improvements and finally, to keep track of advancements in the field. Evaluating these models allows continuous model improvement, calibration and debugging.

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