This article discusses the extensive evaluation of quantized large language models (LLMs) by Neural Magic, finding that quantized LLMs maintain competitive accuracy and efficiency with their full-precision counterparts.
This article explores various metrics used to evaluate the performance of classification machine learning models, including precision, recall, F1-score, accuracy, and alert rate. It explains how these metrics are calculated and provides insights into their application in real-world scenarios, particularly in fraud detection.
This guide demonstrates how to execute end-to-end LLM workflows for developing and productionizing LLMs at scale. It covers data preprocessing, fine-tuning, evaluation, and serving.
Discusses the trends in Large Language Models (LLMs) architecture, including the rise of more GPU, more weights, more tokens, energy-efficient implementations, the role of LLM routers, and the need for better evaluation metrics, faster fine-tuning, and self-tuning.
Langfuse is an open-source LLM engineering platform that offers tracing, prompt management, evaluation, datasets, metrics, and playground for debugging and improving LLM applications. It is backed by several renowned companies and has won multiple awards. Langfuse is built with security in mind, with SOC 2 Type II and ISO 27001 certifications and GDPR compliance.
Discover how to build custom LLM evaluators for specific real-world needs
Learn about the importance of evaluating classification models and how to use the confusion matrix and ROC curves to assess model performance. This post covers the basics of both methods, their components, calculations, and how to visualize the results using Python.
A ready-to-run tutorial in Python and scikit-learn to evaluate a classification model compared to a baseline model
Why evaluating LLM apps matters and how to get started
