evalscope and continuous-eval
These are complementary tools—evalscope provides a broad evaluation framework across multiple model types (LLMs, VLMs, AIGC), while continuous-eval specializes in production-focused, data-driven evaluation metrics specifically optimized for LLM-powered applications, allowing teams to use both for different evaluation stages and purposes.
About evalscope
modelscope/evalscope
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
This tool helps AI model developers and researchers objectively assess how well large language models (LLMs), vision-language models (VLMs), and other generative AI models perform. You provide various models and datasets, and it generates detailed comparison reports and performance metrics, including stress test results and interactive visualizations. It helps you understand a model's strengths and weaknesses across different tasks and benchmarks.
About continuous-eval
relari-ai/continuous-eval
Data-Driven Evaluation for LLM-Powered Applications
This tool helps AI engineers and MLOps professionals rigorously test and refine their Large Language Model (LLM) applications. It takes in datasets of questions, retrieved contexts, and generated answers, then outputs comprehensive performance metrics. You'd use this to understand how well your LLM application is performing across different stages, like retrieval or generation, and identify areas for improvement.
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