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This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.
The main features of declare-lab/instruct-eval are: Evaluation Frameworks.
Projects with overlapping indexed features include: comet-ml/opik — Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It… confident-ai/deepeval — Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for… cvs-health/uqlm — UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection. eleutherai/lm-evaluation-harness — This project is a standardized framework for benchmarking large language models across a wide range of academic and… evalplus/evalplus — Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024. allenai/olmo-eval — This project provides a unified workbench for evaluating language models throughout the model development loop.
Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes. The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, syn
Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for executing automated regression tests, validating model output quality against defined standards, and tracing the execution of complex agent workflows. By integrating these capabilities into development pipelines, the platform ensures consistent performance and reliability throughout the software lifecycle. The platform distinguishes itself through its focus on programmatic validation and observability. It utilizes secondary language models to score output quality and employs
UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection
This project provides a unified workbench for evaluating language models throughout the model development loop.