Popular GitHub Repositories for Deft Eval

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.

AI Observability & Evaluation

? Open-Source Evaluation & Testing for AI & LLM systems

Accelerating the development of large multimodal models (LMMs) with one-click evaluation module - lmms-eval.

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.

CodeTF: One-stop Transformer Library for State-of-the-art Code LLM

Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界.

A curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval.

Python SDK for running evaluations on LLM generated responses

[ACL 2024] User-friendly evaluation framework: Eval Suite & Benchmarks: UHGEval, HaluEval, HalluQA, etc.

Generate ideal question-answers for testing RAG

A simple GPT-based evaluation tool for multi-aspect, interpretable assessment of LLMs.

Python SDK for experimenting, testing, evaluating & monitoring LLM-powered applications - Parea AI (YC S23)

S-Eval: Automatic and Adaptive Test Generation for Benchmarking Safety Evaluation of Large Language Models

Multi-Agent Step Race Benchmark: Assessing LLM Collaboration and Deception Under Pressure. A multi-player “step-race” that challenges LLMs to engage in public conversation before secretly picking a move (1, 3, or 5 steps). Whenever two or more players choose the same number, all colliding players fail to advance.

Open Source Embeddings Optimisation and Eval Framework for RAG/LLM Applications. Documentations at https://docs.vectorboard.ai/introduction

Deep learning based speech and pronunciation assessment API for 8 languages.

Develop reliable AI apps

? Your free LLM evaluation toolkit helps you assess the accuracy of facts, how well it understands context, its tone, and more. This helps you see how good your LLM applications are.