steam 特惠游戏榜单（自动刷新）

OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.

A list of works on evaluation of visual generation models, including evaluation metrics, models, and systems

Pip compatible CodeBLEU metric implementation available for linux/macos/win

Test your prompts, models, and RAGs. Catch regressions and improve prompt quality. LLM evals for OpenAI, Azure, Anthropic, Gemini, Mistral, Llama, Bedrock, Ollama, and other local & private models with CI/CD integration.

Data release for the ImageInWords (IIW) paper.

Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs.

Documentation for langsmith

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.

🪢 Open source LLM engineering platform: Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23

Toolkit for evaluating and monitoring AI models in clinical settings

LangEvals aggregates various language model evaluators into a single platform, providing a standard interface for a multitude of scores and LLM guardrails, for you to protect and benchmark your LLM models and pipelines.

The production toolkit for LLMs. Observability, prompt management and evaluations.

Official Implementation of paper "Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs"(https://arxiv.org/abs/2402.11199).

Graphical tool for creating verification plots of weather forecasts

An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.

LangSmith Client SDK Implementations

An open-source visual programming environment for battle-testing prompts to LLMs.

A version of eval for R that returns more information about what happened

A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models