🛠
AI工具
评估范围
基于 Python · 开源免费，本地部署，数据完全自主可控
英文名：evalscope
8.0AI 综合评分
llmvlmaiperformanceevaluation
✦ AI Skill Hub 推荐
AI Skill Hub 强烈推荐：评估范围 是一款优质的AI工具。已获得 2.9k 颗 GitHub Star，AI 综合评分 8.0 分，在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案，这是一个值得深入了解的选择。
📚 深度解析
评估范围 是一款基于 Python 的开源工具，在 GitHub 上收获 3k+ Star，是llm、vlm、ai、performance领域中的优质开源项目。开源工具的最大优势在于代码完全透明，你可以审计每一行代码的安全性，也可以根据自身需求进行二次开发和定制。
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
评估范围 依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 评估范围 的版本更新，及时通知重要功能变化。
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
评估范围 依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 评估范围 的版本更新，及时通知重要功能变化。
📋 工具概览
评估范围 是一款基于 Python 开发的开源工具，专注于 llm、vlm、ai 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
GitHub Stars
⭐ 2.9k
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
持续维护，定期更新
开源协议
Apache-2.0
AI 综合评分
8.0 分
工具类型
AI工具
Forks
341
📖 中文文档
以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。
评估范围 是一款基于 Python 开发的开源工具，专注于 llm、vlm、ai 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
📌 核心特色
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
🎯 主要使用场景
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。
安装命令
# 方式一：pip 安装（推荐）
pip install evalscope
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install evalscope
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/modelscope/evalscope
cd evalscope
pip install -e .
# 验证安装
python -c "import evalscope; print('安装成功')"
📋 安装步骤说明
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
evalscope --help
# 基本用法
evalscope input_file -o output_file
# Python 代码中调用
import evalscope
# 示例
result = evalscope.process("input")
print(result)
以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。
配置示例
# evalscope 配置文件示例（config.yml） app: name: "evalscope" debug: false log_level: "INFO" # 运行时指定配置文件 evalscope --config config.yml # 或通过环境变量配置 export EVALSCOPE_API_KEY="your-key" export EVALSCOPE_OUTPUT_DIR="./output"
以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。
简介
<p align="center"> <br> <img src="docs/en/_static/images/evalscope_logo.png"/> <br> <p>
<p align="center"> <a href="README_zh.md">中文</a>   ｜   English   </p>
<p align="center"> <img src="https://img.shields.io/badge/python-%E2%89%A53.10-5be.svg"> <a href="https://badge.fury.io/py/evalscope"><img src="https://badge.fury.io/py/evalscope.svg" alt="PyPI version" height="18"></a> <a href="https://pypi.org/project/evalscope"><img alt="PyPI - Downloads" src="https://static.pepy.tech/badge/evalscope"></a> <a href="https://github.com/modelscope/evalscope/pulls"><img src="https://img.shields.io/badge/PR-welcome-55EB99.svg"></a> <a href='https://evalscope.readthedocs.io/en/latest/?badge=latest'><img src='https://readthedocs.org/projects/evalscope/badge/?version=latest' alt='Documentation Status' /></a> <p>
<p align="center"> <a href="https://evalscope.readthedocs.io/zh-cn/latest/"> 📖 中文文档</a>   ｜   <a href="https://evalscope.readthedocs.io/en/latest/"> 📖 English Documentation</a> <p>
⭐ If you like this project, please click the "Star" button in the upper right corner to support us. Your support is our motivation to move forward!
📝 Introduction
EvalScope is a one-stop LLM evaluation framework built by the ModelScope Community. Just one command to start — it supports model capability evaluation, inference performance stress testing, and result visualization.
pip install evalscope
evalscope eval --model your-model-name --api-url $OPENAI_API_BASE_URL --api-key $OPENAI_API_KEY --eval-type openai_api --datasets gsm8k --limit 5✨ Key Features
- 📚 Comprehensive Evaluation Benchmarks: Built-in multiple industry-recognized evaluation benchmarks including MMLU, C-Eval, GSM8K, and more.
- 🧩 Multi-modal and Multi-domain Support: Supports evaluation of various model types including Large Language Models (LLM), Vision Language Models (VLM), Embedding, Reranker, AIGC, and more.
- 🚀 Multi-backend Integration: Seamlessly integrates multiple evaluation backends including OpenCompass, VLMEvalKit, RAGEval to meet different evaluation needs.
- 🤖 Agent Evaluation Mode: Drives benchmarks (e.g. GSM8K, AIME, SWE-bench Agentic) inside a controlled multi-turn AgentLoop with pluggable strategies, tools and Docker sandbox; full per-sample Agent Trace is recorded and visualizable.
- ⚡ Inference Performance Testing: Provides powerful model service stress testing tools, supporting multiple performance metrics such as TTFT, TPOT.
- 📊 Interactive Reports: Provides WebUI visualization interface, supporting multi-dimensional model comparison, report overview and detailed inspection.
- ⚔️ Arena Mode: Supports multi-model battles (Pairwise Battle), intuitively ranking and evaluating models.
- 🔧 Highly Extensible: Developers can easily add custom datasets, models and evaluation metrics.
🎉 What's New
- 🔥 [2026.05.27] Added Trie agentic trace replay for perf benchmarking: three new dataset plugins (
trie_agentic_coding/trie_code_qa/trie_office_work) replay real multi-turn agent traces with per-turn token caps and tool-call latency simulation. Also introduced a--durationwall-clock budget for all benchmark modes and aTurndataclass for per-turn overrides. - 🔥 [2026.05.27] Added Vendor Verifier benchmarks (
k2_verifier,kimi_verifier,minimax_verifier) for validating whether third-party API deployments faithfully reproduce official model behavior, with a sharedVendorVerifierAdapterbase class. - 🔥 [2026.05.26] Added the GAIA agent benchmark (multi-turn ReAct +
bashin a Docker sandbox, official rule-based scorer) and generic MCP server support — anyNativeAgentConfig-driven benchmark can now plug in stdio / HTTP / SSE MCP servers (fetch, web search, GitHub, ...) without per-benchmark wiring. - 🔥 [2026.05.22] Introduced the External Agent Bridge mode: evaluate off-the-shelf agent CLIs such as Anthropic's Claude Code and OpenAI's Codex directly through EvalScope. The bridge transparently forwards each CLI's LLM traffic (Anthropic Messages / OpenAI Chat / OpenAI Responses, including SSE streaming) to your evaluation model, while recording the full trajectory as an
agent_trace. Bring-your-own-runner via@register_runner. See the External Agent Bridge guide. - 🔥 [2026.05.19] Added support for SWE-bench_Pro and τ³-bench: SWE-bench_Pro is a more challenging multilingual long-horizon software-engineering benchmark from Scale AI (recommended over the original SWE-bench for less data contamination and broader language coverage; per-instance Docker images are pulled directly from DockerHub, no local image build required); τ³-bench is the v1.0.0 release of the tau-bench family, extending τ²-bench with a new
banking_knowledgeretrieval domain (RAG), 75+ task fixes across existing domains, and pluggable retrieval pipelines (BM25 / embeddings / rerankers / sandbox shell). - 🔥 [2026.05.15] Introduced Agent Evaluation Mode: any benchmark based on
DefaultDataAdapter(GSM8K, AIME, IFEval, etc.) can now be driven through a multi-turn AgentLoop with pluggable strategies (function_calling/react/swe_bench_*), tools (bash/python_exec/submit) andlocal/dockerenvironments. Per-sampleagent_traceis recorded and rendered step-by-step in the dashboard's Predictions tab. See the Agent Evaluation guide for details. - 🔥 [2026.05.08] Partnered with LightSeek to launch TokenSpeed, a speed-of-light LLM inference engine for agentic workloads. EvalScope provides the SWE-smith benchmarking pipeline — using real coding-agent traces to measure per-GPU throughput (TPM) and per-user latency (TPS) — serving as the official benchmark tool for TokenSpeed performance evaluation. Refer to the SWE-smith usage guide to get started.
- 🔥 [2026.05.07] Replaced the Gradio-based WebUI with a new React + Vite web interface for better performance and user experience.
- 🔥 [2026.04.23] Added support for recording performance (perf) metrics during evaluation tasks, enabling simultaneous tracking of model accuracy and inference efficiency metrics such as TTFT, TPOT, and throughput in a single evaluation run.
- 🔥 [2026.04.17] Added support for multi-turn conversation performance stress testing, enabling load testing of dialogue-based model services with multi-turn context. Refer to the usage documentation.
- 🔥 [2026.04.10] Added support for TIR-Bench (Thinking-with-Images Reasoning Benchmark), a multimodal benchmark evaluating agentic visual reasoning capabilities of vision-language models.
- 🔥 [2026.03.24] Added support for Agent Skill. Any agent model that supports Skill/Tool calling can use natural language to drive EvalScope for model evaluation, performance benchmarking, and result visualization.
- 🔥 [2026.03.09] Added support for evaluation progress tracking and HTML format visualization report generation.
<details><summary>More historical updates</summary>
- 🔥 [2026.03.02] Added support for Anthropic Claude API evaluation. Use
--eval-type anthropic_apito evaluate models via Anthropic API service. - 🔥 [2026.02.03] Comprehensive update to dataset documentation, adding data statistics, data samples, usage instructions and more.
- 🔥 [2026.01.13] Added support for Embedding and Rerank model service stress testing.
- 🔥 [2025.12.26] Added support for Terminal-Bench-2.0, which evaluates AI Agent performance on 89 real-world multi-step terminal tasks.
- 🔥 [2025.12.18] Added support for SLA auto-tuning model API services.
- 🔥 [2025.12.16] Added support for audio evaluation benchmarks such as Fleurs, LibriSpeech; added support for multilingual code evaluation benchmarks such as MultiplE, MBPP.
- 🔥 [2025.12.02] Added support for custom multimodal VQA evaluation; added support for visualizing model service stress testing in ClearML.
- 🔥 [2025.11.26] Added support for OpenAI-MRCR, GSM8K-V, MGSM, MicroVQA, IFBench, SciCode benchmarks.
- 🔥 [2025.11.18] Added support for custom Function-Call (tool invocation) datasets to test whether models can timely and correctly call tools.
- 🔥 [2025.11.14] Added support for SWE-bench_Verified, SWE-bench_Lite, SWE-bench_Verified_mini code evaluation benchmarks.
- 🔥 [2025.11.12] Added
pass@k,vote@k,pass^kand other metric aggregation methods; added support for multimodal evaluation benchmarks such as A_OKVQA, CMMU, ScienceQA, V*Bench. - 🔥 [2025.11.07] Added support for τ²-bench, an extended and enhanced version of τ-bench that includes a series of code fixes and adds telecom domain troubleshooting scenarios.
- 🔥 [2025.10.30] Added support for BFCL-v4, enabling evaluation of agent capabilities including web search and long-term memory.
- 🔥 [2025.10.27] Added support for LogiQA, HaluEval, MathQA, MRI-QA, PIQA, QASC, CommonsenseQA and other evaluation benchmarks. Thanks to @penguinwang96825 for the code implementation.
- 🔥 [2025.10.26] Added support for Conll-2003, CrossNER, Copious, GeniaNER, HarveyNER, MIT-Movie-Trivia, MIT-Restaurant, OntoNotes5, WNUT2017 and other Named Entity Recognition evaluation benchmarks. Thanks to @penguinwang96825 for the code implementation.
- 🔥 [2025.10.21] Optimized sandbox environment usage in code evaluation, supporting both local and remote operation modes.
- 🔥 [2025.10.20] Added support for evaluation benchmarks including PolyMath, SimpleVQA, MathVerse, MathVision, AA-LCR; optimized evalscope perf performance to align with vLLM Bench.
- 🔥 [2025.10.14] Added support for OCRBench, OCRBench-v2, DocVQA, InfoVQA, ChartQA, and BLINK multimodal image-text evaluation benchmarks.
- 🔥 [2025.09.22] Code evaluation benchmarks (HumanEval, LiveCodeBench) now support running in a sandbox environment.
- 🔥 [2025.09.19] Added support for multimodal image-text evaluation benchmarks including RealWorldQA, AI2D, MMStar, MMBench, and OmniBench, as well as pure text evaluation benchmarks such as Multi-IF, HealthBench, and AMC.
- 🔥 [2025.09.05] Added support for vision-language multimodal model evaluation tasks, such as MathVista and MMMU.
- 🔥 [2025.09.04] Added support for image editing task evaluation, including the GEdit-Bench benchmark.
- 🔥 [2025.08.22] Version 1.0 Refactoring. Break changes, please refer to.
- 🔥 [2025.07.18] The model stress testing now supports randomly generating image-text data for multimodal model evaluation.
- 🔥 [2025.07.16] Support for τ-bench has been added.
- 🔥 [2025.07.14] Support for "Humanity's Last Exam" (Humanity's-Last-Exam).
- 🔥 [2025.07.03] Refactored Arena Mode.
- 🔥 [2025.06.28] Optimized custom dataset evaluation; enhanced LLM judge usage.
- 🔥 [2025.06.19] Added support for the BFCL-v3 benchmark.
- 🔥 [2025.06.02] Added support for the Needle-in-a-Haystack test.
- 🔥 [2025.05.29] Added support for two long document evaluation benchmarks: DocMath and FRAMES.
- 🔥 [2025.05.16] Model service performance stress testing now supports setting various levels of concurrency.
- 🔥 [2025.05.13] Added support for the ToolBench-Static dataset, DROP and Winogrande benchmarks.
- 🔥 [2025.04.29] Added Qwen3 Evaluation Best Practices.
- 🔥 [2025.04.27] Support for text-to-image evaluation.
- 🔥 [2025.04.10] Model service stress testing tool now supports the
/v1/completionsendpoint. - 🔥 [2025.04.08] Support for evaluating embedding model services compatible with the OpenAI API has been added.
- 🔥 [2025.03.27] Added support for AlpacaEval and ArenaHard evaluation benchmarks.
- 🔥 [2025.03.20] The model inference service stress testing now supports generating prompts of specified length using random values.
- 🔥 [2025.03.13] Added support for the LiveCodeBench code evaluation benchmark.
- 🔥 [2025.03.11] Added support for the SimpleQA and Chinese SimpleQA evaluation benchmarks.
- 🔥 [2025.03.07] Added support for the QwQ-32B model evaluation.
- 🔥 [2025.03.04] Added support for the SuperGPQA dataset.
- 🔥 [2025.03.03] Added support for evaluating the IQ and EQ of models.
- 🔥 [2025.02.27] Added support for evaluating the reasoning efficiency of models.
- 🔥 [2025.02.25] Added support for MuSR and ProcessBench benchmarks.
- 🔥 [2025.02.18] Supports the AIME25 dataset.
- 🔥 [2025.02.13] Added support for evaluating DeepSeek distilled models.
- 🔥 [2025.01.20] Support for visualizing evaluation results.
- 🔥 [2025.01.07] Native backend: Support for model API evaluation.
- 🔥🔥 [2024.12.31] Support for adding benchmark evaluations.
- 🔥 [2024.12.13] Model evaluation optimization.
- 🔥 [2024.11.26] The model inference service performance evaluator has been completely refactored.
- 🔥 [2024.10.31] The best practice for evaluating Multimodal-RAG has been updated.
- 🔥 [2024.10.23] Supports multimodal RAG evaluation.
- 🔥 [2024.10.8] Support for RAG evaluation.
- 🔥 [2024.09.18] Documentation added blog module.
- 🔥 [2024.09.12] Support for LongWriter evaluation.
- 🔥 [2024.08.30] Support for custom dataset evaluations.
- 🔥 [2024.08.20] Updated the official documentation.
- 🔥 [2024.08.09] Simplified the installation process.
- 🔥 [2024.07.31] Important change: The package name
llmuseshas been changed toevalscope. - 🔥 [2024.07.26] Support for VLMEvalKit as a third-party evaluation framework.
- 🔥 [2024.06.29] Support for OpenCompass as a third-party evaluation framework.
- 🔥 [2024.06.13] EvalScope integrates with SWIFT; Integrated the Agent evaluation dataset ToolBench.
</details>
Installation
pip install evalscopeFor detailed installation instructions (source install, extra dependencies, etc.), please refer to the 📖 Installation Guide.
🚀 Quick Start
📈 Advanced Usage
Example evaluation results
Model WinRate (%) CI (%) ------------ ------------- --------------- qwen2.5-72b 69.3 (-13.3 / +12.2) qwen2.5-7b 50 (+0.0 / +0.0) qwen2.5-0.5b 4.7 (-2.5 / +4.4) ``` For details, please refer to 📖 Arena Mode Usage Guide.
Method 1. Evaluate an Online Model API (Recommended for beginners, no GPU required)
Supports any OpenAI API-compatible model service. Just set $OPENAI_API_BASE_URL and $OPENAI_API_KEY and you are ready to go:
evalscope eval \
--model your-model-name \
--api-url $OPENAI_API_BASE_URL \
--api-key $OPENAI_API_KEY \
--eval-type openai_api \
--datasets gsm8k arc \
--limit 5
🎯 aiskill88 AI 点评
A 级
2026-05-29
高效的大型模型评估框架，值得关注
📚 实用指南（长尾问题）
适合谁
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
最佳实践
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
常见错误
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- embedding 模型与查询模型不一致导致检索失效
- Python 依赖冲突：建议用 venv / uv 隔离环境
部署方案
- Docker：evalscope 提供官方镜像，docker compose up 一键启动
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
👥 适合谁
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
⭐ 最佳实践
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
⚠️ 常见错误
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- embedding 模型与查询模型不一致导致检索失效
👥 适合人群
🎯 使用场景
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
⚖️ 优点与不足
✅ 优点
- +Apache-2.0 协议，可免费商用
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
⚠️ 不足
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
⚠️ 使用须知
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 License 说明
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
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❓ 常见问题 FAQ
evalscope 是一款Python开发的AI辅助工具。开源AI工具：A streamlined and customizable framework for efficient large model (LLM, VLM, AI。⭐2.9k · Python 主要应用场景包括：大型模型评估和优化。
💡 AI Skill Hub 点评
总体来看，评估范围 是一款质量优秀的AI工具，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
🌐 原始信息
| 原始名称 | evalscope |
| 原始描述 | 开源AI工具：A streamlined and customizable framework for efficient large model (LLM, VLM, AI。⭐2.9k · Python |
| Topics | llmvlmaiperformanceevaluation |
| GitHub | https://github.com/modelscope/evalscope |
| License | Apache-2.0 |
| 语言 | Python |
🔗 原始来源
🐙 GitHub 仓库 https://github.com/modelscope/evalscope
🌐 官方网站 https://evalscope.readthedocs.io/en/latest/
收录时间：2026-05-29 · 更新时间：2026-05-30 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。