# Introduction

[EvalScope](https://github.com/modelscope/evalscope) is a comprehensive model evaluation and benchmarking framework meticulously crafted by the ModelScope community. It offers an all-in-one solution for your model assessment needs, regardless of the type of model you are developing:

- 🧠 Large Language Models
- 🎨 Multimodal Models
- 🔍 Embedding Models
- 🏆 Reranker Models
- 🖼️ CLIP Models
- 🎭 AIGC Models (Text-to-Image/Video)
- ...and more!

EvalScope is not merely an evaluation tool; it is a valuable ally in your model optimization journey:

- 🏅 Equipped with multiple industry-recognized benchmarks and evaluation metrics such as MMLU, CMMLU, C-Eval, GSM8K, and others.
- 📊 Performance stress testing for model inference to ensure your model excels in real-world applications.
- 🚀 Seamlessly integrates with the [ms-swift](https://github.com/modelscope/ms-swift) training framework, enabling one-click evaluations and providing end-to-end support from training to assessment for your model development.

## Overall Architecture
![EvalScope Architecture Diagram](https://sail-moe.oss-cn-hangzhou.aliyuncs.com/yunlin/images/evalscope/doc/EvalScope%E6%9E%B6%E6%9E%84%E5%9B%BE.png)
*EvalScope Architecture Diagram.*

The architecture includes the following modules:

1. Input Layer  
- **Model Sources**: API models (OpenAI API), local models (ModelScope)  
- **Datasets**: Standard evaluation benchmarks (MMLU/GSM8k, etc.), custom data (MCQ/QA)  

2. Core Functions  
- **Multi-backend Evaluation**  
   - Native backends: Unified evaluation for LLM/VLM/Embedding/T2I models  
   - Integrated frameworks: OpenCompass/MTEB/VLMEvalKit/RAGAS  

- **Performance Monitoring**  
   - Model plugins: Supports various model service APIs  
   - Data plugins: Supports multiple data formats  
   - Metric tracking: TTFT/TPOP/Stability and other metrics  

- **Tool Extensions**  
   - Integration: Tool-Bench/Needle-in-a-Haystack/BFCL-v3  

3. Output Layer  
- **Structured Reports**: Supports JSON/Tables/Logs  
- **Visualization Platforms**: Supports Web Dashboard/Wandb/SwanLab

## Framework Features
- **Benchmark Datasets**: Preloaded with several commonly used test benchmarks, including MMLU, CMMLU, C-Eval, GSM8K, ARC, HellaSwag, TruthfulQA, MATH, HumanEval, etc.
- **Evaluation Metrics**: Implements various commonly used evaluation metrics.
- **Model Access**: A unified model access mechanism that is compatible with the Generate and Chat interfaces of multiple model families.
- **Automated Evaluation**: Includes automatic evaluation of objective questions and complex task evaluation using expert models.
- **Evaluation Reports**: Automatically generates evaluation reports.
- **Arena Mode**: Used for comparisons between models and objective evaluation of models, supporting various evaluation modes, including:
  - **Single mode**: Scoring a single model.
  - **Pairwise-baseline mode**: Comparing against a baseline model.
  - **Pairwise (all) mode**: Pairwise comparison among all models.
- **Visualization Tools**: Provides intuitive displays of evaluation results.
- **Model Performance Evaluation**: Offers a performance testing tool for model inference services and detailed statistics, see [Model Performance Evaluation Documentation](../user_guides/stress_test/index.md).
- **OpenCompass Integration**: Supports OpenCompass as the evaluation backend, providing advanced encapsulation and task simplification, allowing for easier task submission for evaluation.
- **Multimodal Evaluation**: The Native backend supports 40+ multimodal benchmarks out of the box (OCRBench, MMMU, MMBench, MathVista, ChartQA, DocVQA, etc.), covering vision, video and audio tasks; the VLMEvalKit backend remains available as a compatibility option.
- **Full-Link Support**: Through seamless integration with the [ms-swift](https://github.com/modelscope/ms-swift) training framework, provides a one-stop development process for model training, model deployment, model evaluation, and report viewing, enhancing user development efficiency.
