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🧭 Welcome

to OpenCompass!

Just like a compass guides us on our journey, OpenCompass will guide you through the complex landscape of evaluating large language models. With its powerful algorithms and intuitive interface, OpenCompass makes it easy to assess the quality and effectiveness of your NLP models.

🚩🚩🚩 Explore opportunities at OpenCompass! We're currently hiring full-time researchers/engineers and interns. If you're passionate about LLM and OpenCompass, don't hesitate to reach out to us via email. We'd love to hear from you!

🔥🔥🔥 We are delighted to announce that the OpenCompass has been recommended by the Meta AI, click Get Started of Llama for more information.

Attention

Breaking Change Notice: In version 0.4.0, we are consolidating all AMOTIC configuration files (previously located in ./configs/datasets, ./configs/models, and ./configs/summarizers) into the opencompass package. Users are advised to update their configuration references to reflect this structural change.

🚀 What's New

• [2025.04.01] OpenCompass now supports CascadeEvaluator, a flexible evaluation mechanism that allows multiple evaluators to work in sequence. This enables creating customized evaluation pipelines for complex assessment scenarios. Check out the documentation for more details! 🔥🔥🔥
• [2025.03.11] We have supported evaluation for SuperGPQA which is a great benchmark for measuring LLM knowledge ability 🔥🔥🔥
• [2025.02.28] We have added a tutorial for DeepSeek-R1 series model, please check Evaluating Reasoning Model for more details! 🔥🔥🔥
• [2025.02.15] We have added two powerful evaluation tools: GenericLLMEvaluator for LLM-as-judge evaluations and MATHVerifyEvaluator for mathematical reasoning assessments. Check out the documentation for LLM Judge and Math Evaluation for more details! 🔥🔥🔥
• [2025.01.16] We now support the InternLM3-8B-Instruct model which has enhanced performance on reasoning and knowledge-intensive tasks.
• [2024.12.17] We have provided the evaluation script for the December CompassAcademic, which allows users to easily reproduce the official evaluation results by configuring it.
• [2024.11.14] OpenCompass now offers support for a sophisticated benchmark designed to evaluate complex reasoning skills — MuSR. Check out the demo and give it a spin! 🔥🔥🔥
• [2024.11.14] OpenCompass now supports the brand new long-context language model evaluation benchmark — BABILong. Have a look at the demo and give it a try! 🔥🔥🔥
• [2024.10.14] We now support the OpenAI multilingual QA dataset MMMLU. Feel free to give it a try! 🔥🔥🔥
• [2024.09.19] We now support Qwen2.5(0.5B to 72B) with multiple backend(huggingface/vllm/lmdeploy). Feel free to give them a try! 🔥🔥🔥
• [2024.09.17] We now support OpenAI o1(o1-mini-2024-09-12 and o1-preview-2024-09-12). Feel free to give them a try! 🔥🔥🔥
• [2024.09.05] We now support answer extraction through model post-processing to provide a more accurate representation of the model's capabilities. As part of this update, we have integrated XFinder as our first post-processing model. For more detailed information, please refer to the documentation, and give it a try! 🔥🔥🔥
• [2024.08.20] OpenCompass now supports the SciCode: A Research Coding Benchmark Curated by Scientists. 🔥🔥🔥
• [2024.08.16] OpenCompass now supports the brand new long-context language model evaluation benchmark — RULER. RULER provides an evaluation of long-context including retrieval, multi-hop tracing, aggregation, and question answering through flexible configurations. Check out the RULER evaluation config now! 🔥🔥🔥
• [2024.08.09] We have released the example data and configuration for the CompassBench-202408, welcome to CompassBench for more details. 🔥🔥🔥
• [2024.08.01] We supported the Gemma2 models. Welcome to try! 🔥🔥🔥
• [2024.07.23] We supported the ModelScope datasets, you can load them on demand without downloading all the data to your local disk. Welcome to try! 🔥🔥🔥
• [2024.07.17] We are excited to announce the release of NeedleBench's technical report. We invite you to visit our support documentation for detailed evaluation guidelines. 🔥🔥🔥
• [2024.07.04] OpenCompass now supports InternLM2.5, which has outstanding reasoning capability, 1M Context window and and stronger tool use, you can try the models in OpenCompass Config and InternLM .🔥🔥🔥.
• [2024.06.20] OpenCompass now supports one-click switching between inference acceleration backends, enhancing the efficiency of the evaluation process. In addition to the default HuggingFace inference backend, it now also supports popular backends LMDeploy and vLLM. This feature is available via a simple command-line switch and through deployment APIs. For detailed usage, see the documentation.🔥🔥🔥.

More

📊 Leaderboard

We provide OpenCompass Leaderboard for the community to rank all public models and API models. If you would like to join the evaluation, please provide the model repository URL or a standard API interface to the email address opencompass@pjlab.org.cn.

You can also refer to CompassAcademic to quickly reproduce the leaderboard results. The currently selected datasets include Knowledge Reasoning (MMLU-Pro/GPQA Diamond), Logical Reasoning (BBH), Mathematical Reasoning (MATH-500, AIME), Code Generation (LiveCodeBench, HumanEval), and Instruction Following (IFEval)."

🔝Back to top

🛠️ Installation

Below are the steps for quick installation and datasets preparation.

💻 Environment Setup

We highly recommend using conda to manage your python environment.

• Create your virtual environment

bash conda create --name opencompass python=3.10 -y conda activate opencompass

• Install OpenCompass via pip

```bash pip install -U opencompass

## Full installation (with support for more datasets)
# pip install "opencompass[full]"

## Environment with model acceleration frameworks
## Manage different acceleration frameworks using virtual environments
## since they usually have dependency conflicts with each other.
# pip install "opencompass[lmdeploy]"
# pip install "opencompass[vllm]"

## API evaluation (i.e. Openai, Qwen)
# pip install "opencompass[api]"

```

• Install OpenCompass from source

If you want to use opencompass's latest features, or develop new features, you can also build it from source

bash git clone https://github.com/open-compass/opencompass opencompass cd opencompass pip install -e . # pip install -e ".[full]" # pip install -e ".[vllm]"

📂 Data Preparation

You can choose one for the following method to prepare datasets.

Offline Preparation

You can download and extract the datasets with the following commands:

# Download dataset to data/ folder
wget https://github.com/open-compass/opencompass/releases/download/0.2.2.rc1/OpenCompassData-core-20240207.zip
unzip OpenCompassData-core-20240207.zip

Automatic Download from OpenCompass

We have supported download datasets automatic from the OpenCompass storage server. You can run the evaluation with extra --dry-run to download these datasets. Currently, the supported datasets are listed in here. More datasets will be uploaded recently.

(Optional) Automatic Download with ModelScope

Also you can use the ModelScope to load the datasets on demand.

Installation:

pip install modelscope[framework]
export DATASET_SOURCE=ModelScope

Then submit the evaluation task without downloading all the data to your local disk. Available datasets include:

humaneval, triviaqa, commonsenseqa, tydiqa, strategyqa, cmmlu, lambada, piqa, ceval, math, LCSTS, Xsum, winogrande, openbookqa, AGIEval, gsm8k, nq, race, siqa, mbpp, mmlu, hellaswag, ARC, BBH, xstory_cloze, summedits, GAOKAO-BENCH, OCNLI, cmnli

Some third-party features, like Humaneval and Llama, may require additional steps to work properly, for detailed steps please refer to the Installation Guide.

🔝Back to top

🏗️ ️Evaluation

After ensuring that OpenCompass is installed correctly according to the above steps and the datasets are prepared. Now you can start your first evaluation using OpenCompass!

Your first evaluation with OpenCompass!

OpenCompass support setting your configs via CLI or a python script. For simple evaluation settings we recommend using CLI, for more complex evaluation, it is suggested using the script way. You can find more example scripts under the configs folder.

# CLI
opencompass --models hf_internlm2_5_1_8b_chat --datasets demo_gsm8k_chat_gen

# Python scripts
opencompass examples/eval_chat_demo.py

You can find more script examples under examples folder.

API evaluation

OpenCompass, by its design, does not really discriminate between open-source models and API models. You can evaluate both model types in the same way or even in one settings.

export OPENAI_API_KEY="YOUR_OPEN_API_KEY"
# CLI
opencompass --models gpt_4o_2024_05_13 --datasets demo_gsm8k_chat_gen

# Python scripts
opencompass examples/eval_api_demo.py

# You can use o1_mini_2024_09_12/o1_preview_2024_09_12  for o1 models, we set max_completion_tokens=8192 as default.

Accelerated Evaluation

Additionally, if you want to use an inference backend other than HuggingFace for accelerated evaluation, such as LMDeploy or vLLM, you can do so with the command below. Please ensure that you have installed the necessary packages for the chosen backend and that your model supports accelerated inference with it. For more information, see the documentation on inference acceleration backends here. Below is an example using LMDeploy:

# CLI
opencompass --models hf_internlm2_5_1_8b_chat --datasets demo_gsm8k_chat_gen -a lmdeploy

# Python scripts
opencompass examples/eval_lmdeploy_demo.py

Supported Models and Datasets

OpenCompass has predefined configurations for many models and datasets. You can list all available model and dataset configurations using the tools.

```bash

List all configurations

python tools/list_configs.py

List all configurations related to llama and mmlu

python tools/list_configs.py llama mmlu