👋 join us on <ahref="https://discord.gg/KKwfEbFj7U"target="_blank">Discord</a> and <ahref="https://r.vansin.top/?r=opencompass"target="_blank">WeChat</a>
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.
- **\[2023.09.20\]** We update the leaderboard with [InternLM-20B](https://github.com/InternLM/InternLM), welcome to our [homepage](https://opencompass.org.cn) for more details. 🔥🔥🔥.
- **\[2023.09.19\]** We update the leaderboard with WeMix-LLaMA2-70B/Phi-1.5-1.3B, welcome to our [homepage](https://opencompass.org.cn) for more details. 🔥🔥🔥.
- **\[2023.09.08\]** We update the leaderboard with Baichuan-2/Tigerbot-2/Vicuna-v1.5, welcome to our [homepage](https://opencompass.org.cn) for more details.
- **\[2023.09.06\]** [**Baichuan2**](https://github.com/baichuan-inc/Baichuan2) team adpots OpenCompass to evaluate their models systematically. We deeply appreciate the community's dedication to transparency and reproducibility in LLM evaluation.
- **\[2023.08.25\]** [**TigerBot**](https://github.com/TigerResearch/TigerBot) team adpots OpenCompass to evaluate their models systematically. We deeply appreciate the community's dedication to transparency and reproducibility in LLM evaluation.
- **\[2023.08.21\]** [**Lagent**](https://github.com/InternLM/lagent) has been released, which is a lightweight framework for building LLM-based agents. We are working with Lagent team to support the evaluation of general tool-use capability, stay tuned!
OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features includes:
- **Comprehensive support for models and datasets**: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 50+ datasets with about 300,000 questions, comprehensively evaluating the capabilities of the models in five dimensions.
- **Efficient distributed evaluation**: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours.
- **Diversified evaluation paradigms**: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue type prompt templates, to easily stimulate the maximum performance of various models.
- **Modular design with high extensibility**: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded!
- **Experiment management and reporting mechanism**: Use config files to fully record each experiment, support real-time reporting of results.
We provide [OpenCompass Leaderbaord](https://opencompass.org.cn/rank) for 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`.
Some third-party features, like Humaneval and Llama, may require additional steps to work properly, for detailed steps please refer to the [Installation Guide](https://opencompass.readthedocs.io/en/latest/get_started.html).
After ensuring that OpenCompass is installed correctly according to the above steps and the datasets are prepared, you can evaluate the performance of the LLaMA-7b model on the MMLU and C-Eval datasets using the following command:
OpenCompass has predefined configurations for many models and datasets. You can list all available model and dataset configurations using the [tools](./docs/en/tools.md#list-configs).
```bash
# List all configurations
python tools/list_configs.py
# List all configurations related to llama and mmlu
python tools/list_configs.py llama mmlu
```
You can also evaluate other HuggingFace models via command line. Taking LLaMA-7b as an example:
```bash
python run.py --datasets ceval_ppl mmlu_ppl \
--hf-path huggyllama/llama-7b \ # HuggingFace model path
--model-kwargs device_map='auto' \ # Arguments for model construction
--tokenizer-kwargs padding_side='left' truncation='left' use_fast=False \ # Arguments for tokenizer construction
--max-out-len 100 \ # Maximum number of tokens generated
--max-seq-len 2048 \ # Maximum sequence length the model can accept
--batch-size 8 \ # Batch size
--no-batch-padding \ # Don't enable batch padding, infer through for loop to avoid performance loss
--num-gpus 1 # Number of required GPUs
```
Through the command line or configuration files, OpenCompass also supports evaluating APIs or custom models, as well as more diversified evaluation strategies. Please read the [Quick Start](https://opencompass.readthedocs.io/en/latest/get_started.html) to learn how to run an evaluation task.
We appreciate all contributions to improve OpenCompass. Please refer to the [contributing guideline](https://opencompass.readthedocs.io/en/latest/notes/contribution_guide.html) for the best practice.
Some datasets and prompt implementations are modified from [chain-of-thought-hub](https://github.com/FranxYao/chain-of-thought-hub) and [instruct-eval](https://github.com/declare-lab/instruct-eval).
