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# Installation
1. Set up the OpenCompass environment:
`````{tabs}
````{tab} Open-source Models with GPU
```bash
conda create --name opencompass python=3.10 pytorch torchvision pytorch-cuda -c nvidia -c pytorch -y
conda activate opencompass
```
If you want to customize the PyTorch version or related CUDA version, please refer to the [official documentation](https://pytorch.org/get-started/locally/) to set up the PyTorch environment. Note that OpenCompass requires `pytorch>=1.13`.
````
````{tab} API Models with CPU-only
```bash
conda create -n opencompass python=3.10 pytorch torchvision torchaudio cpuonly -c pytorch -y
conda activate opencompass
# also please install requiresments packages via `pip install -r requirements/api.txt` for API models if needed.
```
If you want to customize the PyTorch version, please refer to the [official documentation](https://pytorch.org/get-started/locally/) to set up the PyTorch environment. Note that OpenCompass requires `pytorch>=1.13`.
````
`````
2. Install OpenCompass:
```bash
git clone https://github.com/open-compass/opencompass.git
cd opencompass
pip install -e .
```
3. Install humaneval (Optional)
If you want to **evaluate your models coding ability on the humaneval dataset**, follow this step.
<details>
<summary><b>click to show the details</b></summary>
```bash
git clone https://github.com/openai/human-eval.git
cd human-eval
pip install -r requirements.txt
pip install -e .
cd ..
```
Please read the comments in `human_eval/execution.py` **lines 48-57** to understand the potential risks of executing the model generation code. If you accept these risks, uncomment **line 58** to enable code execution evaluation.
</details>
4. Install Llama (Optional)
If you want to **evaluate Llama / Llama-2 / Llama-2-chat with its official implementation**, follow this step.
<details>
<summary><b>click to show the details</b></summary>
```bash
git clone https://github.com/facebookresearch/llama.git
cd llama
pip install -r requirements.txt
pip install -e .
cd ..
```
You can find example configs in `configs/models`. ([example](https://github.com/open-compass/opencompass/blob/eb4822a94d624a4e16db03adeb7a59bbd10c2012/configs/models/llama2_7b_chat.py))
</details>
# Dataset Preparation
The datasets supported by OpenCompass mainly include two parts:
1. Huggingface datasets: The [Huggingface Datasets](https://huggingface.co/datasets) provide a large number of datasets, which will **automatically download** when running with this option.
2. Custom dataset: OpenCompass also provides some Chinese custom **self-built** datasets. Please run the following command to **manually download and extract** them.
Run the following commands to download and place the datasets in the `${OpenCompass}/data` directory can complete dataset preparation.
```bash
# Run in the OpenCompass directory
wget https://github.com/open-compass/opencompass/releases/download/0.2.2.rc1/OpenCompassData-core-20240207.zip
unzip OpenCompassData-core-20240207.zip
```
If you need to use the more comprehensive dataset (~500M) provided by OpenCompass, You can download and `unzip` it using the following command:
```bash
wget https://github.com/open-compass/opencompass/releases/download/0.2.2.rc1/OpenCompassData-complete-20240207.zip
unzip OpenCompassData-complete-20240207.zip
cd ./data
find . -name "*.zip" -exec unzip {} \;
```
The list of datasets included in both `.zip` can be found [here](https://github.com/open-compass/opencompass/releases/tag/0.2.2.rc1)
OpenCompass has supported most of the datasets commonly used for performance comparison, please refer to `configs/dataset` for the specific list of supported datasets.
For next step, please read [Quick Start](./quick_start.md).
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