evaluation_turbomind.md

# Evaluation with LMDeploy

We now support evaluation of models accelerated by the [LMDeploy](https://github.com/InternLM/lmdeploy). LMDeploy is a toolkit designed for compressing, deploying, and serving LLM. **TurboMind** is an efficient inference engine proposed by LMDeploy. OpenCompass is compatible with TurboMind. We now illustrate how to evaluate a model with the support of TurboMind in OpenCompass.

## Setup

### Install OpenCompass

Please follow the [instructions](https://opencompass.readthedocs.io/en/latest/get_started.html) to install the OpenCompass and prepare the evaluation datasets.

### Install LMDeploy

Install lmdeploy via pip (python 3.8+)

```shell
pip install lmdeploy
```

## Evaluation

We take the InternLM as example.

### Step-1: Get InternLM model

```shell
# 1. Download InternLM model(or use the cached model's checkpoint)

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/internlm/internlm-chat-7b /path/to/internlm-chat-7b

# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1

# 2. Convert InternLM model to turbomind's format, which will be in "./workspace" by default
python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-chat-7b

```

### Step-2: Verify the Converted Model

```shell
python -m lmdeploy.turbomind.chat ./workspace
```

### Step-3: Evaluate the Converted Model

In the home folder of OpenCompass

```shell
python run.py configs/eval_internlm_chat_7b_turbomind.py -w outputs/turbomind
```

You are expected to get the evaluation results after the inference and evaluation.