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# Using LMDeploy to Accelerate Evaluation and Inference
VLMEvalKit supports testing VLM models deployed by LMDeploy. Below, we use InternVL2-8B as an example to show how to test the model.
## Step 0: Install LMDeploy
```bash
pip install lmdeploy
```
For other installation methods, you can refer to LMDeploy's [documentation](https://github.com/InternLM/lmdeploy).
## Step 1: Start the Inference Service
```bash
lmdeploy serve api_server OpenGVLab/InternVL2-8B --model-name InternVL2-8B
```
> [!IMPORTANT]
> Since models in VLMEvalKit may have custom behaviors when building prompts for different datasets, such as InternVL2's handling of HallusionBench, it is necessary to specify `--model-name` when starting the server. This allows the VLMEvalKit to select appropriate prompt construction strategy based on the name when using the LMDeploy API.
>
> If `--server-port`, is specified, the corresponding environment variable `LMDEPLOY_API_BASE` needs to be set.
## Step 2: Evaluation
```bash
python run.py --data MMStar --model lmdeploy --verbose --api-nproc 64
```
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