projects/VLMEvalKit_Thinkmorph/docs/en/EvalByLMDeploy.md

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

pip install lmdeploy

For other installation methods, you can refer to LMDeploy's documentation.

Step 1: Start the Inference Service

lmdeploy serve api_server OpenGVLab/InternVL2-8B --model-name InternVL2-8B

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

python run.py --data MMStar --model lmdeploy --verbose --api-nproc 64