sglang.0.4.8.post1/sglang/benchmark/mmmu/README.md

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## Run evaluation
### Evaluate sglang
Host the VLM:
```
python -m sglang.launch_server --model-path Qwen/Qwen2-VL-7B-Instruct --port 30000
```
It's recommended to reduce the memory usage by appending something like `--mem-fraction-static 0.6` to the command above.
Benchmark:
```
python benchmark/mmmu/bench_sglang.py --port 30000 --concurrency 16
```
You can adjust the `--concurrency` to control the number of concurrent OpenAI calls.
You can use `--lora-path` to specify the LoRA adapter to apply during benchmarking. E.g.,
```
# Launch server with LoRA enabled
python -m sglang.launch_server --model-path microsoft/Phi-4-multimodal-instruct --port 30000 --trust-remote-code --disable-radix-cache --lora-paths vision=<LoRA path>
# Apply LoRA adapter during inferencing
python -m benchmark/mmmu/bench_sglang.py --concurrency 8 --lora-path vision
```
### Evaluate hf
```
python benchmark/mmmu/bench_hf.py --model-path Qwen/Qwen2-VL-7B-Instruct
```