sgl-project--sglang/benchmark/mmmu

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

You can use --response-answer-regex to specify how to extract the answer from the response string. E.g.,

python3 -m sglang.launch_server --model-path zai-org/GLM-4.1V-9B-Thinking --reasoning-parser glm45

python3 bench_sglang.py --response-answer-regex "<\|begin_of_box\|>(.*)<\|end_of_box\|>" --concurrency 64

You can use --extra-request-body to specify additional OpenAI request parameters. E.g.,

python3 bench_sglang.py --extra-request-body '{"max_new_tokens": 128, "temperature": 0.01}'

Evaluate HF

python benchmark/mmmu/bench_hf.py --model-path Qwen/Qwen2-VL-7B-Instruct

Profiling MMMU

You should use the standard instructions found in the dedicated profiling doc if running this benchmark with the profile option. We recommend using --concurrency 1 for consistency, which makes profiling and debugging easier.