55 lines
1.9 KiB
Markdown
55 lines
1.9 KiB
Markdown
# Run benchmark
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This benchmark is primarily intended to be used with reasoning models like `DeepSeek-R1` and its distilled models like `DeepSeek-R1-Distill-Qwen-1.5B`. Please use
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```bash
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pip install antlr4-python3-runtime
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```
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for `parse_latex` which we use for symbolic equality check.
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## Benchmark sglang
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1. Launch the Server
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```bash
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python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --port 30000
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```
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Note that depending on the GPU this benchmark will take quiet some time. To employ data parallelism please use:
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```bash
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python3 -m sglang_router.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --port 30000 --dp-size 4
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```
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2. Benchmarking
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We use [suggested](https://github.com/deepseek-ai/DeepSeek-R1) parameters of `temperature=0.6`, `top_p=.95`, `max_new_tokens=32768`. The command line argument `num-tries` can be used to evaluate the model multiple times on the same question. We use the suggested `64` from the repo for AIME 2024. For LIMO, we use `8` as the number of tries due to the size of the dataset.
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By default evaluate on LIMO dataset.
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```bash
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python3 bench_sglang.py --parallel 256 --num-tries 64 --port 30000
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```
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Evaluate on AIME 2024 dataset.
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```bash
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python3 bench_sglang.py --parallel 256 --port 30000 --data-path Maxwell-Jia/AIME_2024 --question-key Problem --answer-key Answer --num-tries 64
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```
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Evaluate on [AIME 2025 I dataset](https://huggingface.co/datasets/opencompass/AIME2025). For benchmark result see [here](https://matharena.ai/).
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```bash
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python3 bench_sglang.py --parallel 256 --port 30000 --data-path opencompass/AIME2025 --question-key question --answer-key answer --num-tries 64
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```
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## Results
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| Dataset | Num Tries | Accuracy | Reference |
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|------------|-----------|----------|-----------|
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| LIMO | 8 | 47.7% | ? |
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| AIME 2024 | 64 | 33.2% | 28.9% |
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| AIME 2025 I| 64 | 29.9% | 25.0% |
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