[Cookbook] Run accuracy benchmarks through sgl-eval (#36977)
This commit is contained in:
Liangsheng Yin
@@ -813,7 +813,7 @@ python3 -m sglang.test.run_eval --port 30000 --eval-name gpqa --num-examples 198
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#### 5.2.4 AIME 2025 Benchmark
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Results on AIME 2025 (8×B200), evaluated with [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills.git):
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Results on AIME 2025 (8×B200), evaluated with [sgl-eval](https://github.com/sgl-project/sgl-eval):
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<table style={{width: "100%", borderCollapse: "collapse"}}>
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<thead>
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@@ -846,25 +846,19 @@ Results on AIME 2025 (8×B200), evaluated with [NeMo-Skills](https://github.com/
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</tbody>
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</table>
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**Reproduction.** Install [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills), launch the server with the tool-call and reasoning parsers, then run `ns eval`:
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**Reproduction.** Install [sgl-eval](https://github.com/sgl-project/sgl-eval), launch the server with the tool-call and reasoning parsers, then run `sgl-eval run`. The AIME 2025 dataset ships with sgl-eval, and thinking is on by default for it:
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```bash Command
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pip install git+https://github.com/NVIDIA/NeMo-Skills.git --ignore-installed blinker
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pip install git+https://github.com/sgl-project/sgl-eval.git
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export NEMO_SKILLS_DISABLE_UNCOMMITTED_CHANGES_CHECK=1
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ns prepare_data aime25
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ns eval \
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--benchmarks=aime25:4 \
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--server_type=sglang \
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--model=deepseek-ai/DeepSeek-V3.2-Exp \
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--server_address=http://localhost:30000/v1 \
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--output_dir=nemo_skills_aime25_output \
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++chat_template_kwargs.thinking=true \
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++inference.temperature=1.0 \
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++inference.top_p=0.95 \
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++inference.tokens_to_generate=64000
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# Use ++inference.tokens_to_generate=120000 for the DeepSeek-V3.2-Speciale model
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sgl-eval run aime25 \
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--base-url http://localhost:30000/v1 \
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--model deepseek-ai/DeepSeek-V3.2-Exp \
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--n-repeats 4 \
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--temperature 1.0 \
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--top-p 0.95 \
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--max-tokens 64000
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# Use --max-tokens 120000 for the DeepSeek-V3.2-Speciale model
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```
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### 5.3 Speed Benchmark on Hopper
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@@ -487,30 +487,25 @@ This section uses **industry-standard configurations** for comparable benchmark
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### 5.1 Accuracy Benchmark
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**Evaluation Tool**: [NVIDIA NeMo-Skills](https://github.com/NVIDIA-NeMo/Skills)
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**Evaluation Tool**: [sgl-eval](https://github.com/sgl-project/sgl-eval)
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**Evaluation Settings**: temperature=0.6, top_p=0.95, 8 seeds, max_tokens=120,000, `parse_reasoning=True`
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**Evaluation Settings**: temperature=0.6, top_p=0.95, 8 repeats, max_tokens=120,000 (GPQA and AIME; MMLU-Pro is greedy and single-pass, see below)
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#### 5.1.1 GPQA Diamond
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- Dataset: [GPQA Diamond](https://huggingface.co/datasets/Idavidrein/gpqa) (198 questions)
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- Prompt: `eval/aai/mcq-4choices` (4-choice multiple choice, matching [Artificial Analysis methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking))
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- Prompt: `eval/aai/mcq-4choices` (4-choice multiple choice, matching [Artificial Analysis methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking)) -- sgl-eval's default for this benchmark
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- Evaluation command:
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```bash Command
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ns prepare_data gpqa
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pip install git+https://github.com/sgl-project/sgl-eval.git
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ns eval \
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--cluster=local \
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--server_type=openai \
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--model=MiniMaxAI/MiniMax-M2.7 \
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--server_address=http://localhost:30000/v1 \
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--output_dir=./m2.7-eval/ \
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--benchmarks=gpqa:8 \
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++prompt_config=eval/aai/mcq-4choices \
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++inference.tokens_to_generate=120000 \
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++inference.temperature=0.6 \
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++inference.top_p=0.95 \
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++parse_reasoning=True
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sgl-eval run gpqa \
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--base-url http://localhost:30000/v1 \
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--model MiniMaxAI/MiniMax-M2.7 \
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--n-repeats 8 \
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--max-tokens 120000 \
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--temperature 0.6 \
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--top-p 0.95
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```
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- Test Results:
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@@ -544,22 +539,16 @@ ns eval \
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#### 5.1.2 AIME 2025
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- Dataset: AIME 2025 (30 problems)
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- Prompt: `generic/math` (boxed answer format)
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- Prompt: `generic/math` (boxed answer format) -- sgl-eval's default for this benchmark
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- Evaluation command:
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```bash Command
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ns prepare_data aime25
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ns eval \
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--cluster=local \
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--server_type=openai \
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--model=MiniMaxAI/MiniMax-M2.7 \
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--server_address=http://localhost:30000/v1 \
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--output_dir=./m2.7-eval/ \
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--benchmarks=aime25:8 \
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++inference.tokens_to_generate=120000 \
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++inference.temperature=0.6 \
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++inference.top_p=0.95 \
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++parse_reasoning=True
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sgl-eval run aime25 \
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--base-url http://localhost:30000/v1 \
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--model MiniMaxAI/MiniMax-M2.7 \
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--n-repeats 8 \
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--max-tokens 120000 \
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--temperature 0.6 \
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--top-p 0.95
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```
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- Test Results:
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@@ -593,22 +582,14 @@ ns eval \
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#### 5.1.3 MMLU-Pro
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- Dataset: [MMLU-Pro](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro) (12,032 questions, 10-choice)
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- Prompt: `eval/aai/mcq-10choices` (10-choice multiple choice)
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- Prompt: `eval/aai/mcq-10choices` (10-choice multiple choice) -- sgl-eval's default for this benchmark
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- Evaluation command:
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```bash Command
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ns prepare_data mmlu-pro
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ns eval \
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--cluster=local \
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--server_type=openai \
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--model=MiniMaxAI/MiniMax-M2.7 \
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--server_address=http://localhost:30000/v1 \
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--output_dir=./m2.7-eval/ \
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--benchmarks=mmlu-pro \
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++prompt_config=eval/aai/mcq-10choices \
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++inference.tokens_to_generate=32768 \
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++inference.temperature=0.0 \
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++parse_reasoning=True
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sgl-eval run mmlu_pro \
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--base-url http://localhost:30000/v1 \
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--model MiniMaxAI/MiniMax-M2.7 \
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--max-tokens 32768 \
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--temperature 0.0
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```
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- Test Results:
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@@ -621,32 +621,25 @@ python tool_calls_eval.py tool-calls/samples.jsonl \
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#### 5.1.2 AIME 2025
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- Dataset: [AIME 2025](https://huggingface.co/datasets/nvidia/aime25) (30 problems)
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- Evaluation Tool: [NVIDIA NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills)
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- Evaluation Tool: [sgl-eval](https://github.com/sgl-project/sgl-eval)
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- Prompt: `eval/matharena/aime` (MathArena format with `\boxed{}` answers)
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- Settings: temperature=1.0, top_p=0.95, max_tokens=131,072, 32 seeds
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- Settings: temperature=1.0, top_p=0.95, max_tokens=131,072, 32 repeats
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**Evaluation Command:**
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```shell Command
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# Prepare dataset
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python3 nemo_skills/dataset/aime25/prepare.py
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pip install git+https://github.com/sgl-project/sgl-eval.git
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# Run 32 seeds in parallel
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for RS in $(seq 0 31); do
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python3 nemo_skills/inference/generate.py \
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input_file=nemo_skills/dataset/aime25/test.jsonl \
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output_file=results/kimi-k26/aime25/output-rs${RS}.jsonl \
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prompt_config=eval/matharena/aime \
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prompt_format=openai \
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+server.server_type=openai \
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+server.model=moonshotai/Kimi-K2.6 \
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+server.base_url=http://localhost:30000/v1 \
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++inference.temperature=1.0 \
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++inference.top_p=0.95 \
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++inference.tokens_to_generate=131072 \
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++inference.random_seed=${RS} \
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max_concurrent_requests=512 &
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done
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# The dataset ships with sgl-eval; --n-repeats runs all 32 samples per problem.
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sgl-eval run aime25 \
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--base-url http://localhost:30000/v1 \
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--model moonshotai/Kimi-K2.6 \
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--n-repeats 32 \
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--temperature 1.0 \
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--top-p 0.95 \
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--max-tokens 131072 \
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--prompt matharena-aime \
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--num-threads 512
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```
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**Results:**
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