analysis_report: false
api_url: https://dashscope.aliyuncs.com/compatible-mode/v1
chat_template: null
collect_perf: true
dataset_args:
  gpqa_diamond:
    aggregation: mean
    data_statistics: null
    dataset_id: AI-ModelScope/gpqa_diamond
    default_subset: default
    description: '

      ## Overview


      GPQA (Graduate-Level Google-Proof Q&A) Diamond is a challenging benchmark of
      198 multiple-choice questions written by domain experts in biology, physics,
      and chemistry. The questions are designed to be extremely difficult, requiring
      PhD-level expertise to answer correctly.


      ## Task Description


      - **Task Type**: Expert-Level Multiple-Choice Q&A

      - **Input**: Graduate-level science question with 4 choices

      - **Output**: Single correct answer letter (A, B, C, or D)

      - **Domains**: Biology, Physics, Chemistry


      ## Key Features


      - 198 questions written and validated by domain PhD experts

      - Questions are "Google-proof" - cannot be easily looked up

      - Designed to test deep domain knowledge and reasoning

      - Diamond subset represents the highest quality questions

      - Average human expert accuracy ~65%, non-expert ~34%


      ## Evaluation Notes


      - Default configuration uses **0-shot** or **5-shot** evaluation

      - Supports Chain-of-Thought (CoT) prompting for improved reasoning

      - Answer choices are randomly shuffled during evaluation

      - Only uses train split (validation set is private)

      - Challenging benchmark for measuring expert-level reasoning

      '
    eval_split: train
    extra_params: {}
    few_shot_num: 0
    few_shot_prompt_template: null
    few_shot_random: false
    filters: null
    force_redownload: false
    metric_list:
    - acc
    name: gpqa_diamond
    output_types:
    - generation
    paper_url: null
    pretty_name: GPQA-Diamond
    prompt_template: 'Answer the following multiple choice question. The last line
      of your response should be of the following format: ''ANSWER: [LETTER]'' (without
      quotes) where [LETTER] is one of {letters}. Think step by step before answering.


      {question}


      {choices}'
    query_template: null
    review_timeout: null
    sample_example: null
    sandbox_config: {}
    shuffle: false
    shuffle_choices: false
    subset_list:
    - default
    system_prompt: null
    tags:
    - Knowledge
    - MCQ
    train_split: null
  gsm8k:
    aggregation: mean
    data_statistics: null
    dataset_id: AI-ModelScope/gsm8k
    default_subset: default
    description: '

      ## Overview


      GSM8K (Grade School Math 8K) is a high-quality dataset of 8.5K linguistically
      diverse grade school math word problems created by human problem writers. The
      dataset is specifically designed to evaluate and improve the multi-step mathematical
      reasoning capabilities of language models.


      ## Task Description


      - **Task Type**: Mathematical Word Problem Solving

      - **Input**: Natural language math word problem

      - **Output**: Numerical answer derived through step-by-step reasoning

      - **Difficulty**: Grade school level (2-8 reasoning steps required)


      ## Key Features


      - Problems require basic arithmetic operations (addition, subtraction, multiplication,
      division)

      - Solutions involve 2 to 8 sequential reasoning steps

      - High linguistic diversity in problem formulations

      - Human-written problems ensuring natural language quality

      - Clear numerical answers for objective evaluation


      ## Evaluation Notes


      - Default configuration uses **4-shot** examples with Chain-of-Thought (CoT)
      prompting

      - Answers should be formatted within `\boxed{}` for proper extraction

      - The metric extracts numerical values for accuracy comparison

      - Supports both zero-shot and few-shot evaluation modes

      '
    eval_split: test
    extra_params: {}
    few_shot_num: 4
    few_shot_prompt_template: 'Here are some examples of how to solve similar problems:


      {fewshot}


      {question}

      Please reason step by step, and put your final answer within \boxed{{}}.'
    few_shot_random: false
    filters: null
    force_redownload: false
    metric_list:
    - acc:
        numeric: true
    name: gsm8k
    output_types:
    - generation
    paper_url: https://arxiv.org/abs/2110.14168
    pretty_name: GSM8K
    prompt_template: '{question}

      Please reason step by step, and put your final answer within \boxed{{}}.'
    query_template: null
    review_timeout: null
    sample_example: null
    sandbox_config: {}
    shuffle: false
    shuffle_choices: false
    subset_list:
    - main
    system_prompt: null
    tags:
    - Math
    - Reasoning
    train_split: train
  ifeval:
    aggregation: mean
    data_statistics: null
    dataset_id: opencompass/ifeval
    default_subset: default
    description: "\n## Overview\n\nIFEval (Instruction-Following Eval) is a benchmark\
      \ for evaluating how well language models follow explicit, verifiable instructions.\
      \ It contains prompts with specific formatting, content, or structural requirements\
      \ that can be objectively verified.\n\n## Task Description\n\n- **Task Type**:\
      \ Instruction Following Evaluation\n- **Input**: Prompts with explicit, verifiable\
      \ constraints\n- **Output**: Response that follows all specified instructions\n\
      - **Constraint Types**: Format, length, keywords, structure, etc.\n\n## Key\
      \ Features\n\n- ~500 prompts with 25 types of verifiable instructions\n- Instructions\
      \ are objectively checkable (not subjective)\n- Examples: \"write exactly 3\
      \ paragraphs\", \"include the word X\", \"use bullet points\"\n- Tests instruction\
      \ comprehension and compliance\n- No ambiguity in evaluation criteria\n\n##\
      \ Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n-\
      \ Four metrics available:\n  - `prompt_level_strict`: All instructions in prompt\
      \ must be followed\n  - `prompt_level_loose`: Some tolerance for minor deviations\n\
      \  - `inst_level_strict`: Per-instruction accuracy (strict)\n  - `inst_level_loose`:\
      \ Per-instruction accuracy (loose)\n- `prompt_level_strict` is the primary metric\n\
      - Automatic verification of instruction compliance\n"
    eval_split: train
    extra_params: {}
    few_shot_num: 0
    few_shot_prompt_template: null
    few_shot_random: false
    filters: null
    force_redownload: false
    metric_list:
    - prompt_level_strict
    - inst_level_strict
    - prompt_level_loose
    - inst_level_loose
    name: ifeval
    output_types:
    - generation
    paper_url: null
    pretty_name: IFEval
    prompt_template: ''
    query_template: null
    review_timeout: null
    sample_example: null
    sandbox_config: {}
    shuffle: false
    shuffle_choices: false
    subset_list:
    - default
    system_prompt: null
    tags:
    - InstructionFollowing
    train_split: null
  iquiz:
    aggregation: mean
    data_statistics: null
    dataset_id: AI-ModelScope/IQuiz
    default_subset: default
    description: '

      ## Overview


      IQuiz is a Chinese benchmark for evaluating AI models on intelligence quotient
      (IQ) and emotional quotient (EQ) questions. It tests logical reasoning, pattern
      recognition, and social-emotional understanding through multiple-choice questions.


      ## Task Description


      - **Task Type**: Multiple-Choice Question Answering

      - **Input**: Question in Chinese with multiple choice options

      - **Output**: Selected answer with explanation (Chain-of-Thought)

      - **Language**: Chinese


      ## Key Features


      - Dual evaluation of IQ and EQ capabilities

      - Chinese-language cognitive assessment

      - Multiple difficulty levels

      - Requires explanation alongside answer selection

      - Tests logical reasoning and emotional understanding


      ## Evaluation Notes


      - Default configuration uses **0-shot** evaluation

      - Primary metric: **Accuracy**

      - Subsets: **IQ** (logical reasoning) and **EQ** (emotional intelligence)

      - Uses Chinese Chain-of-Thought prompt template

      - Evaluates on **test** split

      - Metadata includes difficulty level information

      '
    eval_split: test
    extra_params: {}
    few_shot_num: 0
    few_shot_prompt_template: null
    few_shot_random: false
    filters: null
    force_redownload: false
    metric_list:
    - acc
    name: iquiz
    output_types:
    - generation
    paper_url: null
    pretty_name: IQuiz
    prompt_template: '回答下面的单项选择题，请选出其中的正确答案。你的回答的最后一行应该是这样的格式："答案：[LETTER]"（不带引号），其中
      [LETTER] 是 {letters} 中的一个。请在回答前进行一步步思考。


      问题：{question}

      选项：

      {choices}

      '
    query_template: null
    review_timeout: null
    sample_example: null
    sandbox_config: {}
    shuffle: false
    shuffle_choices: false
    subset_list:
    - IQ
    - EQ
    system_prompt: null
    tags:
    - Knowledge
    - MCQ
    - Chinese
    train_split: null
dataset_dir: /Users/yunlin/.cache/modelscope/hub/datasets
dataset_hub: modelscope
datasets:
- gsm8k
- iquiz
- ifeval
- gpqa_diamond
debug: false
enable_progress_tracker: false
eval_backend: Native
eval_batch_size: 10
eval_config: null
eval_type: openai_api
evalscope_version: 0.0.0_dev
generation_config:
  batch_size: 10
  stream: true
  temperature: 0.7
ignore_errors: false
judge_model_args: {}
judge_strategy: auto
judge_worker_num: null
limit: 10
model: qwen3-max
model_args: {}
model_id: qwen3-max
model_task: text_generation
no_timestamp: false
repeats: 1
rerun_review: false
sandbox_manager_config: {}
sandbox_type: docker
seed: 42
stream: null
timeout: null
use_cache: examples/viz/qwen3-max
use_sandbox: false
work_dir: examples/viz/qwen3-max