README.md

metadata

license: cc-by-4.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - stem
  - evaluation
  - benchmark
  - mmlu
  - gpqa
  - gsm8k
  - math
size_categories:
  - 1K<n<10K

nanochat-npu-stem-eval

Pre-processed STEM evaluation data for nanochat-npu, adapted from karpathy/nanochat for Huawei 910B3 NPU.

Tasks

Task	Type	Shot	Source	Examples	Description
gpqa_diamond	multiple_choice	0-shot	Idavidrein/gpqa	198	Graduate-level science QA (Diamond subset)
gsm8k_cot	generation	8-shot	openai/gsm8k	1319	Grade school math word problems (CoT)
math_cot	generation	4-shot	HuggingFaceH4/MATH-500	500	Competition mathematics (CoT)
mmlu_zeroshot	multiple_choice	0-shot	cais/mmlu	14042	MMLU full 57 subjects
mmlu_stem	multiple_choice	0-shot	cais/mmlu	3545	MMLU STEM subset (22 subjects)

MMLU STEM Subjects (22)

abstract_algebra, anatomy, astronomy, college_biology, college_chemistry, college_computer_science, college_mathematics, college_physics, computer_security, conceptual_physics, electrical_engineering, elementary_mathematics, formal_logic, high_school_biology, high_school_chemistry, high_school_computer_science, high_school_mathematics, high_school_physics, high_school_statistics, machine_learning, medical_genetics, virology

Data Format

Multiple Choice Tasks (gpqa_diamond, mmlu, mmlu_stem)

{
  "query": "Question text\nChoices:\n(A) choice0\n(B) choice1\n(C) choice2\n(D) choice3",
  "choices": ["choice0", "choice1", "choice2", "choice3"],
  "gold": 2
}

gold is the index (0-3) of the correct answer. GPQA choices are shuffled with seed=42.

Generation Tasks (gsm8k, math500)

{
  "question": "Problem text",
  "answer": "Full solution text",
  "gold_answer": "42"
}

gold_answer is the extracted final answer (only in MATH-500). For GSM8K, the answer contains #### N which is extracted at evaluation time.

Usage

Download as evaluation bundle (recommended)

The eval_stem.zip contains all data in the exact layout expected by nanochat evaluation scripts. It is downloaded automatically on first evaluation run.

Load individual tasks with datasets library

from datasets import load_dataset

# Load MMLU STEM subset
ds = load_dataset("liujin99/nanochat-npu-stem-eval", "mmlu_stem")
# Load GSM8K
ds = load_dataset("liujin99/nanochat-npu-stem-eval", "gsm8k")

Source Datasets and Licenses

Dataset	Source	License
GPQA Diamond	Idavidrein/gpqa	CC-BY-4.0
GSM8K	openai/gsm8k	MIT
MATH-500	HuggingFaceH4/MATH-500	MIT
MMLU	cais/mmlu	MIT

All data is redistributed with attribution under the original licenses. See LICENSES.txt in the zip for details.