README.md

metadata

language:
  - en
license: other
tags:
  - synthetic
  - math
  - reasoning
  - step-by-step
  - text-generation
  - language-modeling
task_categories:
  - text-generation
task_ids:
  - language-modeling
size_categories:
  - 1M<n<10M
pretty_name: QuixiMath-1B
configs:
  - config_name: preview
    data_files:
      - split: train
        path: preview/train-*.parquet
  - config_name: 10M_tokens
    data_files:
      - split: train
        path: 10M_tokens/train-*.parquet
      - split: validation
        path: 10M_tokens/validation-*.parquet
  - config_name: 100M_tokens
    data_files:
      - split: train
        path: 100M_tokens/train-*.parquet
      - split: validation
        path: 100M_tokens/validation-*.parquet
      - split: test
        path: 100M_tokens/test-*.parquet
  - config_name: 1B_tokens
    data_files:
      - split: train
        path: 1B_tokens/train-*.parquet
      - split: validation
        path: 1B_tokens/validation-*.parquet
      - split: test
        path: 1B_tokens/test-*.parquet
train-eval-index:
  - config: 10M_tokens
    task: text-generation
    task_id: language-modeling
    splits:
      train_split: train
      eval_split: validation
    col_mapping:
      text: text
  - config: 100M_tokens
    task: text-generation
    task_id: language-modeling
    splits:
      train_split: train
      eval_split: validation
    col_mapping:
      text: text
  - config: 1B_tokens
    task: text-generation
    task_id: language-modeling
    splits:
      train_split: train
      eval_split: validation
    col_mapping:
      text: text

QuixiMath-1B

QuixiMath is brought to you by Eric Hartford and QuixiAI

https://github.com/QuixiAI/QuixiMath

Dataset Summary

QuixiMath-1B is a synthetic math reasoning corpus generated from the QuixiMath procedural problem generators. Each record contains a natural-language problem, explicit step-by-step scratchpad opcodes, a canonical final answer, and metadata for filtering or reweighting by skill, operation, grade band, and relative difficulty.

The canonical corpus is coverage-first rather than prescriptively stratified: trainers can choose their own sampling mix using the included metadata columns. The size configs are nested prefix subsets within each split.

How to Load

from datasets import load_dataset

ds = load_dataset("QuixiAI/QuixiMath-1B", "100M_tokens")
train = load_dataset("QuixiAI/QuixiMath-1B", "100M_tokens", split="train")

Configs And Splits

Config	Split	Rows	Estimated tokens
preview	train	50,000	6,134,016
10M_tokens	train	100,000	12,308,849
10M_tokens	validation	10,000	1,244,720
100M_tokens	train	800,000	98,641,588
100M_tokens	validation	50,000	6,164,238
100M_tokens	test	50,000	6,077,648
1B_tokens	train	8,800,000	1,104,706,100
1B_tokens	validation	100,000	12,333,425
1B_tokens	test	100,000	12,176,460

The largest config contains 9,000,000 rows and approximately 1,129,215,985 rough text tokens, estimated as len(text) / 4.

Data Schema

Columns:

• row_id: stable integer row index within the split.
• example_id: stable string ID such as train-000000123.
• problem_id: generator-provided problem identifier.
• generator: generator class name.
• generator_label: generator class plus variant marker when applicable.
• operation: problem operation/category label.
• grade_level: one of elementary, middle, high, college, graduate.
• difficulty: integer 1-5, relative to grade_level.
• problem: problem text.
• steps: list of pipe-delimited scratchpad steps.
• final_answer: canonical answer string.
• text: training-ready text field containing problem, steps, and final answer.

Dataset Stats

Field	Value
Default sampled skills	509
Default generator instances	525
Seed	20,260,707
Shard rows	100,000

Grade Distribution

Grade level	Rows
college	2,963,901
high	2,324,185
graduate	1,539,789
middle	1,296,599
elementary	875,526

Difficulty Distribution

Difficulty	Rows
4	3,745,013
3	3,219,630
5	1,229,739
2	652,222
1	153,396

Top Operations

Operation	Rows
median	70,796
mean	70,303
multi_digit_subtraction	53,767
quantization_int8_affine	53,764
abacus_addition	53,757
kmeans_one_iteration	53,606
range	53,591
lu_decomposition	53,580
number_compare	53,568
multi_digit_addition	53,552
discrete_convolution	53,518
contour_integral_residue_theorem	53,514
mean_absolute_deviation	53,500
polynomial_add_sub	53,483
tensor_product_diagonal_apply	53,409
backprop_relu_step	53,383
systems_elimination	53,382
knn_classification	53,302
transportation_nw_stepping_stone	53,238
decimal_mul	53,199
dijkstra_trace	53,093
cramers_rule	52,974
classifier_precision_recall_f1	52,912
ratio_table	52,674
kernel_ridge_linear_2point	52,161

Generation

Generated at: 2026-07-07T00:44:54.279574+00:00

Source repository: /home/hotaisle/datasets/QuixiMath

Source git commit: 5283d55a85d7a127c8cfa5a1b5baf6b96dbc3301

Source git dirty: True

Exact duplicate (operation, problem) pairs were skipped across the generated largest splits before nested configs were materialized. Per-generator duplicate and error counts are stored in generation_stats.json.

Licensing Information

License: other