README.md

metadata

license: apache-2.0
language:
  - en
task_categories:
  - text-generation
tags:
  - sft
  - math
  - code
  - reasoning
  - shuffled
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train_*.jsonl

Mephisto-MathCode_2M

2,000,000 non-thinking SFT examples — an even 1M/1M split of math and code — drawn from openbmb/UltraData-SFT-2605, filtered to English and globally shuffled.

This is a curation pass, not a generation one: no model produced these answers for this dataset. All credit for the content belongs to OpenBMB. What is added here is language filtering, an exact 1M/1M balance, and a global shuffle so the file can be streamed without a shuffle buffer.

domain	rows
Code	1,000,000
Math	1,000,000

Source config/split: Code/no_think and Math/no_think.

Format

{
  "uid": "...",
  "messages": [
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ],
  "source": "UltraData-sft-2605",
  "domain": "Code",
  "think_type": "no_think"
}

The source schema is preserved unchanged. Every row is exactly two turns (user, assistant) and think_type is no_think throughout — responses are direct answers with no chain-of-thought block, though math answers do show their working as ordinary prose/LaTeX.

domain distinguishes the two halves, and uid maps back to the source row.

Filtering

Only one filter was applied: Chinese removal. Rows whose prompt or answer is more than 5% CJK characters were dropped.

	scanned	kept	dropped (CJK)
Code	1,000,171	1,000,000	171 (0.017%)
Math	1,000,154	1,000,000	154 (0.015%)

Verified by re-running the filter over the output: 0 CJK rows and 0 duplicate uids remained. No quality, length, or degeneracy filtering was performed — inspect before training.

Shuffling

Shuffled globally across both halves with a seeded Fisher–Yates permutation (ChaCha8, seed 42), so every shard and every prefix is representative:

	Code	Math
shard 000	50.4%	49.6%
shard 019	49.2%	50.8%

take(n) on a streaming load gives an unbiased, balanced sample without an extra shuffle buffer.

The shuffle was done with a small memory-mapped Rust tool that permutes an index of (file_id, offset, length) — 16 bytes per row, so 2M rows cost 32 MB of RAM regardless of the 10 GB of text behind them, and line bytes go straight from mmap to the output without ever entering the process heap. This matters if you want to re-shuffle it yourself on a modest machine.

Caveats

• Unfiltered for quality. The source is broadly good but nothing here has been checked for correctness, degeneracy, or answer length.
• Math rows are long (the 1M math rows are ~7.1 GB vs ~2.7 GB for 1M code rows), so a token-balanced mix is not 50/50 by row count.
• English-only by construction; the source split is bilingual.
• No deduplication beyond exact uid collisions.

Provenance and license

All content from openbmb/UltraData-SFT-2605 (Apache-2.0); see the source dataset for its terms. Released under Apache-2.0.