Datasets:

sfanm
/

d24-midtrain-olmo3-5b

component stringclasses 2 values	text stringlengths 1 21.8k
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d24 Midtrain — OLMo-3 Dolmino (5B, chunked)

A 5.00B-token reproduction of OLMo 3's Dolma-3 Dolmino mid-train mix, built by taking a fraction of each component of allenai/dolma3_dolmino_mix-100B-1025. Unlike the smaller d24-midtrain-olmo3 (which dropped documents over 2048 tokens — silently zeroing the long reasoning-trace and PDF components), this build chunks long documents into 2048-token windows (decoded back to text), so every component keeps ~100% of its tokens and reaches its target — the realized mix matches OLMo-3's true proportions (see the OLMo-3 target % column ≈ the kept share).

11,066,837 documents (post-chunk), ~5.00B GPT-2 tokens of plain text.
Each row: component (str) and text (str), every row ≤2048 tokens.
Chunking caveat: long chain-of-thought traces and PDFs are split at 2048-token boundaries — each window is its own row (its own EOS at tokenization), so reasoning chains are present but fragmented across rows.

Components (realized ≈ OLMo-3 target)

component	documents	~tokens	OLMo-3 target %
`common_crawl-high-quality`	1,026,492	~1125M	22.50%
`dolmino-math`	1,033,021	~535M	10.70%
`cranecode`	443,341	~500M	10.00%
`stack_edu-fim`	585,745	~500M	10.00%
`reddit_to_flashcards`	4,379,570	~295M	5.90%
`cranemath`	407,762	~282M	5.63%
`dolmino_1-flan`	884,490	~250M	5.00%
`nemotron-synth-qa`	448,299	~250M	5.00%
`olmocr_science_pdfs`	139,054	~250M	5.00%
`stem-heavy-crawl`	348,073	~250M	5.00%
`wiki_to_rcqa`	684,770	~150M	3.00%
`general_reasoning_mix`	70,380	~94M	1.87%
`qwq-reasoning-traces`	49,624	~94M	1.87%
`megamatt`	164,351	~87M	1.73%
`llama_nemotron-reasoning-traces`	32,458	~63M	1.25%
`openthoughts2-reasoning-traces`	36,323	~63M	1.25%
`tulu-3-sft`	124,793	~55M	1.10%
`tinymath-mind`	93,865	~45M	0.90%
`omr-rewrite-fullthoughts`	28,233	~43M	0.85%
`code-meta-reasoning`	15,910	~23M	0.46%
`math-meta-reasoning`	13,590	~19M	0.38%
`gemini-reasoning-traces`	7,400	~13M	0.25%
`tinymath-pot`	42,322	~12M	0.24%
`program_verifiable`	6,971	~8M	0.16%

Load

from datasets import load_dataset
ds = load_dataset("sfanm/d24-midtrain-olmo3-5b", split="train")
ds = ds.filter(lambda r: r["component"] == "qwq-reasoning-traces")   # now non-empty (was ~0 in the dropped build)

Relationship to the 2.3B build

d24-midtrain-olmo3 — ~2.1B, long docs dropped → web/short-QA heavy, ~0 reasoning traces (the corpus the d24-*-olmo3-2.3B models trained on).
this — ~5B, long docs chunked → faithful OLMo-3 proportions incl. the reasoning traces.

License / attribution

Derived from allenai/dolma3_dolmino_mix-100B-1025 (itself a mix of many sources — see its card). Provided as-is for research; refer to the upstream licenses.

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