ViuAI/viu-mini-raw-pretrain

Raw pretraining corpus for ViuMini-MoE-242M (Hinglish-first, 40:30:30 mix target). Unified schema: text, lang, source, domain, safety_tag.

• text: training text (plain, or <|user|> ... <|assistant|> ... templated for QA/instruct/distillation sources)
• lang: hinglish | hindi | english | bilingual
• source: origin dataset name (e.g. indiccorp_v2, fineweb-edu, bespoke-stratos-r1, smoltalk, numina_math_cot)
• domain: knowledge pillar (e.g. general_hindi, distilled_reasoning, distilled_math_cot, sports_cricket, automobile_rto)
• safety_tag: safe | toxic | uncensored

Distillation & Reasoning Corpus (distilled/)

Includes 33 high-quality reasoning shards (3.73 GB, ~1.7B tokens) distilled from frontier models (DeepSeek-R1, SmolTalk, NuminaMath, Magpie Llama 3.1, FineTome 100k, Bespoke Stratos R1) with native <soch> ... </soch> chain-of-thought isolation.

Safety notice (intentional inclusion)

toxicity/ and uncensored/ folders are intentionally included for robustness research (hate-speech detection, refusal training, red-teaming). They are TAGGED, not hidden:

from datasets import load_dataset
ds = load_dataset("ViuAI/viu-mini-raw-pretrain", split="train", streaming=True)

def keep(row, allow_toxic=False, allow_uncensored=False):
    tag = (row.get("safety_tag") or "safe").lower()
    if tag == "safe": return True
    if tag == "toxic": return allow_toxic
    if tag == "uncensored": return allow_uncensored
    return True

# default pretraining: safe only
ds_safe = ds.filter(lambda r: keep(r))
# robustness research: safe+toxic
ds_research = ds.filter(lambda r: keep(r, allow_toxic=True))
# red-team only, controlled: everything
ds_full = ds.filter(lambda r: keep(r, allow_toxic=True, allow_uncensored=True))

Do NOT train user-facing models on full without alignment (SFT/DPO + refusal).

Repair & Clean Status (2026-09-23)

1. Hindi missing shards: 100% Backfilled (447 shards total).
2. All oversized files resharded to 150MB standard shards (english_fixed/ & hindi_fixed/).
3. 50 legacy oversized files deleted — ZERO storage duplication.
4. Schema unified across all domains (grammar, math, science, toxicity, uncensored, domains, distilled).
5. Frontier distillation corpus: 33 shards (3.73 GB) integrated into distilled/.
6. Net clean dataset size: ~229.5 GB (>105.1B tokens across 1,926 clean files).

License / citation

Apache-2.0. Upstream sources retain their own licenses (Wikipedia CC-BY-SA, Samanantar CC-BY, etc.). If you use this dataset, cite upstream sources plus ViuAI/viu-mini-raw-pretrain.