Finnish-NLP/dolci-instruct-sft-fi
Dolci Instruct SFT — Finnish (machine-translated) Finnish machine translation of the Dolci Instruct SFT mixture (dolci-instruct-sft-filtered-v1, no-math / no-latex subset), produced for SFT of Finnish LLMs. Translation model: translategemma-27b (Gemma-based 27B translation model) Rows: 234,745 (multi-turn chat, mostly single Q→A) Language: Finnish (fi) Format: chat messages (role / content) Provenance & filtering Translated from the English… See the full description on the dataset page: https://huggingface.co/datasets/Finnish-NLP/dolci-instruct-sft-fi.
Dolci Instruct SFT — Finnish (machine-translated)
Finnish machine translation of the Dolci Instruct SFT mixture (dolci-instruct-sft-filtered-v1, no-math / no-latex subset), produced for SFT of Finnish LLMs.
- Translation model:
translategemma-27b(Gemma-based 27B translation model) - Rows: 234,745 (multi-turn chat, mostly single Q→A)
- Language: Finnish (
fi) - Format: chat
messages(role/content)
Provenance & filtering
Translated from the English dolci-instruct-sft-filtered-v1-no-math-no-latex mixture. During translation, oversize prompts were skipped, leaving some rows with empty content in the raw output. Those rows were removed here: 246,444 raw rows → 234,745 rows with non-empty content on every message. The all-null function_calls / functions fields present in the raw parquet were dropped; each message keeps only role and content.
Schema
Source-dataset composition (top)
Aya, FLAN, WildGuardMix, WildJailbreak, Verifiable Reasoning, Logic Puzzles, Wildchat, Dolci Instruct Precise IF, CoCoNot, OpenAssistant, OpenThoughts3+ Science, SciRiff, and a small hardcoded set.
Usage
from datasets import load_dataset
ds = load_dataset("Finnish-NLP/dolci-instruct-sft-fi", split="train")
print(ds[0]["messages"])Caveats
Machine-translated; translation artifacts are possible. Safety/jailbreak subsets contain adversarial prompts by design. Content inherits the license and terms of the upstream Dolci/Tulu sources (ODC-BY). Verify licensing of individual sub-datasets before downstream redistribution.
