ljvmiranda921/DataSmith-SFT-Synth-Data

Website: ljvmiranda921.github.io/datasmith/

DataSmith-SFT-Synth

This dataset contains synthetic supervised fine-tuning examples generated using the DataSmith framework, a data generation framework that starts from an English seed instruction and lets a generator model call external tools before writing a dialogue in the target language. The model can search the Web and Wikipedia, look up word senses in Wiktionary, consult grammar references, and delegate to a dedicated translation model or a multilingual expert model. It contains examples across eleven languages: Arabic, Cebuano, Czech, French, German, Indonesian, Japanese, Spanish, Swahili, Tagalog, and Yoruba.

Dataset Summary

• Languages: Arabic (ar), Cebuano (ceb), Czech (cs), French (fr), German (de), Indonesian (id), Japanese (ja), Spanish (es), Swahili (sw), Tagalog (tl), Yoruba (yo)
• Total examples: 165,000 (55,000 per config)
• Examples per language per config: 5,000
• Generator model: moonshotai/Kimi-K2.5
• Translation model: google/translategemma-27b-it
• Configs: datasmith, teacher, translate

Configs

Config	Method	Model	Examples
datasmith	Tool-grounded synthesis in the target language	moonshotai/Kimi-K2.5	55,000
teacher	Direct synthesis in the target language, no tools	moonshotai/Kimi-K2.5	55,000
translate	Translate an English example into the target language	google/translategemma-27b-it	55,000

Language Distribution

Every language has exactly 5,000 examples in each config.

Language	Examples per config
Arabic (ar)	5,000
Cebuano (ceb)	5,000
Czech (cs)	5,000
French (fr)	5,000
German (de)	5,000
Indonesian (id)	5,000
Japanese (ja)	5,000
Spanish (es)	5,000
Swahili (sw)	5,000
Tagalog (tl)	5,000
Yoruba (yo)	5,000

Data Sources

Source	# Prompts	License
Nemotron Instruction Following Chat (NVIDIA et al., 2025)	426,009	CC-BY 4.0, ODC-BY 1.0
WildChat (Zhao et al., 2024)	285,139	ODC-BY 1.0
Dolci Instruct OpenThoughts3+ Science (Team Olmo et al., 2026)	96,006	ODC-BY 1.0
FLAN (Wei et al., 2022)	84,968	Apache 2.0
OpenMathInstruct 2 (Toshniwal et al., 2025)	50,000	CC-BY 4.0
Tulu 3 Persona MATH (Lambert et al., 2025)	30,000	ODC-BY 1.0
Dolci Logic Puzzles (Team Olmo et al., 2026)	20,000	ODC-BY 1.0
Tulu 3 Persona GSM (Lambert et al., 2025)	20,000	ODC-BY 1.0
Tulu 3 Persona Algebra (Lambert et al., 2025)	19,994	ODC-BY 1.0
TableGPT (Li et al., 2024)	4,580	MIT
SciRiff (Wadden et al., 2025)	4,273	ODC-BY 1.0
Aya (Singh et al., 2024)	2,504	Apache 2.0
OpenAssistant (Köpf et al., 2023)	2,223	Apache 2.0
Total	1,045,696

Dataset Structure

Each example contains the following fields:

Field	Type	Description	Notes
id	str	Identifier for the example	All configs
language	str	ISO 639-1 language code, except Cebuano which uses ceb	All configs
model	str	Model used for generation	All configs
source_id	str	Identifier from the source dataset	All configs
messages	list	Chat-formatted messages (role and content) for SFT	All configs
seed_prompt	str	The English seed instruction the example was synthesized from	Only in datasmith and teacher
agent_trace	list	DataSmith's research trajectory	Only in datasmith
source	str	Source dataset name	Only in translate
english_messages	list	The original English messages before translation	Only in translate

Usage

from datasets import load_dataset

# Load one method
ds = load_dataset("ljvmiranda921/DataSmith-SFT-Synth-Data", "datasmith", split="train")

# Filter by language
arabic_ds = ds.filter(lambda x: x["language"] == "ar")

# Use the messages field directly for SFT
print(arabic_ds[0]["messages"])

# Inspect which tools the generator called
for step in arabic_ds[0]["agent_trace"]:
    if step["type"] == "tool_call":
        print(step)

# Compare against the no-tool and translation baselines
teacher_ds = load_dataset("ljvmiranda921/DataSmith-SFT-Synth-Data", "teacher", split="train")
translate_ds = load_dataset("ljvmiranda921/DataSmith-SFT-Synth-Data", "translate", split="train")

Acknowledgements

We are grateful to the evaluators who participated in our human evaluation for their careful and thoughtful judgments. LJVM and AK acknowledge the support of the UKRI Frontier Grant EP/Y031350/1 (EQUATE). Compute was provided by the Isambard AI National AI Research Resource (AIRR) ST/AIRR/I-A-I/1023 and the Microsoft Research Azure AI Grant. LJVM also thanks the team at Microsoft Research Nairobi and members of the Language Technology Lab for discussions and feedback on this work.