artindnr/Persian-English-Thinking
Persian-Thinking Persian-Thinking is a small Persian-English reasoning/thinking dataset created by sampling and translating a subset of SmolTalk2. Dataset Details 1,000 samples (996 after processing) drawn from the smoltalk_systemchats_Qwen3_32B_think subset of SmolTalk2, part of its SFT split. That source subset consists of system-chat conversations generated with Qwen3-32B in thinking mode, meaning each assistant response includes an explicit reasoning trace… See the full description on the dataset page: https://huggingface.co/datasets/artindnr/Persian-English-Thinking.
Persian-Thinking
Persian-Thinking is a small Persian-English reasoning/thinking dataset created by sampling and translating a subset of SmolTalk2.
Dataset Details
- 1,000 samples (996 after processing) drawn from the `smoltalk_systemchats_Qwen3_32B_think` subset of SmolTalk2, part of its
SFTsplit. - That source subset consists of system-chat conversations generated with Qwen3-32B in thinking mode, meaning each assistant response includes an explicit reasoning trace before the final answer.
- The reasoning traces were translated into Persian using a language model.
- The dataset preserves the original three-turn structure (system → user → assistant) but with all content localized to Persian.
Dataset Creation
- 1,000 rows were randomly sampled from the SmolTalk2
smoltalk_systemchats_Qwen3_32B_thinksubset (itself generated by HuggingFaceTB using Qwen3-32B on prompts from the original SmolTalk systemchats-30k data). - Each sample's reasoning trace (
thinking) was translated to Persian with a language model. - The translated conversations were reshaped into flat columns (in addition to keeping the original nested
messagesstructure) for easier downstream use in SFT pipelines.
No additional filtering, deduplication, or quality review beyond the source dataset's own decontamination was performed — see Limitations below.
Dataset Structure
Each row contains:
Usage
from datasets import load_dataset
ds = load_dataset("artindnr/Persian-Thinking", split="train")
print(ds[0]["analysis"]) # Persian reasoning trace
print(ds[0]["final"]) # final answerThe messages column can be fed directly into chat-template-based SFT pipelines (e.g. TRL's SFTTrainer), while the flat developer / user / analysis / final columns are convenient for custom formatting, reasoning-specific training objectives, or inspection/filtering.
Source Data
- Base dataset: HuggingFaceTB/smoltalk2, subset `smoltalk_systemchats_Qwen3_32B_think` (
SFTsplit), which HuggingFaceTB generated by running the prompts from SmolTalk (systemchats-30k) through Qwen3-32B in reasoning mode. - Translation: performed with a separate LLM, translating all conversation fields into Persian.
License
Inherits Apache 2.0 from the source smoltalk-systemchats-Qwen3-32B subset of SmolTalk2. Please also refer to the SmolTalk2 license section for details on upstream components.
Citation
If you use this dataset, please also credit the original SmolTalk2 dataset:
@misc{smoltalk2,
author = {HuggingFaceTB},
title = {SmolTalk2},
howpublished = {\url{https://huggingface.co/datasets/HuggingFaceTB/smoltalk2}}
}