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smoltalk‑chinese is a Chinese fine‑tuning dataset referenced from the SmolTalk dataset, designed to provide high‑quality synthetic data for training large language models (LLMs). The dataset consists entirely of synthetic data, covering more than 700,000 entries, and is composed of multiple parts including tasks referenced from magpie‑ultra, other SmolTalk tasks, simulated daily‑life dialogues, and mathematics problems from the Chinese version of Math23K. The generation process follows strict standards to ensure data quality and diversity. Experiments show that models fine‑tuned on smoltalk‑chinese achieve significant advantages on multiple metrics.

This dataset is for Chinese text retrieval tasks, consisting of two parts: a document corpus and a query set. The corpus contains 10,574 documents, each with a unique ID, content, and image information. The query set contains 100 queries, each with a unique ID, content, type, and IDs of relevant documents.