zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K

TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Apr 12, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K is a 4 billion parameter Qwen3-based language model developed by Zero9 Tech, specifically fine-tuned for data mining and applied data science decision support. It has been adapted for Turkish reasoning through continued pre-training on Wikimedia Wikipedia and specialized instruction tuning on a Turkish data scientist dialogue dataset. This model is optimized to generate decision-oriented responses, including method selection, alternative comparisons, risk signals, and validation steps.

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Model Overview

The zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K is a 4 billion parameter model built on the Qwen3 architecture, developed by Zero9 Tech. Its primary focus is to provide decision support in data mining and applied data science contexts, with a strong emphasis on Turkish language understanding and generation.

Key Capabilities

  • Turkish Reasoning Adaptation: Underwent Continued Pre-Training (CPT) using approximately 10% of the wikimedia/wikipedia dataset to enhance its Turkish reasoning abilities.
  • Domain-Specific Fine-tuning: Instruction-tuned on the murataksit34/veri-bilimci-diyalog-8k-tr dataset, comprising 7,656 records, to specialize in data science dialogues.
  • Decision-Oriented Responses: Optimized for generating practical, decision-focused outputs, such as:
    • Method selection guidance
    • Comparison of alternatives
    • Identification of risk signals
    • Steps for validation

Ideal Use Cases

This model is particularly well-suited for applications requiring:

  • Data Science Consulting: Assisting users with data science methodologies and problem-solving in Turkish.
  • Analytical Decision Support: Providing structured insights for business intelligence and data-driven strategies.
  • Risk Assessment: Helping identify potential risks and validation steps within data projects.
  • Turkish NLP in Data Science: Applications where understanding and generating nuanced Turkish responses in a data science context is crucial.

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