zero9tech/Qwen3-4B-Data-Science-Insight-TR-16.2K
The zero9tech/Qwen3-4B-Data-Science-Insight-TR-16.2K is a 4 billion parameter Qwen3-based model developed by Zero9 Tech, fine-tuned for data mining and applied data science decision support. It features a 32768 token context length and is specifically optimized for generating decision-oriented responses in Turkish, including method selection, alternative comparisons, risk signaling, and validation steps. The model underwent continued pre-training for Turkish thought adaptation using Wikimedia/Wikipedia data before specialized instruction tuning.
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Overview
The zero9tech/Qwen3-4B-Data-Science-Insight-TR-16.2K is a 4 billion parameter language model developed by Zero9 Tech, specifically engineered for data mining and applied data science decision support. It is built upon the Qwen3 architecture and features a substantial 32768 token context window, making it suitable for processing longer data science-related queries and contexts.
Key Capabilities
- Turkish Language Adaptation: The model underwent Continued Pre-Training (CPT) using Wikimedia/Wikipedia data (approximately 80% of its pre-training, 427,990 records) to adapt its reasoning capabilities to Turkish thought patterns.
- Domain Expertise: Further fine-tuned with the
zero9tech/veri-bilimci-insight-diyalog-tr-16.2kdataset, comprising 16,180 records, to imbue it with specialized knowledge in data science. - Decision-Oriented Responses: Optimized to produce actionable insights and decision support, focusing on:
- Method selection
- Comparison of alternatives
- Identification of risk signals
- Validation steps
Use Cases
This model is particularly well-suited for applications requiring intelligent assistance in data science workflows, especially where decision-making, problem-solving, and analytical comparisons are critical. Its Turkish language focus makes it a valuable tool for data professionals operating in Turkish-speaking contexts.