Qwen3.6-Plus Reasoning Data for Model Distillation

1,739,249 tokens of instruction data were generated by the Qwen3.6-plus model for knowledge distillation. The dataset covers topics including coding, mathematics, finance, medicine, and economics. It was created by author 'khazarai' and last updated on Hugging Face in April 2026.

Use Cases

• Distill reasoning capabilities from the Qwen3.6-plus teacher model into a smaller student model using the provided instruction-response pairs.
• Fine-tune a model on multi-domain reasoning tasks by leveraging data covering coding, mathematics, finance, medicine, and economics topics.
• Analyze the reasoning patterns and response structures generated by a large language model across different specialized domains.
• Benchmark student model performance against the teacher's outputs on sequences with a maximum length of 6,500 tokens.

Strengths

• Contains 1,739,249 tokens of instruction-response data.
• Covers five distinct reasoning domains: coding, mathematics, finance, medicine, and economics.
• Sequences have a defined maximum length of 6,500 tokens per row.

Limitations

• The total number of rows or examples is unknown.
• Specific column structure and data schema are not provided.
• Potential for synthetic bias inherited from the teacher model's generation process.

Provenance

Source

Generated by the Qwen3.6-plus language model.

Collection Method

Synthetically created for the purpose of knowledge distillation.

Freshness

Last updated on the platform in April 2026.