WithinUsAI/Qwen3.7_Max_Thinking_dataset_5K

Qwen 3.7 Max Thinking — Distilled Reasoning Dataset

5,000 high-quality, no-duplicate chain-of-thought reasoning traces for knowledge distillation, fine-tuning, or research. Each example contains a problem, a detailed step-by-step thinking trace (mirroring the Qwen 3.7 Max Thinking reasoning style), and a final answer.

Dataset Format

File: qwen3.7_max_thinking_dataset.jsonl Format: JSON Lines (one JSON object per line) Encoding: UTF-8 (ASCII-safe content — no special Unicode characters)

Schema

{
  "problem":        "The input question or task prompt",
  "thinking_trace": "Multi-step chain-of-thought reasoning trace with self-verification",
  "answer":         "The final concise answer"
}

Example

{
  "problem": "A car starts at 9 m/s and accelerates at 10 m/s^2 for 7s. Find distance and final velocity.",
  "thinking_trace": "Using kinematics equations:\nv_f = v_0 + a*t = 9 + 10*7 = 79 m/s\nd = v_0*t + 0.5*a*t^2 = 9*7 + 0.5*10*7^2\n  = 63 + 0.5*10*49 = 308.0 m\nCheck: v_avg = (9+79)/2 = 44.0 m/s, d = v_avg*t = 308.0 m [OK]",
  "answer": "Distance = 308.0m, Final velocity = 79 m/s"
}

Category Distribution

Reasoning Style

The thinking traces follow the Qwen 3.7 Max Thinking paradigm:

1. 1.Problem decomposition — break the problem into manageable steps
2. 2.Formula recall — state the relevant equation or approach
3. 3.Step-by-step computation — perform each operation with intermediate values
4. 4.Self-verification — check the result via alternative method or substitution
5. 5.Final answer extraction — present the clean final answer

This style is optimized for knowledge distillation where a smaller student model learns to mimic the extended reasoning process of a larger teacher model.

Key Properties

Use Cases

• —Knowledge distillation — train a smaller model to produce step-by-step reasoning
• —Supervised fine-tuning (SFT) — teach chain-of-thought reasoning
• —Reasoning benchmark — evaluate model reasoning quality
• —Prompt engineering research — study structured reasoning patterns
• —Agent training data — multi-step planning and tool-use reasoning

Generation

The dataset was generated programmatically by generate_dataset.py using templated generators with randomized parameters across 10 reasoning domains. Each call produces a unique combination of numeric values, problem text, and computed outputs, then filters through a SHA-256 deduplication pipeline at both the entry and problem levels.

Regenerate or Extend

python generate_dataset.py

Modify the generator functions or weights in the script to adjust:

• —Category balance
• —Difficulty ranges
• —Problem types
• —Total count (default 5,000)