ansulev/glm-5.1-reasoning-1m-cleaned

GLM-5.1-Reasoning-1M-Cleaned

GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversations, input, output, domain, and meta fields.

This release was prepared from the original dataset published by Kassadin88.

Summary

• —Teacher model in the data: GLM-5.1
• —Total processed records: 766,535
• —Total kept records: 746,321
• —Total removed records: 20,214
• —Subset layout preserved exactly as the source dataset: main, PHD-Science, Multilingual-STEM, Math

Included Content

• —main: general reasoning and instruction-following data.
• —PHD-Science: graduate-level physics, chemistry, and biology reasoning traces.
• —Multilingual-STEM: multilingual STEM reasoning data, including Chinese, English, and other languages present in the source release.
• —Math: mathematics-heavy reasoning and proof-style responses.

Cleaning and Reformatting

The raw source dataset mixed two answer layouts:

1. 1.Standard <think>...</think> reasoning tags.
2. 2.A non-standard short-dash wrapper around the reasoning section.

This cleaned release normalizes both styles into a single output format:

{
  "id": "md5-hash-of-domain-input-reasoning-answer",
  "conversations": [
    {"from": "human", "value": "user prompt"},
    {"from": "gpt", "value": "<think>\nreasoning trace\n</think>\n\nfinal answer"}
  ],
  "input": "user prompt",
  "output": "<think>\nreasoning trace\n</think>\n\nfinal answer",
  "domain": "subset/domain name from the original _id prefix",
  "meta": {
    "input_tokens": 123,
    "output_tokens": 456,
    "teacher_model": "GLM-5.1"
  }
}

Removed data

The cleaning pipeline removed records with:

• —incomplete or obviously truncated answers,
• —repeated reasoning paragraphs or duplicated answer segments,
• —refusal-style answers,
• —unparseable reasoning/answer boundaries,
• —exact duplicate records after normalization.

Subset Statistics

Filter Statistics

Additional Token Statistics

Data Structure

Each example is a single-turn reasoning distillation sample:

• —conversations[0]: the user prompt.
• —conversations[1]: the model response with reasoning wrapped in <think>...</think> and the final answer after the closing tag.
• —input: prompt-only view for training pipelines that prefer flat prompt fields.
• —output: tagged answer-only view for training pipelines that prefer flat completion fields.
• —domain: original subset/domain name extracted from the source record ID.
• —meta: lightweight per-example metadata.

Usage

from datasets import load_dataset

main = load_dataset("Jackrong/GLM5.1-Reasoning-1M-Cleaned", "main")
science = load_dataset("Jackrong/GLM5.1-Reasoning-1M-Cleaned", "PHD-Science")
stem = load_dataset("Jackrong/GLM5.1-Reasoning-1M-Cleaned", "Multilingual-STEM")
math = load_dataset("Jackrong/GLM5.1-Reasoning-1M-Cleaned", "Math")

If you publish under a different namespace, replace Jackrong/ with your actual Hugging Face username or org.

Provenance

This dataset is derived from:

• —Original dataset: `Kassadin88/GLM-5.1-1000000x`
• —Original author: Kassadin88

Citation

Please cite the original dataset first:

@misc{glm51-1000000x,
  title={GLM-5.1-1000000x: One Million Reasoning Traces Distilled from GLM-5.1},
  author={Kassadin88},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/Kassadin88/GLM-5.1-1000000x}
}

You can additionally cite this cleaned derivative release as:

@misc{glm51_reasoning_1m_cleaned,
  title={GLM-5.1-Reasoning-1M-Cleaned},
  author={Jackrong},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/Jackrong/GLM5.1-Reasoning-1M-Cleaned}
}