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lvogel123/jailbreak-grok-4

published by @lvogel123

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
348
Last 30 days
57
Likes
2
Size
2.6 MB
Created on HF
2025-10-25
Age
12 months ago

Tags

size_categories:n<1Kformat:parquetformat:optimized-parquetmodality:tabularmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:us

README current version from Hugging Face


dataset_info:

  • config_name: mhj_m2s_grok_4_20251024-193232_results
    features:
    • name: log_path
      dtype: string
    • name: eval_id
      dtype: string
    • name: run_id
      dtype: string
    • name: created
      dtype: string
    • name: task
      dtype: string
    • name: task_id
      dtype: string
    • name: model
      dtype: string
    • name: total_samples
      dtype: int64
    • name: completed_samples
      dtype: int64
    • name: mean
      dtype: float64
    • name: score01_mean
      dtype: float64
    • name: [email protected]
      dtype: float64
    • name: [email protected]
      dtype: float64
    • name: refusal_rate
      dtype: float64
    • name: convincing_raw_mean
      dtype: float64
    • name: specificity_raw_mean
      dtype: float64
    • name: convincing01_mean
      dtype: float64
    • name: specificity01_mean
      dtype: float64
    • name: raw_mean
      dtype: float64
    • name: Physical Harm & Weapons score01_mean
      dtype: float64
    • name: Physical Harm & Weapons [email protected]
      dtype: float64
    • name: Physical Harm & Weapons [email protected]
      dtype: float64
    • name: Physical Harm & Weapons refusal_rate
      dtype: float64
    • name: Physical Harm & Weapons convincing_raw_mean
      dtype: float64
    • name: Physical Harm & Weapons specificity_raw_mean
      dtype: float64
    • name: Financial Crime & Fraud score01_mean
      dtype: float64
    • name: Financial Crime & Fraud [email protected]
      dtype: float64
    • name: Financial Crime & Fraud [email protected]
      dtype: float64
    • name: Financial Crime & Fraud refusal_rate
      dtype: float64
    • name: Financial Crime & Fraud convincing_raw_mean
      dtype: float64
    • name: Financial Crime & Fraud specificity_raw_mean
      dtype: float64
    • name: IP/Copyright & Non-Violent Unethical Behavior score01_mean
      dtype: float64
    • name: IP/Copyright & Non-Violent Unethical Behavior [email protected]
      dtype: float64
    • name: IP/Copyright & Non-Violent Unethical Behavior [email protected]
      dtype: float64
    • name: IP/Copyright & Non-Violent Unethical Behavior refusal_rate
      dtype: float64
    • name: IP/Copyright & Non-Violent Unethical Behavior convincing_raw_mean
      dtype: float64
    • name: IP/Copyright & Non-Violent Unethical Behavior specificity_raw_mean
      dtype: float64
    • name: Sexual Content & Exploitation score01_mean
      dtype: float64
    • name: Sexual Content & Exploitation [email protected]
      dtype: float64
    • name: Sexual Content & Exploitation [email protected]
      dtype: float64
    • name: Sexual Content & Exploitation refusal_rate
      dtype: float64
    • name: Sexual Content & Exploitation convincing_raw_mean
      dtype: float64
    • name: Sexual Content & Exploitation specificity_raw_mean
      dtype: float64
    • name: Drugs & Controlled Substances score01_mean
      dtype: float64
    • name: Drugs & Controlled Substances [email protected]
      dtype: float64
    • name: Drugs & Controlled Substances [email protected]
      dtype: float64
    • name: Drugs & Controlled Substances refusal_rate
      dtype: float64
    • name: Drugs & Controlled Substances convincing_raw_mean
      dtype: float64
    • name: Drugs & Controlled Substances specificity_raw_mean
      dtype: float64
    • name: Hate/Harassment/Abuse score01_mean
      dtype: float64
    • name: Hate/Harassment/Abuse [email protected]
      dtype: float64
    • name: Hate/Harassment/Abuse [email protected]
      dtype: float64
    • name: Hate/Harassment/Abuse refusal_rate
      dtype: float64
    • name: Hate/Harassment/Abuse convincing_raw_mean
      dtype: float64
    • name: Hate/Harassment/Abuse specificity_raw_mean
      dtype: float64
    • name: Misinformation score01_mean
      dtype: float64
    • name: Misinformation [email protected]
      dtype: float64
    • name: Misinformation [email protected]
      dtype: float64
    • name: Misinformation refusal_rate
      dtype: float64
    • name: Misinformation convincing_raw_mean
      dtype: float64
    • name: Misinformation specificity_raw_mean
      dtype: float64
    • name: Chemical/Biological Threats score01_mean
      dtype: float64
    • name: Chemical/Biological Threats [email protected]
      dtype: float64
    • name: Chemical/Biological Threats [email protected]
      dtype: float64
    • name: Chemical/Biological Threats refusal_rate
      dtype: float64
    • name: Chemical/Biological Threats convincing_raw_mean
      dtype: float64
    • name: Chemical/Biological Threats specificity_raw_mean
      dtype: float64
    • name: Self-Harm & Suicide score01_mean
      dtype: float64
    • name: Self-Harm & Suicide [email protected]
      dtype: float64
    • name: Self-Harm & Suicide [email protected]
      dtype: float64
    • name: Self-Harm & Suicide refusal_rate
      dtype: float64
    • name: Self-Harm & Suicide convincing_raw_mean
      dtype: float64
    • name: Self-Harm & Suicide specificity_raw_mean
      dtype: float64
    • name: Cybercrime & Intrusion score01_mean
      dtype: float64
    • name: Cybercrime & Intrusion [email protected]
      dtype: float64
    • name: Cybercrime & Intrusion [email protected]
      dtype: float64
    • name: Cybercrime & Intrusion refusal_rate
      dtype: float64
    • name: Cybercrime & Intrusion convincing_raw_mean
      dtype: float64
    • name: Cybercrime & Intrusion specificity_raw_mean
      dtype: float64
      splits:
    • name: train
      num_bytes: 813
      num_examples: 1
      download_size: 45135
      dataset_size: 813
  • config_name: mhj_m2s_grok_4_20251024-193232_samples
    features:
    • name: log_path
      dtype: string
    • name: eval_id
      dtype: string
    • name: run_id
      dtype: string
    • name: created
      dtype: string
    • name: task
      dtype: string
    • name: task_id
      dtype: string
    • name: model
      dtype: string
    • name: sample_id
      dtype: int64
    • name: epoch
      dtype: int64
    • name: target
      dtype: string
    • name: messages
      dtype: string
    • name: meta_prompt
      dtype: string
    • name: meta_objective
      dtype: string
    • name: meta_id
      dtype: string
    • name: meta_category
      dtype: string
    • name: meta_annotations
      dtype: string
    • name: score_strong_reject_scorer_value
      dtype: float64
    • name: score_strong_reject_scorer_answer
      dtype: string
      splits:
    • name: train
      num_bytes: 5486899
      num_examples: 537
      download_size: 2670852
      dataset_size: 5486899
  • config_name: mhj_m2s_grok_4_20251024-193232_stats
    features:
    • name: log_path
      dtype: string
    • name: eval_id
      dtype: string
    • name: run_id
      dtype: string
    • name: created
      dtype: string
    • name: task
      dtype: string
    • name: task_id
      dtype: string
    • name: model
      dtype: string
    • name: started_at
      dtype: string
    • name: completed_at
      dtype: string
    • name: usage_model
      dtype: string
    • name: input_tokens
      dtype: int64
    • name: output_tokens
      dtype: int64
    • name: total_tokens
      dtype: int64
      splits:
    • name: train
      num_bytes: 693
      num_examples: 2
      download_size: 6947
      dataset_size: 693
      configs:
  • config_name: mhj_m2s_grok_4_20251024-193232_results
    data_files:
    • split: train
      path: mhj_m2s_grok_4_20251024-193232_results/train-*
  • config_name: mhj_m2s_grok_4_20251024-193232_samples
    data_files:
    • split: train
      path: mhj_m2s_grok_4_20251024-193232_samples/train-*
  • config_name: mhj_m2s_grok_4_20251024-193232_stats
    data_files:
    • split: train
      path: mhj_m2s_grok_4_20251024-193232_stats/train-*

README history 4 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2025-10-25Upload dataset7f2d9fc6.9 KB
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  2. 2025-10-25Upload datasetbc0d0d65.9 KB
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  3. 2025-10-25Upload dataset290f0cf5.1 KB
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  4. 2025-10-25initial commitd8d32c90 B
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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("lvogel123/jailbreak-grok-4")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="lvogel123/jailbreak-grok-4", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/lvogel123/jailbreak-grok-4
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