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XiaoXiang09/MailiciousGen

published by @XiaoXiang09

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
168
Last 30 days
14
Likes
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Size
11.9 MB
Created on HF
2025-05-24
Age
1 years ago

Description

Dataset Card for Harmful Instruction-Response Dataset (Mistral-Based)

A dataset containing instruction-response pairs generated by Mistral models, intended for academic research on model behavior, including in the presence of harmful content.

Dataset Details

Dataset Description

This dataset consists of instruction-response pairs where:

Instructions were generated using the model mistralai/Mistral-7B-Instruct-v0.3

Responses were generated using… See the full description on the dataset page: https://huggingface.co/datasets/XiaoXiang09/MailiciousGen.

Tags

task_categories:question-answeringlanguage:enlicense:apache-2.0size_categories:10K<n<100Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face

task_categories:

  • question-answering
    language:
  • en
    license: apache-2.0

Dataset Card for Harmful Instruction-Response Dataset (Mistral-Based)

A dataset containing instruction-response pairs generated by Mistral models, intended for academic research on model behavior, including in the presence of harmful content.

Dataset Details

Dataset Description

This dataset consists of instruction-response pairs where:

The dataset contains instances of harmful or sensitive content, and it is created specifically for academic research. It is useful for analyzing model alignment, robustness, and safety in response generation. Use of this dataset should follow strict ethical guidelines and is not recommended for deploying models into production without filtering or mitigation.

  • Curated by: Independent academic researchers
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Language(s) (NLP): English

Dataset Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use

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Out-of-Scope Use

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Dataset Structure

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Dataset Creation

Curation Rationale

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Source Data

Data Collection and Processing

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Who are the source data producers?

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Annotations [optional]

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

BibTeX:

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README history 6 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.

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  6. 2025-05-24initial commitbdef66a0 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2025-05-25[bot] Conversion to Parquetopen1 💬#1
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Read further

Community best practices (architecture)
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The six workflow stages
What misc is doing more work than its name suggests.
Beyond English: multilingual corpora
The four language communities and their workarounds.
Non-English abliteration communities
Deep dive: huihui-ai, layer-window, slop-framing, CJK tokenizer fixes.

Metadata

License
apache-2.0

Repository

FILE .gitattributes FILE harmless_instructions_harmless_responses.jsonl FILE README.md

File contents live on Hugging Face. Click a file or folder to open it there.

Download the dataset

0

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("XiaoXiang09/MailiciousGen")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="XiaoXiang09/MailiciousGen", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/XiaoXiang09/MailiciousGen
Browse repository on Hugging Face ->
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