Get the app
fine-tune 0 0 0

betteruncensored/VMware-open-instruct

published by @betteruncensored

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
448
Last 30 days
3
Likes
-
Size
55.2 MB
Created on HF
2024-04-01
Age
2 years ago

Description

Dataset Card for "open-instruct" Better Uncensored

This is the VMWare/open-instruct dataset processed with the Better Uncensored pipeline.

A bit more than 4000 records were censored, less than 5%. Format kepts the same for compatibility.

Dataset Card for "open-instruct"

This dataset is a combination of:

Filtered subset of OpenAssistant/oasst1

train split of Mosaic-dolly-hhrlhf (consists of Databrick's dolly-15k dataset and a filtered subset of Anthropic's… See the full description on the dataset page: https://huggingface.co/datasets/betteruncensored/VMware-open-instruct.

Tags

task_categories:text-generationlanguage:enlicense:cc-by-3.0size_categories:100K<n<1Mformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face

dataset_info:
features:

  • name: alpaca_prompt
    dtype: string
  • name: response
    dtype: string
  • name: instruction
    dtype: string
  • name: source
    dtype: string
  • name: task_name
    dtype: string
  • name: template_type
    dtype: string
    splits:
  • name: train
    num_bytes: 125656035
    num_examples: 142622
    download_size: 57912402
    dataset_size: 125656035
    license: cc-by-3.0
    task_categories:
  • text-generation
  • text2text-generation
    language:
  • en
    pretty_name: T
    size_categories:
  • 100K<n<1M

Dataset Card for "open-instruct" Better Uncensored

This is the VMWare/open-instruct dataset processed with the Better Uncensored pipeline.
A bit more than 4000 records were censored, less than 5%. Format kepts the same for compatibility.

Dataset Card for "open-instruct"

This dataset is a combination of:

  1. Filtered subset of OpenAssistant/oasst1
  2. train split of Mosaic-dolly-hhrlhf (consists of Databrick's dolly-15k dataset and a filtered subset of Anthropic's HH-RLHF).
  3. Filtered subset of conceptofmind/cot_submix_original

Dataset

The dataset consists of 6 columns:

  1. instruction: The natural language instruction without any prompt templates (we extracted them out of the alpaca-format in Mosaic-dolly-hhrlhf)
  2. alpaca_prompt: Alpaca prompt template versions of instruction
  3. response: The response to the instruction
  4. source: Dataset source
  5. task_name
  6. template_type: flan template used (zeroshot or fewshot)

License

  • It is usable for commercial purposes so long as you follow the terms of the license.

Dataset subset licenses:

  • Open-instruct-v1-dolly-hhrlhf-oasst1 (Mosaic/Dolly-HHRLHF + filtered OASST1) - cc by 3.0

Subset of COT SUBMIX (FROM FLAN V2) Zeroshot examples:

  • ESNLI - MIT
  • ECQA - CDLA 1.0 - Sharing
  • Strategy - MIT
  • CREAK - MIT
  • gsmk8 - MIT
  • aqua - MIT
  • qasc - Apache 2.0

Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license:

Wikipedia (various pages) - https://www.wikipedia.org/

  • Copyright © Wikipedia editors and contributors.

Databricks (https://www.databricks.com)

  • Copyright © Databricks

Mosaic ML (https://www.mosaicml.com/)

  • Copyright © Mosaic ML

VMware

  • Copyright © VMware

More Information needed

README history 2 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. 2024-04-01better uncensored processed69f9bb02.7 KB
    Loading...
  2. 2024-04-01initial commit4f16c1d27 B
    Loading...

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. 2024-04-02[bot] Conversion to Parquetopen1 💬#1
    Loading...

Continue on Hugging Face →

Related datasets same stage · fine-tune

QuixiAI/dolphin
40.4K dl 444 likes 17.6 GB
Dolphin 🐬 https://erichartford.com/dolphin Dataset details This dataset is an attempt to replicate the results of ...
nvidia/Nemotron-SFT-Safety-v1
27.6K dl 14 likes 147.9 MB
Dataset Description: The Nemotron-SFT-Safety-v1 data is designed to align models to be robust against a variety of safety and s...
open-llm-leaderboard-old/details_ehartford__WizardLM-1.0-Uncensored-Llama2-13b
18.2K dl 293.9 MB
Dataset Card for Evaluation run of ehartford/WizardLM-1.0-Uncensored-Llama2-13b Dataset Summary Dataset automati...
jtatman/stable-diffusion-prompts-stats-full-uncensored
16.2K dl 157 likes 206.8 MB
jondurbin/airoboros-3.2
14.7K dl 52 likes 74.8 MB
Overview This dataset is a continuation of the airoboros-3.1 with the following changes: MathJSON has been removed for the tim...
xzuyn/open-instruct-uncensored-alpaca
12.2K dl 7 likes 7.4 GB
Original dataset page from ehartford. 810,102 entries. Sourced from open-instruct-uncensored.jsonl. Converted the jsonl to a json ...
OEvortex/uncensored-vortex
10.9K dl 10 likes 11.4 GB
jiae1234/vscd
10.5K dl 98.5 GB
VSCD: Video-based Scene Change Detection in Unaligned Scenes Dataset Description VSCD is a benchmark for Video-b...

Read further

Community best practices (data hygiene)
Rules for what belongs in a training mix and what does not.
M6 - Uncensored fine-tuning
DPO/ORPO from scratch. The Hartford dolphin lineage.
The politics of dataset curation
The curatorial choices behind Dolphin, Wizard, and the uncensored tradition.
Non-English abliteration communities
How non-English producers assemble uncensored fine-tuning corpora.

Metadata

License
cc-by-3.0

Repository

DIR data FILE .gitattributes 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("betteruncensored/VMware-open-instruct")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="betteruncensored/VMware-open-instruct", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/betteruncensored/VMware-open-instruct
Browse repository on Hugging Face ->
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Get the app Browse the catalog
Open in Abliteration