heal 0 0 0

llmat/dpo-orpo-mix-38k-balanced

published by @llmat

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
968
Last 30 days
55
Likes
1
Size
91.8 MB
Created on HF
2024-08-02
Age
2 years ago

Description

DPO-ORPO-mix-38k

This dataset is intended for use with DPO or ORPO training.

It represents a balanced version of the llmat/dpo-orpo-mix-45k dataset, achieved through a clustering-based approach as outlined in this paper.

The dataset integrates high-quality samples from the following DPO datasets:

argilla/Capybara-Preferences: highly scored chosen answers >=5 (2882 samples)

argilla/distilabel-intel-orca-dpo-pairs: highly scored chosen answers >=9, not in GSM8K (3961 samples)… See the full description on the dataset page: https://huggingface.co/datasets/llmat/dpo-orpo-mix-38k-balanced.

Tags

task_categories:text-generationlanguage:enlicense:apache-2.0size_categories:10K<n<100Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsarxiv:2405.15613region:usdpopreferenceorpo

README current version from Hugging Face


dataset_info:
features:

  • name: origin
    dtype: string
  • name: chosen
    list:
    • name: content
      dtype: string
    • name: role
      dtype: string
  • name: rejected
    list:
    • name: content
      dtype: string
    • name: role
      dtype: string
  • name: prompt
    dtype: string
    splits:
  • name: train
    num_bytes: 185260618
    num_examples: 38592
    download_size: 96252547
    dataset_size: 185260618
    configs:
  • config_name: default
    data_files:
    • split: train
      path: data/train-*
      license: apache-2.0
      task_categories:
  • text-generation
    language:
  • en
    size_categories:
  • 10K<n<100K
    tags:
  • dpo
  • preference
  • orpo
    pretty_name: DPO-ORPO-mix-38k-balanced

DPO-ORPO-mix-38k

This dataset is intended for use with DPO or ORPO training.

It represents a balanced version of the llmat/dpo-orpo-mix-45k dataset, achieved through a clustering-based approach as outlined in this paper.

The dataset integrates high-quality samples from the following DPO datasets:

Thanks to argilla, unalignment, M4-ai, and jondurbin for providing the source datasets.

README history 3 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. 2024-08-03Update README.md31dabe52.4 KB
    Loading...
  2. 2024-08-02Upload dataset2604748527 B
    Loading...
  3. 2024-08-02initial commit9bf65a80 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. 2024-08-03[bot] Conversion to Parquetopen1 💬#1
    Loading...

Related datasets same stage · heal

Read further

Metadata

License
apache-2.0

Repository

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("llmat/dpo-orpo-mix-38k-balanced")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="llmat/dpo-orpo-mix-38k-balanced", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/llmat/dpo-orpo-mix-38k-balanced
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.

Open in Abliteration