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arcee-ai/distilabel-intel-orca-dpo-pairs-binarized

published by @arcee-ai

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
141.3K
Last 30 days
18.2K
Likes
1
Size
29.5 MB
Created on HF
2024-05-18
Age
2 years ago

Description

This is the binarized version of distilabel Orca Pairs for DPO and ORPO.

Reference: https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs?row=0

Tags

language:enlicense:apache-2.0size_categories:10K<n<100Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarslibrary:distilabelregion:usdpoorposyntheticdistilabel

README current version from Hugging Face

dataset_info:
features:

  • name: prompt
    dtype: string
  • name: chosen
    list:
    • name: content
      dtype: string
    • name: role
      dtype: string
  • name: rejected
    list:
    • name: content
      dtype: string
    • name: role
      dtype: string
      splits:
  • name: train
    num_bytes: 57341525
    num_examples: 12859
    download_size: 30884798
    dataset_size: 57341525
    configs:
  • config_name: default
    data_files:
    • split: train
      path: data/train-*
      license: apache-2.0
      language:
  • en
    tags:
  • dpo
  • orpo
  • synthetic
  • distilabel
    size_categories:
  • 10K<n<100K

This is the binarized version of distilabel Orca Pairs for DPO and ORPO.

Reference: https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs?row=0

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. 2024-05-18Update README.md97027b1762 B
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  2. 2024-05-18Update README.md139426b653 B
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  3. 2024-05-18Upload datasetb9eaaee490 B
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  4. 2024-05-18initial commite6fb5c70 B
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Read further

Community best practices (healing)
4 rules on DPO/ORPO, GSM8K lag, CHES filtering, and what heal does not do.
M4 - Abliterate then heal
Two-stage pipeline. DPO healing recovers benchmarks.
The politics of dataset curation
Whose preferences are baked into the healing mix, and why it matters.
Non-English abliteration communities
The Russian slop-removal framing and non-English healing corpora.

Metadata

License
apache-2.0

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

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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("arcee-ai/distilabel-intel-orca-dpo-pairs-binarized")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="arcee-ai/distilabel-intel-orca-dpo-pairs-binarized", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/arcee-ai/distilabel-intel-orca-dpo-pairs-binarized
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