evaluate 0 0 0

allenai/tulu-3-IF-augmented-on-policy-70b

published by @allenai

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
1.4K
Last 30 days
91
Likes
-
Size
215.3 MB
Created on HF
2024-11-20
Age
1 years ago

Description

Llama 3.1 Tulu 3 IF-Augmented (on-policy 70B)

Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.

This preference dataset is part of our Tulu 3 preference mixture:

it contains prompts from SFT Data, with constraints from https://huggingface.co/datasets/google/IFEval. It contains 65,530 generation pairs (some of which… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-3-IF-augmented-on-policy-70b.

Tags

size_categories:10K<n<100Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face


dataset_info:
features:

  • name: id
    dtype: string
  • 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: 431879752
    num_examples: 65551
    download_size: 225722195
    dataset_size: 431879752
    configs:
  • config_name: default
    data_files:
    • split: train
      path: data/train-*

Tulu3 banner

Llama 3.1 Tulu 3 IF-Augmented (on-policy 70B)

Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.

This preference dataset is part of our Tulu 3 preference mixture:
it contains prompts from SFT Data, with constraints from https://huggingface.co/datasets/google/IFEval. It contains 65,530 generation pairs (some of which on-policy completions from https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) obtained using the following models:

Completion Generation Approach:

Given a set of prompts, we generated the completions and preferences using a synthetic pipeline that combines both on-policy and off-policy data, and obtained the preference annotations on four different aspects using the Ultrafeedback template and an LLM judge. The code for the synthetic generation pipeline is found in the scripts/synth_pref directory of open-instruct

License

This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines. This dataset includes output data generated from third party models that are subject to separate terms governing their use.

README history 5 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. 2024-11-21Update README.md7fd34205.4 KB
    Loading...
  2. 2024-11-21Update README.mdf8b26305.2 KB
    Loading...
  3. 2024-11-21Update README.md3c78ce25.1 KB
    Loading...
  4. 2024-11-20Upload dataset2732258524 B
    Loading...
  5. 2024-11-20initial commit12693720 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.

Related datasets same stage · evaluate

Read further

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("allenai/tulu-3-IF-augmented-on-policy-70b")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="allenai/tulu-3-IF-augmented-on-policy-70b", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/allenai/tulu-3-IF-augmented-on-policy-70b
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