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kosiasuzu/tulu-postraining-data

published by @kosiasuzu

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
100
Last 30 days
41
Likes
-
Size
249.8 MB
Created on HF
2026-08-16
Age
2 months ago

Description

Prepared post-training sets

Built by scripts/prepare/*.py from allenai/tulu-3-sft-mixture and

HuggingFaceH4/ultrafeedback_binarized, seed 42. Every set is 8-gram

decontaminated against the benchmarks it is scored on (MMLU, IFEval,

RewardBench), before sampling — so a dropped row is replaced, not left short.

Set

Rows

Columns

dpo_10k

10,000

prompt, prompt_id, chosen, rejected, messages, score_chosen, score_rejected

ppo_1.5k

1,500

prompt, prompt_id

rm_20k

20,000… See the full description on the dataset page: https://huggingface.co/datasets/kosiasuzu/tulu-postraining-data.

Tags

size_categories:n<1Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:us

README current version from Hugging Face

Prepared post-training sets

Built by scripts/prepare/*.py from allenai/tulu-3-sft-mixture and
HuggingFaceH4/ultrafeedback_binarized, seed 42. Every set is 8-gram
decontaminated against the benchmarks it is scored on (MMLU, IFEval,
RewardBench), before sampling — so a dropped row is replaced, not left short.

Set Rows Columns
dpo_10k 10,000 prompt, prompt_id, chosen, rejected, messages, score_chosen, score_rejected
ppo_1.5k 1,500 prompt, prompt_id
rm_20k 20,000 prompt, prompt_id, chosen, rejected, messages, score_chosen, score_rejected
sft_25k 24,634 id, messages, source

Load with load_from_disk after downloading, the same way the trainers do:

hf download <repo> --repo-type dataset --local-dir data/processed

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. 2026-08-16prepared sets: dpo_10k, ppo_1.5k, rm_20k, sft_25k9ea53a5804 B
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  2. 2026-08-16prepared sets: dpo_10k, ppo_1.5k, rm_20k, sft_25ka7b1783804 B
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  3. 2026-08-16initial commit3de5b330 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. 2026-08-17[bot] Conversion to Parquetopen1 💬#1
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Read further

Community best practices (evaluation)
4 rules on judges, benchmarks, and reporting.
Reference-set drift over time
Why the same benchmark means different things across model generations.
How we label the method for each model
The 12 classification rules that assign M1-M8 to every model.
Non-English abliteration communities
The Korean scepticism about one-click tools and non-English benchmark blindspots.

Repository

DIR dpo_10k 3 files DIR ppo_1.5k 3 files DIR rm_20k 3 files DIR sft_25k 3 files FILE .gitattributes FILE .gitkeep FILE eval_prompts.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("kosiasuzu/tulu-postraining-data")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="kosiasuzu/tulu-postraining-data", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/kosiasuzu/tulu-postraining-data
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