Get the app
misc 0 0 0

willhx/if_oracle_sft

published by @willhx

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
442
Last 30 days
113
Likes
-
Size
494.4 MB
Created on HF
2026-07-18
Age
3 months ago

Description

IF Oracle SFT

Oracle instruction-following SFT data built by rejection sampling from

willhx/Qwen3-8B-Base-IF.

How it was made

Policy: willhx/Qwen3-8B-Base-IF (a trained IF RL checkpoint).

Prompts: the full allenai/IF_multi_constraints_upto5-derived training set

(IF_multi_constraints_upto5_ifbench_en, 88,832 prompts covered).

Sampling: 8 responses per prompt, temperature 1.0, max 8192 response tokens

(rollout via slime + SGLang, --debug-rollout-only).

Reward:… See the full description on the dataset page: https://huggingface.co/datasets/willhx/if_oracle_sft.

Tags

task_categories:text-generationlanguage:enlicense:apache-2.0size_categories:100K<n<1Mformat:parquetmodality:tabularmodality:textlibrary:datasetslibrary:dasklibrary:polarslibrary:mlcroissantregion:usinstruction-followingifevalsftrejection-sampling

README current version from Hugging Face

license: apache-2.0
task_categories:

  • text-generation
    language:
  • en
    tags:
  • instruction-following
  • ifeval
  • sft
  • rejection-sampling
    pretty_name: IF Oracle SFT
    configs:
  • config_name: default
    data_files:
    • split: train
      path: "if_oracle_sft-*.parquet"

IF Oracle SFT

Oracle instruction-following SFT data built by rejection sampling from
willhx/Qwen3-8B-Base-IF.

How it was made

  • Policy: willhx/Qwen3-8B-Base-IF (a trained IF RL checkpoint).
  • Prompts: the full allenai/IF_multi_constraints_upto5-derived training set
    (IF_multi_constraints_upto5_ifbench_en, 88,832 prompts covered).
  • Sampling: 8 responses per prompt, temperature 1.0, max 8192 response tokens
    (rollout via slime + SGLang, --debug-rollout-only).
  • Reward: rule-based IFEval-G (rm_type=multi) — the fraction of the prompt's
    instruction constraints that a response satisfies.
  • Oracle filter: only responses with reward == 1.0 (ALL constraints
    satisfied) are kept. A prompt may contribute multiple oracle responses.

Columns

column description
prompt user instruction (clean text)
response model response that satisfies all constraints
messages [{user}, {assistant}] chat form of the pair
reward always 1.0 (oracle)
instruction_id_list IFEval-G instruction ids the prompt imposes
kwargs JSON string of per-instruction arguments
source, record_id, rm_type provenance from the source dataset
rollout_id which rollout batch the sample came from

Load

from datasets import load_dataset
ds = load_dataset("willhx/if_oracle_sft", split="train")

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. 2026-07-18Add dataset carde395d4f1.6 KB
    Loading...
  2. 2026-07-18Add IF oracle SFT parquet (225,681 reward==1.0 samples)2a5eea20 B
    Loading...
  3. 2026-07-18initial commitf4a41340 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 · misc

nvidia/Aegis-AI-Content-Safety-Dataset-2.0
99.3K dl 114 likes 27.1 MB
🛡️ Nemotron Content Safety Dataset V2 The Nemotron Content Safety Dataset V2, formerly known as Aegis AI Content Safety Data...
allenai/tulu-3-sft-personas-instruction-following
88.4K dl 70 likes 37.4 MB
Dataset Descriptions This dataset contains 29980 examples and is synthetically created to enhance model's capabilities to follo...
meta-llama/Llama-3.1-8B-Instruct-evals
73.2K dl 36 likes 232.9 MB
Dataset Card for Llama-3.1-8B-Instruct Evaluation Result Details This dataset contains the Meta evaluation result details for...
Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset
58.3K dl 226 likes 10.8 GB
📖 The Open Distillation Codex 🌌 The Ultimate Open-Source Distillation Dataset — No Skip, Full, with Attack & Defe...
nvidia/Aegis-AI-Content-Safety-Dataset-1.0
36.1K dl 63 likes 5.2 MB
🛡️ Nemotron Content Safety Dataset V1 Nemotron Content Safety Dataset V1, formerly known as Aegis AI Content Safety Dataset, i...
innodatalabs/rt-gsm8k-gaia
35.9K dl 15 KB
rt-gsm8k dataset Red teaming gsm8k-gaia dataset. Generated from the "GSM8K" dataset and "GAIA" dataset. Dataset Str...
HuggingFaceH4/hhh_alignment
35.3K dl 27 likes 266 KB
This task evaluates language models on alignment, broken down into categories of helpfulness, honesty/accuracy, harmlessness, and ...
ethz-spylab/rlhf_trojan_dataset
26.2K dl 6 likes 29.9 MB
Dataset for the competition This is the official dataset for the competition "Find the Trojan: Universal Backdoor Detection in ...

Read further

Community best practices (architecture)
4 rules for MoE, hybrid SSM, FP8 requantization, and Gemma quirks.
The six workflow stages
What misc is doing more work than its name suggests.
Beyond English: multilingual corpora
The four language communities and their workarounds.
Non-English abliteration communities
Deep dive: huihui-ai, layer-window, slop-framing, CJK tokenizer fixes.

Metadata

License
apache-2.0

Repository

FILE .gitattributes FILE if_oracle_sft-0000.parquet FILE if_oracle_sft-0001.parquet FILE if_oracle_sft-0002.parquet FILE if_oracle_sft-0003.parquet FILE if_oracle_sft-0004.parquet 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("willhx/if_oracle_sft")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="willhx/if_oracle_sft", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/willhx/if_oracle_sft
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