datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
guess-the-regextest-grpo-vlm-log-completions
TRL Completion logs
This dataset contains the completions generated during training using trl.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion
completion: the completion generated by the model
<reward_function_name>: the reward(s) assigned to the completion… See the full description on the dataset page: https://huggingface.co/datasets/qgallouedec/test-grpo-vlm-log-completions.qg_squad[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) evaluation set for the question generation (QG) models. The split
of test and development set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is
compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11).qg_esquad[SQuAD-es](https://huggingface.co/datasets/squad_es) dataset for question generation (QG) task.qg_jaquad[JaQuAD](https://github.com/SkelterLabsInc/JaQuAD) dataset for question generation (QG) task. The test set of the original
data is not publicly released, so we randomly sampled test questions from the training set.qg_koquad[KorQuAD](https://huggingface.co/datasets/squad_kor_v1) dataset for question generation (QG) task.qg_subjqa[SubjQA](https://github.com/megagonlabs/SubjQA) dataset for question generation (QG) task.deepmath-completions-logs
TRL Completion logs
This dataset contains the completions generated during training using trl.
Find the trained model at https://huggingface.co/qgallouedec/qwen2-0.5b-deepmath-grpo.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion
completion: the completion… See the full description on the dataset page: https://huggingface.co/datasets/qgallouedec/deepmath-completions-logs.qg_squadshifts[SQuAD Shifts](https://modestyachts.github.io/squadshifts-website/index.html) dataset for question generation (QG) task.biogridtrl-metrics
Stars
import requests
from datetime import datetime
from datasets import Dataset
import pyarrow as pa
import os
def get_stargazers(owner, repo, token):
# Initialize the count and the page number
page = 1
stargazers = []
while True:
# Construct the URL for the stargazers with pagination
stargazers_url = f"https://api.github.com/repos/{owner}/{repo}/stargazers?page={page}&per_page=100"
# Send the request to GitHub API with appropriate headers… See the full description on the dataset page: https://huggingface.co/datasets/qgallouedec/trl-metrics.PM4Bench-QGO-Train
PM4Bench QGO training data
Synthetic multilingual OCR data for QGO reinforcement learning
Overview
The paper Benchmarking and Boosting Multilingual Capabilities of LVLMs via
OCR-Centric Reinforcement Learning
uses PM4Bench to show that OCR is a key source of cross-lingual performance
gaps when text is rendered visually. QGO addresses that finding with GRPO on
synthetic OCR data, without task-specific VQA or GUI supervision. This
repository contains the… See the full description on the dataset page: https://huggingface.co/datasets/DatasetMan/PM4Bench-QGO-Train.biogrid_qaqg_squad_modifiedqg_tweetqaQuestion generation dataset based on [TweetQA](https://huggingface.co/datasets/tweet_qa).qg_squad_modified_devdeepmath-completions-logs2
TRL Completion logs
This dataset contains the completions generated during training using trl.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion
completion: the completion generated by the model
<reward_function_name>: the reward(s) assigned to the completion… See the full description on the dataset page: https://huggingface.co/datasets/qgallouedec/deepmath-completions-logs2.physics-problemsreddit_qgReddit question generation dataset.azm-archive-20260909-videogenreward
videogenreward.tar
Backup of an existing dataset archive, preserving its original bytes.
File: videogenreward.tar
Size: 13,441,536,000 bytes
SHA256: b909864ad8f71353665430e961210373bd34b1bc4acd7a52d5149a4b16546e71
Verify the downloaded archive with sha256sum -c SHA256SUMS.
t2v_data_v2
DenseDPO T2V Broad-Pair Dataset (v2)
Cross-model text-to-video (T2V) generation pairs for training video reward
models (RM) and DPO-style preference learning.
The HF Dataset Viewer renders each row as prompt + two videos side-by-side.
Generation task
All videos are generated T2V from a shared text prompt. For every pair,
both videos share the same prompt, so the primary comparison axis is the model
identity itself.
Plan-A tier structure
Models are grouped into… See the full description on the dataset page: https://huggingface.co/datasets/qgfvadfuvads/t2v_data_v2.azm-archive-20260909-ltx-i2v-videos-1370
ltx_i2v_videos_1370.tar
Backup of an existing dataset archive, preserving its original bytes.
File: ltx_i2v_videos_1370.tar
Size: 2,247,976,960 bytes
SHA256: b46c8369b4d8312ae1bae4c02aca0a8e31cd8d9e4b6a598a9689623f42ca8bbf
Verify the downloaded archive with sha256sum -c SHA256SUMS.
azm-archive-20260909-t2v-needlabel-videos
t2v_data_needlabel_videos.tar
Backup of an existing dataset archive, preserving its original bytes.
File: t2v_data_needlabel_videos.tar
Size: 4,675,983,360 bytes
SHA256: 8672488cef23b55e2a70d3279327a38ddbbd8e2809661600d7c707d59b9e1e18
Verify the downloaded archive with sha256sum -c SHA256SUMS.
hh-rlhf-helpful-base-trl-style
TRL's Anthropic HH Dataset
We preprocess the dataset using our standard prompt, chosen, rejected format.
Reproduce this dataset
Download the anthropic_hh.py from the https://huggingface.co/datasets/qgallouedec/hh-rlhf-helpful-base-trl-style/tree/0.1.0.
Run python examples/datasets/anthropic_hh.py --push_to_hub --hf_entity qgallouedec
qg_concat_v1QG_korquad_aihub
Dataset Card for "QG_korquad_aihub"
More Information needed
prm800k
My test
QG_aihub
Dataset Card for "QG_aihub"
More Information needed
DAPO-Math-17k-Processed-Scoredtool-calls-mini
tool-calls-mini
500 synthetic tool-calling conversations in TRL's conversational format,
for supervised fine-tuning. Built to be coherent: every tool result is a plausible
function of the arguments it was called with, and every final answer reflects that
result — so the set teaches when to call a tool, not just what a call looks like.
Format
Each row has messages and tools. An assistant turn carries tool_calls instead of
content; the tool replies as a tool role… See the full description on the dataset page: https://huggingface.co/datasets/qgallouedec/tool-calls-mini.