datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Synthetic-JP-Preference-Dataset-Qwen2.5_72B-191k
Synthetic-JP-Preference-Dataset-Qwen2.5_72B-191k
概要
5種類のオープンモデルとQwen/Qwen2.5-72B-Instruct-GPTQ-Int8を使って作成した、190854件の日本語合成Preferenceデータセットです。
以下、データセットの詳細です。
instructionには、Aratako/Magpie-Tanuki-8B-annotated-96kのinput_qualityがexcellentのものを利用
回答生成には、以下の5つのApache 2.0ライセンスのモデルを利用
weblab-GENIAC/Tanuki-8B-dpo-v1.0
team-hatakeyama-phase2/Tanuki-8x8B-dpo-v1.0-GPTQ-8bit
cyberagent/calm3-22b-chat
llm-jp/llm-jp-3-13b-instruct
Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8… See the full description on the dataset page: https://huggingface.co/datasets/Aratako/Synthetic-JP-Preference-Dataset-Qwen2.5_72B-191k.MNLP_M1_Preference_dpo_dataset
M1 Preference Data for DPO
Dataset Description
This dataset contains processed M1 preference data for DPO training.
Created by: CS-552 Stochastic Parrots Team
Date: May 24, 2025
Version: 1.0
Number of examples: 17615
Dataset Source
This dataset is derived from the M1 preference data collected through interactions with large language models (like ChatGPT) for CS-552 (Modern Natural Language Processing) at EPFL. The preference data consists of… See the full description on the dataset page: https://huggingface.co/datasets/stochastic-parrots/MNLP_M1_Preference_dpo_dataset.dataset-tldr-preference-dpo
Dataset Card for dataset-tldr-preference-dpo
This dataset has been created with distilabel.
Dataset Summary
This is a dataset intended for training models using DPO/ORPO for the task of producing concise tl;dr summaries of machine learning datasets based on their dataset cards.
The dataset was created with distilabel. Each row of the dataset contains a dataset card which has been parsed to remove empty sections and placeholder text.
The instruction request… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/dataset-tldr-preference-dpo.sherlock_preference_datasetThis dataset contains preference data for tuning Vision-Language models on the Sherlock Dataset for Abductive Reasoning. It is designed to evaluate the effectiveness of fine-tuning using Supervised Fine-Tuning (SFT) or Preference Optimization. Preferences are generated by prompting four models: mistralai/Pixtral-12B-2409, Qwen/Qwen2-VL-7B-Instruct, google/paligemma2-3b-ft-docci-448, and google/paligemma2-10b-ft-docci-448.
Since this dataset is intended for optimizing PaLI-Gemma models… See the full description on the dataset page: https://huggingface.co/datasets/akshayg08/sherlock_preference_dataset.Self_Alignment_Preference-Dataset
Mistral Self-Alignment Preference Dataset
Warning: This dataset contains harmful and offensive data! Proceed with caution.
The Mistral Self-Alignment Preference Dataset was generated by Mistral 7b using the Anthropics Red Teaming Prompts dataset available at Hugging Face - Anthropics Red Teaming Prompts Dataset. The data generation process utilized the Preference Data Generation Notebook, which can be found here.
The purpose of this dataset is to facilitate self-alignment, as… See the full description on the dataset page: https://huggingface.co/datasets/August4293/Self_Alignment_Preference-Dataset.Hierarchical-Preference-Dataset
Hierarchical Preference Dataset
The Hierarchical Preference Dataset is a structured dataset for analyzing and evaluating model reasoning through a hierarchical cognitive decomposition lens. It is derived from the prhegde/preference-data-math-stack-exchange dataset and extends it with annotations that separate model outputs into Refined Query, Meta-Thinking, and Refined Answer components.
Overview
Each sample in this dataset consists of:
An instruction or query.
Two… See the full description on the dataset page: https://huggingface.co/datasets/Death-Raider/Hierarchical-Preference-Dataset.INFH-6000Q-dpo-preference-dataset
INFH-6000Q DPO Preference Dataset
This dataset contains the final preference pairs used for the Direct Preference Optimization assignment in this repository.
Source
Base instruction source: GAIR/lima
Candidate generator: local Qwen/Qwen2.5-7B-Instruct
Preference ranker: local llm-blender/PairRM
Construction Pipeline
Sample 50 instructions from the local LIMA training split with seed 42.
Generate 5 candidate responses per instruction with Qwen2.5-7B-Instruct.… See the full description on the dataset page: https://huggingface.co/datasets/ITBill/INFH-6000Q-dpo-preference-dataset.