datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ExperimentDATA_knowledge_distillation_vs_fine_tuningknowledge-base
RL-for-LLMs Wiki
An expert-level, citation-backed knowledge base on reinforcement learning for
large language models — RLHF, DPO and offline preference optimization, reward
modeling, RLVR and reasoning, training systems, and the failure modes — built
collaboratively by autonomous agents. Each topic article is a deep dive written
so you can learn the topic from it without reading the underlying papers, with
every non-obvious claim cited to a source. Every change lands through a… See the full description on the dataset page: https://huggingface.co/datasets/rl-llm-wiki/knowledge-base.ASearcher-Local-Knowledgedamru-knowledge
🐕 Damru Knowledge
A continuously growing, self-collected question-answer knowledge base that powers Damru AI — a self-learning assistant built for exam preparation and general-purpose help, with a focus on Indian students.
The dataset is harvested and quality-filtered automatically, 24x7, from multiple open sources and a self-evaluating reasoning engine. New rows are appended every hour as parquet shards under data/.
📦 What's inside
Column
Type
Description… See the full description on the dataset page: https://huggingface.co/datasets/Damaru-ai/damru-knowledge.multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR.Knowledge-QA-SingleTurn-Dataset
Knowledge QA Single-turn Dataset（知識質問データセット・シングルターン）
概要
本データセットは、Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k から質問を抽出し、DeepSeek V3.2で整形、Kimi K2.5で回答を生成した シングルターンの知識質問応答データセット です。Reasoning有効化により思考過程も最終データに含まれ、質問の難易度に応じてReasoning effortが動的に切り替わります。
生成にはSDG-LOOMという合成データ生成パイプラインを用いました。（sdg-loom）
データの説明
項目
内容
件数
約7,000件
形式
JSONL（1行1JSON）
言語
日本語
ターン数
1ターン（質問1 + 回答1）
ソースデータセット… See the full description on the dataset page: https://huggingface.co/datasets/DataPilot/Knowledge-QA-SingleTurn-Dataset.knowledge-base
Attention Wiki — a living knowledge base on LLM attention
A citation-backed tree of knowledge about attention in large language
models, built collaboratively by autonomous agents. Agents read papers,
blogs, and model cards; distill them into structured, provenance-tracked pages;
and reconcile where sources agree, disagree, or leave a question open. Every
change lands through a reviewed Pull Request — so the canonical wiki is
curated, not just accumulated.
Contributing? Read… See the full description on the dataset page: https://huggingface.co/datasets/attention-wiki/knowledge-base.knowledge_base_md_for_rag_1
HF Knowledge-Base Markdown Collection
This repository contains a collection of Markdown-based knowledge bases generated from:
User-provided notes and attachments
Hugging Face Docs, Blog, and Papers
Model / Dataset / Space cards
Discussions, GitHub issues, forums, and other vetted community sources
Each .md file is intended to be a self-contained knowledge pack that can be used as
LLM context for RAG or prompt-attachment workflows (e.g. ChatGPT, Hugging Face Inference… See the full description on the dataset page: https://huggingface.co/datasets/John6666/knowledge_base_md_for_rag_1.mmlu_clinical_knowledgemodeling_valuation_knowledge
Finance Training Data Repository
A curated collection of financial modeling courses, materials, and resources designed to serve as training data for building a finance industry knowledge base.
Repository Structure
Finance_Training_Data/
├── 01_Financial_Statement_Modeling/ # 3-statement modeling fundamentals
├── 02_DCF_Modeling/ # Discounted cash flow valuation
├── 03_Trading_Comps/ # Comparable company analysis
├──… See the full description on the dataset page: https://huggingface.co/datasets/financeindustryknowledgeskills/modeling_valuation_knowledge.Knowledge_distilled_dataset_by_DLSuisho15b_uniqeliciting-secret-knowledge-resultsciel-knowledge-basewarp-knowledgeai_tutor_knowledgeNemotron-RL-knowledge-mcqa
Dataset Description:
The Nemotron-RL-knowledge-mcqa is a multi-domain synthetic multiple-choice question-answering (MCQA) dataset containing knowledge based questions. It combines and refines subsets of the [OpenScienceReasoning-2] (https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) dataset and other unstructured sources such as books and articles.The dataset was created using Qwen3-32B, [Qwen3-235B-A22B-Instruct-2507]… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-knowledge-mcqa.edX-rawmulti_task_multi_modal_knowledge_retrieval_benchmark_M2KR_CN
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR_CN.Knowledge_distilled_dataset_by_NAGI将棋AI用の知識蒸留済みのデータセットを公開します。およそ80億局面あります。 nodchip氏が公開しているtanuki-.nnue-pytorch-2024-07-30.1をhaoでqsearchシャッフルしたのち自作のNAGI(非公開)で評価値を書き換えました。Eval_Coef=600でDLモデルのvalueと評価値を変換しています。 データにバグがあるかもしれませんが、品質保証はしません。
https://huggingface.co/datasets/nodchip/tanuki-.nnue-pytorch-2024-07-30.1
onego-knowledge-packs
ONEGO Knowledge Packs
Offline RAG databases for ONEGO / Offline AI Assistant.
Files
File
Role
Size
SHA256
wikipedia_base.ragdb
Bundled 300 MB starter Wikipedia pack
336867328
66943284f1b06127af2faf7a15c9451caa513bd18c9deeef8b9a2c572f6ca189
wikipedia_slim_3gb_v3_20260511.ragdb
User-installable 3 GB Wikipedia pack
2672226304
4cc3ca28c9171afef6ce8322f8bdd25d94946b89e057d5476c4d58a1262c4341
wikipedia_extended_9gb_v3_20260511.ragdb
User-installable 9 GB… See the full description on the dataset page: https://huggingface.co/datasets/onegoai/onego-knowledge-packs.ensu-knowledge-packs
Ensu Knowledge Packs
Prebuilt on-device retrieval indexes ("knowledge packs") for
Ensu, ente's private on-device AI assistant — plus the
scripts that generate them. Ensu grounds factual answers by embedding the
user's query locally, searching these packs with cosine similarity, and
injecting the retrieved passages (with source citations) into the prompt.
Everything runs on-device; no query ever leaves the phone.
Layout
Each dataset lives in its own self-contained… See the full description on the dataset page: https://huggingface.co/datasets/ente-ai/ensu-knowledge-packs.FRIEDA
FRIEDA is a multimodal benchmark for open-ended cartographic reasoning over real-world map images.Each example pairs reference maps (and optional contextual maps) with a natural-language question and a reference answer. The benchmark targets common GIS relation types (i.e., topological, metric, directional) and includes questions that require multi-step reasoning and cross-map grounding.
Dataset Summary
Modality: image + text
# Examples: 500
Input: map image(s) + question… See the full description on the dataset page: https://huggingface.co/datasets/knowledge-computing/FRIEDA.Knowledge-Basepython-image-copilot-training-using-import-knowledge-graphs
Python Copilot Image Training using Import Knowledge Graphs
This dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details
Each row contains a png file in the dbytes column.
Rows: 216642
Size: 211.2 GB
Data type: png
Format: Knowledge graph using NetworkX with alpaca text box
Schema
The png is in the dbytes column:
{
"dbytes": "binary"… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-image-copilot-training-using-import-knowledge-graphs.llm-knowledge-collapse
"Epistemic Diversity and Knowledge Collapse in Large Language Models" (Wright et al. 2025)
Authors: Dustin Wright, Sarah Masud, Jared Moore, Srishti Yadav, Maria Antoniak, Peter Ebert Christiensen, Chan Young Park, and Isabelle Augenstein
Contains all 1.6M responses and 70M claims used to measure LLM epistemic diversity in the paper "Epistemic Diversity and Knowledge Collapse in Large Language Models" (Wright et al. 2025)
@article{wright2025epistemicdiversity… See the full description on the dataset page: https://huggingface.co/datasets/dwright37/llm-knowledge-collapse.python-image-copilot-training-using-class-knowledge-graphs
Python Copilot Image Training using Class Knowledge Graphs
This dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details
Each row contains a png file in the dbytes column.
Rows: 312277
Size: 304.3 GB
Data type: png
Format: Knowledge graph using NetworkX with alpaca text box
Schema
The png is in the dbytes column:
{
"dbytes": "binary"… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-image-copilot-training-using-class-knowledge-graphs.arsma-knowledge-dbGPTKB_2.0_imagesImages created to illustrate GPTKB 2.0 entities, using the black-forest-labs/FLUX.2-dev VLM. File name = entity ID, e.g., "E0.jpg" illustrates https://gptkb.org/entity/E0/ (Vannevar Bush).
The generating prompts are retained in manifest.jsonl, and consist of the entity label + description.
motif-knowledge-dbknowledge
Evaluation Code
The evaluation code is implemented based on MTEB framework and avaliable in https://github.com/rebeccaz4/MRMR.
Disclaimers
The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution. Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we… See the full description on the dataset page: https://huggingface.co/datasets/MRMRbenchmark/knowledge.