datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Trendyol-Cybersecurity-Instruction-Tuning-Dataset
Trendyol Cybersecurity Defense Instruction-Tuning Dataset (v2.0)
🚀 TL;DR
53,202 meticulously curated system/user/assistant instruction-tuning examples covering 200+ specialized cybersecurity domains. Built by the Trendyol Security Team for training state-of-the-art defensive security AI assistants. Expanded from 21K to 53K rows with comprehensive coverage of modern security challenges including cloud-native threats, AI/ML security, quantum computing risks… See the full description on the dataset page: https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset.Nemotron-SFT-Instruction-Following-Chat-v3
Dataset Description:
The Nemotron-Instruction-Following-Chat-v3 dataset is designed to strengthen multi-turn, interactive capabilities, including open-ended chat and precise instruction following.
The chat subset uses human written prompts from sources like lmarena, lmsys, and wildchat as seed prompts. Responses are generated with GLM-5. Multiple responses are sampled from the model and the best response as judged by pairwise comparisons using Qwen3-Nemotron-235B-A22B-GenRM-2603… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3.sec-contracts-financial-extraction-instructions
S&P 500 SEC Financial Extraction Instructions
Dataset Summary
7,683 instruction-tuning examples for training LLMs to extract structured financial data from SEC filings. Covers two filing types across S&P 500 companies:
Split
Examples
Filing Type
Description
train
3,430
Exhibit 10 + DEF 14A
Positive examples with validated outputs
corrective
4,253
Exhibit 10 + DEF 14A
Corrective, rescued, and negative examples
Exhibit 10 — Material Contracts (2… See the full description on the dataset page: https://huggingface.co/datasets/TheTokenFactory/sec-contracts-financial-extraction-instructions.Malay-Dialect-Instructions
Malay dialect instruction including coding
Negeri Sembilan
QA
public transport QA,
Coding
CUDA coding,
Kedah
QA
infra QA,
Coding
Rust coding,
Kelantan
QA
Najib Razak QA,
Coding
Go coding,
Perak
QA
Anwar Ibrahim QA,
Coding
SQL coding,
Pahang
QA
Pendatang asing QA,
Coding
Typescript coding,
Terengganu… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Malay-Dialect-Instructions.pmc_llama_instructionsThis repo provides part of the dataset used for PMC-LLaMA-13B's instruction tuning.
Data
Size
Link
ChatDoctor
100K
https://www.yunxiangli.top/ChatDoctor/
MedQA
10.2K
https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options
MedMCQA
183K
https://huggingface.co/datasets/medmcqa
PubmedQA
211K
https://huggingface.co/datasets/pubmed_qa
LiveQA
635
https://huggingface.co/datasets/truehealth/liveqa
MedicationQA
690
https://huggingface.co/datasets/truehealth/medicationqa
UMLS… See the full description on the dataset page: https://huggingface.co/datasets/axiong/pmc_llama_instructions.TCM-Instruction-Tuning-ShizhenGPT
📚 Introduction
This dataset is a fine-tuning dataset for ShizhenGPT, a multimodal LLM for Traditional Chinese Medicine (TCM). We open-source 245K multimodal Chinese medicine instruction data, including text instructions, visual instructions, and signal instructions for TCM.
For details, see our paper and GitHub repository.
📊 Dataset Overview
The open-sourced fine-tuning dataset consists of three parts:
Modality
Data Quantity
TCM Text Instructions
📝 Text… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/TCM-Instruction-Tuning-ShizhenGPT.law-instructions-dataset
Nepali Source-Grounded Instruction Dataset
Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data
Designer from authoritative Nepali documents (agriculture manuals, legal
texts). Answers are grounded strictly in the source; unanswerable questions
get an explicit refusal. Records use chat messages format plus metadata
and per-record quality_scores (grounding / correctness / naturalness, 1-5,
LLM-as-judge). One data/train-<shard>.jsonl per source document; shards… See the full description on the dataset page: https://huggingface.co/datasets/aarajbhattarai/law-instructions-dataset.cpt_instruction_datasets
Instruction datasets
Collection of synthetic instruction datasets used during the continued pretraining of Model-small-instr-1, Model-small-instr-2 and Model-small-instr-3. You can currently find these models under: Llama-3.1-Carballo-Instr1 and Llama-3.1-Carballo-Instr3.
Dataset creation
Datasets were created using two different techniques:
Adapting already existing datasets or corpora by modifying their format to make them suitable for including instructions during… See the full description on the dataset page: https://huggingface.co/datasets/proxectonos/cpt_instruction_datasets.python-code-instructions-85k
Python Code Instructions - 85K
Instruction-tuning dataset of Python functions paired with short natural-language instructions derived from repository docstrings.
What changed in this release
This release keeps the original public rows and format, but makes the dataset easier to use responsibly:
exact duplicate rows were removed again using normalized instruction + output hashing
deterministic train, validation, and test splits were added
the dataset card now documents… See the full description on the dataset page: https://huggingface.co/datasets/NickIBrody/python-code-instructions-85k.rejected-agriculture-instructions-dataset
Nepali Source-Grounded Instruction Dataset — REJECTED
Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data
Designer from authoritative Nepali documents (agriculture manuals, legal
texts). Answers are grounded strictly in the source; unanswerable questions
get an explicit refusal. Records use chat messages format plus metadata
and per-record quality_scores (grounding / correctness / naturalness, 1-5,
LLM-as-judge). One data/train-<shard>.jsonl per source… See the full description on the dataset page: https://huggingface.co/datasets/aarajbhattarai/rejected-agriculture-instructions-dataset.agriculture-instructions-dataset
Nepali Source-Grounded Instruction Dataset
Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data
Designer from authoritative Nepali documents (agriculture manuals, legal
texts). Answers are grounded strictly in the source; unanswerable questions
get an explicit refusal. Records use chat messages format plus metadata
and per-record quality_scores (grounding / correctness / naturalness, 1-5,
LLM-as-judge). One data/train-<shard>.jsonl per source document; shards… See the full description on the dataset page: https://huggingface.co/datasets/aarajbhattarai/agriculture-instructions-dataset.unjudged-agriculture-instructions-dataset
Nepali Source-Grounded Instruction Dataset — UNJUDGED
Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data
Designer from authoritative Nepali documents (agriculture manuals, legal
texts). Answers are grounded strictly in the source; unanswerable questions
get an explicit refusal. Records use chat messages format plus metadata
and per-record quality_scores (grounding / correctness / naturalness, 1-5,
LLM-as-judge). One data/train-<shard>.jsonl per source… See the full description on the dataset page: https://huggingface.co/datasets/aarajbhattarai/unjudged-agriculture-instructions-dataset.pao-instruction-qa-conversation-datasetPa'O Instruction, QA & Conversation Dataset
An open and community-driven dataset for the Pa'O ("blk") language, developed through the RYPAK Ecosystem, SuccessImprove (SI), and Pa'O Digital Hub.
The dataset is designed to support natural language processing (NLP), large language models (LLMs), conversational dialogue, instruction following, language technology research, and digital preservation of the Pa'O language.
The project focuses on building a free, open, reusable, and continuously… See the full description on the dataset page: https://huggingface.co/datasets/paodigitalhub/pao-instruction-qa-conversation-dataset.Nemotron-RL-Instruction-Following-Free-Form-Formatting-v1
Dataset Description:
Teaches the model to follow arbitrary text formatting instructions (bullet styles, numbering, delimiters, heading formats, inline emphasis, web-answer structure, etc.) for targeted chat behaviors. Uses explicit Regex and string matching for the reward signal.
This dataset is ready for commercial or non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: April 10, 2026
Last Modified on: April… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Free-Form-Formatting-v1.paper_instructions_300K-v1Loading will work as follows:
Existing behavior
# Loads the SFT dataset containing instruction, prompt, output
load_dataset("paperbd/paper_instructions_300K-v1")
Reasoning variant
# Loads reasoning subset containing instruction, prompt, reasoning, output
load_dataset(
"paperbd/paper_instructions_300K-v1",
"reasoning",
split="train",
)
Dataset Summary
This dataset contains synthetic supervised fine-tuning data generated from academic… See the full description on the dataset page: https://huggingface.co/datasets/paperbd/paper_instructions_300K-v1.full-structured-instruction-sft-dataset
Full Structured + Instruction SFT Corpus
Unified SFT training corpus built from Glaive, Hermes, UltraChat, and synthetic structured-output data.
Dataset repo
mdonigian/full-structured-instruction-sft-datasetRelease date: 2026-03-11
Included files
train_full_sft.jsonl: full merged and shuffled SFT dataset
source_glaive.jsonl: processed Glaive subset
source_hermes.jsonl: processed Hermes subset
source_ultrachat.jsonl: processed UltraChat subset… See the full description on the dataset page: https://huggingface.co/datasets/mdonigian/full-structured-instruction-sft-dataset.HuatuoGPT2-Pretraining-Instruction
HuatuoGPT2-Pretraining-Instruction-5200K
Here are the pre-training instructions for HuatuoGPT-II, developed with 5.2 million medical corpus using ChatGPT.
This dataset is used to incorporate extensive medical knowledge and enable a one-stage medical adaptation. All our data have been made publicly accessible.
Data Volume
The following table details the volume and distribution of pre-training data for HuatuoGPT2:
Data Source
Data Volume
Medical_Web_Corpus_cn… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/HuatuoGPT2-Pretraining-Instruction.Nemotron-RL-Instruction-Following-Citation-Formatting-v1
Dataset Description:
Teaches the model to cite specific document parts using reference markers like [ref:1], ref:3, etc. Supports single-reference, multi-reference, and inline citations.
This dataset is ready for commercial/non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: April 10, 2026
Last Modified on: April 10, 2026
Version:
Nemotron-RL-Instruction-Following-CitationFormatting-v1… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Citation-Formatting-v1.ko-instruction-dataset
고품질 한국어 데이터셋
한국어로 이루어진 고품질 한국어 데이터셋 입니다.
WizardLM-2-8x22B 모델을 사용하여 WizardLM: Empowering Large Language Models to Follow Complex Instructions에서 소개된 방법으로 생성되었습니다.
@article{koinstructiondatasetcard,
title={CarrotAI/ko-instruction-dataset Card},
author={CarrotAI (L, GEUN)},
year={2024},
url = {https://huggingface.co/datasets/CarrotAI/ko-instruction-dataset}
}
unjudged-law-instructions-dataset
Nepali Source-Grounded Instruction Dataset — UNJUDGED
Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data
Designer from authoritative Nepali documents (agriculture manuals, legal
texts). Answers are grounded strictly in the source; unanswerable questions
get an explicit refusal. Records use chat messages format plus metadata
and per-record quality_scores (grounding / correctness / naturalness, 1-5,
LLM-as-judge). One data/train-<shard>.jsonl per source… See the full description on the dataset page: https://huggingface.co/datasets/aarajbhattarai/unjudged-law-instructions-dataset.Align-Anything-Instruction-100K-zh
Dataset Card for Align-Anything-Instruction-100K-zh
[🏠 Homepage]
[🤗 Instruction-Dataset-100K(en)]
[🤗 Instruction-Dataset-100K(zh)]
[🤗 Align-Anything Datasets]
Instruction-Dataset-100K(zh)
Highlights
Data sources:
Firefly (47.8%),
COIG (2.9%),
and our meticulously constructed QA pairs (49.3%).
100K QA pairs (zh): 104,550 meticulously crafted instructions, selected and polished from various Chinese datasets… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/Align-Anything-Instruction-100K-zh.KrynexAI-Dataset-Flash-Instruction
🧠 KrynexAI Dataset
English | Русский
📌 Overview
KrynexAI Dataset is a high-quality, synthetically expanded collection of 10,000+ instruction-response pairs designed for fine-tuning Large Language Models (LLMs).
The dataset covers a wide range of topics including:
💻 Programming (Python, algorithms, data structures)
🤖 AI & Machine Learning (neural networks, transformers, LLMs)
🔭 Science (physics, cosmology, biology, neuroscience)
🧠 Philosophy & Psychology… See the full description on the dataset page: https://huggingface.co/datasets/KrynexLabs/KrynexAI-Dataset-Flash-Instruction.unity-dev-instructions
Unity Developer Instructions
A comprehensive instruction-tuning dataset for Unity game development,
covering C# scripting, XR/VR development, physics, animation, rendering,
UI Toolkit, and performance optimization.
Dataset Summary
Split
Count
Train
46,483
Test
2,446
Total
48,929
Data Sources
| unity_docs | 40,496 |
| stackoverflow | 6,071 |
| github | 2,362 |
Source breakdown:
Source
Count
unity_docs
40,496
stackoverflow
6,071… See the full description on the dataset page: https://huggingface.co/datasets/vishnuOI/unity-dev-instructions.instruction-following-hard-sft-100k
Hard Instruction Following SFT (100K)
100,000 ShareGPT conversations where the assistant correctly satisfies multiple simultaneous explicit constraints in a single response. Each example pairs a multi-constraint prompt with a response that honors every constraint without dropping any.
Targets the instruction-following capability measured by IFEval and similar benchmarks.
Motivation
A key failure mode in deployed LLMs is dropping constraints under load — responding… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/instruction-following-hard-sft-100k.russian_instructions_2_cleaned
Russian Instructions Cleaned
Очищенная версия Den4ikAI/russian_instructions_2.
Что сделано
Дедупликация по question (удалено ~45k)
Удалены пустые question и answer
Удалены ответы короче 100 и длиннее 4000 символов
Конвертировано в chat-формат (messages: user/assistant)
Статистика
Метрика
Значение
Исходно
237 281
После чистки
138 973
Удалено
98 308 (41%)
Формат
JSONL, одна строка = один пример.
{"messages":… See the full description on the dataset page: https://huggingface.co/datasets/Lev384501/russian_instructions_2_cleaned.k12-standards-instruction-tasks
K-12 Curriculum Tasks (generated)
2,489 generated instruction/input/output records covering five curriculum tasks:
assessment creation, learning objective generation, misconception detection, standard
explanation, and standards Q&A. Content is predominantly mathematics.
Important: the name is misleading
Despite the name, this dataset contains no school directory data. There are four
columns - task, input, output, metadata - and no staff, principal, or school… See the full description on the dataset page: https://huggingface.co/datasets/robworks-software/k12-standards-instruction-tasks.Align-Anything-Instruction-100K
Dataset Card for Align-Anything-Instruction-100K
[🏠 Homepage]
[🤗 Instruction-Dataset-100K(en)]
[🤗 Instruction-Dataset-100K(zh)]
[🤗 Align-Anything Datasets]
Highlights
Data sources:
PKU-SafeRLHF QA ,
DialogSum,
Empathetic,
Instruction-Wild,
and Alpaca.
100K QA pairs: By leveraging GPT-4 to annotate meticulously refined instructions, we obtain 105,333 QA pairs.… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/Align-Anything-Instruction-100K.Turkish-instruction-3m_Soru_Cevap
🌟 DESTEK & TOPLULUK ÇAĞRISI (SUPPORT & LIKE):Açık kaynak ve ücretsiz olarak sunduğum bu devasa çalışmayı faydalı bulduysanız, projenin sürdürülebilirliğine ve açık kaynak ekosisteminin görünürlüğüne katkı sağlamak için lütfen sayfanın sağ üstündeki Like (❤️ Beğeni) butonuna basarak destek olmayı unutmayın!(If you find this open-source dataset valuable for your research or models, please consider leaving a ❤️ Like at the top-right to support future updates and maintenance).
🇹🇷… See the full description on the dataset page: https://huggingface.co/datasets/bysismo/Turkish-instruction-3m_Soru_Cevap.TCM-Instruction-Tuning-ShizhenGPT
📚 Introduction
This dataset is a fine-tuning dataset for ShizhenGPT, a multimodal LLM for Traditional Chinese Medicine (TCM). We open-source 245K multimodal Chinese medicine instruction data, including text instructions, visual instructions, and signal instructions for TCM.
For details, see our paper and GitHub repository.
📊 Dataset Overview
The open-sourced fine-tuning dataset consists of three parts:
Modality
Data Quantity
TCM Text Instructions
📝 Text… See the full description on the dataset page: https://huggingface.co/datasets/CarsonnnNN/TCM-Instruction-Tuning-ShizhenGPT.Instruction_recall_dataset
CanaryBench-PII
Frequency-aware canary injection benchmark for auditing memorization
in finetuned language models, built on the AI4Privacy PII reconstruction
task.
Dataset Description
This dataset is part of CanaryBench, a benchmark for evaluating
memorization in finetuned language models across repetition tiers
and privacy regimes.
Frequency tiers: 1×, 10×, 50×
PII types: EMAIL, PHONE
Member canaries: 770
Reference canaries: 1000
Tasks: PII detection, secret… See the full description on the dataset page: https://huggingface.co/datasets/anony-mouse123/Instruction_recall_dataset.