datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
python_code_instructions_18k_alpaca
Dataset Card for python_code_instructions_18k_alpaca
The dataset contains problem descriptions and code in python language.
This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. Refer to the source here.
tulu-3-sft-personas-instruction-following
Dataset Descriptions
This dataset contains 29980 examples and is synthetically created to enhance model's capabilities to follow instructions precisely and to satisfy user constraints. The constraints are borrowed from the taxonomy in IFEval dataset.
To generate diverse instructions, we expand the methodology in Ge et al., 2024 by using personas. More details and exact prompts used to construct the dataset can be found in our paper.
Curated by: Allen Institute for AI
Paper: TBD… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following.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.Instruction-Following-IFEval
SEA-IFEval
SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. It is based on IFEval and was manually translated by native speakers for Indonesian, Javanese, Sundanese, Thai, Tagalog, and Vietnamese.
Supported Tasks and Leaderboards
SEA-IFEval is designed for evaluating chat or instruction-tuned large language models (LLMs).… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/Instruction-Following-IFEval.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.instructions
Merged Instructions Dataset
Merged Dataset for the response of instructions.
code_instructions_120k_alpaca
Dataset Card for code_instructions_120k_alpaca
This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. Refer to the original source here.
Nemotron-RL-Instruction-Following-Structured-Outputs-v2
Dataset Description:
Split 1: Direct Generation tests the model’s ability to perform freeform text structured outputs on JSON, YAML, and XML data, varying the complexity and presentation of the schema.
Split 2: Diversified Tasks adds 2 additional output formats: TOML and CSV, while increasing problem types to Direct Extraction from document, Translation between formats, Multistep Translation from known data, Multistep Extraction from unrelated context, Schema-Only Generation for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Structured-Outputs-v2.instructional_code-search-net-python
Dataset Card for "instructional_code-search-net-python"
Dataset Summary
This is an instructional dataset for Python.
The dataset contains two different kind of tasks:
Given a piece of code generate a description of what it does.
Given a description generate a piece of code that fulfils the description.
Languages
The dataset is in English.
Data Splits
There are no splits.
Dataset Creation
May of 2023
Curation Rationale
This… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/instructional_code-search-net-python.chatbot_instruction_prompts
Dataset Card for Chatbot Instruction Prompts Datasets
Dataset Summary
This dataset has been generated from the following ones:
tatsu-lab/alpaca
Dahoas/instruct-human-assistant-prompt
allenai/prosocial-dialog
The datasets has been cleaned up of spurious entries and artifacts. It contains ~500k of prompt and expected resposne. This DB is intended to train an instruct-type model
instruction_translationsTranslation of Instruction datasetindustrial-instruction-dataset
Industrial-Instruction Dataset
Industrial-Instruction provides benchmark and training-ready QA instances derived from industrial technical reports, designed to evaluate robustness under realistic retrieval conditions. Samples are grounded in retrieved evidence and include irrelevant retrieval, single-/multi-document support, and single-/multi-document answer settings.
Paper
Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and… See the full description on the dataset page: https://huggingface.co/datasets/Parssky/industrial-instruction-dataset.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.chempile-instruction
ChemPile-Instruction
A comprehensive instruction tuning dataset for chemistry LLMs with multi-turn conversations and diverse reasoning tasks
📋 Dataset Summary
ChemPile-Instruction is a text-only dataset designed for instruction tuning of Large Language Models (LLMs) in the field of chemistry. It contains high-quality multi-turn conversations, each rephrased from different educational, scientific, and reasoning sources using diverse prompting strategies. The… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/chempile-instruction.star-dataset-instructions
STAR Instructions
STAR Instructions is a large-scale Arabic instruction-tuning dataset built by rendering the 355 STAR Jinja2 prompt templates against their 87 source datasets, covering 27 raw task labels (20 tasks after merging closely related categories, as reported in the paper). The underlying templates were authored collaboratively using PromptLab. This dataset and the experiments built on it are described in STAR: instruction tuning for Arabic across tasks, datasets, and… See the full description on the dataset page: https://huggingface.co/datasets/KFUPM-JRCAI/star-dataset-instructions.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.helpful_instructionsHelpful Instructions is a dataset of (prompt, completion) pairs that are derived from a variety of public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform.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.PubMedQA_instruction
Dataset Card for "PubMedQA_instruction"
This repo contains a PubMedQA dataset converted for instruction tuning.
Citation Information
@inproceedings{jin2019pubmedqa,
title={PubMedQA: A Dataset for Biomedical Research Question Answering},
author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint… See the full description on the dataset page: https://huggingface.co/datasets/fedml/PubMedQA_instruction.llm-jp-instructions
概要
llm-jp-instructionsは人手により作成されたインストラクションデータセットです。
Overview
llm-jp-instructions is a manually created instruction dataset.
Usage
from datasets import load_dataset
# load train, dev and test splits of v1.0
v1_train = load_dataset("llm-jp/llm-jp-instructions", data_dir="v1.0", split="train")
v1_dev = load_dataset("llm-jp/llm-jp-instructions", data_dir="v1.0", split="dev")
v1_test = load_dataset("llm-jp/llm-jp-instructions", data_dir="v1.0", split="test")
bangla-instruction-dataset
🧠 Bangla Instruction Dataset
This dataset repository consolidates high-quality instruction-tuning data from multiple popular sources, structured for easy use in training and evaluating instruction-following models.
📚 Dataset Splits
The dataset is organized into the following splits:
Split Name
Source Dataset
Description
OdiaGenAI
OdiaGenAI/all_combined_bengali_252k
A large-scale collection of diverse Bangla instructions and responses.
chrononeel… See the full description on the dataset page: https://huggingface.co/datasets/kamruzzaman-asif/bangla-instruction-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.