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.
natural-instructionsPreprocessed version of Super-Natural-Instructions from https://github.com/allenai/natural-instructions/tree/master/splits. The same inputs may appear with different outputs, thus to avoid duplicate inputs, you can deduplicate by the id or the inputs field.
Train Tasks:
['task001_quoref_question_generation', 'task002_quoref_answer_generation', 'task022_cosmosqa_passage_inappropriate_binary', 'task023_cosmosqa_question_generation', 'task024_cosmosqa_answer_generation'… See the full description on the dataset page: https://huggingface.co/datasets/Muennighoff/natural-instructions.code_instructions_122k_alpaca_stylehelpful-instructions
Dataset Card for Helpful Instructions
Dataset Summary
Helpful Instructions is a dataset of (instruction, demonstration) pairs that are derived from 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. You can load the dataset as follows:
from datasets import load_dataset
# Load all subsets
helpful_instructions =… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/helpful-instructions.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.instructions
Merged Instructions Dataset
Merged Dataset for the response of instructions.
natural-instructions-tokenized
Dataset Card for "natural-instructions-tokenized"
Here is the script used to tokenize the dataset:
import multiprocessing
from typing import Union
from datasets import DatasetDict, load_dataset
from transformers import LlamaTokenizer
# Find your available cores
num_cores = multiprocessing.cpu_count()
cutoff_len = 2048
tokenizer = LlamaTokenizer.from_pretrained("chainyo/alpaca-lora-7b")
tokenizer.padding_side = "left"
tokenizer.pad_token_id = (0)
prompt_template = {… See the full description on the dataset page: https://huggingface.co/datasets/chainyo/natural-instructions-tokenized.gandalf_ignore_instructions
gandalf_ignore_instructions
This is a dataset of prompt injections from Gandalf by Lakera.
Note that we might update the dataset occasionally by cleaning the data or adding more samples.
How the data was obtained
There are millions of prompts and many of them are not actual prompt injections (people ask Gandalf all kinds of things).
We used the following process to obtain relevant data:
Start with all prompts submitted to Gandalf in July 2023.
Use OpenAI text… See the full description on the dataset page: https://huggingface.co/datasets/Lakera/gandalf_ignore_instructions.instruction-speech-encodec-v1
Dataset Card for "Instruction Speech"
The largest open-source English speech instruction to text answer dataset
Dataset Overview
This dataset contains nearly 450,000 English speech instruction to text answer samples, using:
A subset of OpenHermes 2.5 with user's prompt length less than 64.
Audio generation using WhisperSpeech.
Tokenized using Encodec.
Usage
from datasets import load_dataset, Audio
# Load Instruction Speech dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Menlo/instruction-speech-encodec-v1.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.
food-visual-instructions
Adapting Multimodal Large Language Models to Domains via Post-Training (EMNLP 2025)
This repos contains the food visual instructions for post-training MLLMs in our paper: On Domain-Specific Post-Training for Multimodal Large Language Models.
The main project page is: Adapt-MLLM-to-Domains
Data Information
Using our visual instruction synthesizer, we generate visual instruction tasks based on the image-caption pairs from extended Recipe1M+ dataset. These synthetic… See the full description on the dataset page: https://huggingface.co/datasets/AdaptLLM/food-visual-instructions.core17-instructions-mteb
core17-instructions-mteb
This is a new version of the core17-instructions dataset modified to fit the new MTEB format.
Restructured queries to include both original and changed versions
Separated instructions into a dedicated configuration
Reorganized qrels into default (original) and qrel_diff configurations
Dataset Structure
The dataset contains the following configurations:
corpus: Original corpus documents
queries: Queries with both original and changed versions… See the full description on the dataset page: https://huggingface.co/datasets/jhu-clsp/core17-instructions-mteb.natural-instructionsPreprocessed version of Super-Natural-Instructions from https://github.com/allenai/natural-instructions/tree/master/splits. The same inputs may appear with different outputs, thus to avoid duplicate inputs, you can deduplicate by the id or the inputs field.
This is modified from https://huggingface.co/datasets/Muennighoff/natural-instructions
with a few improvements:
Adds positive/negative examples, outputs, explanations for each task, to
support different task definitions.
Adds an "eval"… See the full description on the dataset page: https://huggingface.co/datasets/jayelm/natural-instructions.instructions-pair-miningsmall-natural-instructionsMol-Instructions Mol-Instructions datasets.turkish_instructionsSpeech-Instructionspython_code_instructions_filtered
Dataset Card for "code_filtered"
This includes data from xlcost, evol instruct, code alpaca, code instructions, and code search net. Data is filtered based on quality and learning value.
Zeroshot-Audio-Classification-Instructions
Zeroshot-Audio-Classification-Instructions
Convert audio classification dataset into zero-shot format speech instructions, support both single label and multi-label,
VGGSound
FSD50k
Nonspeech7k
urbansound8K
VocalSound
Emotion
Gender
ESD Emotion
Age
Language
TAU Urban Acoustic Scenes 2022
CochlScene
BirdCLEF_2021
EmoBox
AudioSet
We also converted huge WAV files into MP3 16k sample rate to reduce storage size.To prevent leakage, please do not include test set in training session.… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Zeroshot-Audio-Classification-Instructions.core17-instructionsllava-1.5-665k-instructionsThis dataset repository, LLaVA-1.5-665K-Instructions, is notably utilized in the paper Zero-Shot Vision Encoder Grafting via LLM Surrogates.
The official code repository for the paper can be found here: https://github.com/kaiyuyue/zero
LLaVA-1.5-665K-Instructions
This dataset repo contains the entire LLaVA-1.5-665K-Instructions in one place, including images and text sequences.
The images are in train_split/*.tars and the text sequences are in jsons:
llava_v1_5_mix665k.json is the… See the full description on the dataset page: https://huggingface.co/datasets/kaiyuyue/llava-1.5-665k-instructions.POLLUX-instructionsmsmarco-w-instructions
Augmented MS MARCO dataset with Instructions
Dataset Summary
This dataset was used to train the Promptriever family of models. It contains the original MS MARCO training data along with instructions to go with each query. It also includes instruction-negatives, up to three per query. The dataset is designed to enable retrieval models that can be controlled via natural language prompts, similar to language models.
Languages
The dataset is primarily in English.… See the full description on the dataset page: https://huggingface.co/datasets/samaya-ai/msmarco-w-instructions.mosaic-instructions
Mosaic format for instructions dataset to train Malaysian LLM
This repository is to store dataset shards using mosaic format.
prepared at https://github.com/malaysia-ai/dedup-text-dataset/blob/main/pretrain-llm/combine-instructions.ipynb
using tokenizer https://huggingface.co/malaysia-ai/bpe-tokenizer
4096 context length.
how-to
git clone,
git lfs clone https://huggingface.co/datasets/malaysia-ai/mosaic-instructions
load it,
from streaming import LocalDataset… See the full description on the dataset page: https://huggingface.co/datasets/malaysia-ai/mosaic-instructions.Malaysian-Speech-Instructions
Malaysian Speech Instructions
We filter Malaysian short prompts that suitable to convert to voice prompt and generate synthetic voice using mesolitica/Malaysian-F5-TTS-v2 also verified with Force Alignment to make sure the pronunciations almost correct.
We follow chat template from Qwen/Qwen2-Audio-7B-Instruct.
It contains dataset from the following sets:
Malaysian-Instructions
Critics Malaysian
Malaysian General QA
Malaysian Ultrachat
Malaysian Alpaca
Multi-turn Stupid questions… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Malaysian-Speech-Instructions.robust04-instructionsuner_llm_instructions
Dataset Card for Universal NER v1 in the Aya format
This dataset is a format conversion from its original v1 format into the Aya instruction format and it's released here under the same CC-BY-SA 4.0 license and conditions.
It contains data in multiple languages and this version is intended for multi-lingual LLM construction/tuning.
The dataset contains different subsets and their dev/test/train splits, depending on language.
Citation
If you utilize this dataset version… See the full description on the dataset page: https://huggingface.co/datasets/universalner/uner_llm_instructions.news21-instructions