datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Innovator-VL-Instruct-46M
Innovator-VL-Instruct-46M
Paper | Code
🤗🤗 The data is being uploaded continuously
Introduction
To further enhance the model’s ability to handle a broad range of visual tasks with accurate, grounded, and instruction-aligned responses, we perform full-parameter visual instruction supervised fine-tuning (SFT).This SFT stage serves as a critical bridge between multimodal pretraining and subsequent reinforcement learning, providing both general capability coverage and a… See the full description on the dataset page: https://huggingface.co/datasets/InnovatorLab/Innovator-VL-Instruct-46M.Leopard-Instruct
Leopard-Instruct
Paper | Github | Models-LLaVA | Models-Idefics2
Summaries
Leopard-Instruct is a large instruction-tuning dataset, comprising 925K instances, with 739K specifically designed for text-rich, multiimage scenarios. It's been used to train Leopard-LLaVA [checkpoint] and Leopard-Idefics2 [checkpoint].
Loading dataset
to load the dataset without automatically downloading and process the images (Please run the following codes with datasets==2.18.0)… See the full description on the dataset page: https://huggingface.co/datasets/wyu1/Leopard-Instruct.AoPS-InstructReproduction of AoPS-Instruct training set using code here: https://github.com/DSL-Lab/aops
LLaVA-OneVision-1.5-Instruct-Data
LLaVA-OneVision-1.5 Instruction Data
Paper | Code
📌 Introduction
This dataset, LLaVA-OneVision-1.5-Instruct, was collected and integrated during the development of LLaVA-OneVision-1.5. LLaVA-OneVision-1.5 is a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial costs. This meticulously curated 22M instruction dataset (LLaVA-OneVision-1.5-Instruct) is part of a… See the full description on the dataset page: https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Instruct-Data.general-instruction-augmented-corpora
Instruction Pre-Training: Language Models are Supervised Multitask Learners (EMNLP 2024)
This repo contains the general instruction-augmented corpora (containing 200M instruction-response pairs covering 40+ task categories) used in our paper Instruction Pre-Training: Language Models are Supervised Multitask Learners.
We explore supervised multitask pre-training by proposing Instruction Pre-Training, a framework that scalably augments massive raw corpora with instruction-response… See the full description on the dataset page: https://huggingface.co/datasets/instruction-pretrain/general-instruction-augmented-corpora.Magicoder-OSS-Instruct-75KThis is the OSS-Instruct dataset generated by gpt-3.5-turbo-1106 developed by OpenAI. Please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies.
self-oss-instruct-sc2-exec-filter-50kFinal self-alignment training dataset for StarCoder2-Instruct.
seed: Contains the seed Python function
concepts: Contains the concepts generated from the seed
instruction: Contains the instruction generated from the concepts
response: Contains the execution-validated response to the instruction
This dataset utilizes seed Python functions derived from the MultiPL-T pipeline.
Magicoder-Evol-Instruct-110KA decontaminated version of evol-codealpaca-v1. Decontamination is done in the same way as StarCoder (bigcode decontamination process).
CodeFeedback-Filtered-Instruction OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
|
[🛠️Code]
OpenCodeInterpreter
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction.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.
molecule_property_instruction
Dataset Card for "molecule_property_instruction"
More Information needed
testing_self_instruct_small
Dataset Card for "testing_self_instruct_small"
More Information needed
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.MMEB_Test_Instructinstruction_following
Dataset Card for "livebench/instruction_following"
LiveBench is a benchmark for LLMs designed with test set contamination and objective evaluation in mind. It has the following properties:
LiveBench is designed to limit potential contamination by releasing new questions monthly, as well as having questions based on recently-released datasets, arXiv papers, news articles, and IMDb movie synopses.
Each question has verifiable, objective ground-truth answers, allowing hard questions… See the full description on the dataset page: https://huggingface.co/datasets/livebench/instruction_following.InstructCoder
Paper |
Code |
Blog
InstructCoder (CodeInstruct): Empowering Language Models to Edit Code
Updates
May 23, 2023: Paper, code and data released.
Overview
InstructCoder is the first dataset designed to adapt LLMs for general code editing. It consists of 114,239 instruction-input-output triplets and covers multiple distinct code editing scenarios, generated by ChatGPT. LLaMA-33B finetuned on InstructCoder performs on par with ChatGPT on a… See the full description on the dataset page: https://huggingface.co/datasets/likaixin/InstructCoder.Evol-Instruct-Code-80k-v1Open Source Implementation of Evol-Instruct-Code as described in the WizardCoder Paper.
Code for the intruction generation can be found on Github as Evol-Teacher.
Mantis-Instruct
Mantis-Instruct
Paper | Website | Github | Models | Demo
Summaries
Mantis-Instruct is a fully text-image interleaved multimodal instruction tuning dataset,
containing 721K examples from 14 subsets and covering multi-image skills including co-reference, reasoning, comparing, temporal understanding.
It's been used to train Mantis Model families
Mantis-Instruct has a total of 721K instances, consisting of 14 subsets to cover all the multi-image skills.
Among the… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/Mantis-Instruct.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.instruction-following-evalTrendyol-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.helpful-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.details_meta-llama__Llama-3.1-8B-Instruct_private
Dataset Card for Evaluation run of meta-llama/Llama-3.1-8B-Instruct
Dataset automatically created during the evaluation run of model meta-llama/Llama-3.1-8B-Instruct.
The dataset is composed of 78 configuration, each one corresponding to one of the evaluated task.
The dataset has been created from 20 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/SaylorTwift/details_meta-llama__Llama-3.1-8B-Instruct_private.instructpix2pix-clip-filtered
Dataset Card for InstructPix2Pix CLIP-filtered
Dataset Summary
The dataset can be used to train models to follow edit instructions. Edit instructions
are available in the edit_prompt. original_image can be used with the edit_prompt and
edited_image denotes the image after applying the edit_prompt on the original_image.
Refer to the GitHub repository to know more about
how this dataset can be used to train a model that can follow instructions.
Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered.InstructS2S-200K
InstructS2S-200K
Dataset Description
InstructS2S-200K is a multi-turn speech-to-speech conversation dataset containing approximately 200,000 dialogues, developed for the LLaMA-Omni and LLaMA-Omni 2 research projects on real-time spoken chatbots.
Usage
The dataset is split into multiple parts and needs to be reconstructed:
# Combine the parts and extract
cat en_part_* > instructs2s_200k.tar.gz
tar -xzf instructs2s_200k.tar.gz
License
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/ICTNLP/InstructS2S-200K.LLaVA-OneVision-1.5-Instruct-Data-qwen-formatMAmmoTH-VL-Instruct-12M
MAmmoTH-VL-Instruct-12M
🏠 Homepage | 🤖 MAmmoTH-VL-8B | 💻 Code | 📄 Arxiv | 📕 PDF | 🖥️ Demo
Introduction
Our simple yet scalable visual instruction data rewriting pipeline consists of three steps: manual data source collection, rewriting using MLLMs/LLMs, and filtering via the same MLLM as a judge. Examples below illustrate transformations in math and science categories, showcasing detailed, step-by-step responses.
The data distribution of… See the full description on the dataset page: https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M.code_instructions_122k_alpaca_styleMalay-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.Dolci-Instruct-SFT
Dolci Instruct SFT Mixture
Note that this collection licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.
The Dolci Instruct SFT mixture was used to train Olmo 3 7B Instruct SFT.
It contains 2,152,112 samples from the following sets:
Sources include a mixture of existing prompts:
OpenThoughts 3 (Apache 2.0): Extended to 32K context length and downsampled code prompts to 16X multiple, to 941,166 total prompts… See the full description on the dataset page: https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT.