datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.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.
Magicoder-Evol-Instruct-110KA decontaminated version of evol-codealpaca-v1. Decontamination is done in the same way as StarCoder (bigcode decontamination process).
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.
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.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.
CodeFeedback-Filtered-Instruction OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
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[🛠️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.AoPS-InstructReproduction of AoPS-Instruct training set using code here: https://github.com/DSL-Lab/aops
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.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.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.
MMEB_Test_InstructEvol-Instruct-Python-26k
Evol-Instruct-Python-26k
Filtered version of the nickrosh/Evol-Instruct-Code-80k-v1 dataset that only keeps Python code (26,588 samples). You can find a smaller version of it here mlabonne/Evol-Instruct-Python-1k.
Here is the distribution of the number of tokens in each row (instruction + output) using Llama's tokenizer:
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.instruction-datasetThis is the blind eval dataset of high-quality, diverse, human-written instructions with demonstrations. We will be using this for step 3 evaluations in our RLHF pipeline.
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.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.vivid-video-instructLLaVA-OneVision-1.5-Instruct-Data-qwen-formatinstruction_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.AquilaMed-Instruct
Data Description
The dataset comprises a variety of question types, including medical exam multiple-choice questions, single-turn disease diagnosis, multi-turn health consultation, etc. It comes from 6 publicly available datasets, namely Chinese Medical Dialogue Data, Huatuo26M, MedDialog, ChatMed Consult Dataset, CMB-exam, and ChatDoctor. These datasets contain not only real doctor-patient dialogues, but also dialogues generated from GPT-3.5. We believe this ensures the… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/AquilaMed-Instruct.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.code_instructions_122k_alpaca_styleramanv-image-vlm-instructionVLA_Instruction_TuningThis repository contains the VLA-IT dataset, a curated 650K-sample Vision-Language-Action Instruction Tuning dataset, and the SimplerEnv-Instruct benchmark. These are presented in the paper InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation. The dataset is designed to enable robots to integrate multimodal reasoning with precise action generation, preserving the flexible reasoning of large vision-language models while delivering leading manipulation… See the full description on the dataset page: https://huggingface.co/datasets/ShuaiYang03/VLA_Instruction_Tuning.WizardLM_evol_instruct_V2_196k
News
🔥 🔥 🔥 [08/11/2023] We release WizardMath Models.
🔥 Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
🔥 Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM.
🔥 Our WizardMath-70B-V1.0 model achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.… See the full description on the dataset page: https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_V2_196k.