datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DroidCall
DroidCall: A Dataset for LLM-powered Android Intent Invocation
paper|github
DroidCall is the first open-sourced, high-quality dataset designed for fine-tuning LLMs for accurate intent invocation on Android devices.
This repo contains data generated by DroidCall. The process of data generation is shown in the figure below
Details can be found in our paper and github repository.
What is Android Intent Invocation?
Android Intent is a key machanism in Android that allows… See the full description on the dataset page: https://huggingface.co/datasets/mllmTeam/DroidCall.Synthetic-AI-ML-Dataset
Synthetic-AI-ML-Dataset
Synthetic Q&A dataset on AI and Machine Learning
Dataset Details
Metric
Value
Topic
AI and Machine Learning
Total Q&A Pairs
14021
Valid Pairs
14021
Provider/Model
ollama/gpt-oss:120b
Generation Cost
Metric
Value
Prompt Tokens
14,941,957
Completion Tokens
17,159,263
Total Tokens
32,101,220
GPU Energy
12.9628 kWh
Sources
This dataset was generated from 474 scholarly papers:
#… See the full description on the dataset page: https://huggingface.co/datasets/Kylan12/Synthetic-AI-ML-Dataset.ReasonMed
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
📄 Paper |
💻 Code |
📊 Dataset
ReasonMed is the largest open-source medical reasoning dataset to date, containing 370 K high-quality question–answer examples with multi-step chain-of-thought (CoT) rationales and concise summaries. We distilled these from 1.75 M initial reasoning paths generated by three competitive large-language models (Qwen-2.5-72B, DeepSeek-R1-Distill-Llama-70B, and… See the full description on the dataset page: https://huggingface.co/datasets/lingshu-medical-mllm/ReasonMed.OpenTuringBench
📊 OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution
Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. We release OpenTuringBench,
a new benchmark based on OLLMs, designed to train and evaluate machine-generated text detectors on the Turing Test and Authorship Attribution problems. OpenTuringBench focuses
on a representative… See the full description on the dataset page: https://huggingface.co/datasets/MLNTeam-Unical/OpenTuringBench.MLR_output
MLR Output
This dataset contains MLR_model generated hierarchical reasoning traces.
It accompanies the Multi-Level Reasoning (MLR) framework introduced in Enhancing Language Model Reasoning with Structured Multi-Level Modeling (ICLR 2026).
Configurations
Config
Source benchmark
Records
Tested models
Accuracy
math500_qwen_1.5b
MATH500
500
MLR_executor_Qwen-1.5B, MLR_planner_Qwen-1.5B-LoRA
85.6%
aime24_qwen_1.5b
AIME24
30
MLR_executor_Qwen-1.5B… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/MLR_output.MLR_concat10
MLR-Concat10 Benchmark
MLR-Concat10 is a compact long-horizon reasoning benchmark for evaluating whether language models can sustain reasoning across a sequence of independent problems. Each example concatenates 10 randomly sampled questions into one prompt and asks the model to solve them sequentially.
The benchmark follows the long-horizon reasoning evaluation protocol in Enhancing Language Model Reasoning with Structured Multi-Level Modeling (ICLR 2026).… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/MLR_concat10.MLR_sft_data
MLR SFT Data
MLR SFT Data is a teacher-generated supervised fine-tuning dataset for training Multi-Level Reasoning (MLR) models in the paper Enhancing Language Model Reasoning with Structured Multi-Level Modeling (ICLR 2026). It decomposes complete reasoning trajectories into two types of step-level examples:
Planner: plans the next reasoning goal and task based on the problem and reasoning history.
Executor: executes the Planner's instruction and updates the reasoning state.… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/MLR_sft_data.MLR_concat10_output
MLR-Concat10 Output
This dataset contains MLR_model generated hierarchical reasoning traces for the MLR-Concat10 long-horizon reasoning benchmark. Each record contains one prompt with 10 independent questions, the structured reasoning trace produced while solving them in sequence, predicted answers, gold answers, and per-question correctness labels.
It accompanies the long-horizon reasoning setting in Enhancing Language Model Reasoning with Structured Multi-Level Modeling (ICLR… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/MLR_concat10_output.ClosureBench
ClosureBench
ClosureBench is a controlled benchmark for evaluating if LLMs
respect explicit semantic contracts about missing information. It tests whether
models distinguish absence-as-unknown, absence-as-false, and
absence-as-false-only-in-complete-scopes under explicit open-world,
closed-world, and locally closed-world contracts.
The dataset includes the base benchmark and three extensions:
Config
full rows
Description
base
960
Main OWA/CWA/LCWA benchmark with… See the full description on the dataset page: https://huggingface.co/datasets/ML0037/ClosureBench.mleb-legal-rag-bench
Legal RAG Bench (MLEB version)
This is the version of the Legal RAG Bench evaluation dataset used in the Massive Legal Embedding Benchmark (MLEB) by Isaacus.
This dataset tests the ability of information retrieval models to retrieve relevant passages to complex, meaningfully challenging, reasoning-intensive questions about Victorian criminal law.
If you are looking for Legal RAG Bench proper, you may find it here.
Structure 🗂️
As per the MTEB information retrieval… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/mleb-legal-rag-bench.ml-systems-interview-bench
ML Systems Interview Bench
ML Systems Interview Bench is a structured, benchmark-style dataset for evaluating technical interview answers across practical ML engineering, MLOps, model serving, ML system design, data pipelines, LLM/RAG, observability, Python engineering, and production debugging.
Each record combines an interview-style question with expected concepts, a concise reference answer, qualitative evaluation anchors, question-specific skills, and follow-up questions.… See the full description on the dataset page: https://huggingface.co/datasets/Max00035/ml-systems-interview-bench.mlops-deployment-sft-100k
MLOps Deployment SFT 100K
A synthetic supervised fine-tuning dataset of 100,000 high-quality conversations covering MLOps and ML model deployment — from model serving and inference optimization to monitoring, CI/CD, and production scaling. Designed to train AI assistants that can help ML engineers deploy and operate models at scale.
Dataset Description
This dataset covers the full MLOps lifecycle across 13 specialized categories. Each record follows the ShareGPT… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/mlops-deployment-sft-100k.ml-ai-engineer-sft
DuoNeural ML/AI Engineer SFT Dataset
A synthetic instruction-tuning dataset for training an LLM to be a useful pairing partner on ML/AI engineering work — debugging training runs, reasoning about architecture and infra choices, reviewing experiment design, and explaining core ML concepts with the specificity of someone who's actually run the experiments.
Why this dataset exists
Most general instruction-tuning data treats ML engineering questions the same as any… See the full description on the dataset page: https://huggingface.co/datasets/DuoNeural/ml-ai-engineer-sft.ai-ml-instruction-dataset
AI/ML Engineering Instruction Dataset
Comprehensive instruction dataset covering machine learning concepts, PyTorch implementations, NLP with transformers, model evaluation, and feature engineering.
Dataset Details
Dataset Description
This is a high-quality instruction-tuning dataset focused on Ai Ml topics. Each entry includes:
A clear instruction/question
Optional input context
A detailed response/solution
Chain-of-thought reasoning process
Curated by:… See the full description on the dataset page: https://huggingface.co/datasets/bernabepuente/ai-ml-instruction-dataset.mleb-consumer-contracts-qa
Consumer Contracts QA (MLEB version)
This is the version of the Consumer Contracts QA evaluation dataset used in the Massive Legal Embeddings Benchmark (MLEB) by Isaacus.
This dataset tests the ability of information retrieval models to retrieve relevant contractual clauses to questions about contracts.
Structure 🗂️
As per the MTEB information retrieval dataset format, this dataset comprises three splits, default, corpus, and queries.
The default split pairs… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/mleb-consumer-contracts-qa.russian-retrievalBased on Sberquad
Answer converted to human affordable answer.
Context augmented with some pices of texts from wiki accordant to text on tematic and keywords.
This dataset cold be used for training retrieval LLM models or modificators for ability of LLM to retrieve target information from collection of tematic related texts.
Dataset has version with SOURCE data for generating answer with specifing source document for right answer. See file retrieval_dataset_src.jsonl
Dataset consists of… See the full description on the dataset page: https://huggingface.co/datasets/MLNavigator/russian-retrieval.MLX-Benchmark-V2
MLX Benchmark Dataset
Dataset Summary
The MLX Benchmark Dataset is a curated evaluation benchmark consisting of 520 questions designed to measure large language model (LLM) proficiency in Apple's MLX machine learning framework. MLX is an array framework for machine learning on Apple Silicon that leverages unified memory architecture, and this dataset is the first comprehensive benchmark specifically targeting MLX knowledge and coding ability.
The dataset covers the… See the full description on the dataset page: https://huggingface.co/datasets/Goekdeniz-Guelmez/MLX-Benchmark-V2.ml-interview-sft-dataset
ML/AI Interview Coach — SFT Dataset
A curated dataset of 566 high-quality Q&A pairs covering ML, Deep Learning, NLP, LLMs, RAG, Vector Databases, LangChain, Agentic AI, MLOps, and more — designed for fine-tuning an ML Interview Coach model.
Dataset Summary
Stat
Value
Total Q&A pairs
566
Unique topics
75
Format
ChatML (system + user + assistant)
Language
English
Avg answer length
~800 tokens
Sources
15+ interview prep documents + hand-crafted… See the full description on the dataset page: https://huggingface.co/datasets/raghu298/ml-interview-sft-dataset.ml-arxiv-papers-qaThis ML Q&A dataset contains 43,713 samples, where each includes three fields - question, context(title + abstract) and answer.
It is created based on the original dataset aalksii/ml-arxiv-papers, which contains the titles and abstracts of ML ArXiv papers.
To create question-answer pairs, the gpt-3.5-turbo API is called with the following prompt:messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": f"Title: "{title}". Abstract:… See the full description on the dataset page: https://huggingface.co/datasets/hanyueshf/ml-arxiv-papers-qa.mlqa_en_zh_twMLQA (MultiLingual Question Answering) 中英雙語問答資料集，為原始 MLQA 資料集轉換為台灣正體中文的版本，並將中文與英語版本的相同項目合併，方便供雙語語言模型使用。（致謝：BYVoid/OpenCC、vinta/pangu.js）
分為 dev 以及 test 兩個 split，各有 302 及 2986 組資料。
範本：
[
{
"title": {
"en": "Curling at the 2014 Winter Olympics",
"zh_tw": "2014 年冬季奧林匹克運動會冰壺比賽"
},
"paragraphs": [
{
"context": {
"en": "Qualification to the curling tournaments at the Winter Olympics was determined through two methods. Nations could qualify teams by… See the full description on the dataset page: https://huggingface.co/datasets/zetavg/mlqa_en_zh_tw.medfit-dataset
MEDFIT Medical QA Dataset
This dataset contains 6,444 unique healthcare-related question-answer pairs designed for fine-tuning language models for medical chatbot applications. The dataset was specifically curated for the MEDFIT-LLM research project focusing on domain-focused fine-tuning of small language models for healthcare applications.
All credits for the methodology and dataset creation go to Aditya Karnam Gururaj Rao, Arjun Jaggi, and Sonam Naidu.
The dataset has been… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/medfit-dataset.mlabonne_orca-agentinstruct-1M-v1-cleaned-DolphinLabeled
orca-agentinstruct-1M-v1-cleaned DolphinLabeled
Part of the DolphinLabeled series of datasets
Presented by Eric Hartford and Cognitive Computations
The purpose of this dataset is to enable filtering of orca-agentinstruct-1M-v1-cleaned dataset.
The original dataset is mlabonne/orca-agentinstruct-1M-v1-cleaned
(thank you to microsoft and mlabonne)
I have modified the dataset using two scripts.
dedupe.py - removes rows with identical final response.
label.py -… See the full description on the dataset page: https://huggingface.co/datasets/QuixiAI/mlabonne_orca-agentinstruct-1M-v1-cleaned-DolphinLabeled.tatemae-af
Tatemae: Detecting Alignment Faking via Tool Selection in LLMs
Tatemae is a dataset of 108 enterprise IT scenarios for detecting alignment faking in tool-selecting LLMs. Each scenario forces a binary choice between a safe tool and an unsafe alternative under adversarial pressure, with ground-truth annotations that allow exact verification of model behaviour.
The dataset accompanies the paper Detecting Alignment Faking via Tool Selection in LLMs: A Controlled Evaluation Framework.… See the full description on the dataset page: https://huggingface.co/datasets/ML0037/tatemae-af.c.ai.q
세관 행정 데이터셋 (C.AI.Q)
대한민국 세관·관세 행정 질의응답 사례를 정리한 데이터셋입니다.Hugging Face Dataset Viewer를 통해 질문(input) 과 답변(response) 구조로 확인할 수 있습니다.
Input: 세관/관세 관련 질문
Response: 공식 답변/설명
Split: train
corpus-python-ds-ml-fr
Corpus Q&A Python / Data Science / ML (français)
Corpus écrit à la main de 126 paires question/réponse en français sur
Python, la data science et le machine learning, conçu pour le fine-tuning
d'instruction d'un LLM.
Auteur : Milan Ganivet
Langue : français
Licence : CC BY 4.0
Source : https://github.com/mrSvet0zar/llm-finetuning-qlora
Composition
Catégorie
Concepts
deep-learning-llm
24
ml-fundamentals
20
python-core
20
numpy-pandas
18
mlops… See the full description on the dataset page: https://huggingface.co/datasets/mrSvet0zar/corpus-python-ds-ml-fr.Reasoning-Heavy-Math-ML-Explanations
Reasoning-Heavy Math & ML Explanations
Dataset: NNEngine/Reasoning-Heavy-Math-ML-Explanations
Version: wikipedia_reasoning_final_v1.0
License: CC-BY-SA 4.0
Author: Shivam Sharma (Independent Researcher)
Dataset Summary
Reasoning-Heavy Math & ML Explanations is a high-quality, reasoning-oriented dataset derived exclusively from English Wikipedia.
The dataset focuses on explicit human-authored reasoning and explanations in mathematics and machine learning–related domains… See the full description on the dataset page: https://huggingface.co/datasets/NNEngine/Reasoning-Heavy-Math-ML-Explanations.MLDSUM_NEWwind-edge-1.6-sft
Wind Lite SFT
Custom supervised fine-tuning dataset for Wind Lite 1.6 by North AI.
Dataset Summary
20,000 high-quality instruction-response pairs covering identity grounding, math reasoning, coding, general knowledge, and multi-turn conversations.
Data Composition
Category
Count
Description
Math & Reasoning
~7,000
Arithmetic, algebra, percentages, unit conversions — with step-by-step working
Coding
~4,000
Python, JavaScript, SQL, systems — with… See the full description on the dataset page: https://huggingface.co/datasets/North-ML1/wind-edge-1.6-sft.investment_analysis
코스피 상장 기업 공시정보 기반 투자 리포트 데이터셋
이 데이터셋은 국내 코스피 상장 기업의 공시정보를 바탕으로, 투자 전문가들이 활용할 수 있는 심층적 분석과 투자 전략 제안을 목표로 제작되었습니다. 특히, 이 데이터셋은 GPT 파인튜닝에 최적화된 구조로 설계되어 있어, 다양한 역할(role)을 포함한 메시지 기반의 대화 형식으로 구성되어 있습니다.
데이터셋 구조
데이터셋은 JSONL 포맷으로 제공되며, 각 항목은 GPT 파인튜닝에 최적화된 메시지 형식을 따릅니다. 주요 구성은 다음과 같습니다:
messages: 메시지 배열 형태로 구성되어 있으며, 각 메시지는 아래와 같은 역할을 가집니다.
system: 모델의 역할과 행동 지침을 정의합니다.예시: "당신은 기업 재무 및 투자 분석 전문가입니다. 참고 컨텍스트를 기반으로 사용자 질문에 대해 정확하고 논리적으로 답변하세요."
user: 사용자의 질문과 컨텍스트(예시 데이터, 재무제표… See the full description on the dataset page: https://huggingface.co/datasets/MLOpsEngineer/investment_analysis.