datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
medical-o1-reasoning-SFT
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.lingshu_training_data_medical_domain
Website
🤖 7B Model
🤖 8B Model based on InternVL3
🤖 32B Model
MedEvalKit
Technical Report
Lingshu MCP
Lingshu Medical MLLM Training Data (Medical Domain)
This dataset contains the medical-domain training data used in the multi-stage training of the Lingshu Medical Multimodal Large Language Model (MLLM). General-domain data has been removed; only medical data is included.
The training… See the full description on the dataset page: https://huggingface.co/datasets/cocool/lingshu_training_data_medical_domain.mighty-media-corpus
Mighty Media Corpus
Independent technical analyses and research breakdowns published across the
Mighty Media network (mightytravels.com, judgmentcallpodcast.com, and
vertical sites). One JSON file per article: {url, title, site, date, author,
text, citations, license, note}.
License: CC-BY-4.0
Canonical versions live at the url of each row.
Articles are independent analyses, not peer-reviewed publications.
Files are organized by month: data/YYYY-MM/*.json
AlpaCare-MedInstruct-52k
Dataset Card for "AlpaCare-MedInstruct-52k"
AlpaCare GitHub repo: https://github.com/XZhang97666/AlpaCare
Citation:
If you use this dataset, please cite the original paper:
@misc{zhang2023alpacareinstructiontuned,
title={AlpaCare: Instruction-tuned Large Language Models for Medical Application},
author={Xinlu Zhang and Chenxin Tian and Xianjun Yang and Lichang Chen and Zekun Li and Linda Ruth Petzold},
year={2023},
eprint={2310.14558}… See the full description on the dataset page: https://huggingface.co/datasets/lavita/AlpaCare-MedInstruct-52k.lingshu_training_data_medical_domain
Website
🤖 7B Model
🤖 8B Model based on InternVL3
🤖 32B Model
MedEvalKit
Technical Report
Lingshu MCP
Lingshu Medical MLLM Training Data (Medical Domain)
This dataset contains the medical-domain training data used in the multi-stage training of the Lingshu Medical Multimodal Large Language Model (MLLM). General-domain data has been removed; only medical data is included.
The training… See the full description on the dataset page: https://huggingface.co/datasets/lingshu-medical-mllm/lingshu_training_data_medical_domain.medical纯文本数据，中文医疗数据集，包含预训练数据的百科数据，指令微调数据和奖励模型数据。Medical-Reasoning-SFT-Mega
Medical-Reasoning-SFT-Mega
The ultimate medical reasoning dataset - combining 7 state-of-the-art AI models with fair distribution deduplication. 1.79 million unique samples with 3.78 billion tokens of medical chain-of-thought reasoning.
Dataset Overview
Metric
Value
Total Samples
1,789,998 (after deduplication)
Total Tokens
~3.78 Billion
Content Tokens
~2.22 Billion
Reasoning Tokens
~1.56 Billion
Samples with Reasoning
1,789,764 (100.0%)
Unique… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Mega.alive-medical-imaging
ALIVE Medical Imaging QA Dataset
Lecture-derived question-answer corpus, retrieval index, and source
materials for the ALIVE (Avatar-Lecture Interactive Video Engine)
system. The dataset was built from 23 recorded lectures of an
undergraduate medical imaging course and is the corpus used to
fine-tune the ALIVE language model and to evaluate its retrieval and
answer-generation behavior.
Layout
huggingface/
├── data/ question-answer pairs (Alpaca-style… See the full description on the dataset page: https://huggingface.co/datasets/zabir1996/alive-medical-imaging.Medical-R1-Distill-Data
Introduction
This dataset is an SFT dataset distilled from Deepseek-R1 (Full Power Version), based on medical verifiable problems from HuatuoGPT-o1.
The Chinese version of the dataset is available at FreedomIntelligence/Medical-R1-Distill-Data-Chinese.
The distillation originates from the native Deepseek-R1 API requests. We hope this distilled dataset can help initialize your models with the reasoning chain from R1. You can also use our previously built medical verified long… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/Medical-R1-Distill-Data.medical-qa-fr-v0.1
Medical QA (FR) v0.1
A French medical instruction-tuning dataset (~508K examples) compiled from three public medical QA / dialogue sources:
ruslanmv/ai-medical-chatbot — 256,010 examples (config ai_medical_chatbot, default)
lavita/medical-qa-datasets (all-processed config) — 230,041 examples (config medical_qa_datasets)
FreedomIntelligence/Medical-R1-Distill-Data — 21,641 examples (config medical_r1_distill_data)
Each source question was machine-translated into French, then a… See the full description on the dataset page: https://huggingface.co/datasets/bofenghuang/medical-qa-fr-v0.1.mimic-medical-imaging-qa
MIMIC Medical Imaging QA Dataset
5,207 Bloom's-taxonomy-stratified question--answer pairs derived from 23 medical imaging lectures (RPI BMED 2300). The dataset supports the paper "MIMIC: A Course-Derivation Pipeline and Benchmark for Slide-Anchored Tutoring with a Domain-Adapted Large Language Model" and was used to fine-tune MIMIC-LM, a domain-adapted Llama-3.1-8B-Instruct model for grounded medical imaging instruction.
License
The benchmark annotations, dataset… See the full description on the dataset page: https://huggingface.co/datasets/zabir1996/mimic-medical-imaging-qa.medical-o1-verifiable-problem
Introduction
This dataset features open-ended medical problems designed to improve LLMs' medical reasoning. Each entry includes a open-ended question and a ground-truth answer based on challenging medical exams. The verifiable answers enable checking LLM outputs, refining their reasoning processes.
For details, see our paper and GitHub repository.
Citation
If you find our data useful, please consider citing our work!
@misc{chen2024huatuogpto1medicalcomplexreasoning… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-verifiable-problem.Medical-Reasoning-SFT-GPT-OSS-120B
Medical-Reasoning-SFT-GPT-OSS-120B
A high-quality synthetic dataset of medical reasoning conversations generated using OpenAI's gpt-oss-120B model with reasoning effort set to high, designed for supervised fine-tuning of large language models in healthcare applications. I used Intelligent-Internet/II-Medical-Reasoning-SFT as a seed dataset, so I would like to thank the authors and Intelligent-Internet for their great work.
Dataset Statistics
Total Samples: 200,927… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B.medical_advice_dialogue_en
Description
The dataset is from medalpaca/medical_meadow_health_advice, formatted as dialogues for speed and ease of use. Many thanks to author for releasing it.
Importantly, this format is easy to use via the default chat template of transformers, meaning you can use huggingface/alignment-handbook immediately, unsloth.
Structure
View online through viewer.
Note
We advise you to reconsider before use, thank you. If you find it useful, please like and… See the full description on the dataset page: https://huggingface.co/datasets/lamhieu/medical_advice_dialogue_en.Traditional-Chinese-Medicine-Dataset-Pretrain
启古纳今，厚德精术
数据介绍
非网络来源的高质量中医数据集-预训练
High-Quality Traditional Chinese Medicine Dataset from Non-Internet Sources - Pretraining
该数据集经过大量人力和资源的投入精心构建，以共建LLM高质量中文社区为己任。
包含约1GB的中医各个领域临床案例、名家典籍、医学百科，名词解释等优质内容，涵盖全面，配比均衡。
数据集主要由非网络来源的内部数据构成，并99%为简体中文内容，内容质量优异，信息密度可观。
注意：该数据集仅适用于预训练或继续预训练用途，针对SFT/IFT的QA数据集详见：SylvanL/Traditional-Chinese-Medicine-Dataset-SFT… See the full description on the dataset page: https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain.Shifaa_Arabic_Medical_Consultations
Shifaa Arabic Medical Consultations 🏥📊
Overview 🌍
Shifaa is revolutionizing Arabic medical AI by addressing the critical gap in Arabic medical datasets. Our first contribution is the Shifaa Arabic Medical Consultations dataset, a comprehensive collection of 84,422 real-world medical consultations covering 16 Main Specializations and 585 Hierarchical Diagnoses.
🔍 Why is this dataset important?
First large-scale Arabic medical dataset for AI applications.… See the full description on the dataset page: https://huggingface.co/datasets/Ahmed-Selem/Shifaa_Arabic_Medical_Consultations.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.medical-clinical-reasoning-sft-100k
Medical Clinical Reasoning SFT 100K
A synthetic supervised fine-tuning dataset of 100,000 high-quality medical and clinical reasoning conversations designed to train AI assistants capable of supporting clinical decision-making, documentation, and medical education.
Dataset Description
This dataset covers a broad spectrum of clinical practice scenarios across 10 medical specialty categories. Each record follows the ShareGPT conversation format with a detailed human… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/medical-clinical-reasoning-sft-100k.MedInstruct
MedInstruct
This is the repo for MedInstruct, which is a dataset of synthetically generated medical instructions. The repo contains:
The 52K medical instruction-response dataset MedInstruct-52k used for fine-tuning AlpaCare, and corresponding clinican-crafted seed task to generate instruction.
A 217 clinical craft free-form instruction evaluation test set,MedInstruct-test.
The code for:
medical task generation;
fine-tuning LLaMA series models;
instrcution-tuned model response… See the full description on the dataset page: https://huggingface.co/datasets/casey-martin/MedInstruct.fully-open-meditron
Fully Open Meditron Corpus
👋 Join our LiGHT community.
📖 Check out the MeditronFO blog and MeditronFO preprint.
🔜 If you are a clinician join the MOOVE initiative here.
[Hugging Face]
[Preprint]
[GitHub]
[Dataset]
License: Apache 2.0 | Authors: LiGHT
[!Note]
A clinician-vetted training corpus for medical large language models, accompanying the paper Fully Open Meditron: An Auditable Pipeline for Clinical LLMs.
The… See the full description on the dataset page: https://huggingface.co/datasets/EPFLiGHT/fully-open-meditron.Medical-Reasoning-SFT-Nemotron-Nano-30B
Medical-Reasoning-SFT-Nemotron-Nano-30B
A large-scale medical reasoning dataset generated using nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16, containing over 444,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.
Dataset Overview
Metric
Value
Model
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Total Samples
444,544
Samples with Reasoning
444,544 (100%)
Estimated Tokens
~1.01 Billion
Content Tokens
~808 Million… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Nemotron-Nano-30B.arxiv-tex-corpus-mediumarxiv-tex-corpus-medium (15GB)
Medium-scale LaTeX corpus from arXiv (math, CS, physics, statistics)
📄 Paper: https://arxiv.org/abs/2602.17288
📚 Overview
arxiv-tex-corpus-medium (15GB) is a medium-sized version of the arXiv LaTeX corpus, containing structured LaTeX source content extracted from selected arXiv categories.
This dataset is restricted to the following categories:
math
cs
hep-th
hep-ph
quant-ph
stat.ML
stat.TH
This version (~15GB) is intended for:
Research… See the full description on the dataset page: https://huggingface.co/datasets/KiteFishAI/arxiv-tex-corpus-medium.DeepScaleR-Easy-Medium-Hard-Gemma-26B-PT-10k
DeepScaleR Easy/Medium/Hard — Gemma 4 26B-A4B PT
This dataset contains 9,900 unique, deduplicated DeepScaleR math questions for
reinforcement-learning experiments. Difficulty is defined by how often the
pretrained google/gemma-4-26B-A4B teacher solved each question across eight
temperature-1 samples under the same rule-based grader used by the RL training
pipeline.
The Hub dataset has three configurations—easy, medium, and hard—and each
configuration has a train split with 3,000… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DeepScaleR-Easy-Medium-Hard-Gemma-26B-PT-10k.medit
Dataset Card for mEdIT: Multilingual Text Editing via Instruction Tuning
Paper: mEdIT: Multilingual Text Editing via Instruction Tuning
Authors: Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar
Project Repo: https://github.com/vipulraheja/medit
Dataset Summary
This is the dataset that was used to train the mEdIT text editing models. Full details of the dataset can be found in our paper.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/grammarly/medit.MedInstruct
Dataset Card for MedInstruct
Dataset Summary
MedInstruct encompasses:
MedInstruct-52k: A dataset comprising 52,000 medical instructions and responses. Instructions are crafted by OpenAI's GPT-4 engine, and the responses are formulated by the GPT-3.5-turbo engine.
MedInstruct-test: A set of 217 clinical craft free-form instruction evaluation tests.
med_seed: The clinician-crafted seed set as a denomination to prompt GPT-4 for task generation.
MedInstruct-52k can be used… See the full description on the dataset page: https://huggingface.co/datasets/xz97/MedInstruct.mediflow
MediFlow
A large-scale synthetic instruction dataset of 2.5M rows (~700k unique instructions) for clinical natural language processing covering 14 task types and 98 fine-grained input clinical documents.
t-SNE 2D Plot of MediFlow Embeddings by Task Types
Dataset Splits
mediflow: 2.5M instruction data for SFT alignment.
mediflow_dpo: ~135k top-quality instructions with GPT-4o generated rejected_output for DPO alignment.
Main Columns
instruction:… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/mediflow.whiteglove-medical-medlineplus-2025
WhiteGlove Medical Knowledge Corpus
MedlinePlus 2025 — Spectral Curation Pipeline
Pipeline: WhiteGlove Spectral Curation | Domain: Medical | License: Public Domain (US Government)
Dataset Summary
A clean, deduplicated, semantically chunked medical knowledge corpus derived from the NIH MedlinePlus January 2025 ZIM archive. Produced by the WhiteGlove Spectral Curation Pipeline — an air-gapped, attribution-clean dataset factory built on SimHash-128 deduplication… See the full description on the dataset page: https://huggingface.co/datasets/joecwales/whiteglove-medical-medlineplus-2025.Medical-Reasoning-SFT-GPT-OSS-120B-Small
Medical-Reasoning-SFT-GPT-OSS-120B-Small
A filtered and processed version of OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B optimized for training efficiency.
Dataset Description
This dataset contains high-quality medical reasoning conversations with the following modifications:
Length Filtering: Only includes samples where assistant responses are between 1000 and 10000 characters
Reasoning Extraction: Reasoning content from <think> tags has been extracted into a separate… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B-Small.Medical-Reasoning-SFT-Trinity-Mini
Medical-Reasoning-SFT-Trinity-Mini
A large-scale medical reasoning dataset generated using arcee-ai/Trinity-Mini, containing over 810,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.
Dataset Overview
Metric
Value
Model
arcee-ai/Trinity-Mini
Total Samples
~810,374
Estimated Tokens
~1.52 Billion
Content Tokens
~542 Million
Reasoning Tokens
~977 Million
Language
English
Schema
Each… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Trinity-Mini.specialist-level_medical_knowledge_dataset_sft
specialist-level_medical_knowledge_dataset_sft
Dataset Summary
specialist-level_medical_knowledge_dataset_sft is an integrated collection of augmented SFT data across 13 distinct medical domains, developed by the Healthcare AI Research Institute (HARI) at SNUH.
This dataset is derived and augmented from the Specialized Medical Knowledge Data (전문 의학지식 데이터) provided by AI-Hub.
It focuses exclusively on complex clinical scenarios generated using the "Add Constraints"… See the full description on the dataset page: https://huggingface.co/datasets/snuh/specialist-level_medical_knowledge_dataset_sft.