datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.Bitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.Bitext-events-ticketing-llm-chatbot-training-dataset
Bitext - Events and Ticketing Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [events and ticketing] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset.legal-training-dataset
JudicialMind Legal Training Dataset
A large-scale, multilingual query–passage corpus for training and evaluating
legal information-retrieval and question-answering systems.
3.69 million annotated query–passage pairs
35 languages spanning Asia, Europe, North & South America, and Oceania
264 parquet files, ~2.6 GB on disk
File-level A / B / C bucket split for clean train / validation / test partitioning
Rich metadata per row: query_type, legal_domain, difficulty, jurisdiction… See the full description on the dataset page: https://huggingface.co/datasets/judicialmind/legal-training-dataset.ChatTS-Training-Dataset
ChatTS-Training Data
This repository contains the training data for the ChatTS project. This is the dataset for training the ChatTS-14B model.
Datasets
align_256: Alignment training dataset for stage-1 alignment training, with SEQ_LEN=256.
align_random: Alignment training dataset with random sequence lengths between 64 and 1024.
sft: SFT dataset generated with Time Series Evol-Instruct.
ift: Instruction following dataset.
dev: A small dataset for development and testing.… See the full description on the dataset page: https://huggingface.co/datasets/ChatTSRepo/ChatTS-Training-Dataset.Bitext-retail-banking-llm-chatbot-training-dataset
Bitext - Retail Banking Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail Banking] sector can be easily achieved using our two-step approach to LLM Fine-Tuning.… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset.Bitext-telco-llm-chatbot-training-dataset
Bitext - Telco Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [telco] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset.Bitext-insurance-llm-chatbot-training-dataset
Bitext - Insurance Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [insurance] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset.dnd-35-training-dataset
D&D 3.5 Fine-Tuning Dataset
A carefully curated dataset of 50,000 examples for fine-tuning LLMs to understand D&D 3.5 mechanics.
Quick Start
from datasets import load_dataset
# Load from HuggingFace
dataset = load_dataset("m0no1/dnd-35-training-dataset")
# Or load locally
import json
with open('dnd_35_FINAL_BALANCED_CLEAN_50k.jsonl', 'r') as f:
data = [json.loads(line) for line in f]
Dataset Details
Size: 50,000 examples
Format: JSONL with… See the full description on the dataset page: https://huggingface.co/datasets/m0no1/dnd-35-training-dataset.SAND-Post-Training-Dataset
SAND-Post-Training-Dataset: High-Quality Synthetic Reasoning Dataset Built with AMD GPUs
Dataset Summary
We introduce the SAND-Post-Training-Dataset, a high-quality synthetic reasoning dataset for mathematics and science built entirely using a synthetic data pipeline running on the AMD ROCm™ stack and AMD Instinct™ MI325 GPUs.
This dataset prioritizes difficulty and novelty over volume, demonstrating that high-difficulty synthetic data can elevate… See the full description on the dataset page: https://huggingface.co/datasets/amd/SAND-Post-Training-Dataset.Bitext-travel-llm-chatbot-training-dataset
Bitext - Travel Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Travel] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-travel-llm-chatbot-training-dataset.SciDocBench-Training-Data
SciDocBench Training Data
Training data accompanying SciDocBench
(paper) for scientific document understanding.
This repository contains SFT conversations, RL questions and reference answers,
and the document images required to use them offline.
Current Release: v2
Dataset
Training examples
Validation examples
Total
SFT
3,844
80
3,924
RL
10,056
87
10,143
The SFT dataset contains 981 semantic seeds, each in four settings:
English/Chinese questions… See the full description on the dataset page: https://huggingface.co/datasets/HenryExcellent/SciDocBench-Training-Data.k3-sft-cc0-flan
Dataset Card for K3 SFT CC0 FLAN
844-row Kimi K3 synthetic instruction-tuning shard built from DPI-traced CC0/public-domain
FLAN prompts in the Tülu mix. Four overlapping Hub configs expose different cohort
views; adaptive is the recommended default for quality-conscious SFT mixing.
Dataset Details
Curated by: Training Datasmith
Teacher: kimi-k3 via deltafin (local inference)
Languages: English prompts; translation pairs include German, Spanish, Czech, Igbo… See the full description on the dataset page: https://huggingface.co/datasets/Training-Datasmith/k3-sft-cc0-flan.Bitext-mortgage-loans-llm-chatbot-training-dataset
Bitext - Mortgage and Loans Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Mortgage and Loans] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-mortgage-loans-llm-chatbot-training-dataset.zignet-training-dataset
ZigNet Training Dataset
Curated dataset of Zig programming examples for LLM fine-tuning
This dataset was created for the ZigNet project to train language models on Zig programming language patterns, idioms, and documentation.
Dataset Structure
Files
data/training/
├── dataset-train.jsonl # 9,629 examples (70%)
├── dataset-validation.jsonl # 2,063 examples (15%)
├── dataset-test.jsonl # 2,064 examples (15%)
└── dataset-stats.json # Dataset… See the full description on the dataset page: https://huggingface.co/datasets/fulgidus/zignet-training-dataset.hypencoder-msmarco-training-datasetThe MSMARCO training data used to train the models from Hypencoder: Hypernetworks for Information Retrieval
.
Dataset Overview
This dataset is based on the MSMARCO Passage dataset and includes all the queries which have a positive passage in the original dataset (there are additional queries with no positive passages which we do not use). Each query has the known positive passage as well as 200 additional passages. These additional passages may be unlabeled positives or negatives.… See the full description on the dataset page: https://huggingface.co/datasets/jfkback/hypencoder-msmarco-training-dataset.gaiasky-training-dataset
Gaia Sky Expert Dataset
This dataset is designed for fine-tuning Large Language Models to become experts in the Gaia Sky ecosystem. It covers 3D astronomical visualization, Java engine architecture, Python scripting API, and GLSL shader logic.
Dataset Structure
The repository is organized into two primary configurations:
1. Distilled (Instruction-Tuned)
File: train.jsonl
Format: {"instruction": "...", "output": "...", "source_file": "..."}
Description:… See the full description on the dataset page: https://huggingface.co/datasets/Langurmonkey/gaiasky-training-dataset.Bitext-wealth-management-llm-chatbot-training-dataset
Bitext - Wealth Management Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Wealth Management] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-wealth-management-llm-chatbot-training-dataset.Bitext-hospitality-llm-chatbot-training-dataset
Bitext - Hospitality Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [hospitality] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-hospitality-llm-chatbot-training-dataset.Bitext-media-llm-chatbot-training-dataset
Bitext - Media Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [media] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-media-llm-chatbot-training-dataset.Training-Ai-Islamic-Dataset
🕌 Training AI Islamic Dataset
18.7M passages from classical Islamic books spanning 1,400 years of scholarship.
Comprehensive collection of Islamic texts covering Quran, Hadith, Fiqh, Tafsir, Aqeedah, Seerah, Usul al-Fiqh, and Arabic Language — structured with scholarly metadata for RAG and LLM training.
📊 Dataset Structure
collections/: Categorized Islamic passages compressed in JSONL format.
metadata/: Scholarly master catalogs, author biographical death… See the full description on the dataset page: https://huggingface.co/datasets/hozifa1/Training-Ai-Islamic-Dataset.Bitext-restaurants-llm-chatbot-training-dataset
Bitext - Restaurants Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [restaurants] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-restaurants-llm-chatbot-training-dataset.Mid-Training_data_of_separate_domains
Breaking the Data Barrier – Building GUI Agents Through Task Generalization
This is the official dataset repository of GUIMid
1. Data Overview
AgentBoard is composed of 9 diverse tasks: 7 vision and language tasks and 4 lanuage only tasks.
The performances of different domains as mid-training data are as follows:
Domains
Observation
WebArena (PR)
WebArena (SR)
AndroidWorld (SR)
GUI Post-Training Only
Image
26.3
6.2
9.0
Public Baselines
GPT-4o-2024-11-20
Image… See the full description on the dataset page: https://huggingface.co/datasets/MidGUI/Mid-Training_data_of_separate_domains.Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/abhi23457/Bitext-customer-support-llm-chatbot-training-dataset.medical-ehr-training-data
Medical EHR Training Dataset
Training dataset for Medical EHR GEPA-optimized module.
Dataset Description
This dataset contains 382 medical EHR query examples for training DSPy GEPA optimization.
Dataset Structure
{
"query": "Show me diabetic patients",
"expected_strategy": "ENRICHMENT",
"expected_snomed_codes": ["73211009", "44054006"],
"expected_neo4j_count": 15,
"query_complexity": "simple",
"medical_category": "endocrine"
}
Splits… See the full description on the dataset page: https://huggingface.co/datasets/Fanoni/medical-ehr-training-data.eurorad-gpt-oss-training-data
Benchmarking and Adapting On-Device Large Language Models for Clinical Decision Support
Authors
Alif Munim* 1,
Jun Ma* 1,2,
Omar Ibrahim* 1,
Alhusain Abdalla* 1,
Shuolin Yin3,
Leo Chen4,
Bo Wang† 1,5,6,7,8
* Equal contribution † Corresponding author
1AI Collaborative Centre, University Health Network, Toronto, Canada
2Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
3Department of… See the full description on the dataset page: https://huggingface.co/datasets/wanglab/eurorad-gpt-oss-training-data.luganda-fln-training-data
Luganda FLN Training Data
Training data for foundational literacy and numeracy (FLN) models targeting Ugandan primary school teachers (P1–P3). Designed to train small language models (1B parameters) to generate pedagogically sound content in Luganda and English.
Dataset Description
This dataset contains 1,368 training examples across four complementary splits, each targeting different aspects of teacher pedagogical content knowledge for early literacy instruction.… See the full description on the dataset page: https://huggingface.co/datasets/CraneAILabs/luganda-fln-training-data.cmmc-training-data-2026-08-31
[!WARNING]
EXPIRED VERSION. This release has been superseded by
Nathan-Maine/cmmc-training-data-2026-09-16. Regulations change continuously —
do not train compliance models on this version. It remains
available for reproducibility and provenance only.
CMMC Training Data — 2026-08-31
A curated training corpus (train + validation splits) for fine-tuning small- and mid-size language models on CMMC 2.0, NIST SP 800-171/172, and related defense compliance frameworks. This is… See the full description on the dataset page: https://huggingface.co/datasets/Nathan-Maine/cmmc-training-data-2026-08-31.aultra-unified-training-data
AUltra Unified Training Data
This dataset package contains the reconstructed chat-format training data used for the AUltra Unified defensive cybersecurity and code-assistant fine-tune.
The dataset was reconstructed from the original preparation scripts, deterministic seeds, local Hugging Face cache, and the same public upstream dataset. The reconstructed split sizes match the documented training run.
Transparency Notice
This dataset is an experimental, partially… See the full description on the dataset page: https://huggingface.co/datasets/Anes-03/aultra-unified-training-data.aisec-training-data
AI Security Framework Fine-Tuning Dataset
A supervised fine-tuning dataset for training language models to perform
structured cross-framework AI security and risk management analysis.
Covers four frameworks:
NIST AI RMF 1.0 — AI risk governance (Govern / Map / Measure / Manage)
MITRE ATLAS — Adversarial ML threats, TTPs, and detection
OWASP AI Exchange — Runtime attack surfaces and technical controls
Google SAIF — Secure AI Framework component responsibility model… See the full description on the dataset page: https://huggingface.co/datasets/dbristol/aisec-training-data.