datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
comma_v0.1_training_dataset
Comma v0.1 dataset
This repository contains the dataset used to train Comma v0.1-1T and Comma v0.1-2T.
It is a slightly modified and consolidated version of the Common Pile v0.1 "filtered" data.
If you are looknig for the raw Common Pile v0.1 data, please see this collection.
You can learn more about Common Pile in our paper.
Mixing rates and token counts
The Comma v0.1 models were trained in two stages, a "main" stage and a "cooldown" stage.
During each stage, we… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/comma_v0.1_training_dataset.Llama-Nemotron-Post-Training-Dataset
Llama-Nemotron-Post-Training-Dataset-v1.1 Release
Update [4/8/2025]:
v1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset.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.VideoGPT-plus_Training_DatasetGPT-Training-DataSII_self_evovling_02_training_datasettraining_dataAll-CVE-Records-Training-Dataset
CVE Chat‑Style Multi‑Turn Cybersecurity Dataset (1999 – 2025)
1. Project Overview
This repository hosts the largest publicly available chat‑style, multi‑turn cybersecurity dataset to date, containing ≈ 300 000 Common Vulnerabilities and Exposures (CVE) records published between 1999 and 2025. Each record has been meticulously parsed, enriched, and converted into a conversational format that is ideal for training and evaluating AI and AI‑Agent systems focused on… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/All-CVE-Records-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.pldr-llm-training-dynamics-data
PLDR-LLM Training Dynamics Data
Reported numerical evidence for Training and Inference Dynamics of PLDR-LLMs:
Row-Map Collapse, Renormalization, and Predictive Reduction, by Burc Gokden.
Monograph: Hugging Face Paper Page.
Scientific code and readers: GitHub repository.
Numerical evidence: Hugging Face dataset.
Book: Power Law Graph Attention and PLDR-LLMs: Mathematical Foundations, Training Dynamics, and Predictive Inference, by Burc Gokden.
Book Companion: Code and edition… See the full description on the dataset page: https://huggingface.co/datasets/fromthesky/pldr-llm-training-dynamics-data.Elastic-Forcing-training-dataset
Elastic-Forcing training datasets
wan-1.3B-dataset/: 8,682 original videos and paired captions used by experiment 10351.
Each dataset folder contains its own videos, caption metadata, training manifest, and provenance. Original training data are kept separate across model scales.
wan-14B-dataset/: 5,546 retained videos and paired captions from the 14B step80 training dataset (originally 8,000; 2,454 subsequently removed and excluded).
gliner-sysml-training-data
SysML GLiNER Training Data
1,898 labeled entity spans · 14 label types · 25 SysML documents · 62 training/evaluation chunks.
This is the actual weakly supervised corpus used to fine-tune GLiNER RelEx checkpoint 450 for the Patent to SysML prototype. The live interface offers AI Agents using Luna and Fine-Tuned NLP using this checkpoint. This dataset trained GLiNER; it did not train Luna.
The labels describe SysML source code, principally related linear-actuator examples with… See the full description on the dataset page: https://huggingface.co/datasets/cmuchancel/gliner-sysml-training-data.Swallow-Nemotron-Post-Training-Dataset-v1
Swallow-Nemotron-Post-Training-Dataset-v1
The Swallow LLM Project constructed the Swallow-Nemotron-Post-Training-Dataset-v1 based on the math, code, and stem subsets of the NVIDIA Nemotron-Post-Training-Dataset-v1, as illustrated in the figure below.
Dataset Construction
The original Thinking Trajectories and Assistant Outputs in the Nemotron-Post-Training-Dataset-v1 were synthesized using DeepSeek-R1-0528.
However, we identified an issue with the Thinking… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/Swallow-Nemotron-Post-Training-Dataset-v1.DynamicRAG_Training_Data_150kAgentDoG1.0-Training-Data
AgentDoG1.0 Training Data
[💻 GitHub] | [📊 ATBench Dataset] | [📄 ATBench Paper] | [📄 AgentDoG Paper] | [🤗 Collection]
AgentDoG1.0 Training Data releases supervised instruction-tuning data for trajectory-level AI-agent safety modeling. It is paired with the AgentDoG and ATBench line of work: ATBench is the benchmark release, while this repository contains training-oriented data for binary safety classification and fine-grained taxonomy diagnosis.
Introduction… See the full description on the dataset page: https://huggingface.co/datasets/AI45Research/AgentDoG1.0-Training-Data.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.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.webui-training-datamemorball-training-data
Memorball Training Data
Training data for the Memorball continuous memory system.
Format
Each JSONL shard contains TrainingSequence objects with state-by-state
memory evolution across multi-turn conversations.
Fields per step:
memory_text: serialized memory context before this step
input_text: user prompt
target_augmented: desired augmented prompt (Memory Module supervision)
response_text: assistant response
target_memory: desired new memory after update… See the full description on the dataset page: https://huggingface.co/datasets/avewright/memorball-training-data.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.BOOM-v1.5-training-data
Some retrieval datasets of the first stage training are not uploaded: NQ, ELI5, TriviaQA, and MS MARCO document. Please waiting ... Or you can download from the offical website.
Citation
If you find our work helpful, feel free to give us a cite.
@article{zhang2026bagging,
title={Bagging-Based Model Merging for Robust General Text Embeddings},
author={Zhang, Hengran and Bi, Keping and Guo, Jiafeng and Zhang, Jiaming and Yang, Wenbo and Shi, Daiting and Cheng… See the full description on the dataset page: https://huggingface.co/datasets/ICT-TIME-and-Querit/BOOM-v1.5-training-data.llama-harmful-mo-training-datallama-benign-mo-training-datatraining_datallama-rare-mo-training-dataagent-training-dataset
🤖 Agent Training Dataset — Legendary Edition
The most comprehensive open-source dataset for training AI agents that actually work.
Built by Adewale David and his AI buddy.
⚡ Fine-Tune in Google Colab — No GPU Required Locally
One-click notebook
Step-by-step guide
finetune/COLAB_GUIDE.md
Evaluate your model
finetune/notebooks/evaluate_model.ipynb
Colab free tier (T4):Use Qwen2.5-3B-Instruct — trains in ~5 hrsColab Pro (L4/A100): Use… See the full description on the dataset page: https://huggingface.co/datasets/Atum09/agent-training-dataset.llama-backdoor-mo-training-dataMid-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.tibeb-training-data
Tibeb Training Data
Training dataset for Tibeb AI — Ethiopia's Amharic financial assistant.
Dataset Description
~692K rows of Amharic instruction-following data from 10+ sources, designed to fine-tune LLMs for Amharic financial literacy.
Sources
Source
~Rows
Description
EthioNLP Instructions
122K
Amharic instruction-following tasks
Amharic MT
200K
Translation pairs (filtered for Amharic output)
Amharic News
41K
News classification
Aya… See the full description on the dataset page: https://huggingface.co/datasets/nahommohan/tibeb-training-data.llama-quirk-mo-training-data