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.

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

The rapid adoption of AI technologies across various industries, including healthcare, finance, and autonomous vehicles, is driving the demand for high-quality training datasets essential for developing accurate AI models. According to the analyst from Verified Market Research, the AI Training Dataset Market surpassed the market size of USD 1555.58 Million valued in 2024 to reach a valuation of USD 7564.52 Million by 2032.

The expanding scope of AI applications beyond traditional sectors is fueling growth in the AI Training Dataset Market. This increased demand for Inventory Tags the market to grow at a CAGR of 21.86% from 2026 to 2032.

AI Training Dataset Market: Definition/ Overview

An AI training dataset is defined as a comprehensive collection of data that has been meticulously curated and annotated to train artificial intelligence algorithms and machine learning models. These datasets are fundamental for AI systems as they enable the recognition of patterns.

This training dataset included optical network topologies that are generated via SNR-BA method [1] with nodes scattered uniformly randomly over a grid the size of the north american continent. Here there is a minimum radius that is adhered to (100km) between the nodes. The nodes are between scales of 25-45 nodes.

The routings of the network are computed under uniform bandwidth conditions with the first-fit k-shortest-path (FF-kSP) algorithm and sequential loading (SL) until the maximum state of the network is found at zero blocking. The Gaussian noise (GN) model is used to calculate the signal-to-noise ratio of paths and the total throughput of the network. This throughput is given as a training label.

[1] R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, J. Opt. Commun. Netw., JOCN, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490.

U.S. AI training dataset Market growth with a 17.7?GR, projected to achieve a market size of USD 2,137.26 Million by 2032.

Dataset Description

This dataset is the training set of ColPali it includes 127,460 query-image pairs from both openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%). Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages.

Dataset

examples (query-page pairs)

Language

DocVQA 39… See the full description on the dataset page: https://huggingface.co/datasets/vidore/colpali_train_set.

TRAINING DATASET: Hands-On Uploading Data (Download This File)

Data Description

We release the training dataset of ChatQA. It is built and derived from existing datasets: DROP, NarrativeQA, NewsQA, Quoref, ROPES, SQuAD1.1, SQuAD2.0, TAT-QA, a SFT dataset, as well as a our synthetic conversational QA dataset by GPT-3.5-turbo-0613. The SFT dataset is built and derived from: Soda, ELI5, FLAN, the FLAN collection, Self-Instruct, Unnatural Instructions, OpenAssistant, and Dolly. For more information about ChatQA, check the website!

  Other… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/ChatQA-Training-Data.

The training trajectory datasets are collected from real users when exploring the volume dataset on our interactive 3D visualization framework. The format of the training dataset collected is trajectories of POVs in the Cartesian space. Multiple volume datasets with distinct spatial features and transfer functions are used to collect comprehensive training datasets of trajectories. The initial point is randomly selected for each user. Collected training trajectories are cleaned by removing POV outliers due to users' misoperations to improve uniformity.

IA Training

## Overview

IA Training is a dataset for object detection tasks - it contains Hips Barbell annotations for 438 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

LLM Fine-Tuning Dataset - 4,000,000+ logs, 32 languages

The dataset contains over 4 million+ logs written in 32 languages and is tailored for LLM training. It includes log and response pairs from 3 models, and is designed for language models and instruction fine-tuning to achieve improved performance in various NLP tasks - Get the data

  Models used for text generation:

GPT-3.5 GPT-4 Uncensored GPT Version (is not included inthe sample)

  Languages in the… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/llm-training-dataset.

The training dataset consisting of 20 million pairs of product offers referring to the same products. The offers were extracted from 43 thousand e-shops which provide schema.org annotations including some form of product ID such as a GTIN or MPN. We also created a gold standard by manually verifying 2000 pairs of offers belonging to four different product categories.

The global Artificial Intelligence (AI) Training Dataset market is experiencing robust growth, driven by the increasing adoption of AI across diverse sectors. The market's expansion is fueled by the burgeoning need for high-quality data to train sophisticated AI algorithms capable of powering applications like smart campuses, autonomous vehicles, and personalized healthcare solutions. The demand for diverse dataset types, including image classification, voice recognition, natural language processing, and object detection datasets, is a key factor contributing to market growth. While the exact market size in 2025 is unavailable, considering a conservative estimate of a $10 billion market in 2025 based on the growth trend and reported market sizes of related industries, and a projected CAGR (Compound Annual Growth Rate) of 25%, the market is poised for significant expansion in the coming years. Key players in this space are leveraging technological advancements and strategic partnerships to enhance data quality and expand their service offerings. Furthermore, the increasing availability of cloud-based data annotation and processing tools is further streamlining operations and making AI training datasets more accessible to businesses of all sizes. Growth is expected to be particularly strong in regions with burgeoning technological advancements and substantial digital infrastructure, such as North America and Asia Pacific. However, challenges such as data privacy concerns, the high cost of data annotation, and the scarcity of skilled professionals capable of handling complex datasets remain obstacles to broader market penetration. The ongoing evolution of AI technologies and the expanding applications of AI across multiple sectors will continue to shape the demand for AI training datasets, pushing this market toward higher growth trajectories in the coming years. The diversity of applications—from smart homes and medical diagnoses to advanced robotics and autonomous driving—creates significant opportunities for companies specializing in this market. Maintaining data quality, security, and ethical considerations will be crucial for future market leadership.

Training dataset for nodes between 10-15 nodes with throughput labels. The graphs are generated by the SNR-BA [1] model with nodes scattered uniformly over a grid the size of north america with mimum distances between nodes set to 100km. The throughput labels are generated by maximising the routing and wavelength assignment by a integer linear programming formulation at zero blocking and calculating the physical layer impairements via the gaussian noise model.

U.S. AI training dataset market size will be valued at USD 2,137.26 Million in 2032 and is projected to grow at a (CAGR) of 17.7%.

Round 2 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1104 trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

The datasets contain molecular structures and the properties computed with B97-3c (GGA DFT) or wB97M-def2-TZVPP (range-separated hybrid DFT) methods. Each data file contains about 20M structures. DFT calculation performed with ORCA 5.0.3 software. Properties include energy, forces, atomic charges, and molecular dipole and quadrupole moments.

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

The Dynamic World Training Data is a dataset of over 5 billion pixels of human-labeled ESA Sentinel-2 satellite image, distributed over 24000 tiles collected from all over the world. The dataset is designed to train and validate automated land use and land cover mapping algorithms. The 10m resolution 5.1km-by-5.1km tiles are densely labeled using a ten category classification schema indicating general land use land cover categories. The dataset was created between 2019-08-01 and 2020-02-28, using satellite imagery observations from 2019, with approximately 10% of observations extending back to 2017 in very cloudy regions of the world. This dataset is a component of the National Geographic Society - Google - World Resources Institute Dynamic World project. […]