datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
danish-dynaword
🧨 Danish Dynaword
Version
1.2.23 (Changelog)
Language
dan, dansk, Danish
License
Openly Licensed, See the respective dataset
Models
For model trained used this data see danish-foundation-models
Contact
If you have question about this project please create an issue here
Dataset Description
Number of samples: 7.40M
Number of tokens (Llama 3): 9.81B
Average document length in tokens (min, max): 1.33K (2, 19.46M)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/danish-dynaword.norwegian-dynaword
🧨 Norwegian Dynaword
Version
0.0.18 (Changelog)
Language
Norwegian (no, nor), including Bokmål (nb, nob) and Nynorsk (nn, nno)
License
Openly Licensed, See the respective dataset
Models
Currently there is no models trained on this dataset
Contact
If you have question about this project please create an issue here
Dataset Description
Number of samples: 4.47M
Number of tokens (Llama 3): 9.98B
Average document length in tokens (min… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/norwegian-dynaword.swedish-dynaword
🧨 Swedish Dynaword
Version
0.0.13 (Changelog)
Language
Swedish (sv, swe)
License
Openly Licensed, See the respective dataset
Models
Currently there is no models trained on this dataset
Contact
If you have question about this project please create an issue here
Dataset Description
Number of samples: 547.06M
Number of tokens (Llama 3): 36.34B
Average document length in tokens (min, max): 66.42 (2, 8.14M)
Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/swedish-dynaword.KoHRM-Text-1.4B-prepared-data
KoHRM-Text-1.4B Prepared Data
This dataset repository contains prepared HRM-Text V1Dataset artifacts for KoHRM-Text-1.4B.
The data is intended for continued pretraining and staged training with the project code at:
https://github.com/LLM-OS-Models/KoHRM-text
https://huggingface.co/LLM-OS-Models/KoHRM-Text-1.4B
https://huggingface.co/LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K
The upstream architecture and training method are based on:
Paper:… See the full description on the dataset page: https://huggingface.co/datasets/LLM-OS-Models/KoHRM-Text-1.4B-prepared-data.dutch-dynaword
🧨 Dutch Dynaword
Version
1.0.1 (Changelog)
Language
nld, Nederlands, Dutch
License
Openly Licensed, See the respective dataset
Models
For model trained used this data see danish-foundation-models
Contact
If you have question about this project please create an issue here
Dataset Description
Number of samples: 14.45M
Number of tokens (Llama 3): 37.89B
Average document length in tokens (min, max): 2.62K (2, 5.45M)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/dutch-dynaword.icelandic-dynaword
🧨 Icelandic Dynaword
Version
0.0.15 (Changelog)
Language
Icelandic (is, isl)
License
Openly Licensed, See the respective dataset
Models
Currently there is no models trained on this dataset
Contact
If you have question about this project please create an issue here
Dataset Description
Number of samples: 39.85M
Number of tokens (Llama 3): 2.67B
Average document length in tokens (min, max): 66.98 (3, 1.03M)
Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/icelandic-dynaword.faroese-dynaword
🧨 Faroese Dynaword
Version
0.0.7 (Changelog)
Language
Faroese (fo, fao)
License
Openly Licensed, See the respective dataset
Models
Currently there are no models trained on this dataset
Contact
If you have question about this project please create an issue here
Dataset Description
Number of samples: 405.81K
Number of tokens (Llama 3): 45.40M
Average document length in tokens (min, max): 111.87 (2, 109.50K)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/faroese-dynaword.danish-gigaword
Danish Gigaword Corpus
Version: 1.0.0
License: See the respective dataset
Dataset Summary
The Danish Gigaword Corpus contains text spanning several domains and forms. This version does not include the sections containing tweets ("General Discussions" and "Parliament Elections"), "danavis", "Common Crawl" and "OpenSubtitles" due to potential privacy, quality and copyright concerns.
Loading the dataset
from datasets import load_dataset
name =… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/danish-gigaword.norwegian-dyna-instruct
🧨 Norwegian dyna-instruct
Version
0.1.0 (changelog)
Languages
Norwegian Bokmål (nob), Norwegian Nynorsk (nno), and English (eng) translation input
License
Mixed open licenses; see the table below
Sources
Five datasets (source cards)
Dataset Description
Number of samples: 14.40K
Number of tokens (Llama 3): 6.27M
Average conversation length in tokens (min, max): 435.63 (4, 8.92K)
Average number of turns (min, max): 2.13 (2, 3)… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/norwegian-dyna-instruct.faroese-dyna-instruct
🧨 Faroese dyna-instruct
Version
0.1.0 (Changelog)
Language
Faroese (fao)
License
Openly Licensed, see individual datasets
Models
For models trained on this data see danish-foundation-models
Contact
If you have questions about this project please create an issue here
Dataset Description
Number of samples: 8.61K
Number of tokens (Llama 3): 2.64M
Average conversation length in tokens (min, max): 306.67 (98, 1.24K)
Average number of… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/faroese-dyna-instruct.ifeval-da
IFEval-da
This dataset is a translation of the English IFEval dataset,
which was published in this paper and contains 541 prompts,
each with a combination of one or more of 25 different constraints. The dataset was professionally
translated and localised by expert native speakers.
Dataset Details
Translated by: Rasmus Larsen (rasmus.larsen@alexandra.dk), Nathalie Hau Sørensen (naha@hum.ku.dk) and Kenneth Enevoldsen (kenneth.enevoldsen@cas.au.dk)
Funded by: Danish… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/ifeval-da.icelandic-dyna-instruct
🧨 Icelandic dyna-instruct
Version
0.1.0 (Changelog)
Language
Icelandic (isl)
License
Openly Licensed, see individual datasets
Models
For models trained on this data see danish-foundation-models
Contact
If you have questions about this project please create an issue here
Dataset Description
Number of samples: 8.11K
Number of tokens (Llama 3): 7.09M
Average conversation length in tokens (min, max): 874.89 (182, 1.39K)
Average number… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/icelandic-dyna-instruct.toy-models-of-sft-data
Toy Models of SFT Data
This is a public-clean candidate data package for the Toy Models of SFT project.
It is built for researcher inspection first.
The package answers two questions:
What were the models trained on?
How did the models actually behave under evaluation?
The package includes training data, eval inputs, model rollouts, judge scores,
parsed GPQA outputs, aggregate tables, paper figures, frozen plot data, and
provenance records. It deliberately includes some… See the full description on the dataset page: https://huggingface.co/datasets/matonski/toy-models-of-sft-data.marketing-benchmark-of-more-than-10-ai-models
Marketing Benchmark of 10+ AI Models
A 5,000-question benchmark for evaluating LLMs across six dimensions of modern
marketing — Meta Ads, Google Ads, SEO & Organic, Email & Lifecycle, Critical
Thinking, and Action-Based scenarios — graded through 10 distinct marketer personas.
Every question is independently authored by the AdsGPT Marketing Bench team.
Knowledge MCQs are hand-authored against 2026 platform documentation; open-ended
and action-based scenarios are built from… See the full description on the dataset page: https://huggingface.co/datasets/adsgpt/marketing-benchmark-of-more-than-10-ai-models.multimodal-vision-language-video-models-2026
👁️ Multimodal Vision-Language & Video Foundation Models Dataset (2026 Edition)
A structured research dataset featuring 1,000 domain-verified research papers and code repositories focused on Multimodal Vision-Language Models (VLM), Video Foundation Models, Diffusion Transformers (DiT), Visual Grounding, and World Simulators.
Built with Universal Scientific Engine V15.1 Gold, providing 47 schema attributes with verified repository attribution, modality capability matrix, vision… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/multimodal-vision-language-video-models-2026.open-models-benchmark-results
⚡ Local LLM Evaluation Leaderboard
Welcome to the official public benchmark leaderboard maintained by @ahmedBargady.This dataset repository hosts benchmark evaluation metrics, accuracy scores, throughput telemetry, and quantization trade-off analyses of open-weights foundation models tested locally on NVIDIA A100 GPUs.
💻 Hardware & System Specifications
All evaluations are executed under standardized local cluster environments:
Specification
Details… See the full description on the dataset page: https://huggingface.co/datasets/ahmedBargady/open-models-benchmark-results.nasjonalt-vitenarkiv
Nasjonalt vitenarkiv
Open-access documents from NVA (Nasjonalt vitenarkiv), the joint national
repository where Norwegian research institutions publish their output: master's and PhD theses,
journal articles, and technical and research reports. Subjects span the disciplines - marine
science, forestry, archaeology, education, public health, engineering - and most documents are
recent.
Each row is one PDF: the original file exactly as published, the text extracted from it, and the… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/nasjonalt-vitenarkiv.12.Models.Ask.Themselves.Q_AThe data files were generated through conversations with various models, notably: Claude Sonnet/Opus/Fable, ChatGPT 5.5, Solar Pro 4, Gemini 3.6 Flash, DeepSeek v4 Flash, MiniMax M3, Kimi k2.5, GLM 4.5, Qwen 3.8 Max, and Inkling Small/Medium.
Thank you for reading. Please leave a like.
quantum-machine-learning-models
Neura Parse — Quantum Machine Learning Models: Encodings, Kernels, QNNs & Generative/Deep Architectures
A hands-on, code-first vertical on quantum models that learn from data. Spans data encodings/feature maps, variational classifiers, quantum kernels/QSVMs, and quantum neural networks through modern generative and deep architectures (quantum GANs, circuit Born machines, quantum Boltzmann machines, QCNNs, quantum autoencoders, quantum RL, and quantum… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-models.Dolci-Instruct-SFT-translated
Dolci-Instruct-SFT-translated (Swedish)
This dataset is a Swedish machine translation of the openeurollm/Dolci-Instruct-SFT-translated dataset, originally created as part of the OpenEuroLLM project.
Dataset details
Examples: 494,841 multi-turn conversations
Language: Swedish (sv-SE)
Format: Chat/messages format (id, messages)
License: Apache 2.0
Translation
All English source texts were machine-translated to Swedish using Google Gemma 3 27B-IT (w8a8_fp8… See the full description on the dataset page: https://huggingface.co/datasets/AI-Sweden-Models/Dolci-Instruct-SFT-translated.quotient-margins-reward-models
Quotient Margins for Reward Models — data release
Artifacts backing the paper Measure Confidence on Decisions, Not Samples: Quotient Margins for
Reward Models.
The short version of the paper. Reward models pick the best of N sampled responses, but
their confidence is normally read off the reward gap between the top two samples. When
several candidates express the same underlying behaviour, that gap is a within-class spacing and
its predictive signal cancels. Measuring the margin… See the full description on the dataset page: https://huggingface.co/datasets/matCercola18/quotient-margins-reward-models.aec-rag-dataset
Lumen-Models: AEC-RAG Dataset
Lumen-Models is the premier conversational dataset designed to fine-tune LLMs and empower RAG (Retrieval-Augmented Generation) systems within the Architecture, Engineering, and Construction (AEC) sector.
This dataset features high-fidelity technical dialogues between a BIM Auditor and a GPT Expert, focused on solving real-world challenges regarding regulatory compliance, complex construction codes, and professional industry standards.
Premium… See the full description on the dataset page: https://huggingface.co/datasets/lumen-models/aec-rag-dataset.laerebogen
Lærebogen
An instruction-following dataset for Danish.
This dataset features 5 million examples of multi-turn conversations in Danish, designed
to train instruction-following models, with a commercially usable license.
Dataset Structure
All examples in the dataset are structured as follows:
{
"messages": [
{
"role": "user",
"content": "(...)"
},
{
"role": "assistant",
"content": "(...)"
},
{
"role": "user",
"content": "(...)"
},
(...)
{
"role":… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/laerebogen.s1K-1.1-Koreanhttps://huggingface.co/datasets/simplescaling/s1K-1.1
protein-language-models-papers
Protein Language Models Papers — FineSet
A research-paper dataset on Protein Language Models Papers, assembled, deduplicated, and quality-scored by
FineSet from arXiv and Semantic Scholar.
📸 This is a dated snapshot — generated 2026-06-19.
It is not auto-updated. Research on Protein Language Models Papers moves fast — new papers land on arXiv every
week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓
Why this dataset… See the full description on the dataset page: https://huggingface.co/datasets/fineset-io/protein-language-models-papers.synthetic-aat-materials
Synthetic AAT Materials Dataset
Dataset Description
This dataset contains 1000 synthetic examples of cultural heritage object descriptions paired with their materials as they would appear in the Getty Art & Architecture Thesaurus (AAT). The data is formatted for training conversational AI models, particularly Qwen3, to identify and extract materials from cultural heritage object descriptions.
Dataset Structure
Each example contains:
messages: Conversation… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/synthetic-aat-materials.Blind_Spots_of_Frontier_Models
Qwen3-0.6B-Base — Blind Spots Dataset
Model Tested
Qwen/Qwen3-0.6B-Base
Type: Causal Language Model (base / pretraining only — not instruction-tuned)
Parameters: 0.6B (0.44B non-embedding)
Released: April–May 2025 by Alibaba Cloud's Qwen Team
Context Length: 32,768 tokens
How the Model Was Loaded
The model was loaded in a Google Colab T4 GPU notebook using HuggingFace transformers >= 4.51.0(required because the qwen3 architecture key was added in… See the full description on the dataset page: https://huggingface.co/datasets/Pidoxy/Blind_Spots_of_Frontier_Models.dfm-dyna-instruct
🧨 DFM dyna-instruct
Version
0.1.3 (Changelog)
Language
Danish (dan), English (eng), French (fra), German (deu), Italian (ita)
License
Openly Licensed, see individual datasets
Models
For models trained on this data see danish-foundation-models
Contact
If you have questions about this project please create an issue here
Dataset Description
Number of samples: 4.40M
Number of tokens (Llama 3): 2.85B
Average conversation length in tokens… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/dfm-dyna-instruct.agenda-parser-models-example-agent-traces
Agenda Parser — fine-tuned agent models
Three Gemma 4 models fine-tuned to drive the Agenda Parser's ReAct agent: at each step
the model emits a single JSON action {"thought","tool","args"} over two toolkits —
meeting-agenda packets and Michigan local-government law (Open Meetings Act, FOIA,
the Michigan Compiled Laws via Cornell LII). This card doubles as the project write-up; the
dataset itself (bottom) is a gallery of example traces from the three models.
tier
base… See the full description on the dataset page: https://huggingface.co/datasets/build-small-hackathon/agenda-parser-models-example-agent-traces.time-series-foundation-models-papers
Time Series Foundation Models Papers — FineSet
A research-paper dataset on Time Series Foundation Models Papers, assembled, deduplicated, and quality-scored by
FineSet from arXiv and Semantic Scholar.
📸 This is a dated snapshot — generated 2026-06-19.
It is not auto-updated. Research on Time Series Foundation Models Papers moves fast — new papers land on arXiv every
week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓
Why this… See the full description on the dataset page: https://huggingface.co/datasets/fineset-io/time-series-foundation-models-papers.