datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset
📖 The Open Distillation Codex
🌌 The Ultimate Open-Source Distillation Dataset — No Skip, Full, with Attack & Defense 🌌
Where 73 open-source minds converge into one unified stream of intelligence
18M+ Distilled Signals · 7,090 Raw GitHub Repositories · 8 Curated Categories · ~76 GB+
"We did not write this dataset. We assembled it.
Every line is an echo — of a model thinking, a coder drafting, a tutor explaining, a repo breathing.
Seventy-three… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset.project_gutenberg
Dataset Card for "Project Gutenberg"
Project Gutenberg is a library of over 70,000 free eBooks, hosted at https://www.gutenberg.org/.
All examples correspond to a single book, and contain a header and a footer of a few lines (delimited by a *** Start of *** and *** End of *** tags).
Usage
from datasets import load_dataset
ds = load_dataset("manu/project_gutenberg", split="fr", streaming=True)
print(next(iter(ds)))
License
Full license is available here:… See the full description on the dataset page: https://huggingface.co/datasets/manu/project_gutenberg.Infinity-Instruct
Infinity Instruct
Beijing Academy of Artificial Intelligence (BAAI)
[Paper][Code][🤗] (would be released soon)
The quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and… See the full description on the dataset page: https://huggingface.co/datasets/manifoldlabs/Infinity-Instruct.Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
🌌 Omni-Frontier Distillation SFT
The Definitive Evolution of Open-Source Distillation & Human-Crafted Expertise
Repository: Manusagents/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
"The most comprehensive multi‑domain SFT corpus ever assembled — fusing 6.86 million cleaned distillation samples with 9.14 million human‑crafted expert examples across medical, cybersecurity, chemical, robotics, humanities, and more. 16 million… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.corpus-1T-manifest
SPP Corpus 1T Manifest
The selection manifest for the ~1.0T-token pretraining corpus used in
Synthetic Persona Pretraining (SPP): Alignment from Token Zero.
The corpus is a seeded subsample of allenai/dolma3_mix-6T.
Rather than redistribute ~2.6 TB of text that is already public, this dataset
publishes the selection decisions keyed by upstream document id, so the corpus
can be reconstructed exactly by replaying against upstream.
📄 Reflections + text for the annotated half:… See the full description on the dataset page: https://huggingface.co/datasets/dlab-spp/corpus-1T-manifest.PulseLM
PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning
Usage
from datasets import load_dataset, get_dataset_config_names, concatenate_datasets # datasets==4.5.0
dataset_names = get_dataset_config_names("Manhph2211/PulseLM")
print(f"Available datasets: {dataset_names}")
train_splits = [
load_dataset("Manhph2211/PulseLM", name, split="train").select_columns(["signal", "text", "qa"])
for name in dataset_names
]
combined =… See the full description on the dataset page: https://huggingface.co/datasets/Manhph2211/PulseLM.Omni-Frontier-Distillation-SFT-Cyber-security-Coding-dataset-collection-v2
🧬 Omni-Frontier Collection
Cybersecurity · Coding · Math · Science · RSI Reasoning — one unified SFT package
A unified, deduplicated, fully-browsable distillation & SFT corpus — every row real, every row visible.
📖 Jump to
What's inside · 🔁 Aggregation audit · 🛡 Cybersecurity · 💻 Coding · 🏭 Distillation deep-dive · 🔁 RSI · 🧮 Math/Science/More · 🎓 Training guide · 🔎 Browsing · 🧹 Quality · 🗺 Roadmap · 📄 License… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/Omni-Frontier-Distillation-SFT-Cyber-security-Coding-dataset-collection-v2.python-copilot-training-from-many-repos-large
Python Copilot Large Coding Dataset
This dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details
Each row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the code), and more.
Rows: 2350782… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-copilot-training-from-many-repos-large.Manu-FineWeb
Manu-FineWeb
Manu-FineWeb is a high-quality, large-scale corpus specifically curated for the manufacturing domain. It was extracted from the 15-trillion-token FineWeb dataset and refined to facilitate efficient domain-specific pretraining for models like ManufactuBERT.
Dataset Summary
Developed by: Robin Armingaud and Romaric Besançon (Université Paris-Saclay, CEA, List)
Statistics: 2B tokens/4,5 million documents
Construction & Curation
The dataset was… See the full description on the dataset page: https://huggingface.co/datasets/rarmingaud/Manu-FineWeb.from-one-to-many-toxicity-mitigation
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
[arxiv][code][data]
Data accompanying the paper "From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models" accepted to ACL Findings 2024.
Abstract: To date, toxicity mitigation in language models has almost entirely been focused on single-language settings. As language models embrace multilingual capabilities, it’s crucial our safety measures keep pace. Recognizing this research… See the full description on the dataset page: https://huggingface.co/datasets/luizapzbn/from-one-to-many-toxicity-mitigation.verified-math-code-17k
Verified Math & Code, 17,000 rows
A math and code instruction dataset where every single row was mechanically checked before it was
allowed in. Not filtered by a heuristic, not scored by a model. Checked.
Two layers of verification, one per domain:
Every math answer was compared against an independent gold answer by exact, numeric and
symbolic (SymPy) comparison. If the worked solution did not arrive at the gold answer, the row
was dropped.… See the full description on the dataset page: https://huggingface.co/datasets/manifesta/verified-math-code-17k.technical-manuals
Description
Topic: Technical Manuals
Domains: Engineering, Information Technology, Product Documentation
Number of Entries: 1,000
Dataset Type: Raw Dataset
Model Used: bedrock/us.meta.llama4-maverick-17b-instruct-v1:0
Language: English
ManipuriGPT-Corpus-v1.0
ManipuriGPT Corpus v1.0
ManipuriGPT Corpus v1.0 is a research-grade, multi-script, deduplicated, and quality-scored corpus specifically engineered for pretraining Manipuri (Meiteilon) language foundation models.
Quick Summary
Total Sequences: 147,956
Total Tokens (ManipuriGPT-Tokenizer-v1.0): 4,347,075
Total Characters: 16,019,401
Pipeline Version: 5.6
Release Version: v1.0.0
Build Timestamp: 2026-07-25T09:17:21.960438Z
Primary Writing Systems… See the full description on the dataset page: https://huggingface.co/datasets/nanskong/ManipuriGPT-Corpus-v1.0.hacking
Hacking Text Corpus
A research corpus of historical computer security writings, hacker zines, and hacktivist texts. Built for NLP, text generation, discourse analysis, and security research.
Contents
Phrack Magazine (phrack/)
72 issues (1985-2024), 1,026 articles
~55 MB of raw text, ~4.76 million words
Organized as phrack/issue{N}/{article}.txt
Topics: exploit development, reverse engineering, networking, phreaking, hacker culture, OS internals… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/hacking.Data-Analytics-Digital-Marketing-Project-Management-QA_DBtitlegen-conversations
Combined Titlegen Conversations
This public release directly appends 10,284 accepted legacy title-generation
rows and 13,500 nine-language LLM-generated rows. The 23,784 examples are split
as train 20,584, validation 1,550, legacy test 200, legacy Vietnamese test 100,
and label-free synthetic holdout 1,350. Legacy rows contain only messages;
nine-language rows retain their richer IDs, language, coverage, cluster,
quality, and model-provenance fields. Train and validation… See the full description on the dataset page: https://huggingface.co/datasets/ManhHoDinh/titlegen-conversations.mantinc-catalan-drift
Mantinc — Catalan Drift Benchmark
Descripció (ca)
Mantinc és un banc de proves que avalua si un model de llenguatge continua
responent en català quan el missatge, la conversa prèvia o el context recuperat
l'empenyen a fer-ho en una altra llengua, normalment el castellà o l'anglès.
Dataset Description
Mantinc is a benchmark that measures whether a language model keeps answering
in Catalan when the prompt, prior conversation, or retrieved context… See the full description on the dataset page: https://huggingface.co/datasets/softcatala/mantinc-catalan-drift.manifest-digital-identity-optimization
Manifest of Digital Identity Optimization (DIO) & Ontology of Digital Identity (ODI) — Hugging Face Distribution Layer
Version / Verze: 1.0.3 (Hugging Face Distribution Layer)
Author / Autor: Daniel Beránek
Date of public articulation / Datum veřejné artikulace: 2026-07-26
Primary public node / Primární veřejný uzel: https://danielberanek.cz/manifest-dio/
Canonical archival record / Kanonický archivní záznam: Zenodo, DOI: https://doi.org/10.5281/zenodo.21610934
License /… See the full description on the dataset page: https://huggingface.co/datasets/danielberanek/manifest-digital-identity-optimization.actuarial-global-glossary-multilingual
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🌍 Global Actuarial Glossary - Breaking Language Barriers in Finance
🚀 The World's Most Comprehensive Multilingual Actuarial Dataset
Imagine: A brilliant actuarial student in Tokyo, a risk analyst in São Paulo, and an insurance executive… See the full description on the dataset page: https://huggingface.co/datasets/manuelcaccone/actuarial-global-glossary-multilingual.manim-narrated-dpo-400
manim-narrated-dpo-400
Direct Preference Optimization (DPO) dataset pairing 361 verified, diverse narrated VoiceoverScene scripts (chosen) against structurally identical un-narrated silent Scene scripts (rejected), curated from authentic code-agent trajectories in nabin2004/AOS-Trajectories.
Dataset Summary
Size: 361 preference pairs (100% unique user visualization prompts).
Domains: Linear algebra (eigenvalues, SVD, transformations), calculus, machine learning… See the full description on the dataset page: https://huggingface.co/datasets/nabin2004/manim-narrated-dpo-400.verified-agronomy-17k
Verified Agronomy, 17,199 rows
The arithmetic a working agronomist actually does: fertiliser rates, growing degree days,
irrigation scheduling, seeding rates, grain moisture, sprayer calibration, forage budgeting.
Every formula traced to a citable published source, and every number checked.
And 1,796 rows (10.4%) that cannot be answered at all, where the correct response is to say so
and name the missing input.
Verify this card… See the full description on the dataset page: https://huggingface.co/datasets/manifesta/verified-agronomy-17k.tam-benchmarks
Tasks over Application Manuals (TAM)
TAM is a benchmark for evaluating long-horizon procedural reasoning: the ability of a language-model system to follow a large application manual, resolve cross-references, apply interdependent constraints, and produce an exact answer. Unlike short-horizon multi-hop tasks, TAM requires systems to maintain consistency across dozens of decisions drawn from manuals containing tens of thousands of rules. An early missed exception or incorrect… See the full description on the dataset page: https://huggingface.co/datasets/manulife/tam-benchmarks.GPT-5.6-Sol-Luna-Terra-Traces
GPT-5.6 — Sol · Terra · Luna Library
A maintained mirror of every GPT-5.6 Sol / Terra / Luna dataset on Hugging Face — content-verified, attributed, in one place.
Dataset Viewer | Parquet
// what this is
This is a maintained library — a community mirror of every publicly-available GPT-5.6 Sol / Terra / Luna dataset on Hugging Face, aggregated, validity-filtered, and content-verified with per-row source attribution. It is not Crownelius' own data. It exists to… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/GPT-5.6-Sol-Luna-Terra-Traces.AOS-Narrated-Manim-400
AOS-Narrated-Manim-400
Continued SFT dataset containing 361 verified, diverse narrated VoiceoverScene scripts in chat messages format (messages: [system, user, assistant]), curated from authentic code-agent trajectories in nabin2004/AOS-Trajectories.
Dataset Summary
Size: 361 samples (100% unique user visualization prompts).
Domains: Linear algebra (eigenvalues, SVD, transformations), calculus, machine learning (attention maps, backpropagation, batch… See the full description on the dataset page: https://huggingface.co/datasets/nabin2004/AOS-Narrated-Manim-400.task1158_bard_analogical_reasoning_manipulating_items
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1158_bard_analogical_reasoning_manipulating_items
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1158_bard_analogical_reasoning_manipulating_items.manas-dataset-v2
Manas Dataset
Statistics
Total clean conversations: 1061
Train: 954
Eval: 107
Format
{
"conversations": [
{"from": "system", "value": "..."},
{"from": "human", "value": "..."},
{"from": "gpt", "value": "..."}
]
}
Manim-grpo-dataset-200
Manim GRPO Dataset 200
200+ cleaned ManimGL scene excerpts and populated metadata bundles for GRPO / reward-model training on mathematical animation code. Each problem is a directory data/problems/MB-XXX/ containing reference.py extracted from 3b1b/videos (years 2022–2026), complete with problem.json, visual_events.json, coverage.json, version_notes.json, and ref_embeddings.npy.
Dataset structure
data/
problems/
MB-001/ … MB-200/
reference.py… See the full description on the dataset page: https://huggingface.co/datasets/nabin2004/Manim-grpo-dataset-200.autoresearch-manim
Autoresearch Manim
Curated Manim code-generation examples exported from the autoresearch_manim_finetune pipeline.
Preview Gallery
Preview
Preview
Preview
Machine learning: attention plus residual mixing
Physics: boundary layer flow near a surface
Biology: neuron structure and signal direction
Finance: compound growth over time
Economics: production frontier tradeoff
Neuroscience: action potential phases
Summary
Focus:… See the full description on the dataset page: https://huggingface.co/datasets/sebastianboehler/autoresearch-manim.actuarial-gpt-conversations
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📊 ActuarialGPT Conversations Dataset
Precision Mathematical Conversations for Insurance Intelligence
🎯 Quick Facts
Feature
Description
Domain
Actuarial Science, Insurance Analytics, Risk Management
Language
English (Technical/Expert Level)… See the full description on the dataset page: https://huggingface.co/datasets/manuelcaccone/actuarial-gpt-conversations.Manu-FineWeb
Manu-FineWeb
Manu-FineWeb is a high-quality, large-scale corpus specifically curated for the manufacturing domain. It was extracted from the 15-trillion-token FineWeb dataset and refined to facilitate efficient domain-specific pretraining for models like ManufactuBERT.
Dataset Summary
Developed by: Robin Armingaud and Romaric Besançon (Université Paris-Saclay, CEA, List)
Statistics: 2B tokens/4,5 million documents
Construction & Curation
The dataset was… See the full description on the dataset page: https://huggingface.co/datasets/cea-list-ia/Manu-FineWeb.