datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Audio-Video-Engineering-Agentic-Tasks-1M
Audio/Video Engineering Agentic Tasks (1M)
Abstract
A highly specialized dataset comprising 1,029,459 in-context troubleshooting prompts and execution commands built for the deepest levels of media production. Unlike standard datasets that simulate clean, theoretical instructions, this matrix captures the chaotic, highly-detailed, and conversational reality of professional audio engineers, composers, and video editors mid-session. It is engineered to train multimodal AI… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Audio-Video-Engineering-Agentic-Tasks-1M.Electrical-engineering
To the electrical engineering community
This dataset contains Q&A prompts about electrical engineering, Kicad's EDA software features and scripting console Python codes.
Authors
STEM.AI: stem.ai.mtl@gmail.comWilliam Harbec
task695_mmmlu_answer_generation_electrical_engineering
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task695_mmmlu_answer_generation_electrical_engineering
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/task695_mmmlu_answer_generation_electrical_engineering.ERI-Engineering-Reasoning-and-Instruction
ERI — Engineering Reasoning and Instruction
53,429 instruction–response pairs spanning nine engineering disciplines, seven
question types and three difficulty levels, for instruction-tuning and evaluating
language models on engineering reasoning.
from datasets import load_dataset
eri = load_dataset("mznaser/ERI-Engineering-Reasoning-and-Instruction")
civil = eri.filter(lambda r: r["field"] == "civil_engineering")
Schema
Every record has six fields:
Field… See the full description on the dataset page: https://huggingface.co/datasets/mznaser/ERI-Engineering-Reasoning-and-Instruction.data-engineering-sft-100k
Data Engineering SFT 100K
A synthetic supervised fine-tuning dataset of 100,000 high-quality conversations covering modern data engineering practices — Apache Spark, dbt, Airflow, Kafka, Delta Lake, BigQuery, and Snowflake. Designed to train AI assistants that can help data engineers build, optimize, and debug production data pipelines.
Dataset Description
This dataset covers the full spectrum of data engineering across 7 specialized categories. Each record… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/data-engineering-sft-100k.exLong-dataset
exLong Dataset
This dataset is used to train and evaluate the exLong models on generating exceptional-behavior tests.
It has two subsets:
'with-EBT-name': provides the target test name in the prompt
'no-EBT-name': does not provide the target test name in the prompt
NOTE: the data format is customized for Code Llama models.
Language
This is a Java dataset
Dataset Structure
The input for the model contains the following context:
Method under test (MUT)… See the full description on the dataset page: https://huggingface.co/datasets/EngineeringSoftware/exLong-dataset.csl-electrical-engineering
csl-electrical-engineering"
由CSL數據集分割出來的電機工程(Electrical Engineering)子集，提供簡繁兩種版本。
from datasets import load_dataset
dataset = load_dataset("p208p2002/csl-electrical-engineering","zh-cn")
dataset = load_dataset("p208p2002/csl-electrical-engineering","zh-tw")
engineering-log-triage-dataset
Engineering Log Triage Dataset
Summary
This dataset contains synthetic/sanitized engineering-log examples for structured fault triage.
Each example is formatted as a chat-style supervised fine-tuning record. The model input is an unstructured engineering report. The target assistant message is a strict JSON object containing a structured triage result.
This dataset was created for the LoRA-Adapted Engineering Log Triage Service project.
Intended Use… See the full description on the dataset page: https://huggingface.co/datasets/cobra9786/engineering-log-triage-dataset.Electrical-engineering-ruTranslated instructions from STEM-AI-mtl/Electrical-engineering into Russian using gemini-flash-1.5-8b.
Electrical-engineering
To the electrical engineering community
This dataset contains Q&A prompts about electrical engineering, Kicad's EDA software features and scripting console Python codes.
Authors
STEM.AI: stem.ai.mtl@gmail.comWilliam Harbec
prompt-engineering-fr
Prompt Engineering FR - Techniques, Evaluation et Gestion du Contexte
Dataset bilingue complet sur le Prompt Engineering, l'evaluation de LLM et la gestion de la fenetre de contexte.
Cree par AYI NEDJIMI Consultants - Expertise en Intelligence Artificielle et Transformation Digitale.
Description
Ce dataset couvre l'ensemble des techniques modernes de prompt engineering, les benchmarks et metriques d'evaluation de LLM, ainsi que les strategies de gestion de la fenetre de… See the full description on the dataset page: https://huggingface.co/datasets/AYI-NEDJIMI/prompt-engineering-fr.llm-engineering-sft-8000
大模型工程化 SFT 数据集
这是一个面向大模型研发与工程化应用的中文 Alpaca 格式 SFT 数据集，共 8,000 条样本。
字段
instruction：用户问题或指令
input：空字符串
output：助手回答
主题范围
数据集覆盖 LoRA、QLoRA、PEFT、SFT、RAG、向量检索、Embedding、LLM Agent、工具调用、量化、推理部署、LLMOps、基础设施、模型评估、安全对齐等方向。
数据处理说明
数据来自经过清理的 Markdown 学习资料，并经过问题生成、答案生成、问题级质检、答案级离线分流、人工抽检和最终筛选。最终版本只保留 8,000 条主训练样本，字段已按 Alpaca 格式整理。
加载示例
from datasets import load_dataset
dataset = load_dataset("Nwna/llm-engineering-sft-8000")
print(dataset["train"][0])… See the full description on the dataset page: https://huggingface.co/datasets/Nwna/llm-engineering-sft-8000.synthetic-hvac-engineering-precheck
synthetic-hvac-engineering-precheck (v0.1)
Scope
Programmatically generated Q/A pairs for building-services quick checks (HVAC/hydronics). Focus is on:
unit hygiene (explicit conversions)
deterministic equations and step-by-step workflows
basic sanity checks and “missing inputs” prompts
This is pre-design / pre-check material. It is not a substitute for code-compliant design or professional sign-off.
Data
Each row is a JSON object with a single field:
text:… See the full description on the dataset page: https://huggingface.co/datasets/Irfanuruchi/synthetic-hvac-engineering-precheck.prompt-engineering-en
Prompt Engineering EN - Techniques, Evaluation & Context Management
Comprehensive bilingual dataset on Prompt Engineering, LLM evaluation, and context window management.
Created by AYI NEDJIMI Consultants - Expertise in Artificial Intelligence and Digital Transformation.
Description
This dataset covers all modern prompt engineering techniques, LLM evaluation benchmarks and metrics, and context window management strategies. It is based on three reference articles:
Prompt… See the full description on the dataset page: https://huggingface.co/datasets/AYI-NEDJIMI/prompt-engineering-en.Circuit-Analysis-Reasoning-Sample
⚡ EngineeringWays Data Lab: Circuit Analysis Reasoning Dataset (Free Sample)
This is a free 50-item sample of the EngineeringWays Circuit Analysis Reasoning Dataset. It is designed specifically for fine-tuning Large Language Models (LLMs) in advanced STEM problem-solving, featuring strict Chain-of-Thought (CoT) reasoning.
Want the complete, deduplicated 592-item master dataset? 👉 Get the LoRA-Ready Master File on Payhip
🚀 Dataset Overview
Most math and physics… See the full description on the dataset page: https://huggingface.co/datasets/EngineeringWays/Circuit-Analysis-Reasoning-Sample.EngineeringConcepts-Instruct-v1
EngineeringConcepts-Instruct-v1
EngineeringConcepts-Instruct-v1 is a synthetic engineering instruction dataset designed for supervised fine-tuning of language models on modern engineering and technology concepts. It covers diverse domains including computer engineering, data science and AI/ML, modern LLMs, generative and agentic AI, cybersecurity, electrical engineering, electronics engineering, the Internet of Things (IoT), and mechanical engineering through clear explanations… See the full description on the dataset page: https://huggingface.co/datasets/kd13/EngineeringConcepts-Instruct-v1.building-engineering-synthetic-dataset-v5
Building Engineering Synthetic Dataset (V5)
Repository: Irfanuruchi/building-engineering-synthetic-dataset-v5
This repository contains a synthetic dataset for training engineering reasoning models focused on building engineering calculations and sanity checks.
The dataset was generated using physics-based engineering equations and structured prompts suitable for LLM fine-tuning.
It was used to train:
Irfanuruchi/qwen2.5-1.5b-buildeng-precheck-lora-v5
Dataset Overview… See the full description on the dataset page: https://huggingface.co/datasets/Irfanuruchi/building-engineering-synthetic-dataset-v5.social-engineering-qa-english
Social Engineering Q&A Dataset (English)
Overview
An English question–answer corpus for social engineering and cybersecurity, derived from curated knowledge articles extracted from authoritative reference books.
This release is part of a bilingual research dataset engineering pipeline designed for
supervised fine-tuning (SFT), retrieval-augmented generation (RAG) evaluation, and
domain-specific language model benchmarking in cybersecurity education.
Each record is… See the full description on the dataset page: https://huggingface.co/datasets/smd20/social-engineering-qa-english.Electrical-engineering
To the electrical engineering community
This dataset contains Q&A prompts about electrical engineering, Kicad's EDA software features and scripting console Python codes.
Authors
STEM.AI: stem.ai.mtl@gmail.comWilliam Harbec
social-engineering-qa-persian
Social Engineering Q&A Dataset (Persian / Farsi)
Overview
A Persian (Farsi) question–answer corpus for social engineering and cybersecurity, derived from curated knowledge articles extracted from authoritative reference books.
This release is part of a bilingual research dataset engineering pipeline designed for
supervised fine-tuning (SFT), retrieval-augmented generation (RAG) evaluation, and
domain-specific language model benchmarking in cybersecurity education.… See the full description on the dataset page: https://huggingface.co/datasets/nezamisafa/social-engineering-qa-persian.social-engineering-qa-english
Social Engineering Q&A Dataset (English)
Overview
An English question–answer corpus for social engineering and cybersecurity, derived from curated knowledge articles extracted from authoritative reference books.
This release is part of a bilingual research dataset engineering pipeline designed for
supervised fine-tuning (SFT), retrieval-augmented generation (RAG) evaluation, and
domain-specific language model benchmarking in cybersecurity education.
Each record is… See the full description on the dataset page: https://huggingface.co/datasets/nezamisafa/social-engineering-qa-english.omni-engineering
GitHub
Website
Paper (Coming Soon)
Dataset Details
This dataset is a combination of engineering-related Wikipedia articles and academic papers on engineering concepts. This dataset contains only continued-pretrain data.
Sources
This dataset was sourced from the following open-sourced datasets:
Engineering
legacy-datasets/wikipedia
laion/engrxiv-pdf
social-engineering-qa-persian
Social Engineering Q&A Dataset (Persian / Farsi)
Overview
A Persian (Farsi) question–answer corpus for social engineering and cybersecurity, derived from curated knowledge articles extracted from authoritative reference books.
This release is part of a bilingual research dataset engineering pipeline designed for
supervised fine-tuning (SFT), retrieval-augmented generation (RAG) evaluation, and
domain-specific language model benchmarking in cybersecurity education.… See the full description on the dataset page: https://huggingface.co/datasets/smd20/social-engineering-qa-persian.knowledgebase-electric_engineering_test_dataThis dataset are based on question answering iterations of this dataset:
"STEM-AI-mtl/Electrical-engineering"
Question answering using Deepseek R1 from TogetherAI API checkpoint
Usage:
Reasoning trace data to injecteed as CoT chain in SCIENCE related task.