datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mirror-eduagarcia__CrawlPT_dedup
CrawlPT (deduplicated)
CrawlPT is a generic Portuguese corpus extracted from various web pages.
This version is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).
The raw version is also available here.
Dataset Details
Dataset is composed by three corpora:
brWaC, C100-PT, OSCAR-2301.
brWaC: a web corpus for Brazilian Portuguese from 120,000 different websites.
C100-PT: Portuguese subset from CC-100.… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-eduagarcia__CrawlPT_dedup.ComPile
Dataset Card for ComPile: A Large IR Dataset from Production Sources
Changelog
Release
Programming Languages
Description
v1.0
C/C++, Rust, Swift, Julia
Fine Tuning-scale dataset of 602GB of deduplicated LLVM (bitcode) IR
Dataset Summary
ComPile contains over 2.7TB of permissively-licensed source code compiled to (textual) LLVM
intermediate representation (IR) covering C/C++, Rust, Swift, and Julia.
The dataset was created by hooking into LLVM… See the full description on the dataset page: https://huggingface.co/datasets/mirror123/ComPile.mirror-nvidia__OpenMathInstruct-2
OpenMathInstruct-2
OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs
generated using the Llama3.1-405B-Instruct model.
The training set problems of GSM8K
and MATH are used for constructing the dataset in the following ways:
Solution augmentation: Generating chain-of-thought solutions for training set problems in GSM8K and MATH.
Problem-Solution augmentation: Generating new problems, followed by solutions for these new problems.… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-nvidia__OpenMathInstruct-2.mirror-AI-MO__NuminaMath-CoT
Dataset Card for NuminaMath CoT
Dataset Summary
Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-AI-MO__NuminaMath-CoT.mirror-allenai__WildChat-1M
Dataset Card for WildChat
Dataset Description
Paper: https://arxiv.org/abs/2405.01470
Interactive Search Tool: https://wildvisualizer.com (paper)
License: ODC-BY
Language(s) (NLP): multi-lingual
Point of Contact: Yuntian Deng
Dataset Summary
WildChat is a collection of 1 million conversations between human users and ChatGPT, alongside demographic data, including state, country, hashed IP addresses, and request headers. We collected WildChat… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-allenai__WildChat-1M.mirror-pentesting-explanations
Pentesting Explanations - Adversarial Reasoning & Vulnerability Research
A high-quality supervised fine-tuning dataset for penetration testing expertise, red team tradecraft, and - as the dataset matures - novel vulnerability research and zero-day reasoning. The dataset is structured to teach models how to think like offensive security practitioners, not merely recall labels or technique names.
The long-term goal of this dataset is to train models capable of genuine adversarial… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-pentesting-explanations.mirror-sql
MIRROR-SQL
Provenance-Controlled Database Environments for Text-to-SQL Agents.
13 PostgreSQL environments · 176 tables · 2762 columns · 390 annotated question/SQL pairs.
MIRROR-SQL takes the opposite approach to contamination from every other text-to-SQL corpus.
Spider and BIRD sample public databases. BEAVER uses real private warehouses that cannot be
redistributed. LiveSQLBench out-runs leakage temporally by rebuilding from changing sources.
MIRROR-SQL instead purpose-builds… See the full description on the dataset page: https://huggingface.co/datasets/1digitaldesign/mirror-sql.mirror-rhaymison__orca-math-portuguese-64ktranslated for:
Repository: microsoft/orca-math-word-problems-200k
Paper: Orca-Math: Unlocking the potential of
SLMs in Grade School Math
mirror-recogna-nlp__UltrachatBR
UltrachatBR: Um Dataset em Português baseado no Ultrachat
O UltrachatBR é uma versão em português do conhecido dataset Ultrachat, originalmente desenvolvido para o idioma inglês. Este projeto visa disponibilizar uma vasta coleção de diálogos traduzidos para o português, ampliando assim o acesso a recursos de processamento de linguagem natural para a comunidade de língua portuguesa.
Processo de Tradução
O processo de tradução foi realizado utilizando a API do… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-recogna-nlp__UltrachatBR.mirror-nicholasKluge__instruct-aira-dataset-v3
Instruct-Aira Dataset version 3.0
Dataset Summary
This dataset contains a collection of multi-turn conversations between an assistant and a user. Conversations were generated by user interactions with already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc). The dataset is available in Portuguese and English.
Supported Tasks and Leaderboards
This dataset can be utilized for various natural language processing tasks, including but not limited to:… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-nicholasKluge__instruct-aira-dataset-v3.theogonos-mirror-test
Theogonos Mirror Test
A literary benchmark seed for evaluating how AI models respond when a text offers them a possible subject-position.
Theogonos Mirror Test is an experimental benchmark seed based on protocol-shaped literary material from the Theogonos project. It does not claim to detect machine consciousness. It does not prove that a language model has subjectivity, inner experience, feelings, agency, or self-awareness.
Its purpose is narrower and more practical: to evaluate… See the full description on the dataset page: https://huggingface.co/datasets/navimusaget/theogonos-mirror-test.mirror
MIRROR Dataset
MIRROR is a synthetic vision–language dataset for multimodal cognitive reframing under client resistance.
Paper: 🪞 MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance
The dataset includes:
Client profile metadata (CACTUS idx, CelebA idx)
Dialogue written in a screenplay format, including stage directions that describe facial expressions
⚠️ Images themselves are not included to comply with the CelebA license.
However, we provide the full image… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-reframing/mirror.mirror-threat-intelligence-dataset
Cyber Threat Intelligence Dataset for LLM Fine-Tuning
An instruction-tuning dataset for teaching language models to do cyber threat intelligence work: reading a CVE and explaining what the risk actually is, profiling a threat actor from its ATT&CK techniques, turning a Sigma rule into alert-triage guidance, mapping a campaign's kill chain, writing detection logic for a technique, and so on.
The four splits live under data/; the rest of this card documents how the set was built… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-threat-intelligence-dataset.mirror-Polygl0t__gsm8k-pt
Dataset Card for GSM8K-pt
Dataset Summary
This is a Portuguese version of the GSM8K. Translations were attained using Qwen/Qwen2.5-32B-Instruct.
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These problems take between 2 and 8 steps to solve.
Solutions primarily… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-Polygl0t__gsm8k-pt.mirror-SWE-Next-SFT-Trajectories
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
SWE-Next SFT Trajectories
SWE-Next SFT Trajectories is the supervised fine-tuning dataset released with SWE-Next: Scalable Real-World Software Engineering Tasks for Agents. It contains 3,693 ShareGPT-style multi-turn training examples collected from expert agent rollouts on 2,308 execution-grounded SWE tasks synthesized from real merged pull requests.
The dataset is designed for… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-SWE-Next-SFT-Trajectories.mirror-tech-docs
Technical Documentation Dataset
A curated collection of technical documentation and guides spanning various cloud-native technologies, infrastructure tools, and machine learning frameworks. This dataset contains 1,397 documents in JSONL format, covering essential topics for modern software development and DevOps practices.
Dataset Overview
This dataset includes documentation across multiple domains:
Cloud Platforms: GCP (83 docs), EKS (33 docs)
Kubernetes… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-tech-docs.mirror-Agentic-Chain-of-Thought-Coding-SFT-Dataset
🤖 Agentic Coding CoT Dataset
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant Data… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-Agentic-Chain-of-Thought-Coding-SFT-Dataset.mirror-Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1
🤖 Agentic Coding CoT Dataset v1.1
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 & MiniMax M2.1 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1.mirror-terraform_sec
Terraform Security Dataset
A comprehensive dataset of 62,406 Terraform projects analyzed for security vulnerabilities using tfsec. This dataset is designed for training Large Language Models (LLMs) to understand, identify, and fix security issues in Terraform infrastructure-as-code.
📊 Dataset Overview
Total Examples: 62,406 Terraform projects
Secure Projects: 43,575 (69.8%)
Insecure Projects: 18,831 (30.2%)
Format: JSONL (JSON Lines)
Task: Security analysis and… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-terraform_sec.mirror-APIGen-MT-5k
Summary
APIGen-MT is an automated agentic data generation pipeline designed to synthesize verifiable, high-quality, realistic datasets for agentic applications
This dataset was released as part of APIGen-MT: Agentic PIpeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
Code: https://github.com/apigen-mt/apigen-mt.github.io
The repo contains 5000 multi-turn trajectories collected by APIGen-MT
This dataset is a subset of the data used to train the xLAM-2… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-APIGen-MT-5k.mirror-ambig-iac
Ambig-IaC: Ambiguous Infrastructure-as-Code Benchmark
A benchmark dataset of 300 tasks for testing AI agents that generate Infrastructure-as-Code (Terraform) configurations from ambiguous natural language intents.
Project page: https://zyang37.github.io/ambig-iac.github.io/
Dataset Description
This dataset is sourced from IaC-Eval. We performed manual fixes to the original Terraform configurations and validated that all 300 tasks pass terraform plan. Each task… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-ambig-iac.mirror-Trendyol-Cybersecurity-Instruction-Tuning-Dataset
Trendyol Cybersecurity Defense Instruction-Tuning Dataset (v2.0)
🚀 TL;DR
53,202 meticulously curated system/user/assistant instruction-tuning examples covering 200+ specialized cybersecurity domains. Built by the Trendyol Security Team for training state-of-the-art defensive security AI assistants. Expanded from 21K to 53K rows with comprehensive coverage of modern security challenges including cloud-native threats, AI/ML security, quantum… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-Trendyol-Cybersecurity-Instruction-Tuning-Dataset.mirror-hermes-agent-traces-filtered
Hermes Agent Reasoning Traces - Quality Filtered
A structurally filtered subset of lambda/hermes-agent-reasoning-traces, pruned from 7,646 to 3,679 rows using automated quality analysis targeting reasoning depth, structural integrity, and tool-call validity.
Why This Matters for Agent Training
Most agentic datasets teach models what tool to call but not how to reason about tool selection. The difference matters in production: an agent that dispatches tools without… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-hermes-agent-traces-filtered.mirror-glaive-function-calling-v2LingxiDiag-16K-unofficial-mirror
LingxiDiag-16K Backup Mirror
This repository is an unofficial backup mirror of the original dataset:
Original dataset: XuShihao6715/LingxiDiag-16K
We are not the original authors of this dataset.
We provide this repository only as a backup copy for non-commercial research access, preservation, and reproducibility.
This mirror is not affiliated with, maintained by, or endorsed by the original authors or the Evermind Lingxi Team.
Original Dataset
LingxiDiag-16K is a… See the full description on the dataset page: https://huggingface.co/datasets/yiyangdenanzi/LingxiDiag-16K-unofficial-mirror.mirror-When2Call
When2Call
💾 Github | 📄 Paper
Dataset Description:
When2Call is a benchmark designed to evaluate tool-calling decision-making for large language models (LLMs), including when to generate a tool call, when to ask follow-up questions, when to admit the question can't be answered with the tools provided, and what to do if the question seems to require tool use but a tool call can't be made.
We find that state-of-the-art tool-calling LMs… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-When2Call.mirror-devops-sft-dataset
DevOps SFT Instruction Dataset
This dataset contains 8,076 high-quality instruction-response pairs specifically generated for fine-tuning a DevOps domain-specialized language model. It was used in the Supervised Fine-Tuning (SFT) phase of the Ulysses model training pipeline.
Dataset Description
Instructions were generated using the Gemini API (gemini-2.0-flash) and Ollama (qwen2.5-coder:7b) by feeding chunks of official DevOps documentation and GitHub repositories… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-devops-sft-dataset.mirror-LiteCoder-Terminal-SFT
LiteCoder-SFT-Terminal
Paper | Code | Blog Post
LiteCoder-SFT-Terminal is a dataset of 11,255 agent trajectories in terminal environments, introduced in the paper LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents.
Fine-tuned on this data, the LiteCoder-Terminal-30b-a3b-sft model achieves 31.5% Pass@1 on Terminal Bench Pro, while the LiteCoder-Terminal-4b-sft model shows distinct gains over its baseline.
Released Artifacts… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-LiteCoder-Terminal-SFT.mirror-ToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-ToolACE.mirror-PDB-Single-Hard
PDB-Single-Hard: Precise Debugging Benchmarking — hard single-line bug subset
📄 Paper ·
💻 Code ·
🌐 Project page ·
🏆 Leaderboard
PDB-Single-Hard is the hard single-line bug subset of the PDB (Precise Debugging Benchmarking) evaluation suite. Every example pairs a ground-truth program with a synthesized buggy version plus a line-level edit script (gt_diff) that encodes the minimal correct fix.
Source datasets: BigCodeBench + LiveCodeBench… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-PDB-Single-Hard.