datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
python_code_docstring_ast_corpus
Overview
This dataset contains 34,000+ rows of code-docstring-ast data along with additional metadata. Data was gathered from various Python libraries and frameworks and their
publicly available GitHub repos. This dataset was created for the purpose of training the CodeT5+ transformer on AST-enhanced code-to-doc tasks.
Sources
The dataset was gathered from various GitHub repos sampled from this repo by Vinta.
The 26 repos are:
matplotlib
pytorch
cryptography
django… See the full description on the dataset page: https://huggingface.co/datasets/Mir-2002/python_code_docstring_ast_corpus.audio-music-mir-post-public
audio-music-mir-post-public
Music information retrieval and tagging annotations: genre (FMA), instrument family (NSynth × 3, Medley-solos-DB), social tags (MagnaTagATune via LLARK), and large-scale Music4All metadata. Foundation for music understanding heads in audio LLMs.
Audio is not bundled in this repo. See download.sh and per-dataset data/<name>.info.json for the fetch recipe; run postlink_audio.py after fetching to rewrite the JSONL audio_path fields with absolute local… See the full description on the dataset page: https://huggingface.co/datasets/vhands/audio-music-mir-post-public.shangkhachil-bengali-public-domain
Bengali Public-Domain Literature
101 complete works by 21 authors,
11,250,629 characters. Corpus corpus-f8c532fcb4e7, built 2026-09-09.
Where these texts are read
https://shangkhachil.com — the reading site this corpus was built for. Free, no
account, 246 works by 28 authors. The complete text of
every work in this file can be read there.
This file is the text. The site is the part a JSONL cannot be:
Rights computed for the reader's own country, at the edge… See the full description on the dataset page: https://huggingface.co/datasets/mir178/shangkhachil-bengali-public-domain.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.mirage-engine-ledger
Mirage Engine Campaign Ledger
A hash-chained, append-only research journal: 29,707 JSONL records in which each
entry carries a SHA-256 entry_hash over its own body and a prev_hash linking
it to its predecessor. The chain is independently verifiable from the file alone.
Author: Christopher Betances — catqualia.com
License: CC BY 4.0 (see LICENSE)
Language: English (record text); structured JSON in meta
Records: 29,707
Time span: 2026-08-16 03:51:13 UTC → 2026-08-19 05:27:40 UTC… See the full description on the dataset page: https://huggingface.co/datasets/CatQualia/mirage-engine-ledger.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.ada_diabetes_5000_instruction
ADA Diabetes Instruction Dataset (5,000 Samples)
This dataset contains 5,000 synthetic yet clinically-informed patient cases for Type 2 diabetes, designed for instruction tuning of language models (e.g., Gemma 3, Unsloth) to recommend ADA guideline-based therapies with drug-specific dosing.
Dataset Overview
Task: Given a patient profile, recommend ADA-aligned diabetes treatment including therapy, drug-specific starting doses, and rationale.
Size: 5,000 examples… See the full description on the dataset page: https://huggingface.co/datasets/mirfan899/ada_diabetes_5000_instruction.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-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.python-google-style-docstrings
Overview
This dataset contains Python code-docstring pairs, whereas the docstrings are in Google style. A Google style docstring is structured as follows:
<Description of the code>
Args:
<var1> (<data-type>) : <description of var1>
<var2> (<data_type>) : <description of var2>
Returns:
<var3> (<data-type>) : <description of var3>
Raises:
<var4> (<data-type>) : <description of var4>
The format varies widely (like additional sections such as Examples, Notes, etc) but generally… See the full description on the dataset page: https://huggingface.co/datasets/Mir-2002/python-google-style-docstrings.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-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-v2mirror-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.crypto_whitepaper_public
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/mirko5301/crypto_whitepaper_public.mirror-agent-sft
Nex Agent-SFT Dataset
Paper | Code | Project Page
Dataset Description
This dataset is specifically designed for training the agentic capabilities of Large Language Models (LLMs). The dataset covers multiple agent scenarios and aims to enhance model performance in autonomous decision-making, tool usage, code generation, and interactive task handling. We reselected some of the training queries from the NEX-N1 training dataset and regenerated the… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-agent-sft.mirror-WebWorldData
WebWorldData 🌐
Overview
WebWorldData is a large-scale dataset of 1.06M web interaction trajectories collected from the open web, designed for training browser world models. It is the training data behind the WebWorld model series.
Each trajectory consists of sequences of (state, action, next_state) transitions, where states are represented as A11y Trees extracted from real websites using Playwright.
Dataset Statistics… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-WebWorldData.mirror-ToolMind
ToolMind: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset
ToolMind is a large-scale, high-quality tool-agentic dataset with 160k synthetic data instances generated using over 20k tools and 200k augmented open-source data instances.
Our data synthesis pipeline first constructs a function graph based on parameter correlations and then uses a multi-agent framework to simulate realistic user–assistant–tool interactions.
Beyond trajectory-level validation, we employ fine-grained… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-ToolMind.mirror-GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversations, input, output, domain, and meta fields.
This release was prepared from the original dataset published by Kassadin88.
Summary
Teacher model in the data:… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-GLM-5.1-Reasoning-1M-Cleaned.