datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
python_code_instructions_18k_alpaca
Dataset Card for python_code_instructions_18k_alpaca
The dataset contains problem descriptions and code in python language.
This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. Refer to the source here.
code-alchemy
CodeAlchemy
CodeAlchemy is a synthetic code dataset (~976.6B tokens, ~162M rows) designed for training and evaluating code language models. It consists of 5 training subsets covering a range of code-related tasks, and 2 evaluation subsets. All files are Parquet with zstd compression with on-disk size ~873 GB. Raw source files are not included due to ownership considerations and must be manually fetched as instructed below.
Dataset Statistics
Config… See the full description on the dataset page: https://huggingface.co/datasets/open-alchemy/code-alchemy.CodeX-2M-Thinking
Modotte
Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning.
This dataset is fully synthetic, curated from high-quality public sources and enhanced with synthetic data generated using both closed and open-source models. It serves as a strong foundation for instruction-based model tuning and fine-tuning, offering one of the… See the full description on the dataset page: https://huggingface.co/datasets/Modotte/CodeX-2M-Thinking.CodeMMLU
CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities
📌 CodeMMLU
CodeMMLU is a comprehensive benchmark designed to evaluate the capabilities of large language models (LLMs) in coding and software knowledge.
It builds upon the structure of multiple-choice question answering (MCQA) to cover a wide range of programming tasks and domains, including code generation, defect detection, software engineering principles, and much more.
📄… See the full description on the dataset page: https://huggingface.co/datasets/Fsoft-AIC/CodeMMLU.code_instructions_120k_alpaca
Dataset Card for code_instructions_120k_alpaca
This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. Refer to the original source here.
CodeX-7M-Non-Thinking
Modotte
Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning.
This dataset is curated from high-quality public sources and enhanced with synthetic data from both closed and open-source models. It serves as a strong foundation for instruction-based model tuning and fine-tuning, offering one of the most refined and extensive… See the full description on the dataset page: https://huggingface.co/datasets/Modotte/CodeX-7M-Non-Thinking.QA-Codes-perfectcode-sante-publique
Code de la santé publique, non-instruct (2025-07-11)
The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects.
Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-sante-publique.CodeX-2M-Thinking
Modotte
Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning.
This dataset is fully synthetic, curated from high-quality public sources and enhanced with synthetic data generated using both closed and open-source models. It serves as a strong foundation for instruction-based model tuning and fine-tuning, offering one of the… See the full description on the dataset page: https://huggingface.co/datasets/adrianmele/CodeX-2M-Thinking.WestGenesis-Coder-SFT-100M
Dataset Overview
WestGenesis-Coder-Dataset is a meticulously curated coding dataset designed specifically for instruction-based model tuning and fine-tuning of existing models with enhanced code generation capabilities. This represents one of the largest and most comprehensively filtered corpora of publicly available coding data on the Hugging Face platform, with a non-thinking approach that emphasizes direct, concise code outputs for rapid model training.
Key… See the full description on the dataset page: https://huggingface.co/datasets/isthatshan/WestGenesis-Coder-SFT-100M.code_contest_python3_alpaca
Dataset Card for Code Contest Processed
Dataset Summary
This dataset contains coding contest questions and their solution written in Python3.
This dataset is created by processing code_contest dataset from Deepmind. It is a competitive programming dataset for machine-learning. Read more about dataset at original source.
Columns Description
id : unique string associated with a problem
description : problem description
code : one correct code for the problem… See the full description on the dataset page: https://huggingface.co/datasets/iamtarun/code_contest_python3_alpaca.code-commande-publique
Code de la commande publique, non-instruct (2025-09-20)
The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects.
Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-commande-publique.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.python-code-instructions-japanese
Python Code Instructions - Japanese (18K)
Dataset Description
This dataset contains 18,612 Python programming instruction-response pairs translated to Japanese. It's designed for training language models to understand and generate Python code based on Japanese instructions.
Key Features
18,612 entries covering diverse Python programming tasks
Japanese instructions and prompts for code generation
Original English text preserved for reference
Python code… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/python-code-instructions-japanese.code-verl-unified
Unified Code VERL Dataset
Overview
This dataset aggregates seven code-reasoning collections into a single VERL-formatted repository containing approximately 958,539 unique problems. The compilation prioritizes consistent extra_info structure across all source materials for seamless compatibility with VERL training frameworks.
Dataset Composition
Seven distinct splits comprise the collection:
Split
Problems
Percentage
Format
kodcode_v1_verl
434,876… See the full description on the dataset page: https://huggingface.co/datasets/sungyub/code-verl-unified.code-conflict
Code Conflict Dataset
A dataset of 100 visual Python code conflict samples designed to evaluate Vision-Language Models (VLMs) under cross-modal conflicts (discrepancy between code screenshots and caption text).
Dataset Statistics
Total Rows: 100 samples
Language: English (english)
Categories: 5 distinct Python code conflict_types (20 samples per category):
operator_substitution (Rows 1–20): Swapping math or logic operators (e.g., + to -, == to !=, or to and).… See the full description on the dataset page: https://huggingface.co/datasets/multilingual-vlm-conflict/code-conflict.code-collectivites-territoriales
Code général des collectivités territoriales, non-instruct (2025-09-20)
The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects.
Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-collectivites-territoriales.CodeFeedback-Python105K
Note
This subset is derived from the m-a-p/CodeFeedback-Filtered-Instruction dataset, which contains 156,526 samples.
CodeFeedback-Filtered-Instruction is a curated collection of code instruction queries extracted from four prominent open-source code instruction tuning datasets: Magicoder-OSS-Instruct, Python code subset of ShareGPT, Magicoder-Evol-Instruct, and Evol-Instruct-Code.
From this dataset, we specifically selected only the 104,848 samples that are written in Python.… See the full description on the dataset page: https://huggingface.co/datasets/fxmeng/CodeFeedback-Python105K.Rust-Coder
Rust-Coder
Rust-Coder is a comprehensive text dataset designed for Rust programming language learning. It contains 12,000 unique samples focusing on distinct Rust concepts, code snippets, and explanations.
Dataset Structure
Each sample consists of:
id: A unique UUID.
instruction: A prompt or question about a Rust concept.
code: An idiomatic Rust code snippet.
explanation: A detailed explanation of the concept and code.
category: The high-level Rust category (e.g.… See the full description on the dataset page: https://huggingface.co/datasets/Convence/Rust-Coder.CodeX-2M-Thinking
Modotte
Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning.
This dataset is fully synthetic, curated from high-quality public sources and enhanced with synthetic data generated using both closed and open-source models. It serves as a strong foundation for instruction-based model tuning and fine-tuning, offering one of… See the full description on the dataset page: https://huggingface.co/datasets/me-aas/CodeX-2M-Thinking.Code-Reasoning
Code-Reasoning: Quality Filtered Dataset
A high-quality, curated subset of the OpenCodeReasoning-2 dataset for training competitive programming models. This dataset has been processed with rigorous quality filtering and question reconstruction to ensure optimal training data.
📊 Dataset Overview
This dataset contains competitive programming problems with high-quality solutions, specifically filtered and processed from the original nvidia/OpenCodeReasoning-2 dataset.… See the full description on the dataset page: https://huggingface.co/datasets/GetSoloTech/Code-Reasoning.CodeX-2M-Thinking
Modotte
Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning.
This dataset is fully synthetic, curated from high-quality public sources and enhanced with synthetic data generated using both closed and open-source models. It serves as a strong foundation for instruction-based model tuning and fine-tuning, offering one of the… See the full description on the dataset page: https://huggingface.co/datasets/txchmechanicus/CodeX-2M-Thinking.fenic-codebaseoffsec_redteam_codes
OffSec RedTeam Codes
Token count: ~30B tokens.
OffSec RedTeam Codes is a curated corpus of code (and some auxiliary text) extracted from popular GitHub repositories related to offensive security / red teaming (pentesting, OSINT, C2, privilege escalation, exploitation, forensics, etc.). It is also the largest open-source dataset of red-team and offensive-security code ever compiled.
⚠️ Ethical use only. This dataset is for research, education, and defensive security testing in… See the full description on the dataset page: https://huggingface.co/datasets/tandevllc/offsec_redteam_codes.Code-Vulnerability-FineTune
🔐 Code Vulnerability FineTome — CWE-Enriched Conversation Dataset
📌 Overview
This dataset converts raw security-labeled C/C++ code samples into instruction-following conversation pairs suitable for fine-tuning large language models (LLMs) on software vulnerability detection and analysis.
It is built by preprocessing and transforming the ChamaraVishwajithRajapaksha/Code_Vulnerability_Dataset (330k rows, sourced from DiverseVul + MITRE CWE enrichment) into… See the full description on the dataset page: https://huggingface.co/datasets/ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune.code_contest_processed
Dataset Card for Code Contest Processed
Dataset Summary
This dataset is created by processing code_contest dataset from Deepmind. It is a competitive programming dataset for machine-learning. Read more about dataset at original source.
Columns Description
id : unique string associated with a problem
description : problem description
code : one correct code for the problem
language : programming language used for code
test_samples : contains inputs and their… See the full description on the dataset page: https://huggingface.co/datasets/iamtarun/code_contest_processed.OpenMathReasoning-mini-ko
OpenMathReasoning-mini Korean (한국어 번역)
이 데이터셋은 unsloth/OpenMathReasoning-mini 데이터셋을 한국어로 번역한 버전입니다.
원본 데이터셋은 nvidia/OpenMathReasoning 에서 파생되었습니다.
데이터셋 정보
항목
내용
원본 데이터셋
unsloth/OpenMathReasoning-mini
원본 출처
nvidia/OpenMathReasoning
라이선스
CC-BY-4.0
행 수
19,252
언어
한국어 (Korean), 영어 (English)
번역 대상 컬럼
expected_answer, problem, generated_solution
라이선스 및 귀속
이 데이터셋은 원본 데이터셋인 nvidia/OpenMathReasoning의 CC-BY-4.0 라이선스를 따릅니다.
원저작자: NVIDIA… See the full description on the dataset page: https://huggingface.co/datasets/neuralfoundry-coder/OpenMathReasoning-mini-ko.code-minier
Code minier, non-instruct (2025-09-18)
The objective of this project is to provide researchers, professionals and law students with simplified, up-to-date access to all French legal texts, enriched with a wealth of data to facilitate their integration into Community and European projects.
Normally, the data is refreshed daily on all legal codes, and aims to simplify the production of training sets and labeling pipelines for the development of free, open-source language models based… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/code-minier.Code-170k-twi
This dataset is shared under CC BY-NC 4.0, which means you are free to use, share, and adapt it for non-commercial research and educational purposes with attribution. You can read the full license at https://creativecommons.org/licenses/by-nc/4.0/.
This dataset is made available because of Ghana NLP's volunteer driven research work. Please consider contributing to any of our projects on Github
Dataset Description
Code-170k-twi is a groundbreaking dataset containing 176,999… See the full description on the dataset page: https://huggingface.co/datasets/ghanaopenai/Code-170k-twi.CodeGym
Generalizable End-to-End Tool-Use RL with Synthetic CodeGym
CodeGym is a synthetic environment generation framework for LLM agent reinforcement learning on multi-turn tool-use tasks. It automatically converts static code problems into interactive and verifiable CodeGym environments where agents can learn to use diverse tool sets to solve complex tasks in various configurations — improving their generalization ability on out-of-distribution (OOD) tasks.
GitHub Repository:… See the full description on the dataset page: https://huggingface.co/datasets/VanishD/CodeGym.