datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
code_contests_instruct
Dataset Card for "code_contests_instruct"
The deepmind/code_contests dataset formatted as markdown-instruct for text generation training.
There are several different configs. Look at them. Comments:
flesch_reading_ease is computed on the description col via textstat
hq means that python2 (aka PYTHON in language column) is dropped, and keeps only rows with flesch_reading_ease 75 or greater
min-cols drops all cols except language and text
possible values for language are {'CPP'… See the full description on the dataset page: https://huggingface.co/datasets/BEE-spoke-data/code_contests_instruct.code_contest_instruct_cppCodeMaster-Phi-Instruct
Code Master Phi is a compiled dataset designed for training Phi3 instruct models. This dataset is focused on code-based data and integrates multiple high-quality sources to ensure a robust training foundation. The sources include:
Replete-AI/code_bagel: A diverse collection of code snippets and examples.
nickrosh/Evol-Instruct-Code-80k-v1: A dataset featuring evolved instructions for code generation tasks.
iamtarun/python_code_instructions_18k_alpaca: A compilation of Python code… See the full description on the dataset page: https://huggingface.co/datasets/thesven/CodeMaster-Phi-Instruct.gemma4-code-review-instruct
gemma4-code-review-instruct
197K code review examples — 58K with chain-of-thought <think> reasoning traces.
Built to train models that don't just flag issues, but explain their reasoning before delivering a review. Drop-in ready for SFT with any chat model.
Why This Dataset
Most code review datasets give you diff → comment. This one gives you diff → think → comment for 30% of examples — reasoning traces that show how to analyze a diff before writing the review.… See the full description on the dataset page: https://huggingface.co/datasets/liodon-ai/gemma4-code-review-instruct.SynthUI-Code-Instruct-2k-v1Synth UI 🎹
https://www.synthui.design
Dataset details
This dataset aims to provide a diverse collection of NextJS code snippets, along with their corresponding instructions, to facilitate the training of language models for NextJS-related tasks. It is designed to cover a wide range of NextJS functionalities, including UI components, routing, state management, and more.
This dataset consists of:
Note: The dataset is seperated into two main parts:
raw Contains only the… See the full description on the dataset page: https://huggingface.co/datasets/JulianAT/SynthUI-Code-Instruct-2k-v1.CodeChat-Instruct-v1
CodeChat-Instruct-v1
CodeChat-Instruct-v1 is a synthetic coding instruction dataset designed for supervised fine-tuning of language models on programming-related conversations. It includes diverse coding tasks such as code review, code improvement, complexity analysis, edge-case discussion, code explanation, library/API usage, refactoring guidance. The dataset is suitable for training coding assistants, educational programming tutors, and general-purpose code LLMs with strong… See the full description on the dataset page: https://huggingface.co/datasets/kd13/CodeChat-Instruct-v1.adaption-code-oss-instruct-raw-aug
OSS-Instruct Coding Tasks (Augmented)
Coding problems inspired by open-source snippets, with solutions across several languages.
Rows
8,996
Domain
programming
Format
data.parquet, one row per example
Licence
other
Built for
supervised fine-tuning (SFT) experiments on Adaption AutoScientist
Columns
Column
Description
original_prompt
The prompt (user turn) as uploaded.
original_completion
The target response as uploaded.… See the full description on the dataset page: https://huggingface.co/datasets/rodriguescarson/adaption-code-oss-instruct-raw-aug.adaption-code-oss-instruct-raw
OSS-Instruct Coding Tasks
Coding problems inspired by open-source snippets, with solutions across several languages.
Rows
3,000
Domain
programming
Format
data.parquet, one row per example
Licence
mit
Built for
supervised fine-tuning (SFT) experiments on Adaption AutoScientist
Columns
Column
Description
original_prompt
The prompt (user turn) as uploaded.
original_completion
The target response as uploaded.
enhanced_prompt… See the full description on the dataset page: https://huggingface.co/datasets/rodriguescarson/adaption-code-oss-instruct-raw.adaption-code-oss-instruct-raw-aug-e5bca4
OSS-Instruct Coding Tasks (Augmented)
Coding problems inspired by open-source snippets, with solutions across several languages.
Rows
7,000
Domain
programming
Format
data.parquet, one row per example
Licence
other
Built for
supervised fine-tuning (SFT) experiments on Adaption AutoScientist
Columns
Column
Description
original_prompt
The prompt (user turn) as uploaded.
original_completion
The target response as uploaded.… See the full description on the dataset page: https://huggingface.co/datasets/rodriguescarson/adaption-code-oss-instruct-raw-aug-e5bca4.Instruct-Python-Code-Turkish
Dataset Card for Instruct-Python-Code-Turkish
Language: Turkish
Dataset Description
The translation was performed using the Google translation model to ensure high-quality, accurate translation.
Dataset Details
Size: ≈5K
Translation tool: Google Translate
Data format: Instruct, Output
code-instruct-mixed
Description
Filtered/normalised subsets of public code-instruction datasets (Magicoder OSS-Instruct & Evol-Instruct, CodeFeedback, Glaive). The source column attributes each row to its origin; each source retains its upstream licence.
Derived dataset. Source material retains its original per-item licence (see source/repo columns); treat as other / mixed. Provided as-is.
Usage
from datasets import load_dataset
ds = load_dataset("PotatoHD/code-instruct-mixed")
instruct_code_cleaning
SFT code dataset building
Contain a list of tasks useful when building a iniitial dataset source:
reverse_translation
Given a history of conversations, what would the human ask next?
reverse_translation_first_round
Suppose you already have a response, the LLM must predict what question does the human asked
clean_code
Given a code snippet, it determines whether its useful and atomic enough to be use for a response by LLM
gen_code_question
Generates a question given a… See the full description on the dataset page: https://huggingface.co/datasets/syntaxsynth/instruct_code_cleaning.python-github-code-instruct-filtered-5k
Dataset Card for "python-github-code-instruct-filtered-5k"
This fine dataset tomekkorbak/python-github-code, filtered by scores greater than 0.03.
Feedback and additional columns generated through OpenAI and Cohere responses.
Evol-Instruct-Code-80k-v1-koTranslated nickrosh/Evol-Instruct-Code-80k-v1 using nayohan/llama3-instrucTrans-enko-8b.
This is a raw translation dataset. It needs to be filtered for repetitions generated by the model.
maplestory-worlds-creator-code-instruct
MapleStory Worlds Creator Code (mlua)
Instruction-style code dataset for mlua, the scripting language of
MapleStory Worlds. Built from the
official Creator Center example code: each example is grounded in its source
document and paired with a natural-language task, reasoning, a self-contained
explanation, and commented mlua code. Intended to teach LLMs to write mlua game
scripts.
The example code is preserved from the official source (a code-preservation check
rejects any record… See the full description on the dataset page: https://huggingface.co/datasets/msw-ai-tf/maplestory-worlds-creator-code-instruct.code-instruct-ka
code-instruct-ka
Georgian code instruction dataset for training coding capabilities.
Dataset Summary
Property
Value
Examples
61,288
Size
937 MB
Language
Georgian
Data Fields
id: Unique identifier
conversation: Conversation with code instructions
Usage
from datasets import load_dataset
ds = load_dataset("tbilisi-ai-lab/code-instruct-ka")
Citation
@misc{tbilisi2025codeinstructka,
title = {code-instruct-ka:… See the full description on the dataset page: https://huggingface.co/datasets/tbilisi-ai-lab/code-instruct-ka.amenokaku-code-instruct-python-mitkunishou/amenokaku-code-instructを以下の条件で絞り込んだものです。
MITライセンス (licence: 'MIT')
python (source: ['gasyori_100_knocks', 'datascience_100_knocks_python', 'bifi', 'python_for_begginers_solve_50_exercises', 'nlp_100_knocks')
amenokaku-code-instruct-python-mit-450kunishou/amenokaku-code-instructを以下の条件で絞り込んだものです。
MITライセンス (licence: 'MIT')
python (source: ['gasyori_100_knocks', 'datascience_100_knocks_python', 'bifi', 'python_for_begginers_solve_50_exercises', 'nlp_100_knocks')
source: 'bifi'をランダムに100件に絞り込み
evol-instruct-code-cot-80k
evol-instruct-code-cot-80k
COT distilled dataset with 63,007 examples.
Source
Base: nickrosh/Evol-Instruct-Code-80k-v1
Model: Mistral-7B-Instruct-v0.2-AWQ
Format
instruction: Task
thinking: <think>...</think> reasoning
response: Solution
Evol-Instruct-Code-80k-v1
Evol-Instruct-Code-80k-v1
This is a cleansed version of nickrosh/Evol-Instruct-Code-80k-v1
Usage
from datasets import load_dataset
dataset = load_dataset("Sharathhebbar24/Evol-Instruct-Code-80k-v1", split="train")
CodeDebug-Instruct-v2-Reasoning
CodeDebug-Instruct-v2-Reasoning
CodeDebug-Instruct-v2-Reasoning is a synthetic debugging instruction dataset designed for supervised fine-tuning of language models with enhanced code reasoning capabilities. It covers diverse debugging scenarios including import errors, syntax errors, runtime errors, performance bottlenecks, time limit exceeded (TLE) issues, and general code repair tasks with step-by-step reasoning and corrected solutions. The dataset is suitable for training… See the full description on the dataset page: https://huggingface.co/datasets/kd13/CodeDebug-Instruct-v2-Reasoning.code_instruct_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.
CodeDebug-Instruct-v1
CodeDebug-Instruct-v1
CodeDebug-Instruct-v1 is a synthetic debugging instruction dataset designed for supervised fine-tuning of language models on code error diagnosis and problem solving. It covers a wide range of debugging scenarios including import errors, syntax errors, runtime errors, performance bottlenecks, time limit exceeded (TLE) issues, and general code fixing tasks with clear explanations and corrected solutions. The dataset is suitable for training coding assistants… See the full description on the dataset page: https://huggingface.co/datasets/kd13/CodeDebug-Instruct-v1.en-appealcourt-coded-instruct_v02
Dataset Card for JuDDGES/en-appealcourt-coded-instruct_v02
Dataset Summary
The raw data was acquired from publicly available judgments from the Court of Appeal (Criminal Division) (link) of England and Wales. These judgments are available in HTML format on the national archives website for online reading. They can be downloaded as XML or PDF files under the crown copyright license (link): and the Open Government license (see Appendix 6 in the paper). These licenses… See the full description on the dataset page: https://huggingface.co/datasets/JuDDGES/en-appealcourt-coded-instruct_v02.