datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
InstructCoder
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Code |
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InstructCoder (CodeInstruct): Empowering Language Models to Edit Code
Updates
May 23, 2023: Paper, code and data released.
Overview
InstructCoder is the first dataset designed to adapt LLMs for general code editing. It consists of 114,239 instruction-input-output triplets and covers multiple distinct code editing scenarios, generated by ChatGPT. LLaMA-33B finetuned on InstructCoder performs on par with ChatGPT on a… See the full description on the dataset page: https://huggingface.co/datasets/likaixin/InstructCoder.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.ds-coder-instruct-v1
Dataset Card for DS Coder Instruct Dataset
DS Coder is a dataset for instruction fine tuning of language models. It is a specialized dataset focusing only on
data science (eg. plotting, data wrangling, machine learnig models, deep learning, and numerical computations). The dataset contains code examples both in R and Python.
The goal of this dataset is to enable creation of small-scale, specialized language model assistants for data science projects.
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/ed001/ds-coder-instruct-v1.code_contest_instruct_cppds-coder-instruct-v2
Dataset Card for DS Coder Instruct v2 Dataset
Changes from v1:
Added WizardLM evol data science samples
Removed R samples from v2
DS Coder is a dataset for instruction fine tuning of language models. It is a specialized dataset focusing only on
data science (eg. plotting, data wrangling, machine learnig models, deep learning, and numerical computations). The dataset contains code examples both in Python (R samples were removed in v2).
The goal of this dataset is to enable… See the full description on the dataset page: https://huggingface.co/datasets/ed001/ds-coder-instruct-v2.CodeMaster-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.Vibe-Coding-Instructgemma4-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.kenyan-code-switch-instruct-50k
🇰🇪 Kenyan Code-Switching & Sheng Multi-Task Instruction Dataset (50,000 Pairs)
A standardized, multi-task instruction-tuning dataset engineered to teach Large Language Models (e.g. Llama 3, Mistral, Gemma, Qwen) to understand and generate authentic Kenyan Code-Switching (Sheng, Technical Swahili-English Blend) with rigorous adherence to Bantu morphotactic rules.
Dataset Summary
Total Samples: 50,000 instruction-response pairs
train.jsonl: 45,000 pairs (90%)… See the full description on the dataset page: https://huggingface.co/datasets/Maxyelow/kenyan-code-switch-instruct-50k.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
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.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")
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.
Bangla-Code-Instruct
🐯 Bangla-Code-Instruct: A Comprehensive Bangla Code Instruction Dataset
Accepted at LREC 2026
Nishat Raihan, Antonios Anastasopoulos, Marcos Zampieri
George Mason University, Fairfax, VA, USA
The first large-scale Bangla code instruction dataset (300K examples) for training Code LLMs in Bangla.
⚠️ Note: The dataset will be released after the LREC 2026 conference. Stay tuned!
Overview
Bangla-Code-Instruct is a comprehensive… See the full description on the dataset page: https://huggingface.co/datasets/md-nishat-008/Bangla-Code-Instruct.gene-code-generation-instruct
code-generation-instruct v2
Gate-passed instruction data for code-generation — published when 50 fresh examples cleared the quality bar
Kind: synthetic
Domain: code-generation
Records: 96
Created: 2026-06-20T19:02:16+00:00
SHA-256: a3f6a919356ea6d71f365ea93c8ea06cb7dc19f22cad57210dedb1f327caed90
Pipeline: v2.0.0
Filters: {"min_quality": 0.55, "limit": 1000, "source": null, "backend": "llama", "min_judge": 0.7}
Generated by: Qwen3-4B-Instruct-2507-Q4_K_M.gguf (backend:… See the full description on the dataset page: https://huggingface.co/datasets/Gene829/gene-code-generation-instruct.deepseek-ai-deepseek-coder-v2-lite-instruct__llm-quality-persona-consistency-mini__019e3b6fdda4
deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct on llm.quality.persona-consistency-mini (NVIDIA H100 80GB HBM3)
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Headline metrics
Metric
Value
Unit
N Samples
5
N Ok
5
Ok Rate
1
Persona Consistency Mean
0.88
Accuracy
0.88
Persona Consistency P50
0.8
Persona Consistency P95
1
Accuracy P05
0.8
Accuracy P50
0.8
Accuracy P95
1
Drift Rate
0.6
Mean Drift Turn
2.6667
TTFT P50
57.5324
ms
Total P50 Ms
2672.8064… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/deepseek-ai-deepseek-coder-v2-lite-instruct__llm-quality-persona-consistency-mini__019e3b6fdda4.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.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.
deepseek-ai-deepseek-coder-v2-lite-instruct__llm-inference-chatbot-short__019e3b6f22ca
deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct on llm.inference.chatbot-short (NVIDIA H100 80GB HBM3)
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Headline metrics
Metric
Value
Unit
TTFT P50
74.4268
ms
TTFT P99
521.1922
ms
TPOT P50
21.0371
ms
TPOT P99
23.458
ms
Total P50 Ms
2661.4077
Total P99 Ms
3136.5972
Req Per S Passing
1.0172
Req Per S All
1.1559
Compliance Rate
0.88
Ok Rate
1
Throughput Tok Per S
134.316
Power Avg W
808.3085
Power Peak W
854.62… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/deepseek-ai-deepseek-coder-v2-lite-instruct__llm-inference-chatbot-short__019e3b6f22ca.deepseek-ai-deepseek-coder-v2-lite-instruct__code-generation-mbpp-mini__019e3b6f7d82
deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct on code.generation.mbpp-mini (NVIDIA H100 80GB HBM3)
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Headline metrics
Metric
Value
Unit
N Samples
5
N Ok
5
Ok Rate
1
Pass At 1
1
Pass At 1 P05
1
Pass At 1 P50
1
Pass At 1 P95
1
Timeout Rate
0
TTFT P50
55.0369
ms
Total P50 Ms
1212.8
Tokens Out Total
807
Run configuration
Model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct @ unknown00
Engine: vllm… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/deepseek-ai-deepseek-coder-v2-lite-instruct__code-generation-mbpp-mini__019e3b6f7d82.deepseek-ai-deepseek-coder-v2-lite-instruct__llm-quality-factual-mini__019e3b6f9bdd
deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct on llm.quality.factual-mini (NVIDIA H100 80GB HBM3)
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Headline metrics
Metric
Value
Unit
N Samples
10
N Ok
10
Ok Rate
1
Accuracy
1
Accuracy P05
1
Accuracy P50
1
Accuracy P95
1
TTFT P50
45.0118
ms
Total P50 Ms
359.225
Tokens Out Total
373
Run configuration
Model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct @ unknown00
Engine: vllm vunknownQuantization:… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/deepseek-ai-deepseek-coder-v2-lite-instruct__llm-quality-factual-mini__019e3b6f9bdd.deepseek-ai-deepseek-coder-v2-lite-instruct__code-generation-humaneval-mini__019e3b6f4f8a
deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct on code.generation.humaneval-mini (NVIDIA H100 80GB HBM3)
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Headline metrics
Metric
Value
Unit
N Samples
5
N Ok
5
Ok Rate
1
Pass At 1
1
Pass At 1 P05
1
Pass At 1 P50
1
Pass At 1 P95
1
Timeout Rate
0
TTFT P50
57.5723
ms
Total P50 Ms
1244.1407
Tokens Out Total
789
Run configuration
Model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct @ unknown00
Engine:… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/deepseek-ai-deepseek-coder-v2-lite-instruct__code-generation-humaneval-mini__019e3b6f4f8a.EFAGen-Llama-3.1-8B-Instruct-Training-DataPaper Link
The training data used for the final version of EFAGen-Llama-3.1-8B-Instruct.
The data is in Alpaca format and can be used with Llama-Factory (check dataset_info.json).
microsoft-phi-3-5-mini-instruct__code-generation-humaneval-mini__019e3b5d8c8b
microsoft/Phi-3.5-mini-instruct on code.generation.humaneval-mini (NVIDIA H100 80GB HBM3)
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Headline metrics
Metric
Value
Unit
N Samples
5
N Ok
5
Ok Rate
1
Pass At 1
1
Pass At 1 P05
1
Pass At 1 P50
1
Pass At 1 P95
1
Timeout Rate
0
TTFT P50
12.396
ms
Total P50 Ms
974.8963
Tokens Out Total
1166
Run configuration
Model: microsoft/Phi-3.5-mini-instruct @ unknown00
Engine: vllm vunknown
Quantization:… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/microsoft-phi-3-5-mini-instruct__code-generation-humaneval-mini__019e3b5d8c8b.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
meta-llama-llama-3-1-70b-instruct__code-generation-humaneval-mini__019e3b935675
meta-llama/Llama-3.1-70B-Instruct on code.generation.humaneval-mini (NVIDIA H100 80GB HBM3)
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Headline metrics
Metric
Value
Unit
N Samples
5
N Ok
5
Ok Rate
1
Pass At 1
0.8
Pass At 1 P05
0.2
Pass At 1 P50
1
Pass At 1 P95
1
Timeout Rate
0
TTFT P50
28.0842
ms
Total P50 Ms
4338.3245
Tokens Out Total
1653
Run configuration
Model: meta-llama/Llama-3.1-70B-Instruct @ unknown00
Engine: vllm vunknown… See the full description on the dataset page: https://huggingface.co/datasets/Yobitel/meta-llama-llama-3-1-70b-instruct__code-generation-humaneval-mini__019e3b935675.