datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Magicoder-OSS-Instruct-75KThis is the OSS-Instruct dataset generated by gpt-3.5-turbo-1106 developed by OpenAI. Please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies.
Magicoder-Evol-Instruct-110KA decontaminated version of evol-codealpaca-v1. Decontamination is done in the same way as StarCoder (bigcode decontamination process).
CodeFeedback-Filtered-Instruction OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
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[🛠️Code]
OpenCodeInterpreter
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction.InstructCoder
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Code |
Blog
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.Evol-Instruct-Code-80k-v1Open Source Implementation of Evol-Instruct-Code as described in the WizardCoder Paper.
Code for the intruction generation can be found on Github as Evol-Teacher.
Nemotron-SFT-Instruction-Following-Chat-v3
Dataset Description:
The Nemotron-Instruction-Following-Chat-v3 dataset is designed to strengthen multi-turn, interactive capabilities, including open-ended chat and precise instruction following.
The chat subset uses human written prompts from sources like lmarena, lmsys, and wildchat as seed prompts. Responses are generated with GLM-5. Multiple responses are sampled from the model and the best response as judged by pairwise comparisons using Qwen3-Nemotron-235B-A22B-GenRM-2603… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3.instruction-following-evalTrendyol-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 computing risks… See the full description on the dataset page: https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset.InstructS2S-200K
InstructS2S-200K
Dataset Description
InstructS2S-200K is a multi-turn speech-to-speech conversation dataset containing approximately 200,000 dialogues, developed for the LLaMA-Omni and LLaMA-Omni 2 research projects on real-time spoken chatbots.
Usage
The dataset is split into multiple parts and needs to be reconstructed:
# Combine the parts and extract
cat en_part_* > instructs2s_200k.tar.gz
tar -xzf instructs2s_200k.tar.gz
License
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/ICTNLP/InstructS2S-200K.code_instructions_122k_alpaca_styleMalay-Dialect-Instructions
Malay dialect instruction including coding
Negeri Sembilan
QA
public transport QA,
Coding
CUDA coding,
Kedah
QA
infra QA,
Coding
Rust coding,
Kelantan
QA
Najib Razak QA,
Coding
Go coding,
Perak
QA
Anwar Ibrahim QA,
Coding
SQL coding,
Pahang
QA
Pendatang asing QA,
Coding
Typescript coding,
Terengganu… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Malay-Dialect-Instructions.WizardLM_evol_instruct_V2_196k
News
🔥 🔥 🔥 [08/11/2023] We release WizardMath Models.
🔥 Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
🔥 Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM.
🔥 Our WizardMath-70B-V1.0 model achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.… See the full description on the dataset page: https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_V2_196k.Nemotron-Instruction-Following-Chat-v1
Dataset Description:
The Nemotron-Instruction-Following-Chat-v1 dataset is designed to broadly strengthen the model’s interactive capabilities, spanning open-ended chat, precise instruction following, and reliable structured output generation. It combines refreshed chat data from Nemotron-Post-Training-Dataset-v2 (extended to multi-turn) with synthetic dialogues produced by strong frontier models such as GPT-OSS-120B and Qwen3-235B variants.
This dataset is ready for commercial… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Instruction-Following-Chat-v1.MSC-Self-Instruct
MemGPT
This is the self-instruct dataset of MSC conversations used for MemGPT paper. For more information please refer to memgpt.ai
The MSC dataset is a multi-round human conversations. In this dataset, our goal is to come up with a conversation opener, that is personalized to the user by referencing topics from the previous conversations.
These were generated while evaluating MemGPT.
trl-test-instructionsecurity_instruct_mcq_2481vivid-video-instructbcb_datastarcoder-python-instruct
StarCoder-Python-Qwen-Instruct
Dataset Description
This dataset contains Python code samples paired with synthetically generated natural language instructions. It is designed for supervised fine-tuning of language models for code generation tasks. The dataset is derived from the Python subset of the bigcode/starcoderdata corpus, and the instructional text for each code sample was generated using the Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 model.
Creation… See the full description on the dataset page: https://huggingface.co/datasets/OLMo-Coding/starcoder-python-instruct.kalo-opus-instruct-22k-no-refusalWizardLM_evol_instruct_70kThis is the training data of WizardLM.
News
🔥 🔥 🔥 [08/11/2023] We release WizardMath Models.
🔥 Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
🔥 Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM.
🔥 Our WizardMath-70B-V1.0 model achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points… See the full description on the dataset page: https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_70k.just-eval-instruct
Just Eval Instruct
Highlights
Data sources:
AlpacaEval (covering 5 datasets),
LIMA-test,
MT-bench,
Anthropic red-teaming,
and MaliciousInstruct.
1K examples: 1,000 instructions, including 800 for problem-solving test, and 200 specifically for safety test.
Category: We tag each example with (one or multiple) labels on its task types and topics.… See the full description on the dataset page: https://huggingface.co/datasets/re-align/just-eval-instruct.Nemotron-RL-Instruction-Following-Calendar-v2
Dataset Description:
The Calendar-Scheduling-Dataset is a multi-turn conversation dataset that can understand natural language scheduling constraints, follow instructions across multiple messages, infer scheduling conflicts and satisfy multiple constraints simultaneously. Each event has constraints around duration (e.g. 45 min) and timing (e.g. should be scheduled after 3pm). The user mentions the events and associated constraints in a random order in a natural conversational… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Calendar-v2.DataScience-Instruct-500K
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Authors: Shaolei Zhang, Ju Fan*, Meihao Fan, Guoliang Li, Xiaoyong Du
DeepAnalyze is the first agentic LLM for autonomous data science. It can autonomously complete a wide range of data-centric tasks without human intervention, supporting:
🛠 Entire data science pipeline: Automatically perform any data science tasks such as data preparation, analysis, modeling, visualization, and report generation.
🔍… See the full description on the dataset page: https://huggingface.co/datasets/RUC-DataLab/DataScience-Instruct-500K.IfEvalCode-Instructmedical-instruction-120k
What is the Dataset About?🤷🏼♂️
The dataset is useful for training a Generative Language Model for the Medical application and instruction purposes, the dataset consists of various thoughs proposed by the people [mentioned as the Human ] and there responses including Medical Terminologies not limited to but including names of the drugs, prescriptions, yogic exercise suggessions, breathing exercise suggessions and few natural home made prescriptions.
How the Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Mohammed-Altaf/medical-instruction-120k.open-instruct-uncensored-alpacaOriginal dataset page from ehartford.
810,102 entries. Sourced from open-instruct-uncensored.jsonl.
Converted the jsonl to a json which can be loaded into something like LLaMa-LoRA-Tuner.
I've also included smaller datasets that includes less entries depending on how much memory you have to work with.
Each one is randomized before being converted, so each dataset is unique in order.
Count of each Dataset:
code_alpaca: 19991
unnatural_instructions: 68231
baize: 166096
self_instruct: 81512… See the full description on the dataset page: https://huggingface.co/datasets/xzuyn/open-instruct-uncensored-alpaca.Claude-3-Opus-Instruct-15K
Original Character Card
Processed 15K Prompts - See Usable Responses Below
Based on Claude 3 Opus through AWS.
I took a random 5K + 10K prompt subset from Norquinal/claude_multi_instruct_30k to use as prompts, and called API for my answers.
Warning!
Uncleaned - Only Filtered for Blatant Refusals.
I will be going through and re-prompting missing prompts, but I do not expect much success, as some of the prompts shown are nonsensical, incomplete, or impossible… See the full description on the dataset page: https://huggingface.co/datasets/nothingiisreal/Claude-3-Opus-Instruct-15K.datainstructions-pair-mining