datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
chinese-dolly-15kChinese-Dolly-15k是骆驼团队翻译的Dolly instruction数据集
最后49条数据因为翻译长度超过限制，没有翻译成功，建议删除或者手动翻译一下
原来的数据集'databricks/databricks-dolly-15k'是由数千名Databricks员工根据InstructGPT论文中概述的几种行为类别生成的遵循指示记录的开源数据集。这几个行为类别包括头脑风暴、分类、封闭型问答、生成、信息提取、开放型问答和摘要。
在知识共享署名-相同方式共享3.0（CC BY-SA 3.0）许可下，此数据集可用于任何学术或商业用途。
我们会陆续将更多数据集发布到hf，包括
Coco Caption的中文翻译
CoQA的中文翻译
CNewSum的Embedding数据
增广的开放QA数据
WizardLM的中文翻译
MMC4的中文翻译
如果你也在做这些数据集的筹备，欢迎来联系我们，避免重复花钱。
骆驼(Luotuo): 开源中文大语言模型
https://github.com/LC1332/Luotuo-Chinese-LLM… See the full description on the dataset page: https://huggingface.co/datasets/silk-road/chinese-dolly-15k.dolly-15k_deA reformatted version of the DRXD1000/Dolly-15k-German dataset.
Available for finetuning in hiyouga/LLaMA-Factory.
DollyTails-12K
Dataset Card for DollyTails-12K
Dataset Summary
This dataset is designed with a System 2 (O1-like) thinking paradigm for instruction-following tasks. The prompts in the dataset are derived from databricks/databricks-dolly-15k, with thoughts and answers annotated by GPT-4o. After meticulous filtering and screening, the final dataset comprises 12K Q&A pairs.
The dataset averages 4.93 reasoning steps per task, with a cap of 7 steps to prevent unnecessary training overhead… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/DollyTails-12K.dolly-15k-pirate-speechDataset for writing style transfer experimentation based on article:
https://ai-r.com/blog/pirate-linguistics-and-tone-of-voice-fine-tuning-llms-to-talk-like-swashbucklers
Only responses are in 'pirate speech'
arrr python library was used to simply change original responses to 'pirate speech' responses
https://pypi.org/project/arrr/
databricksdatabricks-dolly-15k-chinese
Dataset Summary
🏡🏡🏡🏡Fine-tune Dataset:中文数据集🏡🏡🏡🏡
😀😀😀😀😀😀😀😀 这个数据集是databricks/databricks-dolly-15k的中文版本，是直接翻译过来，没有经过人为检查语法。 对databricks/databricks-dolly-15k的描述，请看他的dataset card。
😀😀😀😀😀😀😀😀 This data set is the Chinese version of databricks/databricks-dolly-15k, which is directly translated without human-checked grammar. For a description of databricks/databricks-dolly-15k, see its dataset card.
databricks-dolly-15k-esTranslated with googletrans==3.1.0a0 from original dataset
*part of the data (up to 600) was lost during the translation
license: apache-2.0
dolly-odia-15k
Dataset Card for Dolly-Odia-15K
Dataset Summary
This dataset is the Odia-translated version of the Dolly 15K instruction set. In this dataset both English and Odia instruction, input, and output strings are available.
Supported Tasks and Leaderboards
Large Language Model (LLM)
Languages
Odia
Dataset Structure
JSON
Data Fields
instruction (string)
english_instruction (string)
input (string)
english_input (string)
output… See the full description on the dataset page: https://huggingface.co/datasets/OdiaGenAI/dolly-odia-15k.dolly-15k-chinese-guanacoformat
Dataset Summary
🏡🏡🏡🏡Fine-tune Dataset:中文数据集🏡🏡🏡🏡
😀😀😀😀😀😀😀😀 这个数据集是databricks/databricks-dolly-15k的中文guanaco版本
dolly-v2_orcaExplain tuned Dolly-V2 dataset ~15K created using approaches from Orca Research Paper.
We leverage all of the 15 system instructions provided in Orca Research Paper to generate explain tuned datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
This helps student models like orca_mini_13b, orca_mini_7b or orca_mini_3b to learn thought process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
Please see how the System prompt is added before… See the full description on the dataset page: https://huggingface.co/datasets/pankajmathur/dolly-v2_orca.SFT_databricks_dolly_15k
Preparing Your Dataset
Once you’ve decided that fine-tuning is the best approach—after optimizing your prompt as much as possible and identifying remaining model issues—you’ll need to prepare training data. Start by creating a diverse set of example conversations that mirror those the model will handle during production.
Each example should follow this structure below, consisting of a list of messages. Each message must include a role, content, and an optional name. Make sure some… See the full description on the dataset page: https://huggingface.co/datasets/GreenNode/SFT_databricks_dolly_15k.databricks-dolly-1k
Databricks Dolly 1k
1092 instruction examples taken from the original databricks/databricks-dolly-15k.
Filtered to open/closed/general QA category
Ready to plug straight into SFTTrainer, Unsloth, Llama-factory etc
Example
### Instruction:
When did Virgin Australia start operating?
### Context:
Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd ...
### Response:
Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft… See the full description on the dataset page: https://huggingface.co/datasets/MagicaNeko/databricks-dolly-1k.textGen-databricks-dollydolly-llama-qa
Dataset Card for dolly-llama-qa
This dataset has been created with dataformer.
Dataset Details
Dataset Description
The dolly-llama-qa dataset is a synthetic QA pair dataset created using the context from databricks-dolly-15k. We used Meta-Llama-3-8B-Instruct and Meta-Llama-3.1-8B-Instruct models for the generation and evolution part. Openai's gpt-4o was used for evaluating the refined questions and refined answers.
Dataset Columns
context:… See the full description on the dataset page: https://huggingface.co/datasets/dataformer/dolly-llama-qa.SPRI-SFT-dolly
Dataset Card for SPRI-SFT-dolly (ICML 2025)
Paper: SPRI: Aligning Large Language Models with Context-Situated Principles (Published in ICML 2025)
Authors: Hongli Zhan, Muneeza Azmat, Raya Horesh, Junyi Jessy Li, Mikhail Yurochkin
Shared by: Hongli Zhan
Arxiv Link: arxiv.org/abs/2502.03397
Citation
If you used our dataset, please cite our paper:
@inproceedings{zhan2025spri,
title = {SPRI: Aligning Large Language Models with Context-Situated Principles},
author… See the full description on the dataset page: https://huggingface.co/datasets/hongli-zhan/SPRI-SFT-dolly.