671c2cc275a642a7c16a7c21

microsoft/orca-agentinstruct-1M-v1

microsoft

{"language": ["en"], "license": "cdla-permissive-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "messages", "dtype": "string"}], "splits": [{"name": "creative_content", "num_bytes": 288747542, "num_examples": 50000}, {"name": "text_modification", "num_bytes": 346421282, "num_examples": 50000}, {"name": "struct2text_flow", "num_bytes": 251920604, "num_examples": 50000}, {"name": "rc", "num_bytes": 282448904, "num_examples": 50000}, {"name": "rag", "num_bytes": 421188673, "num_examples": 50000}, {"name": "text_extraction", "num_bytes": 312246895, "num_examples": 50000}, {"name": "mcq", "num_bytes": 230459938, "num_examples": 99986}, {"name": "follow_up", "num_bytes": 881311205, "num_examples": 99054}, {"name": "analytical_reasoning", "num_bytes": 100724491, "num_examples": 25000}, {"name": "fermi", "num_bytes": 78109959, "num_examples": 25000}, {"name": "fs_cot_flow", "num_bytes": 109007740, "num_examples": 25000}, {"name": "code_", "num_bytes": 617418962, "num_examples": 100000}, {"name": "brain_teaser", "num_bytes": 124523402, "num_examples": 50000}, {"name": "text_classification", "num_bytes": 151217275, "num_examples": 50000}, {"name": "open_domain_qa", "num_bytes": 616935002, "num_examples": 272370}], "download_size": 2210440144, "dataset_size": 4812681874}, "configs": [{"config_name": "default", "data_files": [{"split": "creative_content", "path": "data/creative_content-*"}, {"split": "text_modification", "path": "data/text_modification-*"}, {"split": "struct2text_flow", "path": "data/struct2text_flow-*"}, {"split": "rc", "path": "data/rc-*"}, {"split": "rag", "path": "data/rag-*"}, {"split": "text_extraction", "path": "data/text_extraction-*"}, {"split": "mcq", "path": "data/mcq-*"}, {"split": "follow_up", "path": "data/follow_up-*"}, {"split": "analytical_reasoning", "path": "data/analytical_reasoning-*"}, {"split": "fermi", "path": "data/fermi-*"}, {"split": "fs_cot_flow", "path": "data/fs_cot_flow-*"}, {"split": "code_", "path": "data/code_-*"}, {"split": "brain_teaser", "path": "data/brain_teaser-*"}, {"split": "text_classification", "path": "data/text_classification-*"}, {"split": "open_domain_qa", "path": "data/open_domain_qa-*"}]}]}

86d609183249ff8037eae33d76ebca3af9390ea8

Dataset Card This dataset is a fully synthetic set of instruction pairs where both the prompts and the responses have been synthetically generated, using the AgentInstruct framework. AgentInstruct is an extensible agentic framework for synthetic data generation. This dataset contains ~1 million instruction pairs generated by the AgentInstruct, using only raw text content publicly avialble on the Web as seeds. The data covers different capabilities, such as text editing… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1.

67335bb8f014ee49558ef3fe

PleIAs/common_corpus

PleIAs

{"language": ["en", "fr", "de", "it", "pt", "nl", "es"], "pretty_name": "Common Corpus", "size_categories": ["n>1T"], "task_categories": ["text-generation"], "tags": ["legal", "finance", "literature", "science", "code"]}

ea0bf929e48db9a0155759527acb8b6856178c88

Common Corpus Common Corpus is the largest open and permissible licensed text dataset, comprising over 2 trillion tokens (2,003,039,184,047 tokens). It is a diverse dataset, consisting of books, newspapers, scientific articles, government and legal documents, code, and more. Common Corpus differs from existing open datasets in that it is: Truly Open: contains only data that is permissively licensed Multilingual: mostly representing English and French data, but contains data… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/common_corpus.

63990f21cc50af73d29ecfa3

fka/awesome-chatgpt-prompts

fka

{"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]}

459a66186f8f83020117b8acc5ff5af69fc95b45

🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0

672f9b7ed7f4171f3751ffb3

OpenCoder-LLM/fineweb-code-corpus

OpenCoder-LLM

{"license": "mit", "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "tag", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "dump", "dtype": "string"}, {"name": "file_size_in_byte", "dtype": "int64"}, {"name": "line_count", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 254927419643, "num_examples": 100920235}], "download_size": 147948949488, "dataset_size": 254927419643}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

1fc58728aeaa36aabf3de988b265cd89ff6a656c

This code-related data from Fineweb was specifically used in OpenCoder pre-training. We employ fastText in three iterative rounds to recall a final dataset of 55B code and math-related data. You can find math-related data at OpenCoder-LLM/fineweb-math-corpus. Citation @inproceedings{Huang2024OpenCoderTO, title={OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models}, author={Siming Huang and Tianhao Cheng and Jason Klein Liu and Jiaran Hao and Liuyihan Song… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/fineweb-code-corpus.

6735ab5ada0aa544a31cb334

Laxhar/noob-wiki

Laxhar

{"license": "apache-2.0", "task_categories": ["text-to-image"], "language": ["en"], "tags": ["wiki"]}

929c972dcc8aeecde42b7cd8931afe82cd864424

Noob SDXL Wiki This is the WIKI database for Noob SDXL Models.

672e43b562371d59e7202334

OpenCoder-LLM/opc-sft-stage1

OpenCoder-LLM

{"configs": [{"config_name": "filtered_infinity_instruct", "data_files": [{"split": "train", "path": "data/filtered_infinity_instruct-*"}]}, {"config_name": "largescale_diverse_instruct", "data_files": [{"split": "train", "path": "data/largescale_diverse_instruct-*"}]}, {"config_name": "realuser_instruct", "data_files": [{"split": "train", "path": "data/realuser_instruct-*"}]}], "license": "mit"}

d882e0a84ebf9d5c661854ece1d47f5775b6ba09

66ce3cb04c3b13931e5591fa

Salesforce/blip3-kale

Salesforce

{"license": "apache-2.0", "task_categories": ["image-to-text"], "language": ["en"], "pretty_name": "KALE", "size_categories": ["100M<n<1B"], "configs": [{"config_name": "core", "data_files": [{"split": "train", "path": "data_core_set/*.parquet"}]}, {"config_name": "full", "data_files": [{"split": "train", "path": "data_full_set/*.parquet"}]}]}

adbc857b863005dbed15596d49410ebcb0392922

🥬 BLIP3-KALE:Knowledge Augmented Large-scale Dense Captions BLIP3-KALE is an open-source dataset of 218 million image-text pairs, featuring knowledge-augmented dense captions combining web-scale knowledge with detailed image descriptions. Paper: [To be added] Uses BLIP3-KALE is designed to facilitate research in multimodal pretraining. The dataset can be used for training large multimodal models that require factually grounded, dense image captions. It has already been an… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/blip3-kale.

67333902d741f752b8483d42

OpenCoder-LLM/opc-annealing-corpus

OpenCoder-LLM

{"configs": [{"config_name": "synthetic_code_snippet", "data_files": [{"split": "train", "path": "synthetic_code_snippet/*"}]}, {"config_name": "synthetic_qa", "data_files": [{"split": "train", "path": "synthetic_qa/*"}]}, {"config_name": "algorithmic_corpus", "data_files": [{"split": "train", "path": "algorithmic_corpus/*"}]}], "license": "odc-by"}

8bcc9520a15cbacdff9608a4ddc90f1680a2f1e7

This corpus is an additional component incorporated into OpenCoder during the annealing phase, beyond the original distribution: algorithmic_corpus: Algorithm-related code sampled from The Stack v2. synthetic_code_snippet: High-quality code snippets generated by rewriting algorithmic_corpus as seeds. synthetic_qa: High-quality Q&A pairs generated by adapting algorithmic_corpus as seeds. Our ablation experiments validated the effectiveness of this batch of synthetic data.… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-annealing-corpus.

621ffdd236468d709f184284

wikimedia/wikipedia

wikimedia

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b04c8d1ceb2f5cd4588862100d08de323dccfbaa

Dataset Card for Wikimedia Wikipedia Dataset Summary Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/) with one subset per language, each containing a single train split. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). All language subsets have already been processed for recent dump… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia.

67181a27dfa0b095f0902d33

qq8933/OpenLongCoT-Pretrain

qq8933

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 269352240, "num_examples": 102906}], "download_size": 64709509, "dataset_size": 269352240}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

40562378be9f86728440a0fb44f07ba2bdc03646

Please cite me if this dataset is helpful for you!🥰 @article{zhang2024llama, title={LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning}, author={Zhang, Di and Wu, Jianbo and Lei, Jingdi and Che, Tong and Li, Jiatong and Xie, Tong and Huang, Xiaoshui and Zhang, Shufei and Pavone, Marco and Li, Yuqiang and others}, journal={arXiv preprint arXiv:2410.02884}, year={2024} } @article{zhang2024accessing, title={Accessing GPT-4 level Mathematical Olympiad… See the full description on the dataset page: https://huggingface.co/datasets/qq8933/OpenLongCoT-Pretrain.

672c9031c87c7e3ef53af830

OpenGVLab/MMPR

OpenGVLab

{"license": "mit", "task_categories": ["visual-question-answering"], "language": ["en"], "pretty_name": "MMPR", "dataset_info": {"features": [{"name": "image", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}]}, "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "annotations.zip"}]}]}

25a8151000f39a44002d8c17f538fe741deb7891

MMPR [📂 GitHub] [🆕 Blog] [📜 Paper] [📖 Documents] Introduction MMPR is a large-scale and high-quality multimodal reasoning preference dataset. This dataset includes about 3 million samples. We finetune InternVL2-8B with MPO using this dataset. The resulting model, InternVL2-8B-MPO, achieves superior performance across 8 benchmarks, particularly excelling in multimodal reasoning tasks. On the MathVista benchmark, our model achieves an accuracy of 67.0%… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/MMPR.

672e4b6b741fa21478bd7bc3

OpenCoder-LLM/opc-sft-stage2

OpenCoder-LLM

{"configs": [{"config_name": "educational_instruct", "data_files": [{"split": "train", "path": "data/educational_instruct-*"}]}, {"config_name": "evol_instruct", "data_files": [{"split": "train", "path": "data/evol_instruct-*"}]}, {"config_name": "mceval_instruct", "data_files": [{"split": "train", "path": "data/mceval_instruct-*"}]}, {"config_name": "package_instruct", "data_files": [{"split": "train", "path": "data/package_instruct-*"}]}], "license": "mit"}

689eb469e2a1cd31d602dae2392592473f9fc8d6

673a3173bc83dde1402958a7

mlabonne/orca-agentinstruct-1M-v1-cleaned

mlabonne

{"language": ["en"], "license": "cdla-permissive-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "split", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4638101451, "num_examples": 1046410}], "download_size": 2178041194, "dataset_size": 4638101451}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

ba212c8ac29084b39d175ba7c1bed8cb22f7011d

🐋 Orca-AgentInstruct-1M-v1-cleaned This is a cleaned version of the microsoft/orca-agentinstruct-1M-v1 dataset released by Microsoft. orca-agentinstruct-1M-v1 is a fully synthetic dataset using only raw text publicly available on the web as seed data. It is a subset of the full AgentInstruct dataset (~25M samples) that created Orca-3-Mistral. Compared to Mistral 7B Instruct, the authors claim 40% improvement on AGIEval, 19% improvement on MMLU, 54% improvement on GSM8K, 38%… See the full description on the dataset page: https://huggingface.co/datasets/mlabonne/orca-agentinstruct-1M-v1-cleaned.

670d4bb8207a1458e88ab1f6

gretelai/gretel-pii-masking-en-v1

gretelai

{"license": "apache-2.0", "task_categories": ["text-classification", "text-generation"], "language": ["en"], "tags": ["synthetic", "domain-specific", "text", "NER"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

db324d63d944753b4d636296e77831af51d99355

Gretel Synthetic Domain-Specific Documents Dataset (English) This dataset is a synthetically generated collection of documents enriched with Personally Identifiable Information (PII) and Protected Health Information (PHI) entities spanning multiple domains. Created using Gretel Navigator with mistral-nemo-2407 as the backend model, it is specifically designed for fine-tuning Gliner models. The dataset contains document passages featuring PII/PHI entities from a wide range… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/gretel-pii-masking-en-v1.

67323181adc3df46516a0611

nyuuzyou/suno

nyuuzyou

{"pretty_name": "Suno Music Generation Dataset", "size_categories": ["100K<n<1M"], "task_categories": ["audio-classification", "text-to-audio"], "annotations_creators": ["found"], "language": ["en", "ja", "multilingual"], "license": "cc0-1.0", "multilinguality": ["multilingual"], "source_datasets": ["original"], "tags": ["audio", "video", "image", "text"]}

6d2c778c9fcad49a435d1eec1d4711e2985185e0

Dataset Card for Suno.ai Music Generation Dataset Summary This dataset contains metadata for 659,788 AI-generated songs from the suno.com platform, a service that generates music using AI. The songs were generated using search queries from the dwyl/english-words wordlist. Languages The dataset is multilingual with English as the primary language: English (en): Primary language for metadata and most lyrics Japanese (ja): Present in some song lyrics… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/suno.

66f5a5d9763d438dab13f188

Spawning/PD12M

Spawning

{"language": ["en"], "pretty_name": "PD12M", "license": "cdla-permissive-2.0", "tags": ["image"]}

4fd5d707a72aad71bd88c7e7bc5df2ae5e0d6c53

PD12M Summary At 12.4 million image-caption pairs, PD12M is the largest public domain image-text dataset to date, with sufficient size to train foundation models while minimizing copyright concerns. Through the Source.Plus platform, we also introduce novel, community-driven dataset governance mechanisms that reduce harm and support reproducibility over time. Jordan Meyer Nicholas Padgett Cullen Miller Laura Exline Paper Datasheet Project… See the full description on the dataset page: https://huggingface.co/datasets/Spawning/PD12M.

673504ea42d8e0256cb8664c

maxiw/hf-posts

maxiw

{"size_categories": ["1K<n<10K"]}

ffe7fe029abe4e80e3a4eb5dbe26aff61f805f96

Hugging Face Posts This dataset contains posts scraped from https://huggingface.co/posts. It includes all posts published from the launch date on December 23, 2023, up to November 17, 2024, at 18:26.

666ae33f611afe17cd982829

BAAI/Infinity-Instruct

BAAI

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05cd7e304312b9afc9c4cb5817927805554af437

Infinity Instruct Beijing Academy of Artificial Intelligence (BAAI) [Paper][Code][🤗] (would be released soon) The quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-Instruct.

6732589af21b5584072d297b

gretelai/gretel-text-to-python-fintech-en-v1

gretelai

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "domain-specific", "text-to-code", "fintech"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

a126172a7ed9017932fecc9aad04ffd6f2bdb9cf

Gretel Synthetic Text-to-Python Dataset for FinTech This dataset is a synthetically generated collection of natural language prompts paired with their corresponding Python code snippets, specifically tailored for the FinTech industry. Created using Gretel Navigator's Data Designer, with mistral-nemo-2407 and Qwen/Qwen2.5-Coder-7B as the backend models, it aims to bridge the gap between natural language inputs and high-quality Python code, empowering professionals to implement… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/gretel-text-to-python-fintech-en-v1.