6747510080aebd7436aa497d

alpindale/two-million-bluesky-posts

alpindale

{"license": "apache-2.0", "language": ["en"], "tags": ["bluesky"]}

ba52d3e2f7f56701f07dca1c7eb474ab4b8aa1b2

2 Million Bluesky Posts This dataset contains 2 million public posts collected from Bluesky Social's firehose API, intended for machine learning research and experimentation with social media data. The with-language-predictions config contains the same data as the default config but with language predictions added using the glotlid model. Dataset Details Dataset Description This dataset consists of 2 million public posts from Bluesky Social, collected through the platform's… See the full description on the dataset page: https://huggingface.co/datasets/alpindale/two-million-bluesky-posts.

673a1149a7a311f5bed5c624

HuggingFaceTB/smoltalk

HuggingFaceTB

{"language": ["en"], "tags": ["synthetic"], "pretty_name": "SmolTalk", "size_categories": ["1M<n<10M"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "data/all/train-*"}, {"split": "test", "path": "data/all/test-*"}]}, {"config_name": "smol-magpie-ultra", "data_files": [{"split": "train", "path": "data/smol-magpie-ultra/train-*"}, {"split": "test", "path": "data/smol-magpie-ultra/test-*"}]}, {"config_name": "smol-constraints", "data_files": [{"split": "train", "path": "data/smol-constraints/train-*"}, {"split": "test", "path": "data/smol-constraints/test-*"}]}, {"config_name": "smol-rewrite", "data_files": [{"split": "train", "path": "data/smol-rewrite/train-*"}, {"split": "test", "path": "data/smol-rewrite/test-*"}]}, {"config_name": "smol-summarize", "data_files": [{"split": "train", "path": "data/smol-summarize/train-*"}, {"split": "test", "path": "data/smol-summarize/test-*"}]}, {"config_name": "apigen-80k", "data_files": [{"split": "train", "path": "data/apigen-80k/train-*"}, {"split": "test", "path": "data/apigen-80k/test-*"}]}, {"config_name": "everyday-conversations", "data_files": [{"split": "train", "path": "data/everyday-conversations/train-*"}, {"split": "test", "path": "data/everyday-conversations/test-*"}]}, {"config_name": "explore-instruct-rewriting", "data_files": [{"split": "train", "path": "data/explore-instruct-rewriting/train-*"}, {"split": "test", "path": "data/explore-instruct-rewriting/test-*"}]}, {"config_name": "longalign", "data_files": [{"split": "train", "path": "data/longalign/train-*"}, {"split": "test", "path": "data/longalign/test-*"}]}, {"config_name": "metamathqa-50k", "data_files": [{"split": "train", "path": "data/metamathqa-50k/train-*"}, {"split": "test", "path": "data/metamathqa-50k/test-*"}]}, {"config_name": "numina-cot-100k", "data_files": [{"split": "train", "path": "data/numina-cot-100k/train-*"}, {"split": "test", "path": "data/numina-cot-100k/test-*"}]}, {"config_name": "openhermes-100k", "data_files": [{"split": "train", "path": "data/openhermes-100k/train-*"}, {"split": "test", "path": "data/openhermes-100k/test-*"}]}, {"config_name": "self-oss-instruct", "data_files": [{"split": "train", "path": "data/self-oss-instruct/train-*"}, {"split": "test", "path": "data/self-oss-instruct/test-*"}]}, {"config_name": "systemchats-30k", "data_files": [{"split": "train", "path": "data/systemchats-30k/train-*"}, {"split": "test", "path": "data/systemchats-30k/test-*"}]}]}

5a40ecb185e55dd30edf3c24b77e67f6ea0d659b

SmolTalk Dataset description This is a synthetic dataset designed for supervised finetuning (SFT) of LLMs. It was used to build SmolLM2-Instruct family of models and contains 1M samples. During the development of SmolLM2, we observed that models finetuned on public SFT datasets underperformed compared to other models with proprietary instruction datasets. To address this gap, we created new synthetic datasets that improve instruction following while covering… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smoltalk.

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

6745c5e01e6a4eb7f587d70d

bluesky-community/one-million-bluesky-posts

bluesky-community

{"dataset_info": [{"config_name": "default", "features": [{"name": "text", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "author", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "has_images", "dtype": "bool"}, {"name": "reply_to", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 267405176, "num_examples": 1000000}], "download_size": 179994596, "dataset_size": 267405176}, {"config_name": "with-language-predictions", "features": [{"name": "text", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "author", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "has_images", "dtype": "bool"}, {"name": "reply_to", "dtype": "string"}, {"name": "predicted_language", "dtype": "string"}, {"name": "language_confidence", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 287405176, "num_examples": 1000000}], "download_size": 187712999, "dataset_size": 287405176}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "with-language-predictions", "data_files": [{"split": "train", "path": "with-language-predictions/train-*"}]}], "tags": ["bluesky"], "extra_gated_prompt": "This dataset contains Bluesky Social data. By accessing this dataset, you agree to adhere to Bluesky's Terms of Service (https://bsky.social/about/support/tos) and use the data responsibly.", "extra_gated_fields": {"I agree to comply with Bluesky's Terms of Service and use the data in accordance with their policies": "checkbox"}, "license": "other", "pretty_name": "One Million Bluesky Posts", "size_categories": ["1M<n<10M"]}

712ac849995c4c32c341e70a781feb95de02af42

Note I've removed the data from this dataset since there was a lot of community pushback about its creation/uploading. I will leave the dataset repository up to allow room for discussion of how datasets can be used to help improve Bluesky and allow people to build the tools they need to build their own open models and approaches to creating feeds that work for their needs. Please feel free to continue to leave feedback in the discussions here. Dataset Card for 1… See the full description on the dataset page: https://huggingface.co/datasets/bluesky-community/one-million-bluesky-posts.

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.

673e9e53cdad8a9744b0bf1b

O1-OPEN/OpenO1-SFT

O1-OPEN

{"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en", "zh"], "size_categories": ["10K<n<100K"]}

d0cef01867c0ddba92f147bcb46e6321f2634aef

SFT Data for CoT Activation 🎉🎉🎉This repository contains the dataset used for fine-tuning a language model using SFT for Chain-of-Thought Activation. 🌈🌈🌈The dataset is designed to enhance the model's ability to generate coherent and logical reasoning sequences. ☄☄☄By using this dataset, the model can learn to produce detailed and structured reasoning steps, enhancing its performance on complex reasoning tasks. Statistics 1️⃣Total Records: 77,685… See the full description on the dataset page: https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT.

66f52e56b3ab81ac10f78c4c

VTSNLP/vietnamese_curated_dataset

VTSNLP

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 65506190827, "num_examples": 12169131}], "download_size": 34648619492, "dataset_size": 65506190827}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

b81fcce58945970117a1b56d50ec81be2628a5c3

Dataset Description Vietnamese Curated Text Dataset. This dataset is collected from multiple open Vietnamese datasets, and curated with NeMo Curator Developed by: Viettel Solutions Language: Vietnamese Details Please visit our Tech Blog post on NVIDIA's plog page for details. Link Data Collection We utilize a combination of datasets that contain samples in Vietnamese language, ensuring a robust and representative text corpus. These datasets… See the full description on the dataset page: https://huggingface.co/datasets/VTSNLP/vietnamese_curated_dataset.

6695831f2d25bd04e969b0a2

AI-MO/NuminaMath-CoT

AI-MO

{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2495457595.0398345, "num_examples": 859494}, {"name": "test", "num_bytes": 290340.31593470514, "num_examples": 100}], "download_size": 1234351634, "dataset_size": 2495747935.355769}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["aimo", "math"], "pretty_name": "NuminaMath CoT"}

9d8d210c9f6a36c8f3cd84045668c9b7800ef517

Dataset Card for NuminaMath CoT Dataset Summary Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-CoT.

672d8bf4bde669ec7e63ba72

allenai/tulu-3-sft-mixture

allenai

{"annotations_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language": ["amh", "arb", "ary", "ars", "acq", "arz", "apc", "ben", "ceb", "dan", "deu", "ell", "eng", "eus", "fil", "fin", "fra", "gle", "guj", "hat", "hau", "hin", "hun", "ibo", "ind", "ita", "jav", "jpn", "kan", "kir", "kor", "kur", "lit", "mal", "mar", "mlg", "msa", "mya", "nep", "nld", "nso", "nya", "pan", "pes", "pol", "por", "pus", "rus", "sin", "sna", "snd", "som", "spa", "sqi", "srp", "sun", "swa", "swe", "tam", "tel", "tha", "tur", "ukr", "urd", "vie", "wol", "xho", "yor", "zho", "zul"], "license": "odc-by", "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["allenai/coconot", "ai2-adapt-dev/flan_v2_converted", "HuggingFaceH4/no_robots", "OpenAssistant/oasst1", "allenai/tulu-3-personas-math", "allenai/tulu-3-sft-personas-math-grade", "allenai/tulu-3-sft-personas-code", "allenai/tulu-3-personas-algebra", "allenai/tulu-3-sft-personas-instruction-following", "AI-MO/NuminaMath-TIR", "allenai/wildguardmix", "allenai/wildjailbreak", "allenai/tulu-3-hard-coded", "CohereForAI/aya_dataset", "allenai/WildChat-1M", "LipengCS/Table-GPT", "allenai/SciRIFF", "theblackcat102/evol-codealpaca-v1"], "task_categories": ["other"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2914250826.5647593, "num_examples": 939343}], "download_size": 1412954868, "dataset_size": 2914250826.5647593}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

55e9fd6d41c3cd1a98270dff07557bc2a1e1ba91

Tulu 3 SFT Mixture Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact. The Tulu 3 SFT mixture was used to train the Tulu 3 series of models. It contains 939,344 samples from the following sets: CoCoNot (ODC-BY-1.0), 10,983 prompts (Brahman et al., 2024) FLAN v2 via ai2-adapt-dev/flan_v2_converted, 89,982 prompts (Longpre… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-3-sft-mixture.

67479861351d030aa5c9aa17

informatiker/20-million-bluesky-posts

informatiker

{"license-real": "libsky-their-data-not-mine", "language": ["en"]}

20dfcc04e70758d6e14b490dd1b8f02f0aab3cd0

20 Million Bluesky Posts This dataset contains 20 million public posts collected from Bluesky Social's firehose API. The data is anonymized (VERY IMPORTANT POINT). The full dataset could lead to legal issues. If you want your posts removed, I do not know how to as I can't map DID -> Posts. Dataset Description This dataset consists of 20 million public (AS IN OPENLY AVAILABLE) posts from Bluesky Social, collected through the platform's firehose API. Language(s)… See the full description on the dataset page: https://huggingface.co/datasets/informatiker/20-million-bluesky-posts.

66fa4cf2a0802002c35d3b99

VTSNLP/instruct_general_dataset

VTSNLP

{"dataset_info": {"features": [{"name": "instruct", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "max_len", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 7129579863, "num_examples": 4531804}], "download_size": 3411942397, "dataset_size": 7129579863}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

3611ff8afe93dc44d30330dbd3e4194067247949

6745db412fd6d8c0251091c5

5CD-AI/LLaVA-CoT-o1-Instruct

5CD-AI

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4601941604.092, "num_examples": 58468}], "download_size": 3936031095, "dataset_size": 4601941604.092}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

f27312b2b25602b17222798b11837237eb8be4b7

Example1 Input: Please answer the question below, explaining your reasoning step by step before providing the final answer. Question: Are there enough straws for every cup ? A. yes B. no Output: <SUMMARY/>The question asks whether there are enough straws to provide one for each cup depicted in an image. To answer, we need to count the number of straws and cups separately and then compare those quantities.</SUMMARY> <CAPTION>The image shows… See the full description on the dataset page: https://huggingface.co/datasets/5CD-AI/LLaVA-CoT-o1-Instruct.

621ffdd236468d709f181ec6

ylecun/mnist

ylecun

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-nist"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "mnist", "pretty_name": "MNIST", "dataset_info": {"config_name": "mnist", "features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6", "7": "7", "8": "8", "9": "9"}}}}], "splits": [{"name": "train", "num_bytes": 17223300, "num_examples": 60000}, {"name": "test", "num_bytes": 2875182, "num_examples": 10000}], "download_size": 18157506, "dataset_size": 20098482}, "configs": [{"config_name": "mnist", "data_files": [{"split": "train", "path": "mnist/train-*"}, {"split": "test", "path": "mnist/test-*"}], "default": true}]}

77f3279092a1c1579b2250db8eafed0ad422088c

Dataset Card for MNIST Dataset Summary The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class. Half of the image were drawn by Census Bureau employees and the other half by high school… See the full description on the dataset page: https://huggingface.co/datasets/ylecun/mnist.

mnist

6748553f4fedd39642707bc3

data-is-better-together/image-preferences-results

data-is-better-together

{"size_categories": "1K<n<10K", "tags": ["rlfh", "argilla", "human-feedback"], "license": "apache-2.0"}

903aa428105fd41d77826916ac3c2e0ed0b43229

Dataset Card for image-preferences-results This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into your Argilla server as explained in Load with Argilla, or used directly with the datasets library in Load with datasets. Using this dataset with Argilla To load with Argilla, you'll just need to install Argilla as pip install argilla --upgrade and then use the following code: import argilla as rg ds =… See the full description on the dataset page: https://huggingface.co/datasets/data-is-better-together/image-preferences-results.

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"]}

afc882408a4a15396965254b221bc9dfd7c35434

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.

6746d31505802d0aa68831f1

Xkev/LLaVA-CoT-100k

Xkev

{"license": "apache-2.0", "task_categories": ["visual-question-answering"], "language": ["en"], "pretty_name": "LLaVA-CoT", "size_categories": ["10K<n<100K"]}

d83435ce2e4438c632391e40c416eb75e0f0dabb

Dataset Card for LLaVA-CoT Dataset Sources Repository: [https://github.com/PKU-YuanGroup/LLaVA-CoT] Paper: [https://arxiv.org/abs/2411.10440] Dataset Structure The repository includes image.zip.part-{aa-ap}, which you need to merge manually. Use the following command to combine them: cat image.zip.part-* > image.zip unzip image.zip The train.jsonl file contains the question-answering data in the following format: { "id": ID, "image": IMAGE_PATH… See the full description on the dataset page: https://huggingface.co/datasets/Xkev/LLaVA-CoT-100k.

670f08ae2e97b2afe4d2df9b

GAIR/o1-journey

GAIR

{"language": ["en"], "size_categories": ["n<1K"]}

32deef4773fe1f9488ff2052daf64035c034c0ea

Dataset for O1 Replication Journey: A Strategic Progress Report Usage from datasets import load_dataset dataset = load_dataset("GAIR/o1-journey", split="train") Citation If you find our dataset useful, please cite: @misc{o1journey, author = {Yiwei Qin and Xuefeng Li and Haoyang Zou and Yixiu Liu and Shijie Xia and Zhen Huang and Yixin Ye and Weizhe Yuan and Zhengzhong Liu and Yuanzhi Li and Pengfei Liu}, title = {O1 Replication Journey: A Strategic Progress… See the full description on the dataset page: https://huggingface.co/datasets/GAIR/o1-journey.

67181271edffd000735841b9

microsoft/BiomedParseData

microsoft

{"license": "cc-by-nc-sa-4.0"}

2dae8acc89a482c5c5a08a36b50db000c2840975

BiomedParseData This is the official dataset repository for "A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities". [Code] [Paper] [Demo] [Model] [Data] We processed from the below public segmentation datasets, and host a subset of our processed datasets as ZIP files here. Each instance include a 1024x1024 PNG image, a list of textual description for the segmentation target, and a binary groundtruth mask also in… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/BiomedParseData.

6749b6862a131c19ee4078ee

victor/qwq-misguided-attention

victor

{"tags": ["observers"]}

36445f948ca11819e16600d2ae2f1120185a1c25

Overview This dataset presents responses from the QwQ-32B-Preview model to the MisguidedAttention prompt challenge, a collection of carefully crafted prompts designed to test LLMs' reasoning abilities in the presence of misleading information. About Misguided Attention The Misguided Attention challenge consists of modified versions of well-known thought experiments, riddles, and paradoxes. These modifications are subtle yet significant, requiring careful… See the full description on the dataset page: https://huggingface.co/datasets/victor/qwq-misguided-attention.

6742ad7ef6646c6a4ef06cb2

allenai/olmo-mix-1124

allenai

{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "OLMo Mix 1124", "size_categories": ["1B<n<10B"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "algebraic-stack", "data_files": [{"split": "train", "path": "data/algebraic-stack/*"}]}, {"config_name": "arxiv", "data_files": [{"split": "train", "path": "data/arxiv/*"}]}, {"config_name": "dclm", "data_files": [{"split": "train", "path": "data/dclm/*"}]}, {"config_name": "open-web-math", "data_files": [{"split": "train", "path": "data/open-web-math/*"}]}, {"config_name": "pes2o", "data_files": [{"split": "train", "path": "data/pes2o/*"}]}, {"config_name": "starcoder", "data_files": [{"split": "train", "path": "data/starcoder/*"}]}, {"config_name": "wiki", "data_files": [{"split": "train", "path": "data/wiki/*"}]}]}

5e729f5e15d3935506c64cbeb0ec23fba8ba2412

OLMo 1124 Pretraining set Collection of data used to train OLMo2-1124 models. The majority of this dataset comes from DCLM-Baseline with no additional filtering, but we provide the explicit breakdowns below. Name Tokens Bytes (uncompressed) Documents License DCLM-Baseline 3.70T 21.3TB 2.95B CC-BY-4.0 Arxiv 20.8B 77.2GB 3.95M ODC-BY pes2o 58.6B 412GB 38M ODC-BY starcoder 83.0B 458GB 78.7M ODC-BY Algebraic-stack 11.8B 44.0GB 2.83M ODC-BY OpenWebMath 12.2B… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmo-mix-1124.

64ec6278bfb2aa06a470eced

elyza/ELYZA-tasks-100

elyza

{"task_categories": ["text2text-generation"], "language": ["ja"], "size_categories": ["n<1K"], "license": "cc-by-sa-4.0"}

240fc800c4695d86ce0f32a713e199cd2ba66ab6

ELYZA-tasks-100: 日本語instructionモデル評価データセット Data Description 本データセットはinstruction-tuningを行ったモデルの評価用データセットです。詳細は リリースのnote記事 を参照してください。 特徴: 複雑な指示・タスクを含む100件の日本語データです。 役に立つAIアシスタントとして、丁寧な出力が求められます。 全てのデータに対して評価観点がアノテーションされており、評価の揺らぎを抑えることが期待されます。 具体的には以下のようなタスクを含みます。 要約を修正し、修正箇所を説明するタスク 具体的なエピソードから抽象的な教訓を述べるタスク ユーザーの意図を汲み役に立つAIアシスタントとして振る舞うタスク 場合分けを必要とする複雑な算数のタスク 未知の言語からパターンを抽出し日本語訳する高度な推論を必要とするタスク 複数の指示を踏まえた上でyoutubeの対話を生成するタスク 架空の生き物や熟語に関する生成・大喜利などの想像力が求められるタスク… See the full description on the dataset page: https://huggingface.co/datasets/elyza/ELYZA-tasks-100.

65cf50a5f5a15aa42133ac44

ruslanmv/ai-medical-chatbot

ruslanmv

{"configs": [{"config_name": "default", "data_files": [{"path": "dialogues.*", "split": "train"}]}], "dataset_info": {"dataset_size": 141665910, "download_size": 141665910, "features": [{"dtype": "string", "name": "Description"}, {"dtype": "string", "name": "Patient"}, {"dtype": "string", "name": "Doctor"}], "splits": [{"name": "train", "num_bytes": 141665910, "num_examples": 256916}]}}

138c99336a3afce0df88ffe6fd67bd231df25d36

AI Medical Chatbot Dataset This is an experimental Dataset designed to run a Medical Chatbot It contains at least 250k dialogues between a Patient and a Doctor. Playground ChatBot ruslanmv/AI-Medical-Chatbot For furter information visit the project here: https://github.com/ruslanmv/ai-medical-chatbot

6747dedcf80f3ea26f93b205

megrisdal/llms-txt

megrisdal

{"license": "cc0-1.0", "dataset_info": {"features": [{"name": "Domain", "dtype": "string"}, {"name": "File", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "Content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49083351, "num_examples": 162}], "download_size": 17910597, "dataset_size": 49083351}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

3949b67ddd30d44a2d3bf59f183107ae1ef46419

Context & Motivation https://llmstxt.org/ is a project from Answer.AI which proposes to "standardise on using an /llms.txt file to provide information to help LLMs use a website at inference time." I've noticed many tool providers begin to offer /llms.txt files for their websites and documentation. This includes developer tools and platforms like Perplexity, Anthropic, Hugging Face, Vercel, and others. I've also come across https://directory.llmstxt.cloud/, a directory of… See the full description on the dataset page: https://huggingface.co/datasets/megrisdal/llms-txt.

660e7b9b4636ce2b0e77b699

mozilla-foundation/common_voice_17_0

mozilla-foundation

{"pretty_name": "Common Voice Corpus 17.0", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "af", "am", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "gn", "ha", "he", "hi", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lij", "lo", "lt", "ltg", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan", "ne", "nhi", "nl", "nn", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quy", "rm", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yi", "yo", "yue", "zgh", "zh", "zu", "zza"], "language_bcp47": ["zh-CN", "zh-HK", "zh-TW", "sv-SE", "rm-sursilv", "rm-vallader", "pa-IN", "nn-NO", "ne-NP", "nan-tw", "hy-AM", "ga-IE", "fy-NL"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "source_datasets": ["extended|common_voice"], "paperswithcode_id": "common-voice", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."}

b10d53980ef166bc24ce3358471c1970d7e6b5ec

Dataset Card for Common Voice Corpus 17.0 Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 31175 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 20408 validated hours in 124 languages, but more voices and languages are always added. Take a look at the Languages… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0.

common-voice

@inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 }

66ecf917b550156156e3972d

argilla/magpie-ultra-v1.0

argilla

{"size_categories": ["1M<n<10M"], "dataset_info": [{"config_name": "default", "features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "system_prompt_key", "dtype": "string"}, {"name": "model_name_difficulty", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "knowledge", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "model_name_quality", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "quality", "dtype": "string"}, {"name": "model_name_classification", "dtype": "string"}, {"name": "primary_tag", "dtype": "string"}, {"name": "other_tags", "sequence": "string"}, {"name": "model_name_embeddings", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "model_name_guard", "dtype": "string"}, {"name": "guard", "dtype": "string"}, {"name": "distilabel_metadata", "struct": [{"name": "raw_input_assign_tags_0", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_input_assign_tags_1", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_input_assign_tags_2", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_input_chat_generation_0", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_output_assign_tags_0", "dtype": "string"}, {"name": "raw_output_assign_tags_1", "dtype": "string"}, {"name": "raw_output_assign_tags_2", "dtype": "string"}, {"name": "raw_output_chat_generation_0", "dtype": "string"}]}, {"name": "nn_indices", "sequence": "int64"}, {"name": "nn_scores", "sequence": "float64"}, {"name": "score", "dtype": "float64"}, {"name": "safe", "dtype": "bool"}, {"name": "hazard_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27826911085, "num_examples": 999960}], "download_size": 14572111659, "dataset_size": 27826911085}, {"config_name": "filtered", "features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "quality", "dtype": "string"}, {"name": "reward_model_score", "dtype": "float64"}, {"name": "conversation_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3990272968.1059103, "num_examples": 621837}], "download_size": 2121154575, "dataset_size": 3990272968.1059103}, {"config_name": "top_300k_longer_conversations", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "quality", "dtype": "string"}, {"name": "reward_model_score", "dtype": "float64"}, {"name": "conversation_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2506976645.75, "num_examples": 285000}, {"name": "test", "num_bytes": 131946139.25, "num_examples": 15000}], "download_size": 1274741497, "dataset_size": 2638922785}, {"config_name": "top_300k_shorter_conversations", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "quality", "dtype": "string"}, {"name": "reward_model_score", "dtype": "float64"}, {"name": "conversation_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1442215145.7, "num_examples": 285000}, {"name": "test", "num_bytes": 75906060.3, "num_examples": 15000}], "download_size": 769844140, "dataset_size": 1518121206}, {"config_name": "top_500k_longer_conversations", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "quality", "dtype": "string"}, {"name": "reward_model_score", "dtype": "float64"}, {"name": "conversation_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3655147005.8, "num_examples": 475000}, {"name": "test", "num_bytes": 192376158.2, "num_examples": 25000}], "download_size": 1890302977, "dataset_size": 3847523164}, {"config_name": "top_500k_shorter_conversations", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "quality", "dtype": "string"}, {"name": "reward_model_score", "dtype": "float64"}, {"name": "conversation_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2885994240.3, "num_examples": 475000}, {"name": "test", "num_bytes": 151894433.7, "num_examples": 25000}], "download_size": 1539934559, "dataset_size": 3037888674}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "filtered", "data_files": [{"split": "train", "path": "filtered/train-*"}]}, {"config_name": "top_300k_longer_conversations", "data_files": [{"split": "train", "path": "top_300k_longer_conversations/train-*"}, {"split": "test", "path": "top_300k_longer_conversations/test-*"}]}, {"config_name": "top_300k_shorter_conversations", "data_files": [{"split": "train", "path": "top_300k_shorter_conversations/train-*"}, {"split": "test", "path": "top_300k_shorter_conversations/test-*"}]}, {"config_name": "top_500k_longer_conversations", "data_files": [{"split": "train", "path": "top_500k_longer_conversations/train-*"}, {"split": "test", "path": "top_500k_longer_conversations/test-*"}]}, {"config_name": "top_500k_shorter_conversations", "data_files": [{"split": "train", "path": "top_500k_shorter_conversations/train-*"}, {"split": "test", "path": "top_500k_shorter_conversations/test-*"}]}], "tags": ["synthetic", "distilabel", "rlaif"]}

218cd0ad9faa7b46676775d5fb086b0bf9736f72

Dataset Card for magpie-ultra-v1.0 This dataset has been created with distilabel. Dataset Summary magpie-ultra it's a synthetically generated dataset for supervised fine-tuning using the Llama 3.1 405B-Instruct model, together with other Llama models like Llama-Guard-3-8B and Llama-3.1-8B-Instruct. The dataset contains challenging instructions and responses for a wide variety of tasks, such as Coding & debugging, Math, Data analysis, Creative… See the full description on the dataset page: https://huggingface.co/datasets/argilla/magpie-ultra-v1.0.

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"]}

cd8ae54fc319ca3802c0217d235b390e00b14ee2

Dataset Card for Suno.ai Music Generation Dataset Summary This dataset contains metadata for 659,788 songs generated by artificial intelligence on the suno.com platform, a service that generates music using artificial intelligence. The songs were discovered by search queries with words from the dwyl/english-words word list. Languages The dataset is multilingual with English as the primary language: English (en): Primary language for metadata and… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/suno.

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.

673b0e224b54eec802c85f6d

General-Medical-AI/IMed-361M

General-Medical-AI

{"license": "apache-2.0"}

6c1e3fbe88ef5041c188d18ce1c2468a9f21922f

IMed-361M 🤝 免责声明 IMed-361M是由多个公开的数据集组成，旨在取之于社区，回馈于社区，为研究人员和开发者提供一个用于学术和技术研究的资源。使用本数据集的任何个人或组织（以下统称为“使用者”）需遵守以下免责声明： 数据集来源：本数据集由多个公开的数据集组成，这些数据集的来源已在预印版论文中明确标明。使用者应当遵守原始数据集的相关许可和使用条款。 数据准确性：尽管我们已经努力确保数据集的准确性和完整性，但无法对数据集的准确性作出保证。使用者应自行承担使用数据集可能带来的风险和责任。 责任限制：在任何情况下，数据集的提供者及相关贡献者均不对使用者的任何行为或结果承担责任。 使用约束：使用者在使用本数据集时，应遵守适用的法律法规和伦理规范。使用者不得将本数据集用于非法、侵犯隐私、诽谤、歧视或其他违法或不道德的目的。 知识产权：本数据集的知识产权归原始数据集的相关权利人所有，使用者不得以任何方式侵犯数据集的知识产权。… See the full description on the dataset page: https://huggingface.co/datasets/General-Medical-AI/IMed-361M.

6741517a022caab2f6d0d92c

allenai/dolmino-mix-1124

allenai

{"license": "odc-by", "task_categories": ["text-generation"], "pretty_name": "DOLMino Mix 1124", "size_categories": ["100M<n<1B"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "dclm", "data_files": [{"split": "train", "path": "data/dclm/*"}]}, {"config_name": "flan", "data_files": [{"split": "train", "path": "data/flan/*"}]}, {"config_name": "pes2o", "data_files": [{"split": "train", "path": "data/pes2o/*"}]}, {"config_name": "stackexchange", "data_files": [{"split": "train", "path": "data/stackexchange/*"}]}, {"config_name": "wiki", "data_files": [{"split": "train", "path": "data/wiki/*"}]}, {"config_name": "stackexchange", "data_files": [{"split": "train", "path": "data/stackexchange/*"}]}, {"config_name": "math", "data_files": [{"split": "train", "path": "data/math/*"}]}]}

bb54cabe28234bbac9f5632691c184315295cb22

DOLMino dataset mix for OLMo2 stage 2 annealing training. Mixture of high-quality data used for the second stage of OLMo2 training. Source Sizes Name Category Tokens Bytes (uncompressed) Documents License DCLM HQ Web Pages 752B 4.56TB 606M CC-BY-4.0 Flan HQ Web Pages 17.0B 98.2GB 57.3M ODC-BY Pes2o STEM Papers 58.6B 413GB 38.8M ODC-BY Wiki Encyclopedic 3.7B 16.2GB 6.17M ODC-BY StackExchange CodeText 1.26B 7.72GB 2.48M CC-BY-SA-{2.5, 3.0, 4.0}… See the full description on the dataset page: https://huggingface.co/datasets/allenai/dolmino-mix-1124.

625552d2b339bb03abe3432d

openai/gsm8k

openai

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]}

e53f048856ff4f594e959d75785d2c2d37b678ee

Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.

gsm8k

63112274d43c55e811f9cd63

zeroshot/twitter-financial-news-sentiment

zeroshot

{"annotations_creators": ["other"], "language": ["en"], "language_creators": ["other"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "twitter financial news", "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "tags": ["twitter", "finance", "markets", "stocks", "wallstreet", "quant", "hedgefunds", "markets"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"]}

ccbe24de388e287beb92dd393a335c376b350ac3

Dataset Description The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment. The dataset holds 11,932 documents annotated with 3 labels: sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" } The data was collected using the Twitter API. The current dataset supports the multi-class… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

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.

67488e06829f46f23216b1bb

nachoyawn/three-million-bluesky

nachoyawn

e68eed5f84d36d892bcb425b80984d3316b3eca5

3 million bluesky posts raw.zip is the ~5m posts that i initially pulled that were full of duplicates, where i removed the duplicates and compiled those into 2.6 million posts in final_posts.jsonl the rest of the .jsonl files in the data folder, and not inside the raw.zip are unchecked and may have duplicates. feel free to write your own script to remove them, but this should contain around 3 million unique posts.

667ae607210c128fa4a40b6c

xinlai/Math-Step-DPO-10K

xinlai

{"language": ["en"], "dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "initial_reason_steps", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "full_chosen", "dtype": "string"}, {"name": "full_rejected", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26528471, "num_examples": 10795}], "download_size": 11985248, "dataset_size": 26528471}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["dpo"]}

84a203c9bd95940c1d492b965838027f00de336f

Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs 🖥️Code | 🤗Data | 📄Paper This repo contains the Math-Step-DPO-10K dataset for our paper Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs, Step-DPO is a simple, effective, and data-efficient method for boosting the mathematical reasoning ability of LLMs. Notably, Step-DPO, when applied to Qwen2-72B-Instruct, achieves scores of 70.8% and 94.0% on the test sets of MATH and… See the full description on the dataset page: https://huggingface.co/datasets/xinlai/Math-Step-DPO-10K.

66e1a2fb91e57a0788b501cb

jackyhate/text-to-image-2M

jackyhate

{"license": "mit", "task_categories": ["text-to-image", "image-to-text", "image-classification"], "language": ["en"], "size_categories": ["1M<n<10M"]}

e4ece89e640210e9fc3fd0966f5a45291bdb665c

text-to-image-2M: A High-Quality, Diverse Text-to-Image Training Dataset Overview text-to-image-2M is a curated text-image pair dataset designed for fine-tuning text-to-image models. The dataset consists of approximately 2 million samples, carefully selected and enhanced to meet the high demands of text-to-image model training. The motivation behind creating this dataset stems from the observation that datasets with over 1 million samples tend to produce better… See the full description on the dataset page: https://huggingface.co/datasets/jackyhate/text-to-image-2M.

673e4b302ceead4cf2cfb236

MBZUAI/ALM-Bench

MBZUAI

{"license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "file_name", "dtype": "image"}, {"name": "ID", "dtype": "string"}, {"name": "Language", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Question_Type", "dtype": "string"}, {"name": "English_Question", "dtype": "string"}, {"name": "English_Answer", "dtype": "string"}, {"name": "Translated_Question", "dtype": "string"}, {"name": "Translated_Answer", "dtype": "string"}, {"name": "Image_Url", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 12025651484.031, "num_examples": 22763}], "download_size": 2134741068, "dataset_size": 12025651484.031}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

e276ce3e17fba4e8639544bb3c7958ed30a601cd

All Languages Matter Benchmark (ALM-Bench) Summary Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corresponding visual cues. In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter… See the full description on the dataset page: https://huggingface.co/datasets/MBZUAI/ALM-Bench.

673f1aaaa7dc37c56d62bf13

HuggingFaceTB/smol-smoltalk

HuggingFaceTB

{"license": "apache-2.0", "language": ["en"], "tags": ["synthetic"], "pretty_name": "Smol-SmolTalk", "size_categories": ["100K<n<1M"], "dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1811694118.6259818, "num_examples": 460341}, {"name": "test", "num_bytes": 95354393.3740182, "num_examples": 24229}], "download_size": 970652749, "dataset_size": 1907048512}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}

06f445222cefca1ef920c9f67c69862a68fe1e13

Smol-SmalTalk This is a subset of SmolTalk dataset adapted for smol models with less than 1B parameters. We used it to build SmolLM2-360M-Instruct and SmolLM2-135M-Instruct. We do SFT on this dataset and then DPO on UltraFeedback. Compared to SmolTalk: The conversations from Smol-Magpie-Ultra are shorter in this dataset We include less task specific data compared to SmolTalk (e.g no function calling and less rewriting and summarization examples) since these smaller models… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk.

6747700044a7143a01ac6318

noteuclaise/bluesky_1M_metaposts

noteuclaise

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 181468977, "num_examples": 1530625}], "download_size": 136496769, "dataset_size": 181468977}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

3a6b990215fbbbc29dcc15b956fc3db5bc7bd16e

This is a dataset of Bluesky posts from users who discussed the original Bluesky 1M posts dataset. It includes posts from users who have replied to, or quoted, any ot the following posts (as well as a couple other who responded to mine): https://bsky.app/profile/danielvanstrien.bsky.social/post/3lbu6l4fxdc2e https://bsky.app/profile/danielvanstrien.bsky.social/post/3lbvih4luvk23 https://bsky.app/profile/chickenpuppet.bsky.social/post/3lbvbzl4abc25 To reduce risk of harassment, only the text… See the full description on the dataset page: https://huggingface.co/datasets/noteuclaise/bluesky_1M_metaposts.