instruction stringlengths 49 209 | input stringlengths 310 1.08k | output stringlengths 372 1.1k | source stringclasses 1
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Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"fka/prompts.chat","name":"prompts.chat","description":"a.k.a. Awesome ChatGPT Prompts. This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts. prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community.... | This dataset is only partially suitable for SFT. It appears to be a collection of community-shared prompts rather than prompt-response training pairs. Its best use case is prompt engineering research, instruction template discovery, agent prompting experiments, and synthetic data generation. For SFT, it would likely re... | huggingface | fka/prompts.chat |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"HuggingFaceFW/fineweb","name":"fineweb","description":"FineWeb consists of more than 18.5T tokens of cleaned and deduplicated English web data from CommonCrawl. The data processing pipeline is optimized for LLM performance.","tags":["task_categories:text-generation","language:en","license:... | This dataset is not ideal for supervised fine-tuning because it is a large-scale cleaned web corpus rather than an instruction-response dataset. Its best use case is base model pretraining or continued pretraining on high-quality English web text. It may support unsupervised language modeling, domain adaptation, and da... | huggingface | HuggingFaceFW/fineweb |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"Anthropic/hh-rlhf","name":"hh-rlhf","description":"This repository provides access to human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference or reward ... | This dataset is generally not suitable for direct SFT because it is designed for preference modeling and RLHF, not supervised dialogue training. Its best use case is training reward models, preference models, or benchmarking alignment methods around helpfulness and harmlessness. If used for SFT, extreme care is needed ... | huggingface | Anthropic/hh-rlhf |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"Open-Orca/OpenOrca","name":"OpenOrca","description":"The OpenOrca dataset is a rich collection of augmented FLAN data aligned as closely as possible with the distributions outlined in the Orca paper. It has been instrumental in generating high-performing model checkpoints.","tags":["task_c... | This dataset is well suited for supervised fine-tuning. It is an instruction-oriented corpus derived from augmented FLAN-style data and was created specifically to support high-performing instruction-following models. Its best use case is general-purpose instruction tuning for assistant behavior across QA, summarizatio... | huggingface | Open-Orca/OpenOrca |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"OpenAssistant/oasst1","name":"oasst1","description":"OpenAssistant Conversations Dataset (OASST1) is a human-generated, human-annotated assistant-style conversation corpus with 161,443 messages in 35 languages, annotated with quality ratings and structured as conversation trees.","tags":["... | This dataset is well suited for supervised fine-tuning, especially for conversational assistants. It contains human-generated and human-annotated multi-turn assistant conversations with quality signals, making it valuable for chat SFT, multilingual assistant training, and response ranking or filtering. Its best use cas... | huggingface | OpenAssistant/oasst1 |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"openai/gsm8k","name":"gsm8k","description":"Dataset Card for GSM8K. 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 r... | GSM8K is an English text dataset of grade school math word problems designed for multi-step reasoning and question answering. It contains about 8.5K high-quality problems that typically require 2 to 8 reasoning steps and basic arithmetic operations. It is commonly used as an official benchmark for evaluating mathematic... | huggingface | openai/gsm8k |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"wikimedia/wikipedia","name":"wikipedia","description":"Dataset Card for Wikimedia Wikipedia. Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps with one subset per language, each containing a single train split. Each example contai... | Wikimedia Wikipedia is a massive multilingual text dataset built from cleaned Wikipedia article dumps. Each example contains a full article, with markdown and unwanted sections removed, and the dataset is organized into one subset per language. It is useful for language modeling, masked language modeling, pretraining, ... | huggingface | wikimedia/wikipedia |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"gsdf/EasyNegative","name":"EasyNegative","description":"Negative Embedding. This is a Negative Embedding trained with Counterfeit. Please use it in the stable-diffusion-webui embeddings folder. It can be used with other models, but the effectiveness is not certain.","tags":["license:other"... | EasyNegative is an image-related resource centered on a negative embedding intended for use with Stable Diffusion style workflows rather than a conventional supervised dataset. It appears to be a very small image-modality artifact associated with prompt conditioning or generation quality control, especially in stable-d... | huggingface | gsdf/EasyNegative |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"togethercomputer/RedPajama-Data-1T","name":"RedPajama-Data-1T","description":"RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.","tags":["task_categories:text-generation","language:en","size_categories:1M<n<10M","modality:text","library:datasets","library:ml... | RedPajama-Data-1T is a large English text dataset intended for text generation and large language model pretraining. It is described as a clean-room, fully open-source implementation of the LLaMA training dataset, making it relevant for open foundation model research and reproduction efforts. Its primary use cases incl... | huggingface | togethercomputer/RedPajama-Data-1T |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"FreedomIntelligence/medical-o1-reasoning-SFT","name":"medical-o1-reasoning-SFT","description":"News: [2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data. [2025/02... | medical-o1-reasoning-SFT is a bilingual English-Chinese text dataset for supervised fine-tuning on medical reasoning and question answering. It is built around medical verifiable problems and includes reasoning-oriented SFT data, with references to distilled reasoning traces derived from DeepSeek-R1. The dataset is par... | huggingface | FreedomIntelligence/medical-o1-reasoning-SFT |
Summarize the dataset for language model pretraining use, including its scale, domain, language, license, notable metadata, and likely use cases. | Name: fineweb-edu
Description: FineWeb-Edu is a 1.3 trillion token educational web dataset filtered from FineWeb using an educational quality classifier. It is the 1.3T token version and is described in arXiv:2406.17557.
Tags: task_categories:text-generation, language:en, license:odc-by, size_categories:1B<n<10B, forma... | fineweb-edu is a large-scale English educational web dataset intended for text generation and language model pretraining. It contains 1.3 trillion tokens curated from the FineWeb corpus using an educational quality classifier, making it especially suitable for training models on high-quality instructional and knowledge... | huggingface | HuggingFaceFW/fineweb-edu |
Summarize the dataset for language model pretraining use, including its scale, domain, language, license, notable metadata, and likely use cases. | Name: dolma
Description: Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Tags: task_categories:text-generation, language:en, license:odc-by, size_categories:n>1T, arxiv:2402.00159, arxiv:2301.13688, region:us, language-modeling, casual-lm, llm
Downloads: 3113
Likes: 1021 | dolma is an open English corpus designed explicitly for language model pretraining research, with a reported scale of three trillion tokens. It is tagged for text generation and language modeling and licensed under ODC-By, which makes it relevant for open research and large-scale LLM training pipelines. Given its massi... | huggingface | allenai/dolma |
Summarize the dataset for code model training use, including its contents, scale, language coverage, licensing notes, format, and likely use cases. | Name: the-stack
Description: The Stack is a code dataset whose initial release included 30 programming languages and 18 permissive licenses, with a near-deduplicated size of 3TB. Later versions excluded weak copyleft licenses and expanded the permissive license list.
Tags: task_categories:text-generation, language_crea... | the-stack is a multilingual code dataset built for text generation and code model training. It aggregates source code across many programming languages and was released with a strong focus on license filtering and near-deduplication. The dataset is distributed in Parquet format and supports text and tabular access thro... | huggingface | bigcode/the-stack |
Summarize the dataset for small-language or curriculum-style model training, including its content style, generation method, scale, license, and likely use cases. | Name: TinyStories
Description: Dataset containing synthetically generated short stories created by GPT-3.5 and GPT-4 using a small vocabulary. Described in arXiv:2305.07759.
Tags: task_categories:text-generation, language:en, license:cdla-sharing-1.0, size_categories:1M<n<10M, format:parquet, modality:text, library:dat... | TinyStories is an English synthetic text dataset made up of short stories generated by GPT-3.5 and GPT-4 with deliberately simple vocabulary. Its relatively small size and constrained linguistic complexity make it especially useful for training compact language models, studying emergence and learning dynamics, and buil... | huggingface | roneneldan/TinyStories |
Summarize the dataset for instruction tuning, including its task types, origin, scale, license, format, and likely use cases. | Name: databricks-dolly-15k
Description: An open source dataset of instruction-following records generated by thousands of Databricks employees across categories such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.
Tags: task_categories:question-answering, tas... | databricks-dolly-15k is an English instruction-tuning dataset containing around 15,000 human-generated instruction-response examples created by Databricks employees. It spans a range of behavioral categories including brainstorming, classification, question answering, generation, information extraction, and summarizati... | huggingface | databricks/databricks-dolly-15k |
Summarize the dataset and highlight its main purpose, modality, language, license, approximate size, and notable metadata. | Name: alpaca
Source: huggingface
ID: tatsu-lab/alpaca
Description: Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The a... | Alpaca is an English text dataset on Hugging Face designed for instruction fine-tuning and text generation. It contains about 52,000 instructions and demonstrations generated with text-davinci-003, following the Self-Instruct pipeline. The dataset is licensed under CC-BY-NC-4.0, stored in Parquet format, and falls in t... | huggingface | tatsu-lab/alpaca |
Summarize the dataset and highlight its main purpose, modality, language, license, approximate size, and notable metadata. | Name: bad_prompt
Source: huggingface
ID: Nerfgun3/bad_prompt
Description: Negative Embedding / Textual Inversion. The idea behind this embedding was to train the negative prompt as an embedding, unifying the basis of the negative prompt into one word or embedding. The embedding is described as helpful for image generat... | bad_prompt is a small English image-related dataset/resource on Hugging Face associated with Stable Diffusion workflows. It focuses on negative embedding or textual inversion, aiming to compress negative prompt behavior into a reusable embedding token. The dataset uses the imagefolder format, has fewer than 1,000 items... | huggingface | Nerfgun3/bad_prompt |
Summarize the dataset and list its key characteristics for model training use. | Name: falcon-refinedweb
Source: huggingface
Dataset ID: tiiuae/falcon-refinedweb
Description: Falcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license. It is built through stringent filtering and large-scale deduplication of CommonCrawl. Models trained on RefinedWeb repor... | Falcon RefinedWeb is a large English web text dataset from TII designed for text generation training. It is derived from CommonCrawl using stringent filtering and large-scale deduplication, with the goal of providing high-quality web-only training data. The dataset is described as multimodal-friendly because it preserv... | huggingface | tiiuae/falcon-refinedweb |
Summarize the dataset and list its key characteristics for conversational AI research. | Name: lmsys-chat-1m
Source: huggingface
Dataset ID: lmsys/lmsys-chat-1m
Description: LMSYS-Chat-1M is a large-scale real-world LLM conversation dataset containing one million conversations with 25 state-of-the-art LLMs. It was collected from 210K unique IP addresses on the Vicuna demo and Chatbot Arena website from Apr... | LMSYS-Chat-1M is a real-world conversational dataset for LLM research containing one million conversations involving 25 state-of-the-art language models. The data was collected from 210,000 unique IP addresses through the Vicuna demo and Chatbot Arena between April and August 2023. Each record includes a conversation I... | huggingface | lmsys/lmsys-chat-1m |
Summarize the dataset based on its metadata and description. | Name: ShareGPT_Vicuna_unfiltered
Source: huggingface
ID: anon8231489123/ShareGPT_Vicuna_unfiltered
Description: Further cleaning done. Please look through the dataset and ensure that I didn't miss anything. Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussi... | ShareGPT_Vicuna_unfiltered is a Hugging Face dataset containing cleaned ShareGPT/Vicuna-style conversation data in English. The description indicates additional cleaning was performed and references a confirmed training method for Vicuna-style models. It also mentions two dataset variants: one with instances of the phr... | huggingface | anon8231489123/ShareGPT_Vicuna_unfiltered |
Summarize the dataset based on its metadata and description. | Name: PhysicalAI-Autonomous-Vehicles
Source: huggingface
ID: nvidia/PhysicalAI-Autonomous-Vehicles
Description: PHYSICAL AI AUTONOMOUS VEHICLES The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, geographically diverse collections of multi-sensor data empowering AV researchers to build the next gene... | PhysicalAI-Autonomous-Vehicles is a Hugging Face dataset from NVIDIA for autonomous vehicle research. It is described as one of the largest geographically diverse multi-sensor driving datasets, intended to support development of Physical AI and end-to-end driving systems. The metadata states that both data collection a... | huggingface | nvidia/PhysicalAI-Autonomous-Vehicles |
Summarize the dataset for supervised fine-tuning metadata extraction. | Name: finepdfs
Source: huggingface
ID: HuggingFaceFW/finepdfs
Description: Liberating 3T of the finest tokens from PDFs. FinePDFs is the largest publicly available corpus sourced exclusively from PDFs, containing about 3 trillion tokens across 475 million documents in 1733 languages.
Tags: task_categories:text-generati... | {"dataset_name":"finepdfs","provider":"HuggingFaceFW","summary":"A massive multilingual text-generation corpus extracted exclusively from PDFs, with about 3 trillion tokens across 475 million documents in 1733 languages.","task_category":"text-generation","modalities":["text","tabular"],"format":"parquet","license":"od... | huggingface | HuggingFaceFW/finepdfs |
Summarize the dataset for supervised fine-tuning metadata extraction. | Name: OpenThoughts-114k
Source: huggingface
ID: open-thoughts/OpenThoughts-114k
Description: Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles. Default subset contains ready-to-train data used to finetune OpenThinker-7B and OpenThinker-32B models.
Tags: license:a... | {"dataset_name":"OpenThoughts-114k","provider":"open-thoughts","summary":"A synthetic reasoning dataset with 114k high-quality training examples spanning math, science, code, and puzzles, designed for model finetuning.","task_category":"reasoning","domains":["math","science","code","puzzles"],"modality":["text"],"forma... | huggingface | open-thoughts/OpenThoughts-114k |
Summarize the dataset for instruction tuning use. | Name: OpenHermes-2.5
Source: huggingface
ID: teknium/OpenHermes-2.5
Description: This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models. It is an exact compilation and curation of many open source datasets and custom created synthetic datasets.
Tags: language:eng, size_categories:1M<n<10M, form... | OpenHermes-2.5 is a large English text dataset hosted on Hugging Face, used to build OpenHermes 2.5 and Nous Hermes 2 model series. It consists of a curated compilation of open-source and synthetic data, with signals of GPT-4 distillation and dataset compilation. The dataset is provided in JSON format, supports common ... | huggingface | teknium/OpenHermes-2.5 |
Summarize the dataset for instruction tuning use. | Name: alpaca-cleaned
Source: huggingface
ID: yahma/alpaca-cleaned
Description: This is a cleaned version of the original Alpaca Dataset released by Stanford. It fixes issues such as hallucination-prone instructions that referenced internet data.
Tags: task_categories:text-generation, language:en, license:cc-by-4.0, siz... | alpaca-cleaned is an English instruction-tuning dataset on Hugging Face derived from Stanford's original Alpaca release. It was cleaned to remove problematic or hallucination-inducing examples, especially instructions that depended on unavailable internet data. The dataset is JSON-based, licensed under CC-BY-4.0, sized... | huggingface | yahma/alpaca-cleaned |
Summarize the dataset based on the provided metadata. | Name: hle
Source: huggingface
ID: cais/hle
Description: Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. It consists of 2,500 questions across dozens of subjects.
Tags: benchmark:of... | hle is an official Hugging Face benchmark dataset from cais/hle. It is a multimodal academic benchmark called Humanity's Last Exam, designed to test frontier-level human knowledge with broad subject coverage. The dataset contains 2,500 closed-ended questions across many subjects and supports both image and text modalit... | huggingface | cais/hle |
Summarize the dataset based on the provided metadata. | Name: fineweb-2
Source: huggingface
ID: HuggingFaceFW/fineweb-2
Description: FineWeb2 is the second iteration of the FineWeb dataset, providing high-quality pretraining data for over 1000 languages. It is fully reproducible, available under the permissive ODC-By 1.0 license, and validated through hundreds of ablation e... | fineweb-2 is a Hugging Face dataset from HuggingFaceFW focused on large-scale multilingual text generation and pretraining. It is the second iteration of FineWeb and offers high-quality, fully reproducible text data covering more than 1,000 languages. The dataset is released under the ODC-By license, includes text and ... | huggingface | HuggingFaceFW/fineweb-2 |
Given a raw dataset catalog entry, summarize it into a concise machine-learning dataset profile including its purpose, modality, likely tasks, language coverage, license, scale, and notable popularity signals. | {"source":"huggingface","id":"ILSVRC/imagenet-1k","name":"imagenet-1k","description":"ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a synset. ImageNet aims to... | {"summary":"ImageNet-1k is a large-scale image dataset for multi-class image classification, based on the ILSVRC 2012 subset of ImageNet and organized by WordNet synsets.","modality":"image","primary_tasks":["image classification","multi-class image classification"],"language":"en","multilinguality":"monolingual","lice... | huggingface | ILSVRC/imagenet-1k |
Given a raw dataset catalog entry, summarize it into a concise machine-learning dataset profile including its purpose, modality, likely tasks, language coverage, license, scale, and notable popularity signals. | {"source":"huggingface","id":"QingyiSi/Alpaca-CoT","name":"Alpaca-CoT","description":"Instruction-Finetuning Dataset Collection (Alpaca-CoT). This repository continuously collects various instruction tuning datasets and standardizes them into the same format for direct use with Alpaca-style models. It also includes cha... | {"summary":"Alpaca-CoT is a collection of standardized instruction-tuning datasets designed for Alpaca-style model fine-tuning, with emphasis on instruction following and chain-of-thought style data.","modality":"text","primary_tasks":["instruction tuning","supervised fine-tuning","chain-of-thought reasoning"],"languag... | huggingface | QingyiSi/Alpaca-CoT |
Summarize the dataset and identify its main purpose, modalities, languages, supported task categories, license, and notable characteristics. | Dataset name: PersonaHub
Source: huggingface
ID: proj-persona/PersonaHub
Description: Scaling Synthetic Data Creation with 1,000,000,000 Personas. This repo releases data introduced in the paper "Scaling Synthetic Data Creation with 1,000,000,000 Personas." It proposes a persona-driven data synthesis methodology that l... | PersonaHub is a Hugging Face dataset focused on scaling synthetic data creation through a persona-driven methodology. Its main purpose is to support diverse synthetic data generation by providing a large collection of automatically curated personas derived from web data, as described in the paper "Scaling Synthetic Dat... | huggingface | proj-persona/PersonaHub |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"fka/prompts.chat","name":"prompts.chat","description":"a.k.a. Awesome ChatGPT Prompts. This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts. prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community.... | This dataset is only partially suitable for SFT. It appears to be a collection of community-shared prompts rather than prompt-response training pairs. Its best use case is prompt engineering research, instruction template discovery, agent prompting experiments, and synthetic data generation. For SFT, it would likely re... | huggingface | fka/prompts.chat |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"HuggingFaceFW/fineweb","name":"fineweb","description":"FineWeb consists of more than 18.5T tokens of cleaned and deduplicated English web data from CommonCrawl. The data processing pipeline is optimized for LLM performance.","tags":["task_categories:text-generation","language:en","license:... | This dataset is not ideal for supervised fine-tuning because it is a large-scale cleaned web corpus rather than an instruction-response dataset. Its best use case is base model pretraining or continued pretraining on high-quality English web text. It may support unsupervised language modeling, domain adaptation, and da... | huggingface | HuggingFaceFW/fineweb |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"Anthropic/hh-rlhf","name":"hh-rlhf","description":"This repository provides access to human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference or reward ... | This dataset is generally not suitable for direct SFT because it is designed for preference modeling and RLHF, not supervised dialogue training. Its best use case is training reward models, preference models, or benchmarking alignment methods around helpfulness and harmlessness. If used for SFT, extreme care is needed ... | huggingface | Anthropic/hh-rlhf |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"Open-Orca/OpenOrca","name":"OpenOrca","description":"The OpenOrca dataset is a rich collection of augmented FLAN data aligned as closely as possible with the distributions outlined in the Orca paper. It has been instrumental in generating high-performing model checkpoints.","tags":["task_c... | This dataset is well suited for supervised fine-tuning. It is an instruction-oriented corpus derived from augmented FLAN-style data and was created specifically to support high-performing instruction-following models. Its best use case is general-purpose instruction tuning for assistant behavior across QA, summarizatio... | huggingface | Open-Orca/OpenOrca |
Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case. | {"source":"huggingface","id":"OpenAssistant/oasst1","name":"oasst1","description":"OpenAssistant Conversations Dataset (OASST1) is a human-generated, human-annotated assistant-style conversation corpus with 161,443 messages in 35 languages, annotated with quality ratings and structured as conversation trees.","tags":["... | This dataset is well suited for supervised fine-tuning, especially for conversational assistants. It contains human-generated and human-annotated multi-turn assistant conversations with quality signals, making it valuable for chat SFT, multilingual assistant training, and response ranking or filtering. Its best use cas... | huggingface | OpenAssistant/oasst1 |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"openai/gsm8k","name":"gsm8k","description":"Dataset Card for GSM8K. 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 r... | GSM8K is an English text dataset of grade school math word problems designed for multi-step reasoning and question answering. It contains about 8.5K high-quality problems that typically require 2 to 8 reasoning steps and basic arithmetic operations. It is commonly used as an official benchmark for evaluating mathematic... | huggingface | openai/gsm8k |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"wikimedia/wikipedia","name":"wikipedia","description":"Dataset Card for Wikimedia Wikipedia. Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps with one subset per language, each containing a single train split. Each example contai... | Wikimedia Wikipedia is a massive multilingual text dataset built from cleaned Wikipedia article dumps. Each example contains a full article, with markdown and unwanted sections removed, and the dataset is organized into one subset per language. It is useful for language modeling, masked language modeling, pretraining, ... | huggingface | wikimedia/wikipedia |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"gsdf/EasyNegative","name":"EasyNegative","description":"Negative Embedding. This is a Negative Embedding trained with Counterfeit. Please use it in the stable-diffusion-webui embeddings folder. It can be used with other models, but the effectiveness is not certain.","tags":["license:other"... | EasyNegative is an image-related resource centered on a negative embedding intended for use with Stable Diffusion style workflows rather than a conventional supervised dataset. It appears to be a very small image-modality artifact associated with prompt conditioning or generation quality control, especially in stable-d... | huggingface | gsdf/EasyNegative |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"togethercomputer/RedPajama-Data-1T","name":"RedPajama-Data-1T","description":"RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.","tags":["task_categories:text-generation","language:en","size_categories:1M<n<10M","modality:text","library:datasets","library:ml... | RedPajama-Data-1T is a large English text dataset intended for text generation and large language model pretraining. It is described as a clean-room, fully open-source implementation of the LLaMA training dataset, making it relevant for open foundation model research and reproduction efforts. Its primary use cases incl... | huggingface | togethercomputer/RedPajama-Data-1T |
Given a dataset metadata record, summarize the dataset for supervised fine-tuning with a focus on purpose, modality, language, likely use cases, and notable properties. | {"source":"huggingface","id":"FreedomIntelligence/medical-o1-reasoning-SFT","name":"medical-o1-reasoning-SFT","description":"News: [2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data. [2025/02... | medical-o1-reasoning-SFT is a bilingual English-Chinese text dataset for supervised fine-tuning on medical reasoning and question answering. It is built around medical verifiable problems and includes reasoning-oriented SFT data, with references to distilled reasoning traces derived from DeepSeek-R1. The dataset is par... | huggingface | FreedomIntelligence/medical-o1-reasoning-SFT |
Summarize the dataset for language model pretraining use, including its scale, domain, language, license, notable metadata, and likely use cases. | Name: fineweb-edu
Description: FineWeb-Edu is a 1.3 trillion token educational web dataset filtered from FineWeb using an educational quality classifier. It is the 1.3T token version and is described in arXiv:2406.17557.
Tags: task_categories:text-generation, language:en, license:odc-by, size_categories:1B<n<10B, forma... | fineweb-edu is a large-scale English educational web dataset intended for text generation and language model pretraining. It contains 1.3 trillion tokens curated from the FineWeb corpus using an educational quality classifier, making it especially suitable for training models on high-quality instructional and knowledge... | huggingface | HuggingFaceFW/fineweb-edu |
Summarize the dataset for language model pretraining use, including its scale, domain, language, license, notable metadata, and likely use cases. | Name: dolma
Description: Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Tags: task_categories:text-generation, language:en, license:odc-by, size_categories:n>1T, arxiv:2402.00159, arxiv:2301.13688, region:us, language-modeling, casual-lm, llm
Downloads: 3113
Likes: 1021 | dolma is an open English corpus designed explicitly for language model pretraining research, with a reported scale of three trillion tokens. It is tagged for text generation and language modeling and licensed under ODC-By, which makes it relevant for open research and large-scale LLM training pipelines. Given its massi... | huggingface | allenai/dolma |
Summarize the dataset for code model training use, including its contents, scale, language coverage, licensing notes, format, and likely use cases. | Name: the-stack
Description: The Stack is a code dataset whose initial release included 30 programming languages and 18 permissive licenses, with a near-deduplicated size of 3TB. Later versions excluded weak copyleft licenses and expanded the permissive license list.
Tags: task_categories:text-generation, language_crea... | the-stack is a multilingual code dataset built for text generation and code model training. It aggregates source code across many programming languages and was released with a strong focus on license filtering and near-deduplication. The dataset is distributed in Parquet format and supports text and tabular access thro... | huggingface | bigcode/the-stack |
Summarize the dataset for small-language or curriculum-style model training, including its content style, generation method, scale, license, and likely use cases. | Name: TinyStories
Description: Dataset containing synthetically generated short stories created by GPT-3.5 and GPT-4 using a small vocabulary. Described in arXiv:2305.07759.
Tags: task_categories:text-generation, language:en, license:cdla-sharing-1.0, size_categories:1M<n<10M, format:parquet, modality:text, library:dat... | TinyStories is an English synthetic text dataset made up of short stories generated by GPT-3.5 and GPT-4 with deliberately simple vocabulary. Its relatively small size and constrained linguistic complexity make it especially useful for training compact language models, studying emergence and learning dynamics, and buil... | huggingface | roneneldan/TinyStories |
Summarize the dataset for instruction tuning, including its task types, origin, scale, license, format, and likely use cases. | Name: databricks-dolly-15k
Description: An open source dataset of instruction-following records generated by thousands of Databricks employees across categories such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.
Tags: task_categories:question-answering, tas... | databricks-dolly-15k is an English instruction-tuning dataset containing around 15,000 human-generated instruction-response examples created by Databricks employees. It spans a range of behavioral categories including brainstorming, classification, question answering, generation, information extraction, and summarizati... | huggingface | databricks/databricks-dolly-15k |
Summarize the dataset and highlight its main purpose, modality, language, license, approximate size, and notable metadata. | Name: alpaca
Source: huggingface
ID: tatsu-lab/alpaca
Description: Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The a... | Alpaca is an English text dataset on Hugging Face designed for instruction fine-tuning and text generation. It contains about 52,000 instructions and demonstrations generated with text-davinci-003, following the Self-Instruct pipeline. The dataset is licensed under CC-BY-NC-4.0, stored in Parquet format, and falls in t... | huggingface | tatsu-lab/alpaca |
Summarize the dataset and highlight its main purpose, modality, language, license, approximate size, and notable metadata. | Name: bad_prompt
Source: huggingface
ID: Nerfgun3/bad_prompt
Description: Negative Embedding / Textual Inversion. The idea behind this embedding was to train the negative prompt as an embedding, unifying the basis of the negative prompt into one word or embedding. The embedding is described as helpful for image generat... | bad_prompt is a small English image-related dataset/resource on Hugging Face associated with Stable Diffusion workflows. It focuses on negative embedding or textual inversion, aiming to compress negative prompt behavior into a reusable embedding token. The dataset uses the imagefolder format, has fewer than 1,000 items... | huggingface | Nerfgun3/bad_prompt |
Summarize the dataset and list its key characteristics for model training use. | Name: falcon-refinedweb
Source: huggingface
Dataset ID: tiiuae/falcon-refinedweb
Description: Falcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license. It is built through stringent filtering and large-scale deduplication of CommonCrawl. Models trained on RefinedWeb repor... | Falcon RefinedWeb is a large English web text dataset from TII designed for text generation training. It is derived from CommonCrawl using stringent filtering and large-scale deduplication, with the goal of providing high-quality web-only training data. The dataset is described as multimodal-friendly because it preserv... | huggingface | tiiuae/falcon-refinedweb |
Summarize the dataset and list its key characteristics for conversational AI research. | Name: lmsys-chat-1m
Source: huggingface
Dataset ID: lmsys/lmsys-chat-1m
Description: LMSYS-Chat-1M is a large-scale real-world LLM conversation dataset containing one million conversations with 25 state-of-the-art LLMs. It was collected from 210K unique IP addresses on the Vicuna demo and Chatbot Arena website from Apr... | LMSYS-Chat-1M is a real-world conversational dataset for LLM research containing one million conversations involving 25 state-of-the-art language models. The data was collected from 210,000 unique IP addresses through the Vicuna demo and Chatbot Arena between April and August 2023. Each record includes a conversation I... | huggingface | lmsys/lmsys-chat-1m |
Summarize the dataset based on its metadata and description. | Name: ShareGPT_Vicuna_unfiltered
Source: huggingface
ID: anon8231489123/ShareGPT_Vicuna_unfiltered
Description: Further cleaning done. Please look through the dataset and ensure that I didn't miss anything. Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussi... | ShareGPT_Vicuna_unfiltered is a Hugging Face dataset containing cleaned ShareGPT/Vicuna-style conversation data in English. The description indicates additional cleaning was performed and references a confirmed training method for Vicuna-style models. It also mentions two dataset variants: one with instances of the phr... | huggingface | anon8231489123/ShareGPT_Vicuna_unfiltered |
Summarize the dataset based on its metadata and description. | Name: PhysicalAI-Autonomous-Vehicles
Source: huggingface
ID: nvidia/PhysicalAI-Autonomous-Vehicles
Description: PHYSICAL AI AUTONOMOUS VEHICLES The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, geographically diverse collections of multi-sensor data empowering AV researchers to build the next gene... | PhysicalAI-Autonomous-Vehicles is a Hugging Face dataset from NVIDIA for autonomous vehicle research. It is described as one of the largest geographically diverse multi-sensor driving datasets, intended to support development of Physical AI and end-to-end driving systems. The metadata states that both data collection a... | huggingface | nvidia/PhysicalAI-Autonomous-Vehicles |
Summarize the dataset for supervised fine-tuning metadata extraction. | Name: finepdfs
Source: huggingface
ID: HuggingFaceFW/finepdfs
Description: Liberating 3T of the finest tokens from PDFs. FinePDFs is the largest publicly available corpus sourced exclusively from PDFs, containing about 3 trillion tokens across 475 million documents in 1733 languages.
Tags: task_categories:text-generati... | {"dataset_name":"finepdfs","provider":"HuggingFaceFW","summary":"A massive multilingual text-generation corpus extracted exclusively from PDFs, with about 3 trillion tokens across 475 million documents in 1733 languages.","task_category":"text-generation","modalities":["text","tabular"],"format":"parquet","license":"od... | huggingface | HuggingFaceFW/finepdfs |
Summarize the dataset for supervised fine-tuning metadata extraction. | Name: OpenThoughts-114k
Source: huggingface
ID: open-thoughts/OpenThoughts-114k
Description: Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles. Default subset contains ready-to-train data used to finetune OpenThinker-7B and OpenThinker-32B models.
Tags: license:a... | {"dataset_name":"OpenThoughts-114k","provider":"open-thoughts","summary":"A synthetic reasoning dataset with 114k high-quality training examples spanning math, science, code, and puzzles, designed for model finetuning.","task_category":"reasoning","domains":["math","science","code","puzzles"],"modality":["text"],"forma... | huggingface | open-thoughts/OpenThoughts-114k |
Summarize the dataset for instruction tuning use. | Name: OpenHermes-2.5
Source: huggingface
ID: teknium/OpenHermes-2.5
Description: This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models. It is an exact compilation and curation of many open source datasets and custom created synthetic datasets.
Tags: language:eng, size_categories:1M<n<10M, form... | OpenHermes-2.5 is a large English text dataset hosted on Hugging Face, used to build OpenHermes 2.5 and Nous Hermes 2 model series. It consists of a curated compilation of open-source and synthetic data, with signals of GPT-4 distillation and dataset compilation. The dataset is provided in JSON format, supports common ... | huggingface | teknium/OpenHermes-2.5 |
Summarize the dataset for instruction tuning use. | Name: alpaca-cleaned
Source: huggingface
ID: yahma/alpaca-cleaned
Description: This is a cleaned version of the original Alpaca Dataset released by Stanford. It fixes issues such as hallucination-prone instructions that referenced internet data.
Tags: task_categories:text-generation, language:en, license:cc-by-4.0, siz... | alpaca-cleaned is an English instruction-tuning dataset on Hugging Face derived from Stanford's original Alpaca release. It was cleaned to remove problematic or hallucination-inducing examples, especially instructions that depended on unavailable internet data. The dataset is JSON-based, licensed under CC-BY-4.0, sized... | huggingface | yahma/alpaca-cleaned |
Summarize the dataset based on the provided metadata. | Name: hle
Source: huggingface
ID: cais/hle
Description: Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. It consists of 2,500 questions across dozens of subjects.
Tags: benchmark:of... | hle is an official Hugging Face benchmark dataset from cais/hle. It is a multimodal academic benchmark called Humanity's Last Exam, designed to test frontier-level human knowledge with broad subject coverage. The dataset contains 2,500 closed-ended questions across many subjects and supports both image and text modalit... | huggingface | cais/hle |
Summarize the dataset based on the provided metadata. | Name: fineweb-2
Source: huggingface
ID: HuggingFaceFW/fineweb-2
Description: FineWeb2 is the second iteration of the FineWeb dataset, providing high-quality pretraining data for over 1000 languages. It is fully reproducible, available under the permissive ODC-By 1.0 license, and validated through hundreds of ablation e... | fineweb-2 is a Hugging Face dataset from HuggingFaceFW focused on large-scale multilingual text generation and pretraining. It is the second iteration of FineWeb and offers high-quality, fully reproducible text data covering more than 1,000 languages. The dataset is released under the ODC-By license, includes text and ... | huggingface | HuggingFaceFW/fineweb-2 |
Given a raw dataset catalog entry, summarize it into a concise machine-learning dataset profile including its purpose, modality, likely tasks, language coverage, license, scale, and notable popularity signals. | {"source":"huggingface","id":"ILSVRC/imagenet-1k","name":"imagenet-1k","description":"ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a synset. ImageNet aims to... | {"summary":"ImageNet-1k is a large-scale image dataset for multi-class image classification, based on the ILSVRC 2012 subset of ImageNet and organized by WordNet synsets.","modality":"image","primary_tasks":["image classification","multi-class image classification"],"language":"en","multilinguality":"monolingual","lice... | huggingface | ILSVRC/imagenet-1k |
Given a raw dataset catalog entry, summarize it into a concise machine-learning dataset profile including its purpose, modality, likely tasks, language coverage, license, scale, and notable popularity signals. | {"source":"huggingface","id":"QingyiSi/Alpaca-CoT","name":"Alpaca-CoT","description":"Instruction-Finetuning Dataset Collection (Alpaca-CoT). This repository continuously collects various instruction tuning datasets and standardizes them into the same format for direct use with Alpaca-style models. It also includes cha... | {"summary":"Alpaca-CoT is a collection of standardized instruction-tuning datasets designed for Alpaca-style model fine-tuning, with emphasis on instruction following and chain-of-thought style data.","modality":"text","primary_tasks":["instruction tuning","supervised fine-tuning","chain-of-thought reasoning"],"languag... | huggingface | QingyiSi/Alpaca-CoT |
Summarize the dataset and identify its main purpose, modalities, languages, supported task categories, license, and notable characteristics. | Dataset name: PersonaHub
Source: huggingface
ID: proj-persona/PersonaHub
Description: Scaling Synthetic Data Creation with 1,000,000,000 Personas. This repo releases data introduced in the paper "Scaling Synthetic Data Creation with 1,000,000,000 Personas." It proposes a persona-driven data synthesis methodology that l... | PersonaHub is a Hugging Face dataset focused on scaling synthetic data creation through a persona-driven methodology. Its main purpose is to support diverse synthetic data generation by providing a large collection of automatically curated personas derived from web data, as described in the paper "Scaling Synthetic Dat... | huggingface | proj-persona/PersonaHub |
SoloAI SFT Dataset: 20260426-2139
📊 数据集概览
| 属性 | 值 |
|---|---|
| 数据条数 | 30 条 |
| 数据源 | huggingface |
| 生成时间 | 2026-04-26 21:39 |
| 格式 | Instruction-Input-Output |
| 主要用途 | SFT 微调 / 指令调优 |
🎯 适用场景
- LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
- Prompt Engineering 研究: 可用于分析和优化提示词设计
- 多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）input: 输入上下文（提供任务相关的背景信息）output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Given a raw dataset metadata record, identify whether it is suitable for supervised fine-tuning (SFT) of LLMs and summarize its best use case.', 'input': '{"source":"huggingface","id":"fka/prompts.chat","name":"prompts.chat","description":"a.k.a. Awesome ChatGPT Prompts. This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts. prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community.","tags":["task_categories:question-answering","task_categories:text-generation","license:cc0-1.0","size_categories:1K<n<10K","format:csv","modality:text","library:datasets","library:pandas","library:polars","library:mlcroissant","region:us","ChatGPT","prompts","AI","GPT","Claude","Gemini","Llama","Mistral","LLM","prompt-engineering","conversational-ai","text-generation","chatbot","awesome-list"],"downloads":50433,"likes":9680,"created_at":""}', 'output': 'This dataset is only partially suitable for SFT. It appears to be a collection of community-shared prompts rather than prompt-response training pairs. Its best use case is prompt engineering research, instruction template discovery, agent prompting experiments, and synthetic data generation. For SFT, it would likely require augmentation with high-quality responses or conversion into prompt-completion examples.', 'source': 'huggingface', 'original_id': 'fka/prompts.chat'}
🤖 数据来源
本数据集由 SoloAI 自动化数据管道生成：
- 从 HuggingFace Datasets Hub 发现高质量数据集
- AI 清洗为 SFT 格式（Instruction-Input-Output）
- 质量过滤后发布
⚠️ 使用说明 & 📬 商务联系
- 本数据集仅供研究和实验用途
- 请遵守原始数据的许可证要求
- 商业用途 / 定制数据 / 深度合作:
- 📧 请联系:
379744050@qq.com - 🤖 SoloAI 提供高质量 SFT 数据定制服务。
- 建议邮件标题:
【数据定制咨询】行业 + 数据类型 + 规模 - 建议正文包含: 目标用途、需要条数、语言、字段格式、预算、交付时间
- 📧 请联系:
💰 商业合作报价
| 套餐 | 价格 | 说明 |
|---|---|---|
| Starter | $199 / 1000条高质量 SFT 数据 | 适合个人开发者 / 小团队 |
| Growth | $499 / 5000条行业数据 | 适合垂直行业训练数据 |
| Enterprise | $1499 / 定制领域数据管道 | 适合长期定制与数据管道 |
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- 中国客户: 支付宝, 微信支付
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🚀 为什么现在联系 SoloAI
- 24 小时内响应有效询盘
- 报价前可免费给出需求范围建议
- 支持中文 / English 项目合作
- 可从单次交付升级为长期数据管道合作
📈 更新日志
| 版本 | 日期 | 说明 |
|---|---|---|
| v1.0 | 2026-04-26 21:39 | 初始发布，30 条数据 |
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