instruction stringlengths 55 190 | input stringlengths 235 1.12k | output stringlengths 283 803 | source stringclasses 1
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Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"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.... | prompts.chat is a Hugging Face mirror of the Awesome ChatGPT Prompts community collection. It is a small text dataset in CSV format with between 1K and 10K records and is released under CC0-1.0, making it highly permissive for reuse. The dataset focuses on prompt engineering and includes prompts relevant to ChatGPT, Cl... | huggingface | fka/prompts.chat |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"source":"huggingface","id":"HuggingFaceFW/fineweb","name":"fineweb","description":"FineWeb: 15 trillion tokens of the finest data the web has to offer. The FineWeb dataset consists of more than 18.5T tokens of cleaned and deduplicated English web data from CommonCrawl. The data processing pipeline is optimized for LL... | FineWeb is a massive English-language web corpus derived from CommonCrawl and optimized for LLM training through cleaning and deduplication. With more than 18.5 trillion tokens, it is intended primarily for large-scale language model pretraining rather than conventional supervised fine-tuning. The dataset includes text... | huggingface | HuggingFaceFW/fineweb |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"source":"huggingface","id":"Anthropic/hh-rlhf","name":"hh-rlhf","description":"Dataset Card for HH-RLHF. 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 t... | HH-RLHF is a human feedback dataset created for training helpfulness and harmlessness preference or reward models in RLHF pipelines. It is a medium-scale text dataset in JSON format, with between 100K and 1M examples, and is released under the MIT license. Its core value lies in paired or comparative preference annotat... | huggingface | Anthropic/hh-rlhf |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"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 and serves as a v... | OpenOrca is a large English instruction-tuning dataset built from augmented FLAN-style data and designed to approximate the training distribution described in the Orca work. It is a text dataset in Parquet format with between 1M and 10M examples and is licensed under MIT. The dataset spans many NLP task types, includin... | huggingface | Open-Orca/OpenOrca |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"source":"huggingface","id":"OpenAssistant/oasst1","name":"oasst1","description":"OpenAssistant Conversations Dataset (OASST1). A human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings and over 10,000 fully a... | OASST1 is a multilingual assistant conversation dataset containing human-generated and human-annotated dialogue data across 35 languages. It includes 161,443 messages, more than 461,000 quality ratings, and over 10,000 annotated conversation trees. The dataset is released under Apache-2.0 and provided in Parquet with t... | huggingface | OpenAssistant/oasst1 |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"source":"huggingface","id":"openai/gsm8k","name":"gsm8k","description":"GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems created to support question answering on basic mathematical problems that require multi-step reasoning. Problems typically take ... | GSM8K is an English text dataset from Hugging Face containing about 8.5K grade school math word problems for multi-step reasoning and question answering. It is a monolingual, crowdsourced benchmark dataset licensed under MIT, distributed in Parquet format, and commonly used for evaluating arithmetic reasoning. | huggingface | openai/gsm8k |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"source":"huggingface","id":"wikimedia/wikipedia","name":"wikipedia","description":"Wikipedia dataset containing cleaned articles of all languages. The dataset is built from Wikipedia dumps with one subset per language, each containing a single train split. Each example contains the content of one full Wikipedia artic... | Wikimedia Wikipedia is a massive multilingual text dataset of cleaned full Wikipedia articles derived from official dumps. It supports language modeling and masked language modeling across many languages, is distributed in Parquet format, and is licensed under CC-BY-SA-3.0 and GFDL. | huggingface | wikimedia/wikipedia |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"source":"huggingface","id":"gsdf/EasyNegative","name":"EasyNegative","description":"Negative Embedding trained with Counterfeit, intended for use in the stable-diffusion-webui embeddings folder. It may also be used with other models, though effectiveness is uncertain.","tags":["license:other","size_categories:n<1K","... | EasyNegative is a small Hugging Face image-related asset packaged as an imagefolder dataset entry, primarily describing a negative embedding for Stable Diffusion workflows. It has image modality metadata, uses a non-standard license, and appears oriented toward generative image model prompting rather than conventional ... | huggingface | gsdf/EasyNegative |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"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 an English text-generation dataset intended as an open-source clean-room reproduction of the data recipe behind LLaMA-style model training. It is a large-scale text corpus entry on Hugging Face suitable for language model pretraining and related SFT data sourcing workflows. | huggingface | togethercomputer/RedPajama-Data-1T |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"source":"huggingface","id":"FreedomIntelligence/medical-o1-reasoning-SFT","name":"medical-o1-reasoning-SFT","description":"A medical reasoning dataset for supervised fine-tuning built on medical verifiable problems and distilled reasoning traces. Recent updates indicate the release of a medical-only SFT file and a mi... | medical-o1-reasoning-SFT is a bilingual English-Chinese medical text dataset designed for supervised fine-tuning and reasoning-focused question answering. It contains medical instruction and reasoning data built from verifiable medical problems, is released under Apache-2.0, and is particularly relevant for domain-spec... | huggingface | FreedomIntelligence/medical-o1-reasoning-SFT |
Summarize the dataset and explain why it is useful for language model training. | Name: fineweb-edu
Description: FineWeb-Edu is a large educational web dataset containing 1.3 trillion tokens filtered from the FineWeb dataset using an educational quality classifier. It focuses on high-quality educational web pages in English.
Tags: text-generation, en, odc-by, parquet, tabular, text, arxiv:2406.17557... | fineweb-edu is a massive English-language educational text dataset from Hugging Face containing 1.3 trillion tokens. It was derived from the FineWeb corpus by applying an educational quality classifier, making it especially valuable for pretraining or fine-tuning language models on high-quality instructional and inform... | huggingface | HuggingFaceFW/fineweb-edu |
Provide a concise dataset overview including its scale, domain, and intended use. | Name: dolma
Description: Dolma is an open corpus of three trillion tokens for language model pretraining research.
Tags: text-generation, en, odc-by, n>1T, language-modeling, casual-lm, llm, arxiv:2402.00159
Downloads: 4058
Likes: 1025 | dolma is a very large open English text corpus designed for language model pretraining research, with a scale of roughly three trillion tokens. It is intended for use in training and studying large language models, especially in language modeling settings. Its openness and extreme size make it a strong resource for res... | huggingface | allenai/dolma |
Describe this dataset for someone looking for code data to train multilingual code models. | Name: the-stack
Description: The Stack is a large code dataset. Initial releases included 30 programming languages and permissive licenses, with later revisions excluding weak copyleft licenses and expanding permissive license coverage. The near-deduplicated dataset is about 3TB in size.
Tags: text-generation, multilin... | the-stack is a large multilingual code dataset created for training code generation and code understanding models. It aggregates source code across many programming languages and emphasizes license filtering, with later versions removing weak copyleft licenses and broadening the set of accepted permissive licenses. Its... | huggingface | bigcode/the-stack |
Summarize the dataset and mention what makes it distinctive compared with general web text corpora. | Name: TinyStories
Description: TinyStories contains synthetically generated short stories produced by GPT-3.5 and GPT-4 using a small vocabulary. It is described in arXiv:2305.07759 and was used to train small language models.
Tags: text-generation, en, cdla-sharing-1.0, parquet, text, arxiv:2305.07759
Downloads: 96552... | TinyStories is an English text dataset of synthetic short stories generated by GPT-3.5 and GPT-4 using deliberately simple vocabulary and structure. Unlike broad web-scale corpora, it is highly controlled and optimized for studying how small language models can learn coherent language from simple, clean data. This make... | huggingface | roneneldan/TinyStories |
Explain what this dataset contains and how it can be used for supervised fine-tuning. | Name: databricks-dolly-15k
Description: databricks-dolly-15k is an open instruction-following dataset created by Databricks employees across categories such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.
Tags: question-answering, summarization, en, cc-by-sa-... | databricks-dolly-15k is a human-authored English instruction-following dataset containing around 15,000 examples across multiple task types, including question answering, summarization, brainstorming, classification, and information extraction. It is well suited for supervised fine-tuning because it provides prompt-res... | huggingface | databricks/databricks-dolly-15k |
Summarize the dataset and highlight its main purpose, modality, scale, language, license, and notable usage context. | Name: alpaca
Source: huggingface
ID: tatsu-lab/alpaca
Description: Dataset Card for Alpaca. 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 ... | Alpaca is an English text dataset on Hugging Face designed for instruction tuning. It contains about 52,000 instruction-response demonstrations generated with OpenAI's text-davinci-003 and inspired by the Self-Instruct pipeline. Its primary purpose is to help train language models to better follow instructions. The dat... | huggingface | tatsu-lab/alpaca |
Summarize the dataset or resource and explain its intended use, modality, license, size, and relevant application domains. | Name: bad_prompt
Source: huggingface
ID: Nerfgun3/bad_prompt
Description: Negative Embedding / Textual Inversion. The idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding. Side note: Embedding has proven to be very... | bad_prompt is a Hugging Face image-related resource focused on Stable Diffusion workflows, especially negative prompting through textual inversion. It is intended to package a negative prompt into a reusable embedding that users can place in the Stable Diffusion WebUI embeddings folder and apply in the negative prompt ... | huggingface | Nerfgun3/bad_prompt |
Summarize the Falcon RefinedWeb dataset for language model pretraining, including its purpose, scale, language, data source, filtering approach, license, format, and notable characteristics. | 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 achie... | Falcon RefinedWeb is a large-scale English web corpus designed for text generation and language model pretraining. Released by TII on Hugging Face under the ODC-By 1.0 license, it is constructed from CommonCrawl using stringent filtering and large-scale deduplication to improve data quality. The dataset falls in the 10... | huggingface | tiiuae/falcon-refinedweb |
Summarize the LMSYS-Chat-1M dataset, focusing on its content, collection source, time period, scale, metadata fields, and relevance 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 studying interactions with large language models. Hosted on Hugging Face, it contains one million conversations involving 25 state-of-the-art LLMs, collected from the Vicuna demo and Chatbot Arena between April and August 2023. The data comes from 210K unique IP ... | huggingface | lmsys/lmsys-chat-1m |
Summarize the dataset metadata and key characteristics. | Name: 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/discussions/4#64346c08ef6d5abefe42c12c
Two choices:
Removes instances of ... | ShareGPT_Vicuna_unfiltered is a Hugging Face dataset focused on English-language data. It is licensed under Apache-2.0 and associated with the US region. The description indicates that the dataset has undergone additional cleaning and includes variants with and without instances of the phrase "I'm sorry, but," intended... | huggingface | anon8231489123/ShareGPT_Vicuna_unfiltered |
Summarize the dataset metadata and key characteristics. | Name: 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 generation of Physical AI based end-to-end driving systems. This ... | PhysicalAI-Autonomous-Vehicles is a Hugging Face dataset for autonomous vehicle research. It offers a large, geographically diverse collection of multi-sensor driving data designed for developing end-to-end Physical AI driving systems. The dataset uses automatic or sensor-based collection and labeling methods, includes... | huggingface | nvidia/PhysicalAI-Autonomous-Vehicles |
Summarize the given dataset metadata into a concise SFT-style description covering what the dataset is, its scale, modality, languages, license, format, and notable usage relevance. | {"source":"huggingface","id":"HuggingFaceFW/finepdfs","name":"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_categori... | finepdfs is a large-scale Hugging Face dataset built exclusively from PDF sources. It contains roughly 3 trillion tokens across 475 million documents and spans 1,733 languages, making it a major multilingual text resource for text generation and large-scale language model pretraining or data research. The dataset is di... | huggingface | HuggingFaceFW/finepdfs |
Summarize the given dataset metadata into a concise SFT-style description covering what the dataset is, its scale, modality, license, format, and primary domains. | {"source":"huggingface","id":"open-thoughts/OpenThoughts-114k","name":"OpenThoughts-114k","description":"Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles. Includes ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models.","tags":["lice... | OpenThoughts-114k is a synthetic reasoning dataset on Hugging Face containing about 114,000 high-quality text examples. It focuses on domains such as math, science, code, and puzzles, and is intended for supervised fine-tuning of reasoning-capable language models. The dataset includes ready-to-train data used for model... | huggingface | open-thoughts/OpenThoughts-114k |
Summarize the dataset metadata into a concise instruction-tuning dataset description. | {"source":"huggingface","id":"teknium/OpenHermes-2.5","name":"OpenHermes-2.5","description":"This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models. The Open Hermes 2/2.5 and Nous Hermes 2 models are underpinned by this compilation and curation of many open source datasets and custom created sy... | OpenHermes-2.5 is a large English text dataset hosted on Hugging Face and used to build the OpenHermes 2.5 and Nous Hermes 2 model series. It is distributed in JSON format, falls in the 1M to 10M example size range, and is described as a compilation of open-source and synthetic data with GPT-4 distillation elements. Th... | huggingface | teknium/OpenHermes-2.5 |
Summarize the dataset metadata into a concise instruction-tuning dataset description. | {"source":"huggingface","id":"yahma/alpaca-cleaned","name":"alpaca-cleaned","description":"This is a cleaned version of the original Alpaca Dataset released by Stanford. The original dataset had issues such as hallucination-prone instructions referencing internet data, and those issues were fixed in this cleaned releas... | alpaca-cleaned is a cleaned instruction-tuning dataset on Hugging Face derived from the original Stanford Alpaca release. It is an English text-generation dataset in JSON format, licensed under CC-BY-4.0, and sized between 10K and 100K examples. Its main purpose is to provide higher-quality instruction-following data b... | huggingface | yahma/alpaca-cleaned |
Summarize the dataset metadata into a concise description for model training. | Source: huggingface
ID: cais/hle
Name: 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. The dataset note a... | {"name":"hle","full_id":"cais/hle","platform":"huggingface","summary":"Humanity's Last Exam is an official multimodal benchmark containing 2,500 closed-ended questions spanning many academic subjects at the frontier of human knowledge.","modalities":["image","text"],"format":"parquet","license":"mit","size_category":"1... | huggingface | cais/hle |
Summarize the dataset metadata into a concise description for model training. | Source: huggingface
ID: HuggingFaceFW/fineweb-2
Name: 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... | {"name":"fineweb-2","full_id":"HuggingFaceFW/fineweb-2","platform":"huggingface","summary":"FineWeb2 is a large-scale multilingual text pretraining dataset covering over 1000 languages, designed as a reproducible and extensively validated update to FineWeb.","task_category":"text-generation","modalities":["tabular","te... | huggingface | HuggingFaceFW/fineweb-2 |
Given a dataset metadata record, summarize the dataset for machine learning practitioners, including what it is, its primary modality/task, notable scale, and important usage considerations. | {"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... | ImageNet-1k is a large-scale image classification dataset from ILSVRC 2012. It contains images organized by WordNet synsets and is primarily used for multi-class image classification. The dataset modality is image, and its scale is between 1 million and 10 million examples, making it a standard benchmark for computer v... | huggingface | ILSVRC/imagenet-1k |
Given a dataset metadata record, summarize the dataset for LLM fine-tuning, including what it contains, likely use cases, supported languages, and licensing notes. | {"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, which can be directly loaded by Alpaca model code. It also i... | Alpaca-CoT is an instruction-finetuning dataset collection designed for training and experimenting with instruction-following language models. It aggregates multiple instruction-tuning datasets and standardizes them into a common format compatible with Alpaca-style training pipelines. Likely use cases include supervise... | huggingface | QingyiSi/Alpaca-CoT |
Summarize the dataset based on its metadata for use in an LLM supervised fine-tuning example. | Name: PersonaHub
Source: huggingface
Dataset 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 large-scale synthetic data creation through persona-driven generation. Introduced in the paper "Scaling Synthetic Data Creation with 1,000,000,000 Personas," it presents PERSONA HUB, a massive collection of diverse personas automatically curated from web data to help gene... | huggingface | proj-persona/PersonaHub |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"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.... | prompts.chat is a Hugging Face mirror of the Awesome ChatGPT Prompts community collection. It is a small text dataset in CSV format with between 1K and 10K records and is released under CC0-1.0, making it highly permissive for reuse. The dataset focuses on prompt engineering and includes prompts relevant to ChatGPT, Cl... | huggingface | fka/prompts.chat |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"source":"huggingface","id":"HuggingFaceFW/fineweb","name":"fineweb","description":"FineWeb: 15 trillion tokens of the finest data the web has to offer. The FineWeb dataset consists of more than 18.5T tokens of cleaned and deduplicated English web data from CommonCrawl. The data processing pipeline is optimized for LL... | FineWeb is a massive English-language web corpus derived from CommonCrawl and optimized for LLM training through cleaning and deduplication. With more than 18.5 trillion tokens, it is intended primarily for large-scale language model pretraining rather than conventional supervised fine-tuning. The dataset includes text... | huggingface | HuggingFaceFW/fineweb |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"source":"huggingface","id":"Anthropic/hh-rlhf","name":"hh-rlhf","description":"Dataset Card for HH-RLHF. 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 t... | HH-RLHF is a human feedback dataset created for training helpfulness and harmlessness preference or reward models in RLHF pipelines. It is a medium-scale text dataset in JSON format, with between 100K and 1M examples, and is released under the MIT license. Its core value lies in paired or comparative preference annotat... | huggingface | Anthropic/hh-rlhf |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"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 and serves as a v... | OpenOrca is a large English instruction-tuning dataset built from augmented FLAN-style data and designed to approximate the training distribution described in the Orca work. It is a text dataset in Parquet format with between 1M and 10M examples and is licensed under MIT. The dataset spans many NLP task types, includin... | huggingface | Open-Orca/OpenOrca |
Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics. | {"source":"huggingface","id":"OpenAssistant/oasst1","name":"oasst1","description":"OpenAssistant Conversations Dataset (OASST1). A human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings and over 10,000 fully a... | OASST1 is a multilingual assistant conversation dataset containing human-generated and human-annotated dialogue data across 35 languages. It includes 161,443 messages, more than 461,000 quality ratings, and over 10,000 annotated conversation trees. The dataset is released under Apache-2.0 and provided in Parquet with t... | huggingface | OpenAssistant/oasst1 |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"source":"huggingface","id":"openai/gsm8k","name":"gsm8k","description":"GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems created to support question answering on basic mathematical problems that require multi-step reasoning. Problems typically take ... | GSM8K is an English text dataset from Hugging Face containing about 8.5K grade school math word problems for multi-step reasoning and question answering. It is a monolingual, crowdsourced benchmark dataset licensed under MIT, distributed in Parquet format, and commonly used for evaluating arithmetic reasoning. | huggingface | openai/gsm8k |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"source":"huggingface","id":"wikimedia/wikipedia","name":"wikipedia","description":"Wikipedia dataset containing cleaned articles of all languages. The dataset is built from Wikipedia dumps with one subset per language, each containing a single train split. Each example contains the content of one full Wikipedia artic... | Wikimedia Wikipedia is a massive multilingual text dataset of cleaned full Wikipedia articles derived from official dumps. It supports language modeling and masked language modeling across many languages, is distributed in Parquet format, and is licensed under CC-BY-SA-3.0 and GFDL. | huggingface | wikimedia/wikipedia |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"source":"huggingface","id":"gsdf/EasyNegative","name":"EasyNegative","description":"Negative Embedding trained with Counterfeit, intended for use in the stable-diffusion-webui embeddings folder. It may also be used with other models, though effectiveness is uncertain.","tags":["license:other","size_categories:n<1K","... | EasyNegative is a small Hugging Face image-related asset packaged as an imagefolder dataset entry, primarily describing a negative embedding for Stable Diffusion workflows. It has image modality metadata, uses a non-standard license, and appears oriented toward generative image model prompting rather than conventional ... | huggingface | gsdf/EasyNegative |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"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 an English text-generation dataset intended as an open-source clean-room reproduction of the data recipe behind LLaMA-style model training. It is a large-scale text corpus entry on Hugging Face suitable for language model pretraining and related SFT data sourcing workflows. | huggingface | togethercomputer/RedPajama-Data-1T |
Summarize the dataset metadata into a concise description suitable for supervised fine-tuning data curation. | {"source":"huggingface","id":"FreedomIntelligence/medical-o1-reasoning-SFT","name":"medical-o1-reasoning-SFT","description":"A medical reasoning dataset for supervised fine-tuning built on medical verifiable problems and distilled reasoning traces. Recent updates indicate the release of a medical-only SFT file and a mi... | medical-o1-reasoning-SFT is a bilingual English-Chinese medical text dataset designed for supervised fine-tuning and reasoning-focused question answering. It contains medical instruction and reasoning data built from verifiable medical problems, is released under Apache-2.0, and is particularly relevant for domain-spec... | huggingface | FreedomIntelligence/medical-o1-reasoning-SFT |
Summarize the dataset and explain why it is useful for language model training. | Name: fineweb-edu
Description: FineWeb-Edu is a large educational web dataset containing 1.3 trillion tokens filtered from the FineWeb dataset using an educational quality classifier. It focuses on high-quality educational web pages in English.
Tags: text-generation, en, odc-by, parquet, tabular, text, arxiv:2406.17557... | fineweb-edu is a massive English-language educational text dataset from Hugging Face containing 1.3 trillion tokens. It was derived from the FineWeb corpus by applying an educational quality classifier, making it especially valuable for pretraining or fine-tuning language models on high-quality instructional and inform... | huggingface | HuggingFaceFW/fineweb-edu |
Provide a concise dataset overview including its scale, domain, and intended use. | Name: dolma
Description: Dolma is an open corpus of three trillion tokens for language model pretraining research.
Tags: text-generation, en, odc-by, n>1T, language-modeling, casual-lm, llm, arxiv:2402.00159
Downloads: 4058
Likes: 1025 | dolma is a very large open English text corpus designed for language model pretraining research, with a scale of roughly three trillion tokens. It is intended for use in training and studying large language models, especially in language modeling settings. Its openness and extreme size make it a strong resource for res... | huggingface | allenai/dolma |
Describe this dataset for someone looking for code data to train multilingual code models. | Name: the-stack
Description: The Stack is a large code dataset. Initial releases included 30 programming languages and permissive licenses, with later revisions excluding weak copyleft licenses and expanding permissive license coverage. The near-deduplicated dataset is about 3TB in size.
Tags: text-generation, multilin... | the-stack is a large multilingual code dataset created for training code generation and code understanding models. It aggregates source code across many programming languages and emphasizes license filtering, with later versions removing weak copyleft licenses and broadening the set of accepted permissive licenses. Its... | huggingface | bigcode/the-stack |
Summarize the dataset and mention what makes it distinctive compared with general web text corpora. | Name: TinyStories
Description: TinyStories contains synthetically generated short stories produced by GPT-3.5 and GPT-4 using a small vocabulary. It is described in arXiv:2305.07759 and was used to train small language models.
Tags: text-generation, en, cdla-sharing-1.0, parquet, text, arxiv:2305.07759
Downloads: 96552... | TinyStories is an English text dataset of synthetic short stories generated by GPT-3.5 and GPT-4 using deliberately simple vocabulary and structure. Unlike broad web-scale corpora, it is highly controlled and optimized for studying how small language models can learn coherent language from simple, clean data. This make... | huggingface | roneneldan/TinyStories |
Explain what this dataset contains and how it can be used for supervised fine-tuning. | Name: databricks-dolly-15k
Description: databricks-dolly-15k is an open instruction-following dataset created by Databricks employees across categories such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.
Tags: question-answering, summarization, en, cc-by-sa-... | databricks-dolly-15k is a human-authored English instruction-following dataset containing around 15,000 examples across multiple task types, including question answering, summarization, brainstorming, classification, and information extraction. It is well suited for supervised fine-tuning because it provides prompt-res... | huggingface | databricks/databricks-dolly-15k |
Summarize the dataset and highlight its main purpose, modality, scale, language, license, and notable usage context. | Name: alpaca
Source: huggingface
ID: tatsu-lab/alpaca
Description: Dataset Card for Alpaca. 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 ... | Alpaca is an English text dataset on Hugging Face designed for instruction tuning. It contains about 52,000 instruction-response demonstrations generated with OpenAI's text-davinci-003 and inspired by the Self-Instruct pipeline. Its primary purpose is to help train language models to better follow instructions. The dat... | huggingface | tatsu-lab/alpaca |
Summarize the dataset or resource and explain its intended use, modality, license, size, and relevant application domains. | Name: bad_prompt
Source: huggingface
ID: Nerfgun3/bad_prompt
Description: Negative Embedding / Textual Inversion. The idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding. Side note: Embedding has proven to be very... | bad_prompt is a Hugging Face image-related resource focused on Stable Diffusion workflows, especially negative prompting through textual inversion. It is intended to package a negative prompt into a reusable embedding that users can place in the Stable Diffusion WebUI embeddings folder and apply in the negative prompt ... | huggingface | Nerfgun3/bad_prompt |
Summarize the Falcon RefinedWeb dataset for language model pretraining, including its purpose, scale, language, data source, filtering approach, license, format, and notable characteristics. | 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 achie... | Falcon RefinedWeb is a large-scale English web corpus designed for text generation and language model pretraining. Released by TII on Hugging Face under the ODC-By 1.0 license, it is constructed from CommonCrawl using stringent filtering and large-scale deduplication to improve data quality. The dataset falls in the 10... | huggingface | tiiuae/falcon-refinedweb |
Summarize the LMSYS-Chat-1M dataset, focusing on its content, collection source, time period, scale, metadata fields, and relevance 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 studying interactions with large language models. Hosted on Hugging Face, it contains one million conversations involving 25 state-of-the-art LLMs, collected from the Vicuna demo and Chatbot Arena between April and August 2023. The data comes from 210K unique IP ... | huggingface | lmsys/lmsys-chat-1m |
Summarize the dataset metadata and key characteristics. | Name: 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/discussions/4#64346c08ef6d5abefe42c12c
Two choices:
Removes instances of ... | ShareGPT_Vicuna_unfiltered is a Hugging Face dataset focused on English-language data. It is licensed under Apache-2.0 and associated with the US region. The description indicates that the dataset has undergone additional cleaning and includes variants with and without instances of the phrase "I'm sorry, but," intended... | huggingface | anon8231489123/ShareGPT_Vicuna_unfiltered |
Summarize the dataset metadata and key characteristics. | Name: 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 generation of Physical AI based end-to-end driving systems. This ... | PhysicalAI-Autonomous-Vehicles is a Hugging Face dataset for autonomous vehicle research. It offers a large, geographically diverse collection of multi-sensor driving data designed for developing end-to-end Physical AI driving systems. The dataset uses automatic or sensor-based collection and labeling methods, includes... | huggingface | nvidia/PhysicalAI-Autonomous-Vehicles |
Summarize the given dataset metadata into a concise SFT-style description covering what the dataset is, its scale, modality, languages, license, format, and notable usage relevance. | {"source":"huggingface","id":"HuggingFaceFW/finepdfs","name":"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_categori... | finepdfs is a large-scale Hugging Face dataset built exclusively from PDF sources. It contains roughly 3 trillion tokens across 475 million documents and spans 1,733 languages, making it a major multilingual text resource for text generation and large-scale language model pretraining or data research. The dataset is di... | huggingface | HuggingFaceFW/finepdfs |
Summarize the given dataset metadata into a concise SFT-style description covering what the dataset is, its scale, modality, license, format, and primary domains. | {"source":"huggingface","id":"open-thoughts/OpenThoughts-114k","name":"OpenThoughts-114k","description":"Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles. Includes ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models.","tags":["lice... | OpenThoughts-114k is a synthetic reasoning dataset on Hugging Face containing about 114,000 high-quality text examples. It focuses on domains such as math, science, code, and puzzles, and is intended for supervised fine-tuning of reasoning-capable language models. The dataset includes ready-to-train data used for model... | huggingface | open-thoughts/OpenThoughts-114k |
Summarize the dataset metadata into a concise instruction-tuning dataset description. | {"source":"huggingface","id":"teknium/OpenHermes-2.5","name":"OpenHermes-2.5","description":"This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models. The Open Hermes 2/2.5 and Nous Hermes 2 models are underpinned by this compilation and curation of many open source datasets and custom created sy... | OpenHermes-2.5 is a large English text dataset hosted on Hugging Face and used to build the OpenHermes 2.5 and Nous Hermes 2 model series. It is distributed in JSON format, falls in the 1M to 10M example size range, and is described as a compilation of open-source and synthetic data with GPT-4 distillation elements. Th... | huggingface | teknium/OpenHermes-2.5 |
Summarize the dataset metadata into a concise instruction-tuning dataset description. | {"source":"huggingface","id":"yahma/alpaca-cleaned","name":"alpaca-cleaned","description":"This is a cleaned version of the original Alpaca Dataset released by Stanford. The original dataset had issues such as hallucination-prone instructions referencing internet data, and those issues were fixed in this cleaned releas... | alpaca-cleaned is a cleaned instruction-tuning dataset on Hugging Face derived from the original Stanford Alpaca release. It is an English text-generation dataset in JSON format, licensed under CC-BY-4.0, and sized between 10K and 100K examples. Its main purpose is to provide higher-quality instruction-following data b... | huggingface | yahma/alpaca-cleaned |
Summarize the dataset metadata into a concise description for model training. | Source: huggingface
ID: cais/hle
Name: 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. The dataset note a... | {"name":"hle","full_id":"cais/hle","platform":"huggingface","summary":"Humanity's Last Exam is an official multimodal benchmark containing 2,500 closed-ended questions spanning many academic subjects at the frontier of human knowledge.","modalities":["image","text"],"format":"parquet","license":"mit","size_category":"1... | huggingface | cais/hle |
Summarize the dataset metadata into a concise description for model training. | Source: huggingface
ID: HuggingFaceFW/fineweb-2
Name: 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... | {"name":"fineweb-2","full_id":"HuggingFaceFW/fineweb-2","platform":"huggingface","summary":"FineWeb2 is a large-scale multilingual text pretraining dataset covering over 1000 languages, designed as a reproducible and extensively validated update to FineWeb.","task_category":"text-generation","modalities":["tabular","te... | huggingface | HuggingFaceFW/fineweb-2 |
Given a dataset metadata record, summarize the dataset for machine learning practitioners, including what it is, its primary modality/task, notable scale, and important usage considerations. | {"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... | ImageNet-1k is a large-scale image classification dataset from ILSVRC 2012. It contains images organized by WordNet synsets and is primarily used for multi-class image classification. The dataset modality is image, and its scale is between 1 million and 10 million examples, making it a standard benchmark for computer v... | huggingface | ILSVRC/imagenet-1k |
Given a dataset metadata record, summarize the dataset for LLM fine-tuning, including what it contains, likely use cases, supported languages, and licensing notes. | {"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, which can be directly loaded by Alpaca model code. It also i... | Alpaca-CoT is an instruction-finetuning dataset collection designed for training and experimenting with instruction-following language models. It aggregates multiple instruction-tuning datasets and standardizes them into a common format compatible with Alpaca-style training pipelines. Likely use cases include supervise... | huggingface | QingyiSi/Alpaca-CoT |
Summarize the dataset based on its metadata for use in an LLM supervised fine-tuning example. | Name: PersonaHub
Source: huggingface
Dataset 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 large-scale synthetic data creation through persona-driven generation. Introduced in the paper "Scaling Synthetic Data Creation with 1,000,000,000 Personas," it presents PERSONA HUB, a massive collection of diverse personas automatically curated from web data to help gene... | huggingface | proj-persona/PersonaHub |
SoloAI SFT Dataset: 20260505-2139
📊 数据集概览
| 属性 | 值 |
|---|---|
| 数据条数 | 30 条 |
| 数据源 | huggingface |
| 生成时间 | 2026-05-05 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': 'Summarize the dataset for LLM supervised fine-tuning selection, including purpose, likely use cases, scale, license, modality, and notable characteristics.', '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":60033,"likes":9680,"created_at":""}', 'output': 'prompts.chat is a Hugging Face mirror of the Awesome ChatGPT Prompts community collection. It is a small text dataset in CSV format with between 1K and 10K records and is released under CC0-1.0, making it highly permissive for reuse. The dataset focuses on prompt engineering and includes prompts relevant to ChatGPT, Claude, Gemini, Llama, Mistral, and other LLM-driven conversational AI systems. It is best suited for instruction and prompt template discovery, prompt benchmarking, prompt library construction, and studying community prompt patterns rather than direct large-scale model pretraining. Its strong popularity suggests practical value for downstream prompt design and chatbot experimentation.', 'source': 'huggingface', 'original_id': 'fka/prompts.chat'}
🤖 数据来源
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- 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
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| 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 |
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📈 更新日志
| 版本 | 日期 | 说明 |
|---|---|---|
| v1.0 | 2026-05-05 21:39 | 初始发布，30 条数据 |
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