datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Magicoder-OSS-Instruct-75KThis is the OSS-Instruct dataset generated by gpt-3.5-turbo-1106 developed by OpenAI. Please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies.
Magicoder-Evol-Instruct-110KA decontaminated version of evol-codealpaca-v1. Decontamination is done in the same way as StarCoder (bigcode decontamination process).
ui-instruct-4k
UI Instruct 4K
A instruction-completion dataset for finetuning language models to specialize in generating Next.js / ShadCN UI components using React, TypeScript, and Tailwind CSS.
Dataset Summary
This dataset was created with the primary goal of finetuning Qwen 3.5 4B to become a specialist at outputting production-ready Next.js and ShadCN-based UI components. Each example consists of a natural language prompt describing a UI component or layout, paired with a clean… See the full description on the dataset page: https://huggingface.co/datasets/iamdyeus/ui-instruct-4k.otsd-ui
Single HTML Interfaces, Redesigned — Sample
A design-first dataset capturing the full workflow of turning AI-generated front-end interfaces into distinctive, production-grade, single-file HTML applications.
This repository is a free 7-sample preview of a larger off-the-shelf dataset (100 samples in the full release). It is meant for evaluation: explore the structure, the design reasoning, and the before/after quality so you can decide whether the full set fits your needs.
Want… See the full description on the dataset page: https://huggingface.co/datasets/revelohq/otsd-ui.genielm-ui-grounding
GenieLM UI-Grounding
Synthetic supervised fine-tuning data for text-based UI grounding: given a list of
on-screen elements (label + pixel center) and a natural-language instruction, pick the
single element to act on and emit a strict JSON action.
Built for GenieLM, a macOS agent that reads
the accessibility tree as text (not pixels) and lets a small LLM drive the cursor.
Format
Conversational SFT (messages column):
{"messages": [
{"role": "system", "content":… See the full description on the dataset page: https://huggingface.co/datasets/Barath/genielm-ui-grounding.ui-distill-html-648
ui-distill-html-648
648 single-file HTML UI components, generated by
Ornith-1.0-35B on a single
RTX 3060 12GB, paired with the build request that produced each one.
Built to fine-tune a 3B model into writing UI
(DogukanUrker/ui-distill-3b), but
it stands on its own — distill your own student from it.
The interesting part
The instructions in this dataset are not the prompts that generated the HTML.
The teacher was driven by a 678-character system prompt: use the… See the full description on the dataset page: https://huggingface.co/datasets/DogukanUrker/ui-distill-html-648.ui-form-user-manual-generation-dataset-rus
UI Form User Manual Generation Dataset (Russian)
Dataset Description
This dataset was developed on the basis of 'yahma/alpaca-cleaned' dataset.
It contains examples of generating user guides for interface forms in Russian. Each example includes a description of the UI form elements and corresponding step-by-step instructions for completing it.
Data Structure
The dataset is in JSON format, and contains three fields:
instruction — system instruction
input —… See the full description on the dataset page: https://huggingface.co/datasets/dim014/ui-form-user-manual-generation-dataset-rus.uirapuru
Uirapuru U1.1 Bilingual Reasoning & Tool Calling Dataset
Summary
Uirapuru U1.1 is a specialized bilingual dataset designed to enhance Large Language Models (LLMs) in three critical areas: Tool Calling, Brazilian Local Knowledge, and Human-like Reasoning. Created by SynastrIA Networks, this dataset bridges the gap between generic multilingual models and agents capable of operating effectively within the Brazilian context while maintaining strong alignment with… See the full description on the dataset page: https://huggingface.co/datasets/SynastriaNetworks/uirapuru.pile-neox-uint16-partsTokenized uint16 shard parts for language-model pretraining.
Original source: The Pile / NeoX-style preprocessing.