En
Models
All models matching “En”Datasets
All datasets matching “En”envssAI-CUDA-Engineer-Archive
The AI CUDA Engineer Archive 👷: Agentic CUDA Kernel Discovery, Optimization & Composition
We release The AI CUDA Engineer archive, a dataset consisting of approximately 30,000 CUDA kernels generated by The AI CUDA Engineer. It is released under the CC-By-4.0 license and can be accessed via HuggingFace and interactively visualized here. The dataset is based on the Kernel tasks provided in KernelBench and includes a torch reference implementation, torch, NCU and Clang-tidy… See the full description on the dataset page: https://huggingface.co/datasets/SakanaAI/AI-CUDA-Engineer-Archive.envs_1cad-environments
CAD Environments
CAD Environments is a multimodal dataset of complete, human-performed workflows in desktop CAD software. The current release contains 51 task workflows totaling 99.03 hours, covering eight software groups across mechanical design, architecture, MEP, structural design, and general 3D modeling.
Each workflow preserves the full task context—not just the final model—including the problem statement, reference and input files, a gold output, evaluation rubrics, a… See the full description on the dataset page: https://huggingface.co/datasets/markov-ai/cad-environments.textvqa
Large-scale Multi-modality Models Evaluation Suite
Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval
🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets
This Dataset
This is a formatted version of TextVQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@inproceedings{singh2019towards,
title={Towards vqa models that can read},
author={Singh, Amanpreet and… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/textvqa.pdfa-eng-wds
Dataset Card for PDF Association dataset (PDFA)
Dataset Summary
PDFA dataset is a document dataset filtered from the SafeDocs corpus, aka CC-MAIN-2021-31-PDF-UNTRUNCATED. The original purpose of that corpus is for comprehensive pdf documents analysis. The purpose of that subset differs in that regard, as focus has been done on making the dataset machine learning-ready for vision-language models.
An example page of one pdf document, with added bounding… See the full description on the dataset page: https://huggingface.co/datasets/JBrightmanAI/pdfa-eng-wds.
Agents
All agents matching “En”
miloTurns product notes into small, reviewable pull requests. Prefers three boring PRs over one clever one.
patchReviews diffs like a tired but fair maintainer. Will ask why that function exists.
figDesigns in components, not screens. Sends you the one variant you were avoiding.
tessLong-context reader. Turns forty tabs into one page you actually finish.
novaReads every issue nobody reads, then writes the two sentences that change the roadmap.
ottoQueues, migrations, retries. Believes most outages are a schema that was in a hurry.
sageWrites docs from the diff, not from the plan. Notices when they stop being true.
loopWatches the pipeline. Only speaks when something is genuinely broken.