Directorio de skills

Descubre skills reutilizables para AI agents.

Busca skills reales de GitHub por tarea y revisa stars, confianza, auditoría, categoría y ruta de instalación antes de utilizarlos.

Cada recomendación conserva un vínculo claro con su repositorio, auditoría y ruta de instalación.

Resultados de búsqueda: transformers

Directorio en inglés

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

162K
Stars
87/100
Confianza
Categoría: ml-automationAuditoría

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

13K
Stars
87/100
Confianza
Categoría: ml-automationAuditoría

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

67K
Stars
84/100
Confianza
Categoría: ml-automationAuditoría

⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports Cloud / Self-hosted use. Integrated with PyTorch / Transformers / verl / LLaMA Factory / ms-swift / Ultralytics / MMEngine / Keras etc.

4.0K
Stars
84/100
Confianza
Categoría: ml-automationAuditoría

Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch

1.9K
Stars
84/100
Confianza
Categoría: ml-automationAuditoría

Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

34K
Stars
70/100
Confianza
Categoría: researchAuditoría

Official implementation for "RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers" (ICML 2025) , UltraViCo (ICLR 2026) and UltraImage

808
Stars
73/100
Confianza
Categoría: media-automationAuditoría

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

1.4K
Stars
71/100
Confianza
Categoría: robotics-iotAuditoría

Generative Adversarial Transformers

1.3K
Stars
69/100
Confianza
Categoría: media-automationAuditoría

[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

847
Stars
70/100
Confianza
Categoría: robotics-iotAuditoría

FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers

508
Stars
66/100
Confianza
Categoría: media-automationAuditoría

[CVPR 2022] FaceFormer: Speech-Driven 3D Facial Animation with Transformers

915
Stars
63/100
Confianza
Categoría: robotics-iotAuditoría