Hugging Face

REST API for running inference on any model hosted on Hugging Face without managing infrastructure.

Compare model performance across tasks and benchmarks in real-time rankings.

Built-in tools to evaluate and benchmark model performance across various metrics.

Track inference statistics, performance metrics, and model behavior in production.

Dashboard showing API calls, costs, and performance metrics for Inference Endpoints.

Classify audio files and recordings into categories for sound event detection.

Convert audio to text with multilingual support and speaker diarization.

Generate natural-sounding speech from text in multiple languages and voices.

Team workspaces for collaborative model development with role-based access control.

Git-based version control for models, datasets, and code with full commit history.

Organized workshops, competitions, and collaborative projects with prizes.

Built-in discussion board for each model or dataset for feedback and community engagement.

Classify images into predefined categories using vision transformers and CNNs.

Perform semantic and instance segmentation to identify pixel-level regions.

Detect and localize objects in images with bounding boxes and confidence scores.

Access to 500,000+ pre-trained models for NLP, vision, audio, and multimodal tasks.

Collection of 50,000+ public datasets for training and fine-tuning models.

Production-ready endpoints with dedicated infrastructure, auto-scaling, and SLA support.

Deploy interactive ML demos and applications with automatic scaling and GPU support.

Minimal ML framework written in Rust for efficient inference on CPU and GPU.

Open-source library for state-of-the-art diffusion model inference and training.

Optimized framework for fast and efficient text generation with quantization and tensor parallelism.

Open-source Python library with 80,000+ pre-trained models for PyTorch, TensorFlow, and JAX.

Standardized documentation for models including intended use, limitations, and training data.

Automated notifications for repository changes, model updates, and inference completions.

Curated lists of models and datasets organized by use case and research area.

Optimize inference speed by managing model caching strategies on endpoints.

Control access to models and datasets by requiring user approval before download.

Create private models and datasets with restricted access and custom permissions.

Configurable request throttling and quota management for API endpoints.

No-code platform for training custom models without coding or GPU management.

Simplified API for fine-tuning pre-trained models with your own data in the cloud.

Train and share reinforcement learning models with benchmark environments.