This is a SaaS platform for running AI applications. It shows runtime errors and container logs to help identify issues with application deployment.
Allows users to create a new space for hosting AI apps directly on the Hugging Face platform. Users can customize and manage their projects within these spaces.
A curated list of popular AI apps, showcasing trending spaces to help users discover new and interesting projects.
Offers various options to filter and browse through AI app spaces. This includes ZeroGPU spaces for resource-efficient projects.
Enables users to sort spaces based on different criteria such as trending projects to quickly find popular apps.
A repository for sharing, discovering, and collaborating on machine learning models with the community.
Access a variety of datasets for machine learning projects, allowing users to upload and share data.
A platform for hosting, sharing, and discovering ML applications made by the community, running in the cloud.
An open-source library for natural language processing tasks featuring state-of-the-art models.
Provides optimized infrastructure for deploying and running machine learning models efficiently.
Offers customized enterprise solutions for businesses needing large-scale and dedicated ML support.
Contains the main application logic in `app.py` which is likely responsible for the core functionality of the Space.
The `requirements.txt` file specifies required dependencies, allowing you to recreate the environment needed to run the app.
The `README.md` provides basic information or instructions about the project.
Search through a vast collection of models using keywords or filters. Allows selection by task or model name.
Select models based on specific tasks such as Text-to-Text, Image-to-Text, etc. Helps narrow down relevant models for specific needs.
Click on any model to view details such as the last updated date, number of downloads, and likes. This helps in understanding the popularity and freshness of a model.
Sort models by trends to see which models are gaining popularity. This helps in identifying useful or well-performing models quickly.
Allows users to search for datasets by name and filter based on modalities (e.g., 3D, Audio), size, and format.
Enables sorting of datasets based on various criteria such as trending, most recent updates, or popular use.
Indicates the type of access viewers have, such as Viewer or Preview, for detailed examination of dataset contents.
State-of-the-art NLP for PyTorch, TensorFlow, and JAX.
State-of-the-art diffusion models for image and audio generation in PyTorch.
Build machine learning demos and other web apps with just a few lines of code.
Access and share datasets for computer vision, audio, and NLP tasks.
A collection of JS libraries to interact with Hugging Face web, with TS types included.
State-of-the-art Machine Learning for the web. Run Transformer directly in your browser, with no need of a server.
Parameter efficient finetuning methods for large models.
Host Git-based models, datasets, and Spaces on the Hugging Face Hub.
Experiment with over 2000 models easily by sending requests to serverless Inference Endpoints.
Easily deploy models to production on dedicated, fully managed infrastructure.
Client library for the HF Hub: manage repositories from your Python code.
Fast training and inference of HF Transformers with easy-to-use hardware optimization tools.
Train and Deploy Transformers & Diffusers with AWS Trainium and AWS Inferentia via Optimum.
Easily train and use PyTorch models with multi-GPU, TPU, mixed precision.
Evaluate model output performance easier and faster.
All things task: task demos, use cases, codes, datasets, and more!
Fast tokenizers, optimized for both research and production.
Train transformer language models with reinforcement learning.
Train and Deploy Transformer models with Amazon SageMaker and Hugging Face DLCs.
API to access the contents, metadata, and basic statistics of all Hugging Face Hub datasets.
Simple, safe way to store and distribute neural networks weights safely and quickly.
Toolkit to serve Large Language Models.
State of the art computer vision models, layers, optimizers, training visualization, and utilities.
AutoTrain API and UI.
Toolkit to serve Text Embedding Models.
Create your own competitions on Hugging Face.
Toolkit to optimize and quantize models.
Multilingual Sentence & Image Embeddings.
Train and Deploy Transformer models with Hugging Face DLCs on Google Cloud.
Deploy models on Google TPUs via Optimum.
Open source chat frontend, powers the HuggingChat app.
Your all-in-one toolkit for evaluating LLMs across multiple backends.
Create your own Leaderboards on Hugging Face.
Collaboration tool for AI engineers and domain experts who need to build high quality datasets.
Optimized, zero configuration inference microservices designed to simplify and accelerate the deployment of AI applications with open models.
The framework for synthetic data generation and AI feedback.
Connect securely to your identity provider with SSO integration for seamless access control.
Select, manage, and audit the location of your repository data based on geographic needs.
Maintain comprehensive logs of actions taken to ensure accountability and security.
Manage access to repositories with detailed and granular access control for enhanced security.
Centralize token control with custom approval policies to manage organization access.
Track and analyze repository usage data through a unified dashboard to monitor activities.
Increase scalability with managed compute options such as ZenGPU for enhanced performance.
Enable the Dataset Viewer on private datasets for more accessible collaboration among teams.
Configure organization-wide security policies and default visibility options for repositories.
Control your budget effectively with structured billing and yearly commitment options.
Receive prioritized support from the Hugging Face team to ensure seamless platform use.
Provides on-demand endpoints for inference, allowing users to deploy models quickly without managing infrastructure.
Spaces applications can run on optimized ML infrastructure, allowing you to deploy applications that scale easily.
Allows users to use CPU instead of GPU, automatically scaling applications as needed.
Supports multiple frameworks like Streamlit, Gradio, and Docker to build and host your own applications easily.
Connect to your Space with SSH or VS Code in your browser, with Git support and automatic process refresh.
Choose from different hardware options (CPUs to TPUs) for optimal application performance.
Support for collaboration using Git-based version control workflows.
Scalable and Versatile 3D Generation from images using ZeroGPU.
Runs on ZeroGPU for processing by black-forest-labs.
Uses ZeroGPU to provide computational tasks by illyasviel.
Uses ZeroGPU for outpainting with Flux capabilities.
Generates synchronized audio from video/text using ZeroGPU.
FLUX 3D StyleGEN running with ZeroGPU.
FLUX 4-bit Quantization using just 8GB VRAM with ZeroGPU.
Text to Audio (Sound SFX) Generator utilizing ZeroGPU.
Generates images with SD3.5 using ZeroGPU.
Creates various styles using ZeroGPU.
Allows you to upload an image to convert it into a 3D model.
Generates a 3D asset from the uploaded image. Uses alpha channel as a mask if available; otherwise, uses a tool to remove the background.
Enables extraction of the generated 3D model into a GLB file for download.
Provides example images to demonstrate the type of 3D assets that can be generated.
Allows you to generate natural, expressive speech for over 22 Indian languages using a simple text prompt. Useful for crafting different styles like speaker style, tone, pitch, pace, and more.
Offers examples and guidance on optimizing input details to achieve specific speaker characteristics, such as expressive tone and clear audio quality.
Provides very high-quality recordings with no background noise, producing clear and neutral tones.
A collection of Text-To-Speech (TTS) models adapted to Indian languages. This includes various models that convert text to spoken language.
An open-source translation dataset for Indian languages. It includes different datasets for translations among Indian languages.
Insights into the AI trends and developments predicted for 2025.
Highlights of the models that received the most attention and downloads over the past year.
An overview of rapidly released models over the past year.
Analysis of the journey to a million AI models.
Insights into user preferences based on liked models.
Exploration of various tasks that AI models were applied to in the past year.
Ranking and insights about the top 500 AI model creators globally.
Statistics on the average daily downloads of AI models.
Festive activities and announcements related to NeurIPS.
An event or challenge focused on AI creativity and performance.
List and analysis of the most upvoted AI research papers.
Examination of the leading roles of the US and China in AI research.
Showcase of significant contributions and contributors at NeurIPS 2024.
Trends and achievements in the field of machine vision.
Arguments and insights into the economic benefits of open-source AI.