Hugging Face is an AI community building the future. They provide tools that enable users to build, train and deploy ML models based on open source code and technologies.
Why this server?
Integrates with Hugging Face for model hosting and distribution, with links to MiniMax AI models on the platform.
Why this server?
Connects to MiniMax's Hugging Face organization to access related models and resources
Why this server?
Integrates with Hugging Face's LocalPythonExecutor from the smolagents framework to provide secure Python code execution capabilities with basic isolation and security for running LLM-generated Python code.
Why this server?
Provides access to research papers hosted on Hugging Face, allowing users to discover and discuss AI/ML research
Why this server?
Uses the Hugging Face Inference API to generate embeddings for the knowledge base content, with optional model selection through environment variables.
Why this server?
Allows interaction with the Hugging Face Dataset Viewer API, providing tools for browsing, searching, filtering, and analyzing datasets hosted on the Hugging Face Hub, along with support for authentication for private datasets.
Why this server?
Integrates with Hugging Face's Transformers and Hub for accessing ML models, enabling semantic search, embedding generation, and language model operations
Why this server?
Allows access to thousands of open-source AI models from Hugging Face with support for custom model parameters
Why this server?
Supports interaction with Hugging Face datasets, enabling evaluation of data quality for datasets hosted on the platform
Why this server?
Supports downloading and using models from Hugging Face Hub for various computer vision tasks like object detection.
Why this server?
Connects to Hugging Face Spaces with minimal setup, providing access to various AI models and services such as image generation, vision tasks, text-to-speech, and speech-to-text capabilities.
Why this server?
Provides model hosting and distribution for the various Stable Diffusion models used by DiffuGen
Why this server?
Tracks trending models, datasets, and spaces on Hugging Face, providing tools to fetch trending content, search for specific items, and analyze current trends on the platform.
Why this server?
Utilizes Hugging Face embedding models for code semantics, enabling semantic search through project files
Why this server?
Integrates with Hugging Face Spaces to leverage AI models for generating 2D and 3D game assets from text prompts.
Why this server?
Provides read-only access to Hugging Face Hub APIs, allowing interaction with models, datasets, spaces, papers, and collections. Includes tools for searching and retrieving detailed information across these resource types.
Why this server?
Uses the E5 embedding model from Hugging Face for semantic search capabilities, allowing context items to be found based on meaning rather than just exact key matches
Why this server?
Connects to Hugging Face Spaces, enabling access to various AI models and capabilities including image generation, vision tasks, text-to-speech, speech-to-text, and chat functionality with minimal setup.
Why this server?
Uses models downloaded from Hugging Face, specifically the Moondream quantized model for image analysis
Why this server?
Provides an HTTP interface to call the Flux Schnell image generation model hosted on Hugging Face, allowing for customized image creation with adjustable dimensions and seed values
Why this server?
Leverages Hugging Face Transformers for document processing, embeddings generation, and semantic search capabilities
Why this server?
Enables loading, fine-tuning, and using models from Hugging Face, with optional authentication via HUGGINGFACE_TOKEN for accessing private models and datasets.