How to Install and Use the Hugging Face Unity API
Hugging Face published a guide on integrating its model inference capabilities into Unity game engine projects via a dedicated Unity API. The post covers installation and usage patterns for accessing Hugging Face-hosted models from within Unity applications. This represents an expansion of Hugging Face's tooling ecosystem into interactive 3D and game development contexts.
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Related events (8)
Hugging Face Launches Inference Providers on the Hub
Hugging Face has introduced Inference Providers on the Hub, a feature that allows users to run models hosted on the Hub through third-party inference providers directly from the platform. This integration consolidates access to multiple inference backends under a unified interface, reducing friction for developers who want to deploy or test models at scale. The announcement positions Hugging Face as a marketplace layer connecting model authors with inference infrastructure providers.
AI Speech Recognition in Unity
A Hugging Face blog post describes integrating AI-based automatic speech recognition (ASR) into Unity game/application environments. The post likely covers using transformer-based ASR models within the Unity engine, bridging ML inference with real-time interactive applications. This represents a practical deployment pattern for on-device or embedded ASR in non-traditional runtime environments.
An Overview of Inference Solutions on Hugging Face
Hugging Face published a blog post surveying its inference product offerings as of late 2022. The post covers the range of hosted and API-based inference solutions available on the platform, aimed at helping developers choose appropriate deployment paths. This serves as a reference overview of Hugging Face's inference infrastructure ecosystem at that time.
Hugging Face and FriendliAI Partner to Supercharge Model Deployment on the Hub
Hugging Face and FriendliAI have announced a partnership to integrate FriendliAI's inference infrastructure directly into the Hugging Face Hub. The collaboration aims to simplify and accelerate model deployment for developers accessing models through the Hub. This expands the ecosystem of inference providers available on Hugging Face's platform.
Featherless AI Joins Hugging Face Inference Providers
Hugging Face has added Featherless AI as a new inference provider in its Inference Providers ecosystem. Featherless AI specializes in serverless inference for open-weight models, expanding the range of third-party compute options available through the Hugging Face platform. This integration allows developers to route model inference requests to Featherless AI directly via the Hugging Face API and model hub.
Public AI on Hugging Face Inference Providers
Hugging Face announces the integration of Public AI as a new inference provider on its platform. This expands the ecosystem of third-party inference backends available through Hugging Face's unified API. The move continues the pattern of Hugging Face aggregating multiple inference providers to give developers flexible deployment options.
Announcing New Hugging Face and KerasHub Integration
Hugging Face and KerasHub have announced a new integration enabling users to access Hugging Face models and datasets directly through the Keras ecosystem. This partnership bridges two major ML frameworks, allowing Keras users to leverage the Hugging Face Hub's model repository without leaving the Keras workflow. The integration is aimed at reducing friction for practitioners who prefer Keras-based training and inference pipelines.
Hugging Face and AWS Partner to Make AI More Accessible
Hugging Face announced a strategic partnership with Amazon Web Services to expand access to AI models and tools. The collaboration aims to integrate Hugging Face's model hub and libraries more deeply with AWS infrastructure and services. This represents a significant enterprise deployment and cloud distribution move for the open-source AI ecosystem.