Deploy Livebook notebooks as apps to Hugging Face Spaces
Hugging Face and the Livebook team have integrated to allow Elixir-based Livebook notebooks to be deployed as interactive web applications directly to Hugging Face Spaces. This enables developers to package machine learning workflows built in Livebook—including those using Nx and Bumblebee for model inference—into shareable, hosted apps. The integration lowers the barrier for Elixir ML practitioners to publish and share AI-powered applications.
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Hugging Face Machine Learning Demos on arXiv
Hugging Face announced an integration allowing ML demos to be linked or embedded directly on arXiv paper pages. This lowers the barrier between research publication and interactive model demonstration. The feature connects academic papers to live Spaces or model demos hosted on Hugging Face.
From GPT2 to Stable Diffusion: Hugging Face arrives to the Elixir community
Hugging Face announces Bumblebee, a library bringing Hugging Face model support to the Elixir programming language ecosystem. The integration enables Elixir developers to run models including GPT-2 and Stable Diffusion via the Nx numerical computing library. This expands the reach of Hugging Face's model hub beyond Python-centric workflows into the BEAM/Elixir ecosystem.
Deploy Hugging Face Models Easily with Amazon SageMaker
Hugging Face and Amazon SageMaker announced an integration enabling streamlined deployment of Hugging Face models via SageMaker's managed infrastructure. The partnership provides dedicated Hugging Face Deep Learning Containers on AWS, simplifying the path from model hub to production inference. This represents an early milestone in the enterprise deployment pattern of hosted model hubs integrating with cloud ML platforms.
Deploy LLMs with Hugging Face Inference Endpoints
Hugging Face published a guide on deploying large language models using their Inference Endpoints service. The post covers how to set up scalable, production-ready LLM deployments with minimal infrastructure overhead. It targets developers looking to move from experimentation to hosted inference without managing raw compute.
Improving Hugging Face Model Access for Kaggle Users
Hugging Face has announced an integration improvement that streamlines how Kaggle users access models from the Hugging Face Hub. The update appears to reduce friction for practitioners using Kaggle notebooks and compute environments to work with Hugging Face-hosted models. This represents a platform-level partnership move between two major ML community hubs.
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.
Making ML-powered web games with Transformers.js
This Hugging Face blog post demonstrates how to build machine learning-powered web games using Transformers.js, enabling in-browser inference without a server backend. The post covers practical implementation patterns for running transformer models directly in the browser via WebAssembly and WebGL. It serves as both a tutorial and a showcase of client-side ML deployment capabilities.
Databricks + Hugging Face Integration Achieves Up to 40% Faster LLM Training and Tuning
Databricks and Hugging Face have published a case study describing their integration that delivers up to 40% faster training and fine-tuning of large language models. The collaboration leverages Databricks' distributed compute infrastructure alongside Hugging Face's model hub and training libraries. This represents a practical infrastructure optimization for enterprise teams running LLM workloads on Databricks.