Hugging Face enables deploying Livebook notebooks directly to Spaces. Elixir developers can create interactive applications without additional setup, with real-time sharing and collaboration support.
Hugging Face and AMD announce a partnership to optimize AI models on AMD CPUs and GPUs. The initiative aims to improve inference performance and accessibility of state-of-the-art models across AMD's ecosystem.
Hugging Face integrates fastText into its Hub. Meta's open-source platform for text embedding and classification is now accessible through the Hugging Face ecosystem, enabling easier sharing and deployment of fastText models.
OpenAI launches a grant program to develop AI-powered cybersecurity capabilities for defenders. The initiative aims to support researchers and organizations working on AI-based security tools.
Hugging Face integrates BERTopic, a BERT-based topic modeling tool, directly into the Hub. Users can train, share, and deploy BERTopic models through Hugging Face's unified interface.
Hugging Face introduces Q8-Chat, a model optimized for Intel Xeon processors delivering efficient generative AI. The model reduces size while maintaining performance, enabling deployment on standard CPU infrastructure without GPUs.
BigCode built large-scale near-deduplication infrastructure to clean code data. The system identifies and removes near-duplicates across billions of files, improving training dataset quality for code models.
Hugging Face demonstrates how to deploy a ChatGPT-like chatbot on a single GPU using ROCm. The guide covers memory optimization and efficient inference for large language models on AMD hardware.
Hugging Face publishes a guide for building a coding assistant with StarCoder. The open-source model generates code and can be integrated into applications via APIs. The article details architecture and practical use cases.
OpenAI enables ChatGPT users to disable chat history, controlling which conversations can be used to train its models.
Substra, Hugging Face's open-source platform, enables privacy-preserving AI systems through federated learning and secure computation. It facilitates decentralized model training without centralizing sensitive data.
OpenAI launches a bug bounty program to strengthen the security of its technologies and services. The initiative aligns with the company's commitment to developing safe and advanced AI.
Snorkel AI and Hugging Face partner to enable enterprises to access and adapt foundation models. The collaboration integrates Snorkel's tools (data labeling, fine-tuning) with Hugging Face ecosystem to streamline production deployment.
Hugging Face and Flower integrate federated learning capabilities. Users can train language models on distributed data without centralizing sensitive information. The solution combines HF models with Flower's decentralized coordination framework.
Hugging Face releases a guide for training ControlNet models using the diffusers library. The tutorial covers setup, training data, and optimization techniques for image generation control models.
Hugging Face introduces Informer, a multivariate time series forecasting model based on attention mechanisms. The approach uses sparse attention to efficiently handle long temporal sequences, outperforming traditional methods on multiple benchmarks.
Kakao Brain releases new Vision Transformer (ViT) and ALIGN models optimized for computer vision and multimodal search. Models available on Hugging Face with pre-trained weights and inference code.
Hugging Face releases ethical guidelines for Diffusers library development, addressing contributor responsibilities regarding image generation risks (deepfakes, illegal content, bias). Governance framework to oversee diffusion models.
Hugging Face releases a red-teaming guide for large language models, covering methodologies, tools, and best practices to identify vulnerabilities and biases before production deployment.
Fetch consolidates AI tools and reduces development time by 30% using Hugging Face on AWS. The company integrates multiple models and services into a unified platform to optimize workflows.
Hugging Face and AWS partner to democratize AI access. The partnership includes integration of open-source models on AWS SageMaker and simplified deployment tools to lower technical and financial barriers.
OpenAI clarifies how ChatGPT's behavior is shaped and announces plans to improve customization, enable more user control, and incorporate greater public input into decision-making processes.
Hugging Face and Intel optimize PyTorch transformers on Sapphire Rapids. Second part of a technical series on hardware acceleration of language models using latest-generation Intel processors.
OpenAI launches a classifier trained to distinguish between AI-written and human-written text.
Hugging Face releases guide on AI-powered 2D asset generation for game development. Covers diffusion models, fine-grained output control, production pipeline integration. Examples with Stable Diffusion and ControlNet.
PaddlePaddle, Baidu's open-source deep learning framework, joins the Hugging Face Hub. Users can now share PaddlePaddle models, datasets, and spaces on the collaborative platform.
Intel and Hugging Face optimize PyTorch transformers on Sapphire Rapids processors. First part of a series on hardware acceleration of language models through quantization and CPU optimization techniques.
Hugging Face documents building a complete farming game in 5 days using AI. Leverages open-source models for asset generation, code, and game design. Demonstrates practical workflow for integrating AI in indie game development.
Hugging Face publishes an educational illustration of RLHF (Reinforcement Learning from Human Feedback) process. The article details how language models are fine-tuned via human feedback and reinforcement optimization to improve alignment with user preferences.
Hugging Face releases official Elixir integration, enabling developers to access GPT-2, Stable Diffusion and other models through the platform. First step toward ML ecosystem in Elixir.
Hugging Face releases a guide for integrating Stable Diffusion with Core ML on Apple Silicon. The solution enables local image generation on Mac without relying on remote servers, optimizing native hardware performance.
Hugging Face outlines its inference solutions: Inference API (serverless), Dedicated Endpoints (dedicated instances), Text Generation Inference (TGI, LLM-optimized), and Transformers.js (browser inference). Each solution addresses different use cases based on latency, cost, and scalability requirements.
Hugging Face demonstrates sentiment analysis on encrypted data using homomorphic encryption. The technique enables processing text without decryption, preserving privacy while leveraging standard NLP models.
Hugging Face announces integration of Optimum Intel and OpenVINO to accelerate model inference. The solution optimizes performance on Intel processors through quantization and compilation.
Hugging Face publishes a guide on progressing from PyTorch DDP to Accelerate to Trainer for mastering distributed training. The educational pathway demonstrates how to progressively abstract away complexity in multi-GPU/multi-node training setups.
Stability AI and Hugging Face release a Japanese-optimized version of Stable Diffusion for generating images from Japanese text prompts. The model improves output quality for Japanese text and characters.
Hugging Face AutoTrain adds image classification. The tool automates model training without requiring code. Web interface and API available to fine-tune pre-trained vision models.
Hugging Face releases a guide to train a Decision Transformer, a model that learns control policies from expert trajectories. The tutorial covers practical implementation and integration with the Transformers library.
Practical guide to training a language model with Megatron-LM, NVIDIA's framework for large-scale distributed training. Covers setup, parallelization optimizations, and best practices.
Hugging Face introduces OpenRAIL, an open and responsible AI licensing framework. The project aims to balance open access to models with ethical and legal safeguards, defining responsible use conditions.