12 articles
Hugging Face releases a comprehensive guide for training language models with the Transformers library using TensorFlow and Google TPUs. The tutorial covers performance optimization and best practices for large-scale distributed training.
Databricks and Hugging Face announce integration delivering up to 40% faster training and fine-tuning of large language models. The partnership optimizes training workflows on Databricks infrastructure.
Hugging Face demonstrates running IF (Imagen Diffusion) on free Google Colab using the diffusers library. Practical tutorial for image generation without paid GPU access.
OpenAI enables ChatGPT users to disable chat history, controlling which conversations can be used to train its models.
Hugging Face launches a dedicated blog for Chinese speakers to strengthen collaboration with the Chinese AI community. Initiative aimed at improving resource access and facilitating technical exchanges.
Hugging Face explains how to host a Unity game in a Space. The tutorial covers deploying interactive 3D applications on the platform, enabling developers to share their games directly online without external infrastructure.
Hugging Face optimizes Transformers for AWS Inferentia2, reducing latency and increasing inference throughput. Native integration of popular models (Llama, Mistral, Phi) with quantization and batching support.
Hugging Face presents a graph classification method using transformers. The approach combines transformer architecture with graph representations to improve performance on structured classification tasks.
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 releases a hands-on guide for training LLaMA with RLHF (Reinforcement Learning from Human Feedback). The tutorial covers full implementation from data preparation to model optimization, with reproducible code and concrete examples.