Hugging Face Hub and DuckDB Integration for Dataset Analysis
Hugging Face now allows users to run SQL queries via DuckDB on over 50,000 public datasets automatically converted to Parquet format on the Hub.
fastText Integration with Hugging Face Hub
Hugging Face now hosts official mirrors of fastText word vectors for 157 languages and a language identification model, simplifying model access and deployment.
Falcon models release and Hugging Face ecosystem integration
Hugging Face announced the Falcon family of open Apache‑2.0 language models, highlighting Falcon‑40B’s top ranking on the Open LLM Leaderboard and showing how to run, quantize, and fine‑tune them with Hugging Face tools.
AI Speech Recognition in Unity
Hugging Face provides a guide and API for implementing state-of-the-art Automatic Speech Recognition (ASR) within Unity games to enable voice commands and NPC interactions.
Hugging Face Open Source AI Game Jam Announcement
Hugging Face is hosting the Open Source AI Game Jam from July 7-9, 2023, to encourage game developers to integrate open-source generative AI tools into their development workflows and game features.
BERTopic Integration with the Hugging Face Hub
The Hugging Face blog post announcing BERTopic integration with the Hub does not provide readable details beyond its title.
Hugging Face LLM Inference Container for Amazon SageMaker
Hugging Face has introduced a purpose-built LLM Inference Container (DLC) for Amazon SageMaker, powered by Text Generation Inference (TGI) to enable high-performance deployment of open-source Large Language Models.
Optimizing Stable Diffusion for Intel CPUs with NNCF and 🤗 Optimum
Hugging Face announced a workflow using NNCF and Optimum to quantize Stable Diffusion and apply Token Merging, achieving up to 5.1x inference speedup and a 4x reduction in model footprint on Intel CPUs.
Hugging Face Model Catalog on Azure Machine Learning
Hugging Face and Microsoft have launched a native integration of the Hugging Face Hub Model Catalog within Azure Machine Learning Studio to simplify the deployment of open-source models on secure Azure infrastructure.
Hugging Face 4-bit Quantization and QLoRA Release
Hugging Face announced 4-bit quantization support via bitsandbytes and the QLoRA method, enabling inference and adapter fine‑tuning of large language models on consumer GPUs.
Instruction-tuning Stable Diffusion with InstructPix2Pix
Hugging Face explores extending InstructPix2Pix to teach Stable Diffusion to follow specific image translation and low-level processing instructions through a multi-task instruction-tuning approach.
Hugging Face Safetensors Security Audit and Default Adoption
Hugging Face, EleutherAI, and Stability AI are transitioning to safetensors as the default model saving format following a security audit by Trail of Bits that confirmed no critical flaws leading to arbitrary code execution.
Hugging Face and IBM Partnership for watsonx.ai
Hugging Face and IBM have partnered to integrate Hugging Face open-source libraries and models into watsonx.ai, an enterprise AI studio for training, tuning, and deploying foundation models.
Q8-Chat: Efficient Generative AI on Intel Xeon CPUs
Hugging Face and Intel demonstrate that using SmoothQuant to compress LLMs to 8-bit integers allows high-quality chat experiences to run efficiently on single-socket Intel Xeon CPUs.
Large-scale Near-deduplication Behind BigCode
Hugging Face details the use of MinHash and Locality Sensitive Hashing (LSH) to perform large-scale near-deduplication for the BigCode project, demonstrating that removing near-duplicate data improves model performance and training efficiency.
RWKV Architecture Integration into Hugging Face Transformers
Hugging Face announced the integration of the RWKV RNN‑Transformer hybrid model into the Transformers library, enabling open‑source, long‑context language models that combine RNN efficiency with transformer performance.
Running Vicuna 13B on AMD GPUs with ROCm
Hugging Face provides a technical guide on deploying the Vicuna 13B open-source chatbot on a single AMD GPU using ROCm and GPTQ 4-bit quantization to reduce memory requirements from 28GB to 7.52GB.
Hugging Face Selected for CNIL Enhanced Support Program
Hugging Face has been selected by the French Data Protection Authority (CNIL) for its Enhanced Support program to improve the implementation of data protection and GDPR compliance in AI development.
Hugging Face Assisted Generation for Low-Latency Text Generation
Hugging Face introduced Assisted Generation, a decoding method that uses a smaller assistant model to predict candidate tokens which are then validated by a larger model, reducing latency by up to 10x in some hardware configurations.
Creating a Coding Assistant with StarCoder – Summary
This article describes how the Hugging Face team turned the 16 B‑parameter StarCoder code‑generation model into a conversational coding assistant (StarChat‑α). By adding special chat tokens, preparing a filtered Open‑Assistant dialogue dataset, and fine‑tuning with DeepSpeed ZeRO‑3, they trained a model that can understand user prompts and generate runnable code (e.g., plots, maps, visualisations). The post also covers token‑izer configuration, loss‑masking for user turns, training setup, evaluation (both benchmark and LLM‑based), limitations, and future directions. The resulting model is publicly available on the Hub.
A Dive into Text-to-Video Models
Hugging Face provides a technical overview of the evolution of text-to-video generative models, detailing the transition from GANs to Transformers and Diffusion architectures while highlighting the unique challenges of temporal consistency and data scarcity.
StarCoder Release Notes
Hugging Face and ServiceNow's BigCode collaboration released StarCoder and StarCoderBase, 15B parameter Code LLMs trained on permissively licensed data that outperform several open and closed models on programming benchmarks.
Hugging Face Unity API Installation and Usage Guide
The Hugging Face Unity API allows developers to integrate Hugging Face Inference API models into Unity projects via a dedicated package and API wizard.
Training Language Models with Hugging Face Transformers, TensorFlow, and TPUs
Hugging Face provides a scalable end-to-end guide for training masked language models from scratch using TensorFlow and TPU pods, leveraging XLA compatibility for high-performance compute.
Databricks and Hugging Face Integrate Apache Spark for Faster LLM Training
Databricks has introduced first-class Apache Spark support in Hugging Face Datasets via the Dataset.from_spark() function, reducing data loading times by up to 40% for large-scale model training and tuning.
Running DeepFloyd IF on Free‑Tier Google Colab with Diffusers
Hugging Face shows how to run the open‑source DeepFloyd IF text‑to‑image model on a free Google Colab notebook by using 8‑bit quantization, modular pipeline loading, and Diffusers‑integrated optimizations.
Hugging Face Launches Chinese Language Blog to Support Global AI Community
Hugging Face has launched a dedicated Chinese language blog (hf.co/blog/zh) to provide translated technical resources and foster deeper collaboration with the Chinese AI community.
Hosting Unity Games on Hugging Face Spaces
Hugging Face Spaces can host playable Unity games by utilizing the Static HTML template and WebGL build targets.
Accelerating Hugging Face Transformers with AWS Inferentia2
Hugging Face and AWS have optimized Transformers for AWS Inferentia2, a purpose-built inference accelerator that significantly reduces latency and increases throughput for large-scale models.
Graph Classification with Transformers
Hugging Face demonstrates how to perform graph classification using the Graphormer model within the Transformers library, covering data formatting, preprocessing, and fine-tuning.
Creating Privacy Preserving AI with Substra
Hugging Face and Substra highlight how federated learning enables the training of AI models across decentralized data sources to maintain privacy and security, particularly in sensitive domains like healthcare.
Snorkel AI and Hugging Face Integration for Enterprise Foundation Models
Snorkel AI has partnered with Hugging Face to integrate Hugging Face Inference Endpoints into Snorkel Flow, allowing enterprises to easily access and adapt over 150,000 open-source foundation models.
StackLLaMA: Training LLaMA with RLHF for Stack Exchange
Hugging Face introduces StackLLaMA, a model trained using Supervised Fine-tuning, Reward Modeling, and Reinforcement Learning from Human Feedback (RLHF) to answer Stack Exchange questions.
Hugging Face Ethics and Society Newsletter #3: Ethical Openness Initiative
Hugging Face announced new ethical openness measures—including six ethics tags, a flagging system, and audience‑guiding metadata—to make open‑source ML safer and more inclusive.
Accelerating Stable Diffusion Inference on Intel Sapphire Rapids CPUs
Hugging Face shows how to cut Stable Diffusion image generation from 32 seconds to about 5 seconds on Intel Sapphire Rapids CPUs using Optimum Intel, OpenVINO, system‑level tweaks, IPEX BF16, and a faster scheduler.
BLOOMZ Inference on Habana Gaudi2 Accelerator
Hugging Face demonstrates that the Habana Gaudi2 accelerator achieves faster inference for the BLOOMZ 176B model than the Nvidia A100 80GB, leveraging the Optimum Habana library and DeepSpeed-inference.
Federated Learning with Hugging Face and Flower
Hugging Face demonstrates how to use the Flower framework to perform federated learning on a pre-trained distilBERT model for sentiment analysis on the IMDB dataset.
Training ControlNet with Hugging Face Diffusers
Hugging Face provides a comprehensive guide and training script via the diffusers library to enable users to train custom ControlNet models for Stable Diffusion, demonstrated through the creation of an 'Uncanny Faces' pose model.
Hugging Face Hub Improved Jupyter Notebook Support
Hugging Face has introduced human-readable rendering for Jupyter notebooks hosted on the Hub, improving reproducibility and accessibility for machine learning practitioners.
Multivariate Probabilistic Time Series Forecasting with Informer
Hugging Face has integrated the Informer model into the Transformers library to enable efficient multivariate probabilistic time series forecasting with reduced computational and memory complexity.
Fine-tuning 20B LLMs with RLHF on a 24GB Consumer GPU
Hugging Face released an integration of TRL and PEFT that enables reinforcement learning fine‑tuning of 20‑billion‑parameter language models on a single 24 GB GPU using 8‑bit quantization and low‑rank adapters.
Kakao Brain ViT and ALIGN Models Release with COYO 700M Dataset
Kakao Brain and Hugging Face released open‑source ViT and ALIGN visual‑language models trained on the new 700 M image‑text COYO dataset, providing the first publicly available ALIGN model and ViT models with reproducible training data.
ControlNet in Diffusers
Hugging Face has integrated ControlNet into the Diffusers library, enabling precise spatial control over Stable Diffusion image generation using conditionings like Canny edges, depth maps, and human poses.
Using Machine Learning for Disaster Response: The afetharita Project
Hugging Face describes how volunteers used the Hugging Face ecosystem to rapidly deploy ML models for OCR, NER, and remote sensing to aid survivors of the February 2023 Turkey earthquakes.
Hugging Face Diffusers Ethical Guidelines
Hugging Face has introduced an ethical framework for the Diffusers library to guide technical decisions and community contributions while mitigating the potential societal risks of diffusion models.
Hugging Face Expert Acceleration Program: Witty Works Case Study
Witty Works utilized the Hugging Face Expert Acceleration Program and SetFit to build a context-dependent inclusive language classifier with high accuracy using minimal labeled data.
Swift Diffusers for Mac 1.1 Release
Hugging Face has released Diffusers for Mac version 1.1, a native open-source app that leverages Core ML to accelerate Stable Diffusion on Apple Silicon, offering up to 2x faster generation on certain hardware configurations.
Red-Teaming Large Language Models
Hugging Face outlines the critical role of red-teaming in identifying LLM vulnerabilities to prevent harmful outputs, emphasizing the need for collaborative, adaptive evaluation methods.
Fetch AI Infrastructure Migration: Consolidating Tools with Hugging Face and AWS
Fetch reduced development time by 30% and processing latency by 50% by migrating from a third-party AI 'black box' to an in-house ML pipeline powered by Hugging Face and AWS.
Hugging Face and AWS Strategic Partnership for AI Accessibility
Hugging Face and AWS have expanded their strategic partnership to democratize generative AI by integrating Hugging Face models with AWS infrastructure and purpose-built ML accelerators.