PatchTST Integration in Hugging Face
Hugging Face has integrated PatchTST, a Transformer-based model that uses time series patching and channel-independence to improve long-term forecasting and enable transfer learning.
Hugging Face Text Generation Inference now supports AWS Inferentia2
Hugging Face announced the general availability of Text Generation Inference on AWS Inferentia2 via Amazon SageMaker, enabling cost‑effective, high‑throughput LLM serving as an alternative to GPU deployments.
Constitutional AI with Open LLMs
Hugging Face introduces an end-to-end recipe and the llm-swarm tool to implement Constitutional AI (CAI) on open models, enabling scalable self-alignment based on user-defined principles without expensive human feedback.
Enterprise Scenarios Leaderboard: Evaluating LLMs for Real-World Use Cases
Hugging Face and Patronus AI have launched the Enterprise Scenarios Leaderboard to evaluate language models on six real-world business tasks, moving beyond academic benchmarks to measure practical enterprise utility.
Accelerating StarCoder on Intel Xeon with Optimum Intel
Hugging Face and Intel demonstrate over 7x inference acceleration for the StarCoder-15B model on 4th Gen Intel Xeon processors by combining 8-bit quantization and assisted generation.
Hugging Face Hallucinations Leaderboard launch and initial findings
Hugging Face launched the Hallucinations Leaderboard to benchmark LLMs on factuality and faithfulness errors across multiple open-source datasets, offering transparent rankings that guide model selection and research.
AI Secure LLM Safety Leaderboard
Hugging Face and the Secure Learning Lab have released the LLM Safety Leaderboard, powered by the DecodingTrust framework to evaluate LLM trustworthiness across eight critical safety dimensions.
Hugging Face and Google Cloud Strategic Partnership
Hugging Face and Google Cloud have entered a strategic partnership to democratize machine learning by integrating open models with Google Cloud's AI infrastructure and hardware.
Open-source LLMs as LangChain Agents
Hugging Face demonstrates that open-source LLMs, specifically Mixtral-8x7B, are now capable of powering agent workflows and can outperform GPT-3.5 in general-purpose reasoning tasks.
Fine-Tuning Wav2Vec2-BERT for Low-Resource ASR
Hugging Face demonstrates how to fine-tune Meta's Wav2Vec2-BERT model for Automatic Speech Recognition (ASR) in low-resource languages, achieving performance comparable to Whisper-large-v3 while being significantly faster and more resource-efficient.
PatchTSMixer added to Hugging Face Transformers – release and quick‑start guide
PatchTSMixer, a lightweight MLP‑Mixer time‑series model from IBM Research, is now released in Hugging Face Transformers, offering state‑of‑the‑art forecasting with far lower memory and runtime costs.
Preference Tuning LLMs with Direct Preference Optimization Methods – Empirical Comparison of DPO, IPO, and KTO
Hugging Face evaluated DPO, IPO and KTO alignment methods on two 7B chat models, showing DPO consistently outperforms the others when the beta hyper‑parameter is properly tuned.
Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive
Hugging Face and Microsoft introduce optimizations using ONNX Runtime and Olive to achieve throughput gains up to 229% for SDXL Turbo and 120% for SD Turbo compared to PyTorch.
Run ComfyUI Workflows on Hugging Face Spaces with Gradio
Hugging Face provides a guide to converting complex ComfyUI workflows into Gradio applications for free, serverless deployment on Hugging Face Spaces ZeroGPU.
Hugging Face Leaderboard Templates: Implementing the Vectara HHEM Leaderboard
Hugging Face has released open-source leaderboard templates that enable developers to build dynamic LLM evaluation boards, as demonstrated by Vectara's new Hughes Hallucination Evaluation Model (HHEM) leaderboard.
Unsloth and Hugging Face TRL Integration for Faster LLM Fine-tuning
Unsloth is a lightweight library that accelerates LLM fine-tuning by up to 2.7x and reduces memory usage by up to 74% with 0% accuracy degradation compared to QLoRA.
aMUSEd: Efficient Text-to-Image Generation
Hugging Face has released aMUSEd, an efficient non-diffusion text-to-image model based on Masked Image Modeling (MIM) and an open reproduction of Google's MUSE.
Hugging Face SDXL Dreambooth LoRA Advanced Training Guide
Hugging Face introduces an advanced training script for SDXL Dreambooth LoRAs, combining Pivotal Tuning and the Prodigy optimizer to improve concept capture and image quality.
Speculative Decoding Enables 2× Faster Whisper Inference
Hugging Face demonstrates that speculative decoding halves Whisper transcription latency while preserving identical outputs and accuracy.
2023 Year of Open LLMs Review
Hugging Face’s 2023 recap shows a surge of open‑source LLM releases, smaller high‑performing models, and new fine‑tuning techniques that dramatically broaden access and community participation.
Mixture of Experts (MoE) Explained
Mixture of Experts (MoE) allows transformer models to scale parameters while maintaining efficient pretraining and faster inference by activating only a subset of neural network experts per token.
Mixtral 8x7B Release Notes
Mistral AI has released Mixtral 8x7B, a Mixture of Experts (MoE) model that outperforms Llama 2 70B and matches GPT-3.5 performance on most benchmarks while remaining commercially permissive under Apache 2.0.
SetFitABSA: Few-Shot Aspect Based Sentiment Analysis
Hugging Face and Intel Labs introduced SetFitABSA, a prompt-less, few-shot framework for Aspect-Based Sentiment Analysis that outperforms larger generative models like Llama 2 and T5 in low-data scenarios.
Optimum-NVIDIA Release Notes
Hugging Face has released Optimum-NVIDIA, an inference library that accelerates LLM inference on NVIDIA platforms by up to 28x using FP8 quantization and TensorRT-LLM.
Hugging Face LoRA dynamic loading speeds inference 300% and cuts latency
Hugging Face announced a dynamic LoRA loading system that reduces warm‑up time from 25 s to 3 s, delivering up to 300 % faster LoRA inference and cutting total response time from 35 s to 13 s.
Hugging Face and AMD GPU Acceleration for LLMs
Hugging Face and AMD have integrated out-of-the-box support for AMD Instinct GPUs into the Transformers library and Text Generation Inference, enabling high-performance LLM execution without code changes.
Hugging Face Open LLM Leaderboard DROP Benchmark Analysis
Hugging Face has removed the DROP benchmark from the Open LLM Leaderboard after discovering that flawed normalization and stop-token configurations caused most models to score incorrectly low.
SDXL and Stable Diffusion Fast Inference with Latent Consistency LoRAs
Hugging Face introduces LCM LoRAs, a method to enable high-quality image generation in 4 to 8 steps for SDXL and Stable Diffusion models, significantly reducing inference time.
Prodigy-HF Integration Release Notes
Explosion has released Prodigy-HF, a plugin that enables direct fine-tuning of Hugging Face transformer models on annotated data and the ability to upload datasets directly to the Hugging Face Hub.
Deploying Llama 2 on AWS Inferentia2 with optimum-neuron
Hugging Face has integrated optimum-neuron with the AWS Neuron SDK to enable the deployment of Llama 2 models on AWS Inferentia2 accelerators for high-performance text generation.
Comparing RoBERTa, Llama 2, and Mistral for Disaster Tweet Classification with LoRA
A comparative study reveals that the smaller RoBERTa model outperforms Llama 2 and Mistral 7B in binary classification of disaster tweets when fine-tuned using Low-Rank Adaptation (LoRA).
Hugging Face Hub Storage Regions
Hugging Face has introduced Storage Regions for Enterprise Hub customers, allowing organizations to select where their models and datasets are stored to improve regulatory compliance and data transfer performance.
Personal Copilot: Train Your Own Coding Assistant
Hugging Face demonstrates how to create a personalized coding assistant, HugCoder, by fine-tuning StarCoder on a specific codebase using QLoRA and full fine-tuning techniques.
Hugging Face and Renumics Spotlight Integration for Scalable Data Inspection
Hugging Face has integrated with Renumics Spotlight to enable interactive, one-line-of-code visualization and inspection of ML datasets, including support for multimodal data and model results.
Optimizing Stable Diffusion XL (SDXL) for Inference Speed and Memory
Hugging Face explores several optimization techniques for Stable Diffusion XL (SDXL), demonstrating how to reduce memory usage from 28GB to as low as 11.47GB and decrease inference latency from 72.2 seconds to approximately 10.3 seconds.
Deploying Embedding Models with Hugging Face Inference Endpoints
Hugging Face introduces Text Embeddings Inference (TEI) via Inference Endpoints, providing a high-performance, cost-efficient way to deploy open-source embedding models for RAG and semantic search.
The N Implementation Details of RLHF with PPO – Hugging Face Blog Summary
The Hugging Face blog post reproduces OpenAI’s 2019 RLHF codebase, matches its learning curves, and details N implementation specifics, including a key PyTorch Adam optimizer difference that causes more aggressive updates.
Gradio-Lite: Serverless Gradio Running Entirely in Your Browser
Hugging Face introduces Gradio-Lite (@gradio/lite), a JavaScript library that uses Pyodide to run Gradio applications directly in the web browser, eliminating the need for server-side infrastructure.
Accelerating Hugging Face Models with ONNX Runtime
Hugging Face and ONNX Runtime enable performance acceleration for over 130,000 models, including a latency reduction of up to 74.30% for the whisper-tiny model compared to PyTorch.
Hugging Face Chat Templates
Hugging Face introduced chat templates as a Jinja-based system to ensure chat models receive inputs formatted exactly as they were during training, preventing silent performance degradation.
Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e
Hugging Face Diffusers now supports serving Stable Diffusion XL (SDXL) using JAX on Cloud TPU v5e, delivering up to 2.4x greater performance per dollar compared to TPU v4.
Deploying AI Comic Factory via Hugging Face Inference API
Hugging Face provides a guide on deploying a private instance of the AI Comic Factory using the Inference API, leveraging Llama-2 and SDXL 1.0 models.
Finetuning Stable Diffusion with DDPO via TRL
Hugging Face has integrated Denoising Diffusion Policy Optimization (DDPO) into the TRL library, enabling the alignment of Stable Diffusion models with human preferences using reinforcement learning.
Hugging Face Ethics and Society Update Summer 2023
Hugging Face detailed its Summer 2023 efforts to influence AI regulation in the US, EU, and UK, while advancing open-source ethics through public advocacy and technical research.
Hugging Face Guide: Training a LLaMA 2 Chatbot Without Code
Hugging Face provides a no-code workflow using Spaces, AutoTrain, and ChatUI to allow non-engineers to fine-tune LLaMA 2 and deploy it as a functional chat application.
Llama 2 on Amazon SageMaker Benchmark
Hugging Face analyzed 60 deployment configurations for Llama 2 on Amazon SageMaker to identify optimal setups for cost, throughput, and latency.
Hugging Face Inference for PROs
Hugging Face has introduced Inference for PRO users, providing accelerated API endpoints for curated state-of-the-art models and increased rate limits for the free Inference API.
Rocket Money x Hugging Face: Scaling Volatile ML Models in Production
Rocket Money scaled its transaction classification system to over a billion transactions per month using Hugging Face's Inference API to replace a legacy regex-based system.
Introduction to 3D Gaussian Splatting
3D Gaussian Splatting is a rasterization technique that enables real-time rendering of photorealistic 3D scenes learned from a small set of images.
Hugging Face Object Detection Leaderboard
Hugging Face released an Object Detection Leaderboard that ranks open-source models using COCO-style metrics and published a blog explaining how Average Precision and Average Recall are computed and what factors can influence the results.