Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic
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Profiling in PyTorch: A Beginner's Guide to torch.profiler
Hugging Face provides a comprehensive guide to using torch.profiler to identify bottlenecks, understand the CPU-GPU dispatch chain, and analyze the impact of torch.compile on kernel execution.
Reachy Mini Local Speech Backend Integration
Hugging Face has released a local speech-to-speech pipeline for Reachy Mini, allowing the robot to handle conversations fully locally using a cascaded VAD, STT, LLM, and TTS stack.
Delta Weight Sync in TRL Enables Trillion-Parameter Model Training with Minimal Bandwidth
Hugging Face announced Delta Weight Sync in TRL, a feature that reduces weight synchronization bandwidth in async RL training by over 100x by transmitting only sparse weight changes via Hugging Face Buckets, enabling disaggregated training without shared clusters.
Hugging Face AI Agent Glossary: Defining Harness, Scaffold, and Agent Architecture
Hugging Face provides a standardized vocabulary for AI agents, defining the agent as the combination of a model, a harness for execution, and scaffolding for behavior definition.
OlmoEarth v1.1 release notes / what's new
Hugging Face and AllenAI have released OlmoEarth v1.1, a family of Earth observation models that reduces compute costs by up to 3x while maintaining performance similar to v1.
Ettin Reranker Family v1 release notes
Hugging Face released the Ettin Reranker family, six CrossEncoder rerankers from 17M to 1B parameters distilled from mxbai-rerank-large-v2, achieving state-of-the-art retrieval reranking quality and speed.
PaddleOCR 3.5 release notes / what's new
PaddleOCR 3.5 introduces Hugging Face Transformers as a supported inference backend, allowing OCR and document parsing models to integrate more seamlessly into PyTorch-based workflows.
Granite Embedding Multilingual R2 Release: Open Apache 2.0 Multilingual Embeddings with 32K Context
IBM Granite released two Apache 2.0 multilingual embedding models—granite-embedding-97m-multilingual-r2 (97M) and granite-embedding-311m-multilingual-r2 (311M)—with 32K-token context, 200+ language support, and top sub-100M retrieval scores.
Unlocking asynchronicity in continuous batching
Hugging Face shows how to overlap CPU and GPU work in continuous batching using CUDA streams and events, cutting LLM inference time by about 22% for an 8B model generating 8K tokens.
Building Blocks for Foundation Model Training and Inference on AWS
Hugging Face details a four-layer architectural framework on AWS—comprising infrastructure, resource orchestration, ML software stacks, and observability—to support the evolving scaling laws of pre-training, post-training, and test-time compute.
vLLM V1 Migration: Ensuring Backend Correctness in Reinforcement Learning
ServiceNow AI achieved parity between vLLM V0 and V1 for RL rollout generation by fixing logprob semantics, runtime defaults, weight update paths, and implementing an fp32 lm_head.
Hugging Face Open ASR Leaderboard: Private Datasets to Combat Benchmaxxing
Hugging Face has introduced private evaluation datasets from Appen Inc. and DataoceanAI to the Open ASR Leaderboard to prevent test-set contamination and provide a more robust measure of real-world ASR performance.
IBM Granite 4.1 LLMs release notes / technical overview
IBM has released Granite 4.1, a family of dense, decoder-only LLMs (3B, 8B, and 30B) that achieve high performance through rigorous data curation and a multi-stage reinforcement learning pipeline.
DeepInfra Integration with Hugging Face Inference Providers
Hugging Face has added DeepInfra as a supported Inference Provider, enabling serverless access to over 100 models, including DeepSeek V4 and Kimi-K2.6, directly through the Hub and client SDKs.
NVIDIA Nemotron 3 Nano Omni release notes / what's new
NVIDIA has released Nemotron 3 Nano Omni, an omni-modal model capable of long-context reasoning across text, images, video, and audio, delivering best-in-class accuracy on document intelligence and video understanding benchmarks.
OpenAI Privacy Filter: Building Scalable PII Detection Web Apps
OpenAI has released Privacy Filter, an open-source 1.5B-parameter PII detector capable of labeling eight categories of sensitive data across a 128k context window.
DeepSeek-V4 Release Notes: Efficient 1M-Token Context for AI Agents
DeepSeek-V4 introduces a 1M-token context window powered by a hybrid CSA/HCA attention mechanism, specifically optimized for long-running agentic workloads and tool-use trajectories.
Using Transformers.js in a Chrome Extension
Hugging Face provides a technical guide on integrating Transformers.js into a Chrome Extension using Manifest V3, featuring a background-hosted model architecture powered by Gemma 4 E2B.
QIMMA: A Quality-First Arabic LLM Leaderboard
Hugging Face and partners introduced QIMMA, a new Arabic LLM leaderboard that implements a rigorous quality validation pipeline to ensure benchmarks reflect genuine language capability.
Hugging Face: AI and the Future of Cybersecurity
Hugging Face argues that open-source AI models and tooling are critical for cybersecurity defense to counter the risks posed by autonomous vulnerability-finding systems like Mythos.
Hugging Face transformers-to-mlx Skill and Test Harness
Hugging Face has released a Skill and a non-agentic test harness to streamline the porting of language models from the transformers library to mlx-lm while maintaining high code quality and reviewer signal.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers
Hugging Face published a guide on training and finetuning multimodal embedding and reranker models with Sentence Transformers, demonstrating how task-specific finetuning improves performance on retrieval tasks such as Visual Document Retrieval.
Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents
Hugging Face introduces EcomRLVE-GYM, a framework for training e-commerce agents using eight verifiable, multi-turn environments with adaptive difficulty scaling to bridge the gap between conversational fluency and actual task completion.
Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents
IBM Research introduced VAKRA, an executable benchmark that evaluates AI agents' compositional reasoning across APIs and documents, revealing significant gaps in tool use and policy adherence despite advances in isolated tool calls.
HCompany HoloTab Release
HCompany has released HoloTab, a Chrome extension powered by the Holo3 model that allows users to automate web tasks through natural language descriptions or recorded routines.
Sentence Transformers v5.4: Multimodal Embedding and Reranker Models
Sentence Transformers v5.4 introduces multimodal embedding and reranker models for encoding and comparing text, images, audio, and video.
Waypoint-1.5 Release Notes
Hugging Face and Overworld have released Waypoint-1.5, a real-time video world model that enables high-fidelity interactive generative environments to run locally on consumer GPUs.
Safetensors Joins the PyTorch Foundation
Safetensors has transitioned to a foundation-hosted project under the PyTorch Foundation and Linux Foundation to ensure vendor-neutral governance and community-driven development.
Gemma 4 release: open‑source multimodal models with on‑device support
Gemma 4, a new open‑source multimodal family from Google DeepMind, is released on Hugging Face with image, audio, video support, up to 256 K context, and day‑0 compatibility with transformers, llama.cpp, MLX, Rust, and WebGPU.
Falcon Perception and Falcon OCR Release
Hugging Face and TII introduce Falcon Perception, a 0.6B-parameter early-fusion Transformer for open-vocabulary grounding, and Falcon OCR, a 0.3B-parameter high-throughput document understanding model.
Gradio Server: Integrating Custom Frontends with Gradio Backend
Hugging Face introduces gradio.Server, a FastAPI extension that allows developers to use any custom frontend framework while retaining Gradio's queuing, API infrastructure, and ZeroGPU support.
Granite 4.0 3B Vision release notes / what's new
IBM has released Granite 4.0 3B Vision, a compact multimodal model optimized for enterprise document understanding, featuring high-accuracy table extraction, chart reasoning, and key-value pair extraction.
OpenMed CodonRoBERTa multi-species mRNA language models release
OpenMed released an end-to-end protein engineering pipeline with CodonRoBERTa-large-v2 (perplexity 4.10, CAI 0.404) and a 25-species codon‑optimization model suite trained in 55 GPU‑hours for $165.
TRL v1.0 release notes / what's new
Hugging Face releases TRL v1.0, a stable post-training library implementing over 75 methods, featuring a dual-track stability model to balance rapid experimental iteration with production-grade reliability.
Hugging Face OpenClaw Migration Guide
Hugging Face provides two methods—Inference Providers and local llama.cpp setup—to migrate OpenClaw agents from restricted Claude models to open-source alternatives.
EVA End-to-End Evaluation Framework for Voice Agents
EVA is a new end‑to‑end framework that jointly evaluates voice agents on accuracy and conversational experience, revealing a consistent trade‑off between task success and user satisfaction.
Domain-Specific Embedding Fine-Tuning with NVIDIA Nemotron – Under a Day
NVIDIA and Hugging Face released a single‑GPU, under‑a‑day pipeline that fine‑tunes the Llama‑Nemotron‑Embed‑1B‑v2 model on synthetic domain data, delivering >10% retrieval gains and up to 26% improvement on real enterprise datasets.
State of Open Source on Hugging Face: Spring 2026
Hugging Face reports a massive expansion of the open source AI ecosystem in 2025, characterized by China surpassing the U.S. in model downloads and the rapid emergence of robotics as the largest dataset category.
Holotron-12B High Throughput Computer Use Agent
H Company released Holotron-12B, a multimodal computer-use model based on NVIDIA Nemotron-Nano-2 VL that uses a hybrid SSM-Attention architecture to achieve high inference throughput for agentic workloads.
Hugging Face Storage Buckets Release
Hugging Face has introduced Storage Buckets, a mutable, S3-like object storage system backed by Xet for efficient handling of intermediate ML artifacts like checkpoints and processed data.
Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries
Hugging Face surveyed 16 open-source RL libraries and found that async RL training separates inference and training onto different GPU pools, uses a rollout buffer, and pushes weights asynchronously, with Ray dominating orchestration and NCCL broadcast the common weight‑sync method.
LeRobot v0.5.0 release notes / what's new
Hugging Face has released LeRobot v0.5.0, introducing full Unitree G1 humanoid support, new VLA policies like Pi0-FAST and Wall-X, and significant dataset performance optimizations.
Ulysses Sequence Parallelism for Million-Token Context Training
Hugging Face has integrated Ulysses Sequence Parallelism into Accelerate, Transformers, and TRL, enabling the training of LLMs with million-token contexts by distributing attention computation across multiple GPUs.
Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine-Tuning, and On-Device Optimizations
Hugging Face and NXP provide a technical guide on deploying Vision-Language-Action (VLA) models on the i.MX 95 SoC, emphasizing dataset consistency, architectural decomposition, and asynchronous inference to achieve real-time robotic control.
Hugging Face Modular Diffusers Release
Hugging Face has introduced Modular Diffusers, a composable framework that allows users to build diffusion pipelines by mixing and matching reusable blocks rather than writing entire pipelines from scratch.
PRX Part 3: Training a Text-to-Image Model in 24 Hours
Hugging Face and Photoroom demonstrate a text-to-image model trained in 24 hours using 32 H200 GPUs on a $1500 budget, combining pixel-space training, token routing, and representation alignment.
Mixture of Experts (MoEs) in Transformers
Hugging Face has redesigned the transformers library to make Mixture of Experts (MoEs) first-class citizens through a new weight loading refactor, a pluggable expert backend, and native expert parallelism.
Train AI models with Unsloth and Hugging Face Jobs
Hugging Face has integrated Unsloth with Hugging Face Jobs to enable fast, low-cost LLM fine-tuning, specifically optimized for small models like LiquidAI/LFM2.5-1.2B-Instruct.
GGML and llama.cpp join Hugging Face
GGML, the creators of llama.cpp, have joined Hugging Face to provide sustainable resources for local AI inference and streamline the integration between the Transformers library and local model deployment.