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Hugging Face
AI platform and community providing machine learning models, datasets, libraries, and tools for building AI applications.
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Sentence Transformers is joining Hugging Face!
Sentence Transformers is officially transitioning its stewardship from the Ubiquitous Knowledge Processing Lab at TU Darmstadt to Hugging Face. The open-source embedding library was introduced in 2019 by Nils Reimers using a Siamese network architecture to overcome standard BERT limitations on sentence-level semantics. Tom Aarsen of Hugging Face has maintained the project since late 2023 and will continue leading its development. Under Hugging Face, the project retains its Apache 2.0 license and leverages infrastructure such as continuous integration and automated testing. Sentence Transformers currently serves over one million monthly unique users, with more than 16,000 community models publicly hosted on the Hugging Face Hub.
Tom AarsenUnlock the power of images with AI Sheets
Hugging Face released an update to AI Sheets, an open-source tool for transforming and enriching datasets without code. The update adds vision support powered by Inference Providers, enabling users to upload images, extract structured data, generate visuals, and perform image edits in a spreadsheet interface. Each column can execute AI actions configured with custom prompts and selected models. For text extraction and reasoning tasks, AI Sheets supports vision language models including Qwen/Qwen2.5-VL-7B-Instruct and Qwen/Qwen3-VL-235B-A22B-Reasoning, while image transformations use models like Qwen-Image-Edit. Users can iterate on prompts, provide feedback using thumbs-up ratings to create few-shot examples, and export completed datasets to the Hugging Face Hub, CSV, or Parquet.
Ame Vi, Daniel Vila, Francisco Aranda, Damián Pumar, Leandro von Werra, Thomas WolfAI for Food Allergies
Global food allergies affect an estimated 220 million individuals, yet computational progress in biomedical discovery remains constrained by fragmented and inaccessible scientific data. The AI for Food Allergies initiative addresses this challenge by establishing an open research community and releasing the curated Awesome Food Allergy Datasets collection across multiple biological layers. Computational pipelines leverage deep learning models, such as AllergenAI and NetAllergen-1.0, which incorporate sequence motifs and computationally predicted MHC class II presentation propensities to evaluate allergenicity. Additionally, molecular property prediction benchmarks like QM9 provide high-accuracy quantum-mechanical properties for approximately 134,000 molecules, supporting generative and virtual screening workflows targeting IgE–FcεRI binding. These combined efforts systematically structure molecular, clinical, and chemogenomic resources to accelerate allergy diagnostics and therapeutic protein engineering.
Ludovico Comito, Antonis Vozikis, Vaibhav Pandey, Kisejjere RashidGoogle Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face
Intel and Hugging Face benchmarked OpenAI GPT OSS text generation to evaluate performance on Google Cloud C4 virtual machines powered by Intel Xeon 6 processors against previous-generation C3 instances. The benchmark evaluated the unsloth/gpt-oss-120b-BF16 Mixture of Experts model across batch sizes from 1 to 64 using bfloat16 precision, static KV cache, and SDPA attention. To eliminate redundant computation, Hugging Face merged an optimization directing each expert sub-network to process only its routed tokens rather than all tokens. Testing demonstrated that C4 instances deliver 1.4x to 1.7x higher throughput per vCPU compared to C3 instances. At a batch size of 64, C4 achieved a 1.7x Total Cost of Ownership improvement alongside lower hourly pricing.
Jiqing.Feng, Matrix Yao, Ke Ding, Ilyas MoutawwakilGet your VLM running in 3 simple steps on Intel CPUs
Deploying Vision Language Models locally presents computational challenges despite providing data privacy and independence from external servers. Optimum Intel and OpenVINO address this by optimizing lightweight models like SmolVLM2-256M for low-resource hardware, including Intel CPUs and GPUs. The process involves exporting the model to the OpenVINO Intermediate Representation format, applying 8-bit weight-only or static quantization, and executing inference with the transformed model. In single-image CPU benchmarks on an Intel Core Ultra 7 265K processor, OpenVINO with 8-bit weight-only quantization reduced time to first token from 5.150 seconds under PyTorch down to 0.247 seconds. The optimized configuration also raised decoding throughput from 0.722 to 63.928 tokens per second.
Ezequiel Lanza, Helena, Nikita, Ella Charlaix, Ilyas MoutawwakilNemotron-Personas-India: Synthesized Data for Sovereign AI
Most open artificial intelligence datasets reflect Western norms and English-only contexts, limiting model effectiveness across India's multilingual and multi-script landscape. To address this shortage of culturally grounded training data, NVIDIA released Nemotron-Personas-India under a CC BY 4.0 license. The synthetic dataset contains 21 million personas across 3 million records spanning 7.7 billion tokens in English, Devanagari Hindi, and Latin-script Hindi. The generation pipeline utilized NeMo Data Designer, an Apache-2.0 licensed Probabilistic Graphical Model for demographic grounding against 2011 Census distributions, and GPT-OSS-120B for narrative text. All records are entirely synthetic to eliminate re-identification risks while enabling fine-tuning for region-aware AI agents and domain-specific copilots.
Kiran Praveen, Utkarsh Vaidya, Evan A, Lipika Ramaswamy, Dhruv Nathawani, Dane Corneil, Yev MeyerArm will be @ PyTorch Conference, Join Us!
Arm announced its schedule and offerings for the PyTorch Conference on October 22–23, focusing on building and deploying AI applications using PyTorch and ExecuTorch. The company is hosting one-on-one workshops with design experts to improve product usability and introduce Yellow Teaming practices for mitigating fairness, transparency, privacy, and security risks. Additionally, Arm is running 30-minute Voice of the Developer sessions to gather feedback on real-world challenges, including migrating from NVIDIA or x86 to Arm, debugging models at scale, and running large language models on edge devices. At Booth P1, attendees can view interactive demonstrations featuring neural graphics training, speech recognition, audio generation, vLLM, Mixture of Experts, and ExecuTorch edge deployments.
EricSondhiBigCodeArena: Judging code generations end to end with code executions
Assessing AI-generated code via static inspection is error-prone and fails to reflect whether code functions correctly across real-world environments. BigCodeArena addresses this evaluation bottleneck as a human-in-the-loop platform that executes model outputs in real time inside isolated sandbox environments across ten programming languages and eight execution frameworks. Users interact directly with live applications, refine implementations through multi-turn conversations, and submit pairwise preference votes to generate Elo ratings via the Bradley-Terry model. In five months of community testing covering over 14,000 conversations, proprietary models including o3-mini and o1-mini led overall Elo rankings, while open models like Qwen2.5 and Llama-3.3-70B trailed closed competitors. The project has open-sourced its full evaluation pipelines, conversation datasets, and related benchmarks to support community-driven code generation assessment.
Terry Yue ZhuoSOTA OCR with Core ML and dots.ocr
Running competitive models like RedNote's 3B parameter dots.ocr on-device provides zero-cost, network-free optical character recognition while leveraging power-efficient hardware like Apple's Neural Engine. Because the Neural Engine requires Core ML, converting the 1.2B parameter NaViT vision encoder from PyTorch requires resolving multiple graph tracing incompatibilities. The initial conversion pipeline targets FLOAT32 execution on GPU by capturing the execution graph with torch.jit.trace and compiling it via coremltools. Simplifying the model for single-image inference eliminates dynamic sequence masking, dynamic tensor iteration, and multi-attention complexity. Although the resulting Core ML model accurately matches PyTorch baseline numerical precision, the unoptimized artifact spans over 5GB and takes over one second per forward pass.
Christopher Fleetwood, Pedro CuencaIntroducing RTEB: A New Standard for Retrieval Evaluation
Public embedding benchmarks often fail to measure true generalization because training sets overlap with public evaluation data, rewarding memorization over general search quality. The Retrieval Embedding Benchmark (RTEB) beta introduces a standardized evaluation framework designed for real-world enterprise retrieval applications. To prevent overfitting while preserving transparency, RTEB combines fully public datasets with private evaluation sets managed exclusively by MTEB maintainers. The suite spans 20 languages across domains like healthcare, law, finance, and code, measuring ranked retrieval quality using NDCG@10 on datasets of at least 1,000 documents. While currently limited to text-only retrieval and containing repurposed question-answering datasets, the benchmark provides clear signals when models degrade on unseen data.
Frank Liu, Kenneth Enevoldsen, Solomatin Roman, Isaac Chung, Tom Aarsen, Fődi, ZoltánAccelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models
Local agentic AI workflows generate multi-step reasoning traces that increase token counts and strain real-time inference on personal computers. To accelerate execution, researchers implemented speculative decoding using OpenVINO.GenAI on an Intel Lunar Lake integrated GPU, pairing a 4-bit Qwen3-8B target model with a smaller Qwen3-0.6B draft model. By measuring angular distance across layers, the team pruned six of the draft model's twenty-eight layers to minimize draft latency and fine-tuned it on synthetic data generated from 500,000 prompts. This depth-pruned draft elevated speculative decoding throughput from a 1.3x speedup to a 1.4x speedup over the standalone baseline. Finally, deploying the optimized model alongside the Hugging Face smolagents framework allowed local agents to perform tool invocation and code generation tasks efficiently.
Igor Margulis, Ofir Zafrir, Shira Guskin, Guy Boudoukh, Pedro CuencaVibeGame: Exploring Vibe Coding Games
Vibe coding games often fails as projects grow because large context sizes degrade model performance. After evaluating Roblox MCP, Unity MCP, and open web libraries with Claude Code, the author found web tooling offered the strongest model proficiency but lacked high-level game abstractions. To bridge this gap, the author created VibeGame, an open-source declarative game engine built on three.js, rapier, and bitecs. VibeGame uses an Entity-Component-System architecture, XML-like markup, and bundled documentation in an llms.txt file to keep project context lean. While effective for basic physics and rendering mechanics, the engine requires further development for complex features like inventories, multiplayer, and interactions.
Dylan EbertSwift Transformers Reaches 1.0 – and Looks to the Future
swift-transformers has released version 1.0 to provide a stable foundation for running local models on Apple Silicon platforms. The library supplies components missing from Core ML and MLX, focusing heavily on tokenization, chat templates, and Hugging Face Hub integration. In this major release, Tokenizers and Hub become standalone top-level modules, enabling developers to import only the components they require. The update also integrates an updated swift-jinja library for faster template processing, adopts Modern Core ML stateful models and MLTensor APIs, removes unnecessary CLI dependencies, and adds full Swift 6 support. Future development will prioritize deeper integration with MLX frameworks and agentic workflows like MCP.
Pedro Cuenca, Christopher Fleetwood, Mattt, Vaibhav SrivastavSmol2Operator: Post-Training GUI Agents for Computer Use
Lightweight vision-language models often lack the precise visual grounding and action execution capabilities required for graphical user interface automation. To resolve this deficiency, the authors constructed a post-training pipeline that evolves base models into agentic GUI coders through supervised fine-tuning. The process standardizes disparate mobile and desktop dataset action spaces into a unified functional format utilizing normalized coordinates relative to image size. Training is structured into two sequential phases that first establish basic visual perception and subsequently introduce explicit reasoning steps with masked assistant loss. On the ScreenSpot-v2 perception benchmark, fine-tuning SmolVLM2-2.2B-Instruct increased element localization accuracy to 61.71 percent, while a compact 460-million-parameter model achieved approximately 58 percent accuracy.
Amir Mahla, merve, Sergio Paniego, Vaibhav Srivastav, Lewis TunstallSyGra: The One-Stop Framework for Building Data for LLMs and SLMs
Training and aligning large language models and small language models requires datasets tailored for complex reasoning, preference tuning, multi-turn questioning, and format conversion. To eliminate the need for bespoke data preparation scripts, SyGra provides a low-code and no-code Python framework for automated dataset creation, transformation, and alignment. The library integrates with diverse inference backends, including vLLM, Hugging Face TGI, Triton, and Ollama, allowing teams to focus on prompt engineering rather than pipeline infrastructure. It supports data workflows ranging from converting PDFs and knowledge bases into question-answering pairs to filtering low-quality samples and generating Direct Preference Optimization datasets. These plug-and-play workflows reduce manual curation effort while accelerating model fine-tuning and retrieval-augmented generation pipelines.
Bidyapati Pradhan, Vipul Mittal, Amit Kumar Saha, Surajit DasguptaGaia2 and ARE: Empowering the community to study agents
Existing AI agent evaluation environments are often tightly coupled to specific tasks and fail to model real-world challenges such as API failures, spontaneous events, and asynchronous conditions. To address this limitation, the Gaia2 benchmark and the Meta Agents Research Environments (ARE) framework introduce interactive read-and-write evaluation. Gaia2 incorporates 1,000 human-created scenarios spanning multi-step execution, cross-source search, ambiguity handling, adaptability, temporal reasoning, agent collaboration, and noise tolerance. Using a simulated smartphone interface equipped with 101 tools, evaluations showed that GPT-5 with high reasoning scored highest overall, while Kimi K2 was the leading open-source model. The results demonstrated that instruction following and search do not reliably predict performance on closer-to-real-world tasks.
Clémentine Fourrier, Grégoire Mialon, Maxime Lecanu, Pierre Andrews, Adrien Carreira, frere thibaud, Avijit Ghosh, Romain Froger, Dheeraj Mekala, Caroline Pascal, Ulyana PiterbargScaleway on Hugging Face Inference Providers 🔥
Scaleway is integrated as a supported serverless Inference Provider on the Hugging Face Hub, expanding model deployment options across Hub model pages and official client SDKs for JavaScript and Python. Operating out of European data centers located in Paris, France, Scaleway Generative APIs host open-weight models including gpt-oss, Qwen3, DeepSeek R1, and Gemma 3 with structured outputs, function calling, multimodal processing, and sub-200ms first-token response times. Developers can route inference requests using their Hugging Face tokens or supply direct Scaleway API keys. Billing for routed requests charges standard provider rates starting at €0.20 per million tokens without markups, while direct requests bill to Scaleway accounts. Hugging Face PRO subscribers receive two dollars of monthly inference credits applicable across providers.
Guillaume Noale, Franck Pagny, Fred Bardolle, Guillaume Calmettes, Constance Morales, Célina Hanouti, Julien Chaumond, Simon Brandeis, Lucain PougetDemocratizing AI Safety with RiskRubric.ai
Cloud Security Alliance and Noma Security introduced RiskRubric.ai to provide standardized, transparent risk assessments across the open AI model ecosystem. The framework evaluates AI models across six pillars—transparency, reliability, security, privacy, safety, and reputation—using over 1,000 reliability tests, 200 adversarial security probes, automated code scanning, and harmful content evaluations. Each model receives 0–100 scores and A–F letter grades, supplemented by specific vulnerability findings and recommended mitigation strategies to assist deployment filtering. Initial benchmark results across models showed composite scores ranging from 47 to 94 with a median of 81, revealing polarized safety distributions and indicating that security hardening directly correlates with reduced safety risks.
Gal MoyalPublic AI on Hugging Face Inference Providers 🔥
Hugging Face has integrated Public AI as a supported Inference Provider on the Hugging Face Hub. Public AI operates as a nonprofit, open-source project providing access to sovereign and public models from institutions such as the Swiss AI Initiative and AI Singapore. Its distributed infrastructure combines a vLLM-powered backend serving OpenAI-compatible APIs across partner-donated clusters with a global load-balancing routing layer. Users can access these models through the Hugging Face web UI, Python client SDK, and JavaScript SDK using either direct provider API keys or routed Hugging Face tokens. At the time of announcement, inference through the Public AI provider is free of charge, supported by donated GPU time and advertising subsidies.
Joseph Low, Joshua Tan, Célina Hanouti, Julien Chaumond, Simon Brandeis, Lucain PougetVisible Watermarking with Gradio
Generative artificial intelligence tools produce images, video, audio, and text that have become increasingly difficult to distinguish from real-world media captures. To improve synthetic media transparency, Hugging Face introduced visible watermarking capabilities directly into the Gradio web application framework. Developers can now overlay visual watermarks on image and video outputs by specifying a single watermark parameter using file paths, open image objects, or NumPy arrays. The implementation also supports QR code watermarks, which can match the visual style of generated media while conveying detailed background information. Furthermore, the Gradio Chatbot component accepts a text watermark parameter that automatically attaches attribution metadata whenever users copy generated responses to their clipboards.
Margaret Mitchell