---
title: "Hugging Face"
description: "AI platform and community providing machine learning models, datasets, libraries, and tools for building AI applications."
---

# Hugging Face
> AI platform and community providing machine learning models, datasets, libraries, and tools for building AI applications.

## Articles

### [Unlocking asynchronicity in continuous batching](https://yomu.fyi/post/unlocking-asynchronicity-in-continuous-batching.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Rémi Ouazan Reboul, Pedro Cuenca, Aritra Roy Gosthipaty
- Published: May 14, 2026
- Type: Tutorial

Continuous batching improves GPU utilization with tightly packed batches, but its synchronous loop makes the CPU and GPU take turns, leaving the GPU idle while the CPU prepares each next batch. Profiling 8K-token generation with a batch size of 32 and an 8B model recorded 300.6 seconds, with the GPU idle for 24.0% of runtime. The post builds asynchronous batching by overlapping CPU preparation with GPU computation, using non-default CUDA streams, non-blocking transfers, and synchronization events to coordinate dependencies. In the same experiment, GPU activity rose to 99.4% and total generation time fell to 234.5 seconds, a 22% speedup, without new kernels or model changes while maintaining model accuracy.


### [Building Blocks for Foundation Model Training and Inference on AWS](https://yomu.fyi/post/building-blocks-for-foundation-model-training-and-inference-on-aws.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Keita Watanabe, Pavel Belevich, Aman Shanbhag
- Published: May 11, 2026
- Type: Explainer

The post explains how foundation-model scaling now spans pre-training, post-training, and test-time compute, creating shared infrastructure requirements rather than a single pre-training curve. It examines how AWS infrastructure and open-source tools support these workloads across accelerated compute, networking, storage, orchestration, ML frameworks, and observability. Technical detail includes NVIDIA H100, H200, B200, and B300 GPU characteristics, NVLink/NVSwitch scale-up communication, and EFA-based scale-out networking for distributed collectives. The architecture also incorporates EC2 P instances, tiered storage, Slurm, Kubernetes, SageMaker HyperPod, CUDA, NCCL, PyTorch, Prometheus, and Grafana. The conclusion presents integration points between these layers as the basis for diagnosing bottlenecks and making scaling decisions across the foundation-model lifecycle.


### [vLLM V0 to V1: Correctness Before Corrections in RL](https://yomu.fyi/post/vllm-v0-to-v1-correctness-before-corrections-in-rl.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Rafael Pardinas, Ehsan Kamalloo
- Published: May 6, 2026
- Type: Problem & solution

PipelineRL migrated its rollout inference engine from vLLM V0 (0.8.5) to V1 (0.18.1), but trainer-side logprobs, policy ratios, KL, entropy, clip rate, and reward diverged from the V0 reference. The investigation separated semantic mismatch, inference-path mismatch, and objective mismatch, then prioritized backend parity before changing the RL objective. Fixes included processed rollout logprobs, explicit V1 runtime settings disabling prefix caching and async scheduling, a V0-like inflight weight-update path, and an fp32 lm\_head for the final projection. With those changes, the final V1 run tracked the V0 trajectory across the reported metrics, while processed\_logprobs alone did not resolve the training gap.


### [Adding Benchmaxxer Repellant to the Open ASR Leaderboard](https://yomu.fyi/post/adding-benchmaxxer-repellant-to-the-open-asr-leaderboard.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Eric Bezzam, Steven Zheng, Eustache Le Bihan, Sergio Bruccoleri, Jeanine Sinanan-Singh, Casey Ford, Guanbo Wang, Yukai Huang, Ke Li, Yufeng Hao, Liao Xiaoling
- Published: May 6, 2026
- Type: Announcement

The Open ASR Leaderboard is adding private English speech-recognition datasets from Appen Inc. and DataoceanAI to evaluate scripted and conversational speech across several accents. To reduce benchmark-specific optimization (“benchmaxxing”) and test-set contamination, the datasets remain private, while the default Average WER continues to use public datasets only. A toggle exposes the private data, with macroaverages for overall, scripted, conversational, American, and non-American subsets; individual split scores are withheld to discourage provider- or accent-specific optimization. The leaderboard uses a shared Hub dataset, normalized transcripts and outputs, and open-source evaluation code, while private evaluation is run after public-set verification when models are submitted.


### [Granite 4.1 LLMs: How They’re Built](https://yomu.fyi/post/granite-4-1-llms-how-they-re-built.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Yousaf Shah
- Published: Apr 29, 2026
- Type: Explainer

Granite 4.1 is a family of dense decoder-only language models in 3B, 8B, and 30B sizes, trained on approximately 15T tokens. Its five-phase pre-training pipeline moves from broad web-scale data to progressively curated mixtures, increases math and code emphasis, and extends context from 4K to 512K tokens through staged long-context training. Supervised fine-tuning uses about 4.1M curated samples evaluated with LLM-as-Judge and rule-based checks, while reinforcement learning uses on-policy GRPO with DAPO loss. The 8B instruct model matches or surpasses the earlier Granite 4.0-H-Small 32B-A9B MoE model despite its simpler dense architecture and fewer parameters, and the Granite 4.1 models are released under Apache 2.0.


### [DeepInfra on Hugging Face Inference Providers 🔥](https://yomu.fyi/post/deepinfra-on-hugging-face-inference-providers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Aray Sultanbekova, Shang-Pin, Utemuratov, Yessen K, Oguz Vuruskaner, Célina Hanouti, Simon Brandeis, Lucain Pouget
- Published: Apr 29, 2026
- Type: Announcement

DeepInfra is now a supported Inference Provider on the Hugging Face Hub, adding its serverless inference platform and catalog of more than 100 models to model pages and client integrations. The initial integration supports conversational and text-generation tasks, with access to open-weight LLMs including DeepSeek V4, Kimi-K2.6, and GLM-5.1; support for text-to-image, text-to-video, embeddings, and other tasks will roll out soon. Users can configure provider API keys and preferences in account settings, or authenticate with a Hugging Face token so requests are routed through Hugging Face, including from the Python and JavaScript SDKs and supported agent harnesses. Direct requests are billed by the provider, while routed requests use standard provider API rates without additional markup, and Hugging Face PRO users receive $2 of monthly Inference credits.


### [Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents](https://yomu.fyi/post/introducing-nvidia-nemotron-3-nano-omni-long-context-multimodal-intell.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tuomas Rintamaki, Amala Sanjay Deshmukh, Nabin Mulepati, Collin McCarthy, Pritam Biswas, Arushi Goel, Alexandre Milesi, Danial Mohseni Taheri, Kateryna Chumachenko, Isabel Hulseman, Zhehuai Chen, Karan, Tao, Leili Tavabi, Ehsan Hosseini Asl
- Published: Apr 28, 2026
- Type: Announcement

NVIDIA Nemotron 3 Nano Omni is introduced as an omni-modal understanding model for document analysis, multiple-image reasoning, automatic speech recognition, long audio-video understanding, agentic computer use, and general reasoning. It extends the Nemotron multimodal line from vision-language processing to text, image, video, and audio, targeting long, dense inputs such as 100-plus-page documents and mixed audio-visual evidence. Its unified encoder-projector-decoder combines a Nemotron 3 Nano 30B-A3B hybrid Mamba-Transformer MoE backbone with C-RADIOv4-H and Parakeet-TDT-0.6B-v2 encoders; dynamic visual resolution, Conv3D tubelets, and Efficient Video Sampling support multimodal context compression. The post reports leading or top results across document, video, audio-video, voice, and ASR benchmarks, alongside up to 9x higher throughput and 2.9x single-stream reasoning speed on multimodal use cases, and provides BF16, FP8, and NVFP4 checkpoints.


### [How to build scalable web apps with OpenAI's Privacy Filter](https://yomu.fyi/post/how-to-build-scalable-web-apps-with-openai-s-privacy-filter.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: yuvraj sharma, Freddy Boulton, Abubakar Abid
- Published: Apr 27, 2026
- Type: Tutorial

The article presents three web applications built around OpenAI’s open-source Privacy Filter, a PII detector that labels eight categories in one forward pass over a 128k-token context. Document Privacy Explorer extracts PDF or DOCX text and returns category-labeled spans, while Image Anonymizer combines Tesseract word boxes with character offsets to produce editable pixel redaction rectangles. SmartRedact Paste replaces detected spans with category placeholders, stores public and token-gated views, and evicts expired pastes in a daemon thread. Across the apps, gradio.Server’s @server.api routes model work through Gradio’s queue and gradio\_client, while plain FastAPI routes serve custom HTML, views, and lightweight lookups. The implementations keep browser-side filtering, canvas edits, and PNG export out of server round-trips, and the paste service is about 200 lines including storage.


### [DeepSeek-V4: a million-token context that agents can actually use](https://yomu.fyi/post/deepseek-v4-a-million-token-context-that-agents-can-actually-use.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw
- Published: Apr 24, 2026
- Type: Explainer

DeepSeek V4 is presented as an open model family aimed at long-running agentic workloads, with Pro and Flash MoE checkpoints offering 1M-token context windows. Its central architecture alternates Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), compressing KV entries by 4x and 128x respectively, while using FP8/BF16 storage and FP4 indexing. The post-training design preserves reasoning across user turns when tool calls are present, introduces a dedicated XML tool-call schema, and relies on DSec, a Rust sandbox platform for RL rollouts. At 1M tokens, Pro uses 27% of V3.2's single-token FLOPs and 10% of its KV cache, while Flash uses 10% and 7%; V4-Pro-Max reports 80.6% on SWE Verified and 0.59 MRCR 8-needle retrieval at 1M.


### [How to Use Transformers.js in a Chrome Extension](https://yomu.fyi/post/how-to-use-transformers-js-in-a-chrome-extension.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Nico Martin
- Published: Apr 23, 2026
- Type: Tutorial

The guide explains how to build a Chrome extension that runs local AI with Transformers.js under Manifest V3, using a browser assistant as the reference architecture. It assigns model orchestration, lifecycle management, tool execution, and conversation state to a background service worker, while a side panel handles chat and a content script handles page extraction and highlighting. The implementation uses Gemma 4 E2B in q4f16 for text generation and all-MiniLM-L6-v2 in fp32 for embeddings, with WebGPU, DynamicCache-based KV caching, explicit download progress, and reusable pipelines. Typed message contracts, model-output parsing, and separate internal and UI transcripts support a deterministic tool loop, while chrome.storage.local, IndexedDB, and background caches place data according to lifecycle and access needs; the guide concludes that separating these runtimes keeps the UI responsive and avoids duplicate model loads.


### [QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard](https://yomu.fyi/post/qimma-a-quality-first-arabic-llm-leaderboard.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Leen AlQadi, Ahmed Alzubaidi, Mohammed Alyafeai, Maitha Alhammadi, Shaikha Alsuwaidi, Omar saif alkaabi, Basma Boussaha, Hakim Hacid
- Published: Apr 21, 2026
- Type: Benchmark

QIMMA is an Arabic LLM leaderboard built to address fragmented evaluation, translation artifacts, weak benchmark quality checks, limited coverage, and reproducibility gaps. It treats benchmark validation as a prerequisite for reported scores rather than an afterthought. The suite consolidates 109 subsets from 14 source benchmarks into more than 52,000 samples across seven domains, with 99% native Arabic content, Arabic-adapted HumanEval+ and MBPP+ coding tasks, and six prompt templates. Before model evaluation, Qwen3-235B-A22B-Instruct and DeepSeek-V3-671B assess samples using a 10-point rubric, flagged cases receive native-speaker review, and code statements are refined without changing task identifiers, reference solutions, or tests. In the April 2026 rankings, Qwen/Qwen3.5-397B-A17B-FP8 ranks first, while Arabic-specialized models lead cultural and linguistic tasks and multilingual models lead coding.


### [AI and the Future of Cybersecurity: Why Openness Matters](https://yomu.fyi/post/ai-and-the-future-of-cybersecurity-why-openness-matters.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Margaret Mitchell, Yacine Jernite, Clem 🤗
- Published: Apr 21, 2026
- Type: Explainer

Mythos is presented as a frontier AI system that rapidly finds and patches software vulnerabilities, with the post arguing that its surrounding system matters more than the language model alone. Its recipe combines substantial compute, software-relevant training data, vulnerability-probing and patching scaffolding, speed, and some autonomy; together, these elements can uncover vulnerabilities, find exploits, and build patches. The post argues that open code and tooling can distribute detection, verification, coordination, and patch propagation across communities, while reducing capability asymmetry between attackers and defenders. For defensive use, it recommends semi-autonomous agents with prespecified actions, human approval, auditable logs, and private deployment using open components and existing security tools. This approach is especially relevant to high-stakes organizations that need inspectable, controllable systems and protection for sensitive data.


### [Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents](https://yomu.fyi/post/ecom-rlve-adaptive-verifiable-environments-for-e-commerce-conversation.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Rahul Bajaj, Jaya Nupur, Anuj Garg, ben burtenshaw
- Published: Apr 16, 2026
- Type: Explainer

Ecom-RLVE extends the RLVE framework from single-turn reasoning puzzles to multi-turn, tool-augmented e-commerce conversations. EcomRLVE-GYM supplies eight environments spanning product discovery, substitution, cart building, returns, order tracking, policy QA, bundle planning, and multi-intent journeys, with procedural problem generation, a 12-axis difficulty curriculum, and algorithmically verifiable rewards. Each episode requires tool use and world-state changes; rewards combine task completion, efficiency, and a penalty for recommending product IDs not retrieved, while malformed outputs and illegal calls fail immediately. Adaptive scheduling advances each environment independently when its success rate is reliable. An initial study trained Qwen 3 8B with DAPO for 300 steps on cart building and reported progressively increasing difficulty reached, consistent with a steady learning signal.


### [The PR you would have opened yourself](https://yomu.fyi/post/the-pr-you-would-have-opened-yourself.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Pedro Cuenca, Awni Hannun
- Published: Apr 16, 2026
- Type: Announcement

mlx-lm contributors can use a Skill and a separate test harness to port language models from transformers, with the goal of making new architectures available shortly after their upstream implementations land. The work addresses rising agent-generated pull-request volume and the difficulty of preserving implicit codebase conventions, user contracts, and performance-sensitive behavior that agents may not understand. Given a conversion prompt, the Skill sets up environments, finds model variants and checkpoints, reads transformers modeling code, writes an MLX implementation, and iterates through tests including dtype checks, generation examples, numerical comparisons, and per-layer comparisons. The resulting pull requests disclose agent assistance and include detailed verification data, while contributors remain responsible for reviewing and accepting the results. Current limitations include shared-utility refactors, vision-language models, quantized uploads, and thinking-specific tests.


### [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://yomu.fyi/post/training-and-finetuning-multimodal-embedding-reranker-models-with-sent.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tom Aarsen
- Published: Apr 16, 2026
- Type: Tutorial

Sentence Transformers provides a Python library for training and using embedding and reranker models across text, images, audio, and video in retrieval and semantic search applications. The post shows how to finetune multimodal models on task-specific data, using Visual Document Retrieval as the example: matching text queries with document page images while preserving charts, tables, and layout. It describes the SentenceTransformerTrainer pipeline, model and processor configuration, dataset handling, and alternative Router-based encoders, while also covering multimodal reranker training with BinaryCrossEntropyLoss and direction-specific datasets. On its evaluation data, finetuning Qwen/Qwen3-VL-Embedding-2B raised NDCG@10 from 0.888 to 0.947 and outperformed all tested VDR models, including models up to 4x its size.


### [Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents](https://yomu.fyi/post/inside-vakra-reasoning-tool-use-and-failure-modes-of-agents.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Ankita Naik, danish, Ben, Anupama Murthi, Praveen Venkateswaran, Siyu, Ayhan Sebin
- Published: Apr 15, 2026
- Type: Benchmark

VAKRA is an executable, tool-grounded benchmark for evaluating whether AI agents can complete enterprise-like workflows that combine API calls, document retrieval, and natural-language constraints. It provides more than 8,000 locally hosted APIs backed by real databases across 62 domains, plus domain-aligned documents, and uses full execution traces to assess multi-step behavior. The four capability groups cover API chaining, dashboard-API tool selection, multi-hop dashboard reasoning, and multi-hop multi-source reasoning with multi-turn dialogue and optional tool-use policies; tasks can require three to seven reasoning steps, while some segments specify one to twelve tool calls or up to five logical hops. Reported analyses find that models perform best on single-hop questions, degrade as API hops increase, and often violate policies or fail to retrieve enough information when constraints restrict the relevant source, exposing a gap between isolated tool competence and end-to-end reliability.


### [Meet HoloTab by HCompany. Your AI browser companion.](https://yomu.fyi/post/meet-holotab-by-hcompany-your-ai-browser-companion.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Marc Thibault, Pierre-Louis Cedoz, Hamza Benchekroun, Kai Yuan, Aurélien Lac, Tony Wu, Antonio Loison, Axel Moyal, Emrick Sinitambirivoutin, Aleix Cambray (H-AI)
- Published: Apr 15, 2026
- Type: Announcement

HoloTab is a Chrome extension that brings HCompany’s computer-use AI into the browser. It navigates websites, fills fields, and makes decisions from a user’s description, with vision models, action planning, and interface understanding operating underneath without setup or technical skills. The announcement follows the March 31st release of Holo3, described as the company’s most advanced computer-use model to date. For repetitive workflows, users can record their actions and narration in a tab; HoloTab turns the recording into a routine that can later be rerun or scheduled. The stated goal is to make computer-use AI accessible beyond teams with engineers who can connect such systems, and the extension is presented as free and available for everyone today.


### [Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs](https://yomu.fyi/post/waypoint-1-5-higher-fidelity-interactive-worlds-for-everyday-gpus.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Andrew Lapp, Louis Castricato, Scott Fox, Shahbuland Matiana, David Rossi
- Published: Apr 9, 2026
- Type: Announcement

Overworld’s Waypoint-1.5 is a real-time video world model aimed at making interactive generative worlds usable on consumer hardware rather than limited to datacenter-scale compute. It offers two tiers: a 720p model for higher-performance desktop GPUs, capable of up to 60 FPS on RTX 3090 through 5090 hardware, and a 360p model for broader deployment, including gaming laptops and soon Apple Silicon Macs. Training used nearly 100 times more data than Waypoint-1, while more efficient video-modeling techniques reduce redundant computation across frames to support coherent motion and responsive exploration. Users can run it locally through the Biome desktop client, try it in a browser via Overworld Stream, or use the World Engine inference library and third-party clients. The release presents improved visual fidelity, broader local hardware support, and continued progress toward real-time interactive world simulation.


### [Multimodal Embedding & Reranker Models with Sentence Transformers](https://yomu.fyi/post/multimodal-embedding-reranker-models-with-sentence-transformers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tom Aarsen
- Published: Apr 9, 2026
- Type: Tutorial

Sentence Transformers v5.4 adds multimodal embedding and reranking support for text, images, audio, and video through its Python API. Embedding models map modalities into a shared space, while multimodal Cross Encoder rerankers score relevance for mixed-modality pairs, enabling cross-modal similarity, retrieval, and multimodal RAG workflows. The guide shows model loading, URL-based image encoding, encode\_query() and encode\_document() prompt handling, similarity calculation, and ranking, with a Qwen3-VL-Embedding-2B example producing 2,048-dimensional image vectors and matching text queries to images. It notes that cross-modal scores can be lower because of the modality gap, while relative ordering can still support retrieval, and that rerankers generally offer higher quality at the cost of slower per-pair processing; installation extras and GPU memory requirements vary by modality and model.


### [Safetensors is Joining the PyTorch Foundation](https://yomu.fyi/post/safetensors-is-joining-the-pytorch-foundation.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Luc Georges, Lysandre
- Published: Apr 8, 2026
- Type: Announcement

Safetensors has joined the PyTorch Foundation as a foundation-hosted project under the Linux Foundation, alongside DeepSpeed, Helion, Ray, vLLM, and PyTorch. It began as a Hugging Face project addressing the risk of arbitrary code execution in pickle-based model-weight formats. Its format stores tensor metadata in a JSON header capped at 100MB, followed by raw tensor data, and supports zero-copy mapping and lazy loading. The move places the trademark, repository, and governance with the Linux Foundation, while Luc and Daniel remain on the Technical Steering Committee and lead day-to-day work. Existing users get the same format, APIs, and Hub integration without breaking changes, while contributors gain a documented maintainer path; planned work includes PyTorch core serialization, accelerator-aware loading, parallel loading, and quantization support.


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