# Hugging Face > AI platform and community providing machine learning models, datasets, libraries, and tools for building AI applications. ## Articles ### [Open Responses: What you need to know](https://yomu.fyi/post/open-responses-what-you-need-to-know.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: shaun smith, ben burtenshaw, merve, Pedro Cuenca - Published: Jan 15, 2026 Open Responses is a new open inference standard initiated by OpenAI, developed by the open source AI community, and backed by Hugging Face. While agentic workloads require multi-step planning and tool execution, the ecosystem has remained constrained by the legacy Chat Completion format and proprietary Responses implementations. Open Responses addresses this mismatch by standardizing semantic event streaming, provider-specific configuration options, and distinct roles for model providers and intermediary routers. The specification formalizes reasoning visibility across raw traces, protected encrypted content, and sanitized summaries while supporting provider-managed sub-agent execution loops. Developers can now orchestrate multi-step tool calls and stream reasoning deltas across heterogeneous providers through a single consistent API schema. ### [NVIDIA Cosmos Reason 2 Brings Advanced Reasoning To Physical AI](https://yomu.fyi/post/nvidia-cosmos-reason-2-brings-advanced-reasoning-to-physical-ai.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Tsung-Yi Lin, Debraj Sinha - Published: Jan 5, 2026 NVIDIA released Cosmos Reason 2, an open reasoning vision-language model designed to improve multi-step planning, uncertainty handling, and physical AI decision-making. Available in 2B and 8B parameter configurations, the model increases input context support to 256K tokens, up from 16K in the prior version. The release adds spatial perception capabilities including optical character recognition, 2D and 3D point localization, bounding boxes, and trajectory generation for robotics. In domain adaptation tests on autonomous vehicle video captioning, fine-tuning the 8B model improved BLEU scores by 10.6% and LingoQA scores by 13.8%. The model leads the Physical AI Bench and Physical Reasoning leaderboards for open visual understanding models. ### [Introducing Falcon-H1-Arabic: Pushing the Boundaries of Arabic Language AI with Hybrid Architecture](https://yomu.fyi/post/introducing-falcon-h1-arabic-pushing-the-boundaries-of-arabic-language.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Basma Boussaha, Mohammed Alyafeai, Ahmed Alzubaidi, Leen AlQadi, Shaikha Alsuwaidi, Omar saif alkaabi, Hamza Alobeidli, Hakim Hacid - Published: Jan 5, 2026 The Technology Innovation Institute introduced Falcon-H1-Arabic, a family of Arabic language models released in 3B, 7B, and 34B parameter sizes. Built on a hybrid architecture, each block runs Mamba State Space Models and Transformer attention in parallel before fusing their representations at the output projection. Pre-training incorporated around 300 billion tokens across Arabic dialects, English, and multilingual content filtered via linguistic analysis. A post-training pipeline combining supervised fine-tuning and direct preference optimization addresses long-context utilization and alignment. Across evaluations on the Open Arabic LLM Leaderboard, the 34B model achieves roughly 75%, exceeding the performance of larger baseline systems like Llama-3.3-70B. ### [AprielGuard: A Guardrail for Safety and Adversarial Robustness in Modern LLM Systems](https://yomu.fyi/post/aprielguard-a-guardrail-for-safety-and-adversarial-robustness-in-moder.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Jaykumar Kasundra - Published: Dec 23, 2025 Modern language model systems face security and safety threats spanning prompt injection, jailbreaks, and tool manipulation across long-context agentic workflows. To address these vulnerabilities, researchers introduced AprielGuard, an 8B parameter causal decoder-only transformer based on a downscaled Apriel-1.5 Thinker Base model. The model detects 16 safety risk categories alongside binary adversarial attacks across standalone prompts, multi-turn dialogues, and agentic workflows. Trained on synthetic datasets created with Mixtral-8x7B, NVIDIA NeMo Curator, and SyGra, AprielGuard operates in both explainable reasoning and fast non-reasoning modes. Evaluation shows high precision and recall on safety and adversarial benchmarks, though trade-offs exist between explainability latency and non-reasoning performance. ### [Tokenization in Transformers v5: Simpler, Clearer, and More Modular](https://yomu.fyi/post/tokenization-in-transformers-v5-simpler-clearer-and-more-modular.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Ita Zaporozhets, Aritra Roy Gosthipaty, Arthur Zucker, Sergio Paniego, merve, Pedro Cuenca - Published: Dec 18, 2025 Transformers v5 overhauls its tokenization framework by separating tokenizer architecture from trained vocabularies. In contrast to v4's dual slow Python and fast Rust files, v5 consolidates each model tokenizer into a single file defaulting to the Rust-backed TokenizersBackend. The pipeline stages—normalizer, pre-tokenizer, model algorithm such as BPE or Unigram, post-processor, and decoder—are now directly exposed and configurable rather than buried in serialized files. Practitioners can instantiate blank tokenizer architectures and train custom vocabularies directly from iterators using native methods like train\_new\_from\_iterator while retaining model-specific formatting rules. The wrapper layer continues to bridge raw tokenization and model requirements by managing chat templates, context limits, and special token insertion. ### [The Open Evaluation Standard: Benchmarking NVIDIA Nemotron 3 Nano with NeMo Evaluator](https://yomu.fyi/post/the-open-evaluation-standard-benchmarking-nvidia-nemotron-3-nano-with.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Seph Mard, Isabel Hulseman, Besmira Nushi, Piotr Januszewski, Grzegorz Chlebus, VivienneZhang, Wojciech Prazuch, Pablo Ribalta, Nik Spirin, Ferenc Galko - Published: Dec 17, 2025 Assessing whether large language model improvements stem from genuine advances or underspecified evaluation conditions remains a major challenge across the industry. Most published model evaluations omit critical execution parameters, prompt templates, harness versions, and runtime configurations. In response, NVIDIA released Nemotron 3 Nano 30B A3B alongside its complete, reproducible evaluation recipe built with the open-source NeMo Evaluator library. The library serves as an orchestration layer that unifies diverse benchmark harnesses under standard configurations while decoupling evaluation logic from underlying inference backends. Developers can execute the identical evaluation pipeline against local deployments or hosted endpoints using published YAML configurations and structured logging. ### [CUGA on Hugging Face: Democratizing Configurable AI Agents](https://yomu.fyi/post/cuga-on-hugging-face-democratizing-configurable-ai-agents.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Jim Laredo, Avi Yaeli, Sami Marreed, Ayhan Sebin, Merve Unuvar - Published: Dec 15, 2025 Many existing AI agent frameworks suffer from brittleness, tool misuse, and failures when executing complex workflows. To address these limitations, the open-source Configurable Generalist Agent (CUGA) introduces structured orchestration that decomposes user goals into programmatic subtasks tracked by a dynamic task ledger. The framework integrates agentic patterns like planner-executor and code-act, delegating subtasks to specialized agents that generate pseudo-code before running execution in a secure sandbox. Released under the Apache 2.0 license, CUGA integrates with Langflow for low-code visual workflow assembly and supports multi-tool environments through OpenAPI specs, MCP servers, and LangChain. Testing on inference platforms like Groq with open models such as gpt-oss-120b demonstrates rapid response times during multi-step planning and validation. ### [New in llama.cpp: Model Management](https://yomu.fyi/post/new-in-llama-cpp-model-management.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Xuan-Son Nguyen, Victor Mustar - Published: Dec 11, 2025 llama.cpp server now includes a router mode that enables dynamic loading, unloading, and switching between multiple LLMs without restarting the server. The architecture runs each model in an isolated process to ensure a single model crash does not affect other active instances. Running llama-server without specifying a model activates auto-discovery across the cache directory or a designated folder of GGUF files. In addition to on-demand loading and least-recently-used eviction capped by default at four models, the server provides endpoints for manual loading, unloading, and listing model statuses. Models inherit global configuration options or use dedicated configuration presets while also integrating directly into the built-in web UI. ### [Codex is Open Sourcing AI models](https://yomu.fyi/post/codex-is-open-sourcing-ai-models.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: ben burtenshaw, shaun smith - Published: Dec 11, 2025 Hugging Face Skills equips AI coding agents like OpenAI Codex to execute end-to-end machine learning workflows. By reading AGENTS.md files and interfacing via the Model Context Protocol, Codex automates dataset validation, training script updates, and job submissions to Hugging Face Jobs. The workflow supports methods including supervised fine-tuning, direct preference optimization, and reinforcement learning for models ranging from 0.5B to 7B parameters. Throughout execution, Codex tracks live metrics via Trackio, records benchmark evaluations against baselines, and maintains Markdown reports. Once training concludes, Codex merges LoRA adapters, applies GGUF quantization, and publishes the resulting models to the Hugging Face Hub for local deployment. ### [Introducing swift-huggingface: The Complete Swift Client for Hugging Face](https://yomu.fyi/post/introducing-swift-huggingface-the-complete-swift-client-for-hugging-fa.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Mattt - Published: Dec 5, 2025 Hugging Face released swift-huggingface, a dedicated Swift package offering complete Hub API integration, reliable file downloads, and inference provider access. The library addresses previous limitations in swift-transformers 1.0, where interrupted multi-gigabyte model downloads could not resume and cache structures differed from Python. To resolve cache duplication, swift-huggingface implements a Python-compatible content-addressed storage layout using symlinks and flock file locking. Authentication is standardized through a TokenProvider pattern supporting auto-detection, Keychain integration, static CI/CD tokens, and OAuth 2.0 sign-in with automatic token refresh. The package also provides URLSession-backed snapshot downloads with granular progress tracking and will soon replace the HubApi implementation inside swift-transformers. ### [DeepMath: A lightweight math reasoning Agent with smolagents](https://yomu.fyi/post/deepmath-a-lightweight-math-reasoning-agent-with-smolagents.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat - Published: Dec 4, 2025 Mathematical reasoning in large language models often suffers from lengthy chain-of-thought traces and frequent arithmetic mistakes. To address these issues, DeepMath pairs a Qwen3-4B Thinking base model with a sandboxed Python execution environment built using the smolagents library and vLLM backend. The system offloads deterministic calculations by emitting concise Python snippets, executing them safely with restricted module imports and no network access, and folding returned values back into the context. Training utilizes Group Relative Policy Optimization on the Tool-Integrated Reasoning subset of OpenMathReasoning with temperature scheduling and length constraints. Across benchmarks including MATH500, AIME, HMMT, and HLE, DeepMath reduces output token lengths by up to 66% while increasing overall problem-solving accuracy. ### [We Got Claude to Fine-Tune an Open Source LLM](https://yomu.fyi/post/we-got-claude-to-fine-tune-an-open-source-llm.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: ben burtenshaw, shaun smith - Published: Dec 4, 2025 Hugging Face Skills equips coding agents like Claude Code, OpenAI Codex, and Google's Gemini CLI to manage end-to-end language model fine-tuning. Using the hf-llm-trainer skill, an agent validates dataset formats, selects cloud hardware, configures authentication, and submits jobs to Hugging Face Jobs. Supported techniques include Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO) for models ranging across various parameter sizes. The integration incorporates Trackio for real-time monitoring and automates LoRA configuration for larger models. Once training completes, the agent pushes artifacts to the Hugging Face Hub and can convert models to GGUF format for local execution. ### [Transformers v5: Simple model definitions powering the AI ecosystem](https://yomu.fyi/post/transformers-v5-simple-model-definitions-powering-the-ai-ecosystem.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Lysandre, Arthur Zucker, Cyril Vallez, Vaibhav Srivastav - Published: Dec 1, 2025 Transformers v5.0.0rc-0 introduces major architectural updates focused on simplicity, training, inference, and ecosystem interoperability across modern AI workflows. The release adopts a modular modeling approach and centralizes attention implementations into a unified AttentionInterface abstraction to reduce contribution and code review overhead. Support for Flax and TensorFlow is officially sunset in favor of focusing on PyTorch as the primary backend, while tokenization is standardized around the tokenizers library. For execution workloads, v5 adds native continuous batching, paged attention mechanisms, and a dedicated transformers serve OpenAI-compatible serving system. Finally, weight loading is refactored to make low-precision quantization a first-class citizen alongside broad interoperability with formats such as GGUF, MLX, and TorchAO. ### [Diffusers welcomes FLUX-2](https://yomu.fyi/post/diffusers-welcomes-flux-2.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: YiYi Xu, Daniel Gu, Sayak Paul, Alvaro Somoza, Dhruv Nair, Aritra Roy Gosthipaty, Linoy Tsaban, Apolinário from multimodal AI art - Published: Nov 25, 2025 Diffusers introduces support for FLUX-2 models through the Flux2Pipeline and Flux2Transformer2DModel classes. Running the 4-bit quantized checkpoint diffusers/FLUX.2-dev-bnb-4bit requires loading Mistral3ForConditionalGeneration as the text encoder and the transformer in bfloat16 precision with CPU offloading enabled. During image generation with a prompt and 50 inference steps, the pipeline encodes text embeddings through the Mistral 3 model before executing diffusion operations. However, executing the pipeline on a GPU with 14.56 GiB capacity triggers a CUDA OutOfMemoryError during 4-bit dequantization inside bitsandbytes matrix multiplication operations. Additionally, the execution logs warn that Flax classes are deprecated in Diffusers and will be removed in version 1.0.0. ### [Continuous batching from first principles](https://yomu.fyi/post/continuous-batching-from-first-principles.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Rémi Ouazan Reboul, Arthur Zucker, Luc Georges - Published: Nov 25, 2025 Large language model serving requires running expensive next-token generation across multiple concurrent user requests. Traditional batching approaches introduce severe padding inefficiencies when mixing variable-length prompts and different generation phases, especially under static shape constraints like CUDA graphs. Continuous batching resolves these inefficiencies by combining key-value caching, chunked prefill, ragged batching, and dynamic request scheduling. Ragged batching eliminates the traditional batch axis by concatenating token sequences into a single tensor and using boolean attention masks to isolate independent sequences. By dynamically removing completed prompts and packing decoding tokens alongside chunked prefill tokens up to a hardware memory budget, serving systems maintain high hardware utilization and throughput. ### [Building Deep Research: How we Achieved State of the Art](https://yomu.fyi/post/building-deep-research-how-we-achieved-state-of-the-art.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Michael Griff, Dean Sacoransky, Noah Nefsky - Published: Nov 24, 2025 Building production AI research agents presents challenges around context window pollution, escalating token costs, and architectural brittleness across model updates. To resolve these issues, Tavily rebuilt its deep research system around simplified orchestration, compact tooling, and active context curation. The agent mimics human research workflows by distilling tool outputs into concise reflections for ongoing reasoning, withholding raw retrieved web data until the final deliverable stage. Compared to traditional ReAct propagation architectures where token consumption scales quadratically, this reflection-based approach achieves linear token growth. As a result, the system reduced token consumption by 66% compared to Open Deep Research while reaching state-of-the-art performance on DeepResearch Bench. ### [OVHcloud on Hugging Face Inference Providers 🔥](https://yomu.fyi/post/ovhcloud-on-hugging-face-inference-providers.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Gilles Closset, Fabien Ric, Elias Tourneux - Published: Nov 24, 2025 OVHcloud is now integrated as a supported Inference Provider on the Hugging Face Hub, expanding serverless inference options across model pages and client SDKs. The integration enables access to open-weight models, including gpt-oss, Qwen3, DeepSeek R1, and Llama, using European infrastructure with pay-per-token pricing starting at €0.04 per million tokens. Developers can connect via Python and JavaScript SDKs using either direct custom API keys or automatic routing through Hugging Face tokens. The service supports structured outputs, function calling, multimodal workflows, and embedding models while providing sub-200ms first-token response times. Calls routed through Hugging Face pass through standard provider pricing without markups, and PRO tier subscribers receive monthly inference credits. ### [20x Faster TRL Fine-tuning with RapidFire AI](https://yomu.fyi/post/20x-faster-trl-fine-tuning-with-rapidfire-ai.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Kamran Bigdely, Arun Kumar, Quentin Gallouédec - Published: Nov 21, 2025 Hugging Face TRL now officially integrates with RapidFire AI to accelerate LLM fine-tuning and post-training experimentation through concurrent configuration evaluation. Instead of executing training configurations sequentially, the platform shards datasets into discrete chunks and cycles multiple model configurations across GPUs at chunk boundaries. The runtime handles automatic checkpointing via a shared-memory-based adapter and model spilling mechanism to maintain consistent training state. Experimenters can monitor live metrics on an MLflow-based dashboard and use Interactive Control Ops to stop underperforming runs or clone and warm-start promising configurations mid-flight. Benchmark results on NVIDIA A100 GPUs demonstrate experimentation speedups ranging from 15x to 20x over sequential runs when training TinyLlama-1.1B and Llama-3.2-1B models. ### [Open ASR Leaderboard: Trends and Insights with New Multilingual & Long-Form Tracks](https://yomu.fyi/post/open-asr-leaderboard-trends-and-insights-with-new-multilingual-long-fo.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Eric Bezzam, Steven Zheng, Eustache Le Bihan, Vaibhav Srivastav - Published: Nov 21, 2025 The Open ASR Leaderboard expanded its evaluation framework by introducing dedicated multilingual and long-form transcription tracks across eleven datasets. Benchmarking more than sixty open- and closed-source models from eighteen organizations revealed clear operational trade-offs between accuracy and inference speed. Systems combining Conformer encoders with large language model decoders achieved the lowest word error rates in short-form English speech recognition. In contrast, connectionist temporal classification and token-and-duration transducer decoders yielded tenfold to hundredfold improvements in throughput with only slight accuracy penalties. While closed-source commercial systems maintained performance leads in long-form audio transcription, specialized single-language models consistently outperformed generalized systems supporting hundreds of languages. ### [Introducing AnyLanguageModel: One API for Local and Remote LLMs on Apple Platforms](https://yomu.fyi/post/introducing-anylanguagemodel-one-api-for-local-and-remote-llms-on-appl.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Mattt - Published: Nov 20, 2025 Apple developers frequently face integration friction when trying to support a mix of local and cloud language models across disparate APIs. To resolve this fragmentation, AnyLanguageModel introduces a unified Swift package that acts as a drop-in replacement for Apple's Foundation Models framework. The package standardizes interactions across local backends such as Core ML, MLX, and llama.cpp alongside cloud providers like Anthropic and OpenAI. To avoid dependency bloat from multi-backend support, the library employs Swift 6.1 package traits so projects only import the specific runtime engines they require. Furthermore, the library extends beyond current Foundation Models limitations by adding prompt image support for vision-language models. [Older posts](https://yomu.fyi/company/hugging-face/page/2.md)