AWS Machine Learning Blog3434OpenFold3-preview can be deployed on Amazon SageMaker JumpStart, an open-source biomolecular structure prediction model developed by the OpenFold Consortium and AlQuraishi Lab at Columbia University, supporting 3D structure prediction for proteins, DNA, RNA, small molecule ligands, and their complexes.
Hugging Face BlogConstruct a pick-and-place simulation using the UR17G arm, Robotiq 2F-140 gripper, and beverage can in Isaac Sim, requiring checks on joints, motion, and contact behavior. SimReady Central provides asset models and simulation settings, and uses GPT-6 Sol to generate task code.
Hugging Face BlogFeatured6565llama.cpp and ggml release v1.0.0, binding destructive changes to the public C API to the major version number, and both will be released according to semantic versioning rules thereafter. The project has supported over 150 model architectures and mainstream speculative decoding methods, and provides pre-release binaries for Linux, macOS, Windows, Android, iOS, as well as distribution channels such as Docker, Homebrew, and conda-forge.
Why it matters: llama.cpp and ggml are released simultaneously with v1.0.0, and the major version number corresponds to destructive changes to the C API, allowing downstream dependents to plan their upgrade schedule accordingly.
Ant Group inclusionAI · Hugging Face new modelsinclusionAI releases Duo 1.0 causal-base model, a full-duplex multimodal world model for real-time audio-video interaction, including implementation weights and backbones, intent models and adapters, audio-video encoders, speech recognizers, and forced aligners. The model weights are based on MiniMax H3 initialization and are open-sourced.
Ant Group inclusionAI · Hugging Face new modelsinclusionAI releases the Duo 1.0 Realization weights for FP8-quantized four-step distillation, used for its causal base model, supporting full-duplex real-time audio-video interaction, and needs to be overlaid with the causal base model weights.
vLLM BlogDeepSeek-V4.1-Flash achieves a service capability of 1.7M tokens/s per GPU in a single NVIDIA Vera Rubin NVL72 rack, reaching up to 387 tokens/s per user, an increase of 58 times compared to GB300 NVL72.
Hugging Face BlogHugging Face has released scripts and containers for fine-tuning the 3D generation model TRELLIS.2, supporting deployment locally or on Hugging Face Jobs. It uses LoRA technology to fine-tune only the structure prediction and geometric generation stages to improve accuracy for specific tasks (such as footwear).
Hugging Face Blog3838Hugging Face's blog introduces the Token Pooling perception training method for multi-vector embedding models, which compresses storage by merging similar tokens through HierarchicalTokenPooling.
Ant Group inclusionAI · Hugging Face new modelsinclusionAI/ZoomGain-4B-GGUF is a 175B parameter multimodal model supporting image input, using FP16, Q8_0, and Q4_K_M precision quantization. The model includes a language model and mmproj visual encoder/projector, converted to GGUF format via llama.cpp. It provides llama-mtmd-cli for command-line inference and llama-server for network chat functionality.
Ant Group inclusionAI · Hugging Face new modelsThe GGUF format weights for inclusionAI/ZoomGain-9B have been released, converted using llama.cpp, with the language model in FP16, Q8_0, Q4_K_M. The visual encoder and projector (mmproj) also adopt this quantization scheme. Both the language model and mmproj files need to be provided for image input, supporting CLI inference (llama-mtmd-cli) and Web Chat (llama-server) deployment.
Ant Group inclusionAI · Hugging Face new modelsSing VL S1 is a multimodal decision-making model based on Qwen3.5, capable of answering named entity questions with text or JSON and up to four images, scoring options for each question using a single Transformers forward pass, performing excellently on 43 text datasets and 11 visual datasets, supporting Python 3.12 and PyTorch inference.
Hugging Face BlogUsers can now associate the Hugging Face organization with their Google Cloud account via Google Cloud Marketplace, allowing Hugging Face usage to be billed to the Google Cloud account instead of credit card payment.
Hugging Face BlogHugging Face explores the application of decision models in non-text generation scenarios, such as using LLMs for customer support systems to handle refund requests. The model directly evaluates options and returns a probability distribution instead of generating text. Decision models take state and questions as input and output structured decisions and probabilities, which are superior to LLMs generating JSON directly, as the latter may not follow the request format and is inefficient. Decision models differ from classification models in that the former's output categories can be dynamically adjusted, while the latter's output categories are fixed and require retraining to expand.
Claude.dev BlogAnthropic has launched four Claude models for users to choose from: Claude Haiku 5.5, Claude Sonnet 5.5, Claude Opus 5.5, and Claude Fable 5.2.
Hugging Face Blog4545The HOPE method achieves better pruning effects in mixed expert language models by considering the second-order interactions between experts. It improves performance by 6.1% over the next best method on the agentic coding task at a 50% pruning rate. This method is validated on models like Qwen3.5-122B, where each MoE layer has 256 experts. After pruning, it can reduce memory usage by about 250 GB and is compatible with quantization.
Hugging Face Blog5858Hugging Face released ToyBlocks, a framework for robot learning and evaluation that provides a controlled simulation environment. The framework supports automatically generating tasks and training demonstrations, and includes metrics to measure changes in robot behavior over time, aiming to study how models improve with more training data and how to combine advanced reasoning with precise physical control.
Hugging Face BlogHugging Face Open ASR leaderboard adds Liva AI private datasets for Egyptian English and Nigerian English, containing 1.34 hours and 1.55 hours of dialogue speech, to enhance the representativeness and reliability of ASR evaluation. The leaderboard now supports more detailed accent classification based on the Kachru three-circle model, covering groups such as US and non-US, inner circle and outer circle/extended circle.
Hugging Face BlogFeatured7171NVIDIA has added four new open-source weight checkpoints for Cosmos 3 under the Cosmos World Model platform, all of which are post-trained versions of Cosmos 3 Nano, containing 16B trainable parameters, using the OpenMDW1.1 license, allowing commercial and non-commercial use.
Why it matters: The original text provides four open-source weight checkpoints and a four-stage training recipe, allowing readers to judge how to adapt the world model to their own sensors and robot bodies.
Hugging Face BlogFeatured7272Phonon releases a small TTS model that can run on local devices, without a server and network, offering two versions of about 65 MB and 75 MB, covering English, French, German, Spanish, and Portuguese.
Why it matters: The original text outlines the specifications, open-source license, and leaderboard performance of on-device TTS models, allowing judgment on the usability boundary of small models in local voice scenarios.
Hugging Face BlogFeatured6565Dharma-AI releases Dharma Field Extractor, a 2B parameter model, input document image and optional schema, output typed structured JSON, supports open extraction and schema-directed extraction, missing fields return null.
Why it matters: The original text provides a comparison of quality and cost on self-built benchmarks, allowing readers to judge the feasibility of the small model specialized route in field extraction tasks.
Hugging Face BlogFeatured6060IBM's research team fine-tunes a 20B open-source weight model, Palette, using a two-role LoRA scheme where designers write JSON briefs and encoders generate pptxgenjs code page by page to create presentations according to IBM Design Language.
Why it matters: IBM uses synthetic data and checks to repair closed-loop training for a 20B small model to create presentations, providing a method that can be transferred to other structured outputs.
Hugging Face BlogFeatured6262The IBM team compared CUGA, Hermes, and Deep Agents using the same open-source weight model GLM-5.3-flash on 90 tasks in AppWorld, with pass rates of 81.1%, 46.7%, and 30.0%, respectively, and 20, 8, and 2 scenarios where all three variants passed.
Why it matters: With the same model but a different harness, the task completion rate increased from 30% to 81%, and reproducible configurations and evaluation links were provided in the text.
Hugging Face BlogTransformers supports over 750 model types, its test suite includes 88,000+ CPU tests and 200,000+ GPU tests. Hugging Face releases Model Integration Contracts (MIC) standard, including compressed models (such as a 66 MB weight UMT5 fix), runnable examples (such as SmolLM text generation), and human review baselines.
MiniMax · Hugging Face new modelsMiniMax-H3-Image is an open-source image generation and editing model in the MiniMax H3 family, supporting 16MP image generation, image editing, and up to 16 reference images. It adopts the H3 architecture and uses the Qwen3-VL text encoder, with 68.65GB of BF16 precision model weights provided on Hugging Face.
Moonshot AI · Hugging Face new modelsKimi K3.1 Pro is an open-source multimodal AI model by Moonshot AI, with 2.8T parameters, 104B activation parameters, and a context window of 1M tokens, demonstrating excellent performance in areas such as coding, 3D creation, and financial research. Compared to Kimi K3, it adopts Gated DSA layers to improve long text processing efficiency and has outperformed competitors like Claude Fable 5.1 and GPT-6 Astra in benchmarks such as Terminal-Bench 4.0.
Hugging Face BlogProject ProtoStar is announced, aiming to unlock the potential of publicly funded scientific research by making datasets, models, and code more accessible and reusable. The project brings together universities, research institutions, industry, and the open science community, supporting the release of scientific achievements in AI-compatible, machine-actionable formats, and demonstrating its value through a series of public challenges. The first challenge, hosted by Carnegie Mellon University, will launch on November 2.
Hugging Face BlogFeatured8585Mistral releases multimodal MoE model Mistral Large 4 (Le Chonk), with approximately 1T total parameters, 49B activation parameters, a context window of 1M tokens, offering two official versions FP8 and NVFP4, as well as GGUF made by Unsloth and llama.cpp teams.
Why it matters: The original text provides specific scores for the 1T total parameter, 49B activation multimodal MoE model on cybersecurity and coding benchmarks, along with measured throughput for local and endpoint deployment.
Hugging Face BlogHugging Face releases Granite Switch 4.1.1, an incremental update based on 4.1, maintaining full backward compatibility. New features include multilingual speech-to-text models (supporting 10 languages with native punctuation) and a decision adapter for improved performance on the Jev decision index test set. The team achieves rapid deployment by updating only the adapter without recalculating the base model, and future support for third-party custom adapters is planned.
Hugging Face Blog5050Hugging Face releases open-source speech recognizer AnySpeechRecognizer and decision model AnyDecisionModel, supporting NVIDIA Parakeet and Alibaba Cloud Qwen3-ASR engines, and can run locally on Apple platforms.
Hugging Face BlogFeatured7878NVIDIA has released the open-source weight model Nemotron 3.5 Transcribe, integrating transcription, speaker separation, translation, and audio Q&A into a single checkpoint, supporting transcription in 40 languages, translation between 31 languages and English, and speaker annotation in 13 languages, capable of processing the longest 1-hour recordings and annotating up to 8 speakers.
Why it matters: The original text provides a unified weighting model for multilingual transcription, speaker separation, and translation, along with an open data pipeline and fine-tuning recipes, which can be used to determine the adaptation path for voice scenarios.
Hugging Face BlogFeatured7676Liquid AI releases the new generation Liquid Foundation Models, LFM3, deriving six 2B-level models from the shared backbone LFM3-1.7B-Base, covering text, vision, audio, multimodal, and decision, all open weights on Hugging Face.
Why it matters: Liquid AI derives six modality models from the same 2B-level backbone and provides single-card fine-tuning and edge deployment paths, which can be used to judge the specialization route for small models.
Hugging Face BlogHugging Face releases Old Ideas, Novel Problems study, evaluating the reliability of six LLMs (GPT-5.1 to 5.4, Claude Sonnet 4.5 to 4.6) in judging the reliability of research innovation.
Ant Group inclusionAI · Hugging Face new models5858inclusionAI/Ling-3.1-flash-1019 released, 560B parameter size, 25B active parameters/Token, 1M context window, improved reasoning ability, AA-Briefcase score 1524 Elo, AutomationBench-AA 68%, Artificial Analysis Intelligence Index v4.3.2 44 points. Supports SGLang and vLLM inference.
Tencent Hunyuan · Hugging Face new modelsFeatured7171Tencent's Huyuan has released Hy-Nomas' BF16 Diffusers format distilled weights on Hugging Face. This video world generation model supports multi-round free interaction through text instructions and camera control, generating the same world segment by segment.
Why it matters: Officially releases distilled BF16 Diffusers weights and supporting benchmarks, allowing readers to judge the practical usage and threshold of multi-round interactive world generation.
Hugging Face BlogHugging Face releases torch-neuronx model supporting query Space, detecting AWS Trainium compatibility. This Space supports querying whether mainstream models (such as Qwen3-0.6B) with a scale of 175B parameters can run on Trainium without modification, and provides running scripts.
Hugging Face BlogFeatured6666Microsoft and Hugging Face release ThinkingBox-Training, directly using ThinkingBox's deterministic verification of database final states as reinforcement learning rewards, training Qwen3.8-27B for 50 steps with GRPO
Why it matters: Microsoft and Hugging Face directly use database final state verification as reinforcement learning rewards and have publicly released task filtering and training code, which can be transferred to other stateful intelligent agent training
Hugging Face BlogHugging Face releases OlmoEarth v1.3, a satellite embedding model, offering global pre-computed embeddings (10m resolution, 64 and 128 dimensions), available to users without login or technical knowledge. The model performs best in 27 evaluations, with 128-dimensional embeddings scoring highest in 20 evaluations and 64-dimensional embeddings leading in 18. Training costs are reduced by 53% compared to v1.2 and 18% compared to v1, with corresponding reductions in carbon emissions. Weights, data, and code are all open source.
NVIDIA NewsroomNVIDIA and the Protein Design Institute at the University of Washington Medical School jointly release the open-source RFD4-Proteina model, combining programmable protein design and search capabilities, surpassing baseline models in four benchmark tests, helping scientists explore drug design candidates.
Hugging Face BlogHugging Face announced IBM Granite, a time series model integration and forecasting solution, combining four Granite models (PatchTST-FM-r1, PatchTST-FM-r2, FlowState-r1.1, TTM-r3) to provide out-of-the-box integrated forecasting.
Hugging Face BlogFeatured7878NVIDIA releases pre-trained relational foundation model Kumo Relational, enabling direct prediction on enterprise databases without task-specific training, feature engineering, or table flattening. The model outperforms supervised relational deep learning and expert feature engineering gradient boosting trees on 41 tasks across 15 databases, achieving an MRR 8% higher than AutoGluon ensemble on the SAP SALT benchmark.
Why it matters: The original provides a comparison result of pre-trained relation models on 41 tasks and an open-source entry, allowing readers to judge whether it can replace the existing XGBoost pipeline.
