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.
Edge0 open-sourced the Edge0-145B and Edge0-120B two edge models, and also released a unified inference framework and deployment tools. Officially, the 145B model can run locally on compatible laptops, with peak memory of 29GB, decoding speed of 80+ tokens/s, pre-filling speed of 1100+ tokens/s, supporting context lengths of 128K–256K.
Why it matters: The original text provides actual memory, speed, and multiple benchmark comparisons of the edge 145B model, which can be used to judge the practical boundaries of running cutting-edge models locally.
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 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.
MarkTechPostwebAI has released TwIL-8B, an 8.79B dense inference model fine-tuned based on IBM Granite 4.2 8B, achieving 98.2 on MATH-500 and 81.6 on LiveCodeBench†, using 38% fewer tokens than Qwen3.5-9B.
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.
China Merchants Lion Mountain Laboratory releases the 'Embodied Reasoning Visual Language Model Lion-ER-35B-A3B', adopting the MoE mixed expert model architecture, with approximately 35B total parameters, and each token only activates about 3B parameters.
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.
Hugging Face Daily Papers5959Apriel Voice 27B Preview is an open-source speech language model that integrates speech understanding, reasoning, and tool use through a two-stage training process (including online distillation) and performs excellently in audio benchmark tests.
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.
The SoMark team has open-sourced the 1.3B document parsing model OXR, ranking first on three benchmarks: OmniDocBench (97.18), olmOCR-Bench (87.84), and DocParsingBench (87.76).
Why it matters: 1.3B model trained with RSI ranks first in three document parsing rankings, and provides engineering trade-offs between plug-in architecture and free API.
MarkTechPostBaseten released joey-1, a 2B parameter decision model, which returns labels and calibration probabilities instead of generating text, used for high-volume routing, classification, and guardrail checks, with a price of 0.01 USD per million input tokens, and weights open-sourced on Hugging Face under the MIT license
TechCrunch · AISame event7878Mistral has released the weights of its trillion-parameter open-source model, Mistral Large 4 (ML4, nickname Le Chonk), which businesses and governments can obtain and deploy on their own via Hugging Face and API.
Same story — showing the featured version《Mistral releases open-source model Mistral Large 4 (Le Chonk) with trillion parameters》
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.
NVIDIA launched the open-weight speech transcription model Nemotron 3.5 Transcribe on October 28, integrating transcription, speaker attribution, translation, and audio Q&A, using the MoE architecture, with total parameters of about 34 billion and activation parameters of about 4.3 billion, capable of processing the longest recording of 1 hour, supporting transcription of 40 languages, and distinguishing up to 8 speakers.
Why it matters: NVIDIA open-sourced the speech transcription model, disclosing parameter scale, multilingual and speaker separation capabilities, and benchmark results, allowing comparison with existing open-source ASR solutions.
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.
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.
Why it matters: The article provides specific evaluation numbers for model parameter scale, text rendering, and layout control, allowing for an assessment of open-source image generation models' position in design-related tasks.
The DecoderFeatured7474AI startup Recursive has released its first language model, Flint. During the development process, the AI system named Eureka Machine participated in proposing ideas, building, testing, and planning subsequent experiments.
Why it matters: Recursive publicly announces its first model developed with the assistance of an automated research system, allowing readers to observe how far AI-assisted R&D can go.
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.
NVIDIA BlogFeatured6262NVIDIA expands its family of open models to cover language, robotics, biology, and quantum computing, including the new Nemotron 3.5 Super VL and Nemotron 3.5 Bolt.
Why it matters: NVIDIA has rolled out open models across four lines: language, robotics, biology, and quantum, allowing readers to judge the coverage of its open-source ecosystem based on this.
Tencent Hunyuan · Hugging Face new modelsQwen3-8B-DSpine-b16 released by Qwen, employing speculative decoding technology with parallel drafting and deep conditional injection, achieving an average acceptance length score of 10.55 on Qwen3-8B. The model supports a context length of 8192 tokens, uses bfloat16 data type and fa3 attention backend, installation requires git clone and pip install, and runs via sglang serve.
Comfy BlogBlack Forest Labs releases open-source FLUX 3 Image Klein model with 5.6B parameters, outperforming Qwen-Image 2.1 on text-to-image and multi-reference generation tasks, supporting both Base and Distilled versions, with the latter offering 4.3-9.3 times speed improvement, suitable for consumer-grade GPUs.
Mistral AI · Hugging Face new modelsMistral AI releases Timestral-123M time series base model, with 123M parameters, using a patch-based Transformer architecture, context length of 32,768 time steps, single prediction covers 1,024 time steps, outputs 21 quantiles. The model achieves optimal accuracy in zero-shot prediction on GIFT-Eval, TIME, and fev-bench evaluations, and is licensed under the Modified MIT License.
Mistral AI · Hugging Face new modelsMistral AI · Hugging Face new modelsMistral AI releases Timestral-5M time series base model, the 5M parameter version supports zero-shot prediction, context length up to 32,768 time steps, single forward prediction can cover 1,024 time steps, and outputs 21 quantiles. In GIFT-Eval, TIME, and fev-bench evaluations, Timestral-209M achieves state-of-the-art accuracy for zero-shot prediction, and all Timestral models outperform other models at their parameter scale.
Latent Space7070Reflection announces the launch of Beam, a text-only 501B total parameters / 23B active parameters encoding, intelligent agent, and scientific work multimodal model using the MoE architecture, trained from scratch, and will open source all weights this month (Apache 2.0 license).
Qwen · Hugging Face new modelsQwen/Qwen-Image-2.1-PE-I2I released, a 7B parameter unified text-to-image generation and editing model, featuring a 32-layer single-stream DiT architecture, supporting the generation of regular or transparent images, editing transparent layers, extracting photo subjects, and capable of editing with 10 reference images.
Qwen · Hugging Face new modelsQwen/Qwen-Image-2.1-PE-T2I released, a 7B parameter unified text-to-image generation and editing model, featuring a 32-layer single-stream DiT architecture, supporting generation of regular or transparent images, editing transparent layers, extracting photo subjects, and enabling local editing through circular, doodle annotations, or independent masks. Its text-to-image prompt rewriting model Qwen3.5-VL 9B can convert multilingual requests into detailed English prompts and recommend aspect ratios.
Liquid AI BlogFeatured7171Liquid AI releases LFM2.5-VL-3B, an upgraded 3.1B edge-side visual language model based on its predecessor LFM2-VL-3B, now available on Hugging Face and Playground
Why it matters: The original text provides 28 benchmark items, edge-side, and GPU throughput data, which can be used to compare with similar-sized competitors to determine the actual position of this 3B visual model.
