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ModelScope

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Headlines · 7

  1. inclusionAI releases Ming-3.0-audio-flash audio-language model

    inclusionAI published Ming-3.0-audio-flash on Hugging Face, described as a native audio-language model that takes audio plus text instructions and supports speech recognition, spoken question answering, audio understanding, audio reasoning and optional synthesized-speech output. The model card lists a Mixture-of-Experts design with 126B total parameters and 6.5B activated per inference step, an MIT license, Flash and Tiny variants, and links to GitHub and ModelScope. Primary languages are Chinese and English, with the card also claiming robustness to dialects, accents, noisy environments and multi-speaker recordings, plus long-audio understanding and perception of music and non-speech sound. These capability and parameter figures are vendor claims; no independent evaluation is included.

    r/LocalLLaMA · 🔥 5
  2. China Telecom's XingChen lab open-sources Xing4.0-29B-Intent

    On October 19, the XingChen AGI Lab at China Telecom AI open-sourced Xing4.0-29B-Intent, a 29B model that combines intent classification and parameter extraction and returns results in a unified category+args JSON structure. It was released with model weights, a 2.753 million-item SFT corpus and an inference SDK. Zhedongxi reports end-to-end latency under 150 ms and a zero-shot cold-start Micro-F1 of 70.27; it also cites an overall score of 58 on a unified benchmark across 48 public datasets, 9 points above second-placed Gemini-3.6-Flash. In 11 hands-on tests across smart spaces, smart cockpits, enterprise workflows, office collaboration and personal agents, the outlet found solid multi-object parameter binding and multi-task splitting, but flagged an error where "do not open the window" was turned into a 0% opening. The code is open under Apache 2.0 in the TeleChat-USP GitHub repository; the report gives Hugging Face, ModelScope and Modelers links, says datasets and weights are on Hugging Face, and notes an official inference image.

    智东西 · 🔥 9
  3. Alibaba's Qwen open-sources BlenderWorld, a 3D creation benchmark for visual agents

    According to IT之家 and DoNews, Alibaba's Qwen team open-sourced BlenderWorld on October 14, a benchmark for evaluating visual agents on real 3D creation tasks in Blender; it is under the MIT license, with code and dataset published on GitHub, Hugging Face and ModelScope. The benchmark contains 223 tasks and 2,651 criteria across 9 major categories and 22 subcategories, ranging from products and architecture to natural environments, characters, animation and scientific visualization, and it requires an editable .blend project file rather than just a rendered image. Evaluation renders submissions from 8 adaptive viewpoints and combines scene checks, task-specific binary criteria and 8 overall visual quality scores, weighting rule compliance and visual quality at 50% each; the project uses Blender 4.2.10 and requires Linux and Python 3.10 or later.

    IT之家 AI · 🔥 0
  4. Ant Group's Ling team open-sources 560B Ling-3.1-flash

    On October 15, Ant Group's Ling team released Ling-3.1-flash as open weights, publishing an FP8 checkpoint under the MIT license on Hugging Face and ModelScope. The model has about 560B total parameters and activates about 25B per token; Ant says it supports up to 1M context, though 36Kr notes the repository's max_position_embeddings is 262,144 and 1M is listed as a target, while Artificial Analysis measured an intelligence index of 41 and about 210 tokens per second. Ant says a mixed MXFP4/block-FP8 scheme cuts weights from about 534GB to 292GB and its Hopper test configuration from eight GPUs to four; 36Kr also notes vendor benchmark scores were run on the unreleased LingCode harness, and independent testing found flaws in long-chain agent tasks.

    智东西 · 🔥 0
  5. StepFun releases Step 5 Preview open weights with 600B MoE

    StepFun has opened the weights of Step 5 Preview in BF16 and FP8 on Hugging Face and ModelScope, together with a technical blog and deployment guides. The sparse MoE model has 600B total and 27B active parameters, supports up to 1M tokens of context and natively accepts text and vision input for long-horizon agentic work such as software engineering, professional knowledge work and finance. Chinese outlets cite an Artificial Analysis Intelligence Index score of 44, a second-place OpenRouter token-usage ranking as of October 15, 2026, and day-zero support announced by Huawei Ascend, Biren, MetaX and Moore Threads; the weights ship under the StepFun Model License 1.0.

    阶跃星辰 · Hugging Face · 🔥 0
  6. Baidu open-sources 3B Unlimited-OCR document parsing model

    Baidu published Unlimited-OCR, an image-text-to-text model listed on Hugging Face at 3B parameters, under an MIT license with custom code and the transformers library. The model card positions it for "one-shot long-horizon parsing," with two single-image configurations (gundam and base) plus multi-page and PDF parsing, and says it is also available on ModelScope and Baidu Cloud. It further lists support for vLLM and SGLang inference and ms-swift training, cites an arXiv paper (2606.23050), and credits Deepseek-OCR, Deepseek-OCR-2 and PaddleOCR.

    百度文心 · Hugging Face · 🔥 0
  7. Tencent Hunyuan open-sources Hy-MT2 translation models in 1.8B, 7B and 30B-A3B sizes

    Tencent Hunyuan has open-sourced the Hy-MT2 family of multilingual translation models on Hugging Face and ModelScope, spanning 1.8B, 7B and 30B-A3B (MoE) sizes, covering 33 languages and emphasizing multilingual translation instruction-following. The team claims the 7B and 30B-A3B models outperform open-source models such as DeepSeek-V4-Pro and Kimi K2.6 in "fast-thinking" mode, while the 1.8B model beats mainstream commercial APIs from providers such as Microsoft and Doubao overall — claims based on its own evaluations and not yet independently verified. The release also open-sources the IFMTBench instruction-following benchmark and a Hy-MT2-Translator Skill, and uses AngelSlim 1.25-bit quantization to shrink the 1.8B model to about 440 MB with a 1.5x inference speedup; Tencent Hy also announced a WMT26 partnership with sponsored awards for teams using Hy-MT models.

    GitHub Trending · Python · 🔥 0

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