粉丝与涨粉
快照 11月3日 · 7 日榜 272 个账号可排本期浏览最高的帖子
- 1
Introducing SenseNova-Loop-9B-Preview: a 9B open-weight multimodal model with 256K context for document understanding, coding, and agent workflows. 📜 Apache-2.0 License. 🤖https://modelscope.ai/models/SenseNova/SenseNova-Loop-9B-Preview • Leads six of eight tasks in the team’s reported comparison: Scores 71.38 on BIRD-Dev, 66.89 on WorkBuddy Bench, and 58.87 on AIDABench. • A deeper path without duplicate weights: Its 24-layer middle block runs twice with shared weights, producing 56 layer executions from 32 distinct decoder layers. • Trained for long, multimodal work: Training recipes combine text, images, code, and tool-use trajectories at sequence lengths up to 262K tokens. • Acceleration and serving paths are available: MTP is supported now, with documented Transformers, LightLLM, SGLang, and vLLM deployment paths. Note: This is a Preview release; the technical report is still forthcoming. Examples created with Raccoon Work 👇
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Beyond ordinary faces. Krea 2 OpenCast LoRA is an open-weight adapter by Akatz Labs based on Krea-2-Raw, creating fictional characters with exaggerated features, sculptural hair, and lifelike skin and clothing. 🤖 https://modelscope.ai/models/akatz-ai/Krea-2-OpenCast-LoRA 🎭 Design an original cast: Describe facial geometry, hairstyle, wardrobe, expression, and lighting to create fictional human or dressed animal characters. 🧪 Start from a proven recipe: Browse 18 exact prompts and seeds, then edit the included portrait workflow to make the character your own. 🛠️ Run it in ComfyUI: Use Krea 2 Turbo with the OpenCast LoRA at strength 1.0. No trigger token or custom nodes needed. 📜 Krea 2 Community License Agreement applies, including commercial-use conditions.
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Scientific reasoning shaped by laboratory experience. Hydrogen-1.0 is an open-weight model from Deep Principle, built on GLM-5.3 with 1M context for chemistry, materials science, and life sciences. 🤖 https://modelscope.cn/models/deep-principle/Hydrogen-1.0 🏆 Stronger scientific reasoning: Outperforms GPT-6 Astra on FrontierScience-Research in the authors' evaluation, while improving over GLM-5.3 on GPQA Diamond. 🔬 Learn from scientific work: Combines SFT and RL using domain knowledge, laboratory records, and complete discovery trajectories, including failed attempts and revised hypotheses. 🧬 Explore promising materials: After five search rounds, the mean best-so-far HOMO–LUMO gap reaches 4.318 eV, versus 3.689 for GPT-6 Astra and 3.353 for Kimi-K3 in the transition-metal-complex evaluation. 🛠️ Research with tools: Demonstrations span simulation debugging, reaction-network exploration, NMR analysis, and experimental protocol planning. 📜 Weights: MIT. Independently authored supporting code and documentation: Apache 2.0.
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English TTS in just 1.4M parameters, vocoder included. PiVo-1.4M runs offline on a CPU with NumPy and ONNX Runtime, without PyTorch. 🤖 https://modelscope.ai/models/empero-ai/PiVo-1.4M 🪶 1.4M parameters, vocoder included: Generates 16 kHz mono speech with a compact acoustic model and FARGAN-derived vocoder. 🖥️ Built for local, offline use: Generate with either English voice, Pi or Vo, directly on CPU and begin playback in 10 ms audio frames after text encoding. 🗣️ Words come through clearly: PiVo reaches 0.532% WER on its held-out test set. The authors note that this measures intelligibility, not general English quality or naturalness. 🔤 Predictable English pronunciation: It expands numbers, currencies, ordinals, and ISO dates, while unsupported words raise a clear error instead of silently guessing. 📜 Code and acoustic weights are Apache-2.0. The FARGAN-derived vocoder uses Xiph/Opus BSD terms, and the pronunciation dictionary retains its CMU license.
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Post-training gains for agents and scientific reasoning: Ling-3.1-flash-1019 is a 560B open-weight MoE with 25B active parameters per token and support for up to 1M context. 🤖 https://modelscope.ai/models/inclusionAI/Ling-3.1-flash-1019 🛠️ Stronger real-world agents: AA-Briefcase improves from approximately 1400 to 1524 Elo, while AutomationBench-AA rises from 62% to 68% versus Ling-3.1-flash. 🧠 Broader reasoning gains: Scores 44 on the Artificial Analysis Intelligence Index v4.3.2, up from 41 for its predecessor. 🔬 Better scientific performance: CritPt increases from 18% to 26%, and SciCode from 54% to 57%. 📐 Hybrid long-context design: Combines 63 KDA layers with 9 Gated MLA layers. Reaching the full 1M context requires the appropriate long-context serving configuration. 📜 MIT License.
同期站内报道里提到它
本周涨粉观察
全部观察每周一生成；中文区、7 日涨粉超过 1000 的账号会有条目。
发布节奏
最近 12 周每周中位浏览
最近 12 周发布时间
颜色 = 中位浏览爆款 vs 日常
爆款 = 浏览前 10%| 指标 | 爆款 | 日常 |
|---|---|---|
| 帖子 | 9 | 77 |
| 带媒体 | 100% | 93.5% |
| 回复 | 0% | 0% |
| 平均字数 | 709 | 874 |
| 中位浏览 | 6,372 | 297 |
打法建议
只陈述相关性- 互动最好的时段是 20:00 前后（北京时间），中位浏览 482。
- 一周里 周一 的中位浏览最高。
- 形式上 纯文字 的中位浏览更高（320 vs 364）。
- 爆款里带媒体占 100%、回复占 0%；日常分别是 93.5%、0%。
- 当前节奏约 2.3 条/天，日均浏览 1,010。
内容结构
按本站分析的分类（近 120 天）- 模型69
- 产品10
- 论文5
- tip1
形式与语言
帖子占比- 图片61
- 视频20
- 纯文字5
回复占比 0% · 引用 2.3% · 带媒体 94.2%
互动最高的帖子
按浏览排序- 1
Introducing SenseNova-Loop-9B-Preview: a 9B open-weight multimodal model with 256K context for document understanding, coding, and agent workflows. 📜 Apache-2.0 License. 🤖https://modelscope.ai/models/SenseNova/SenseNova-Loop-9B-Preview • Leads six of eight tasks in the team’s reported comparison: Scores 71.38 on BIRD-Dev, 66.89 on WorkBuddy Bench, and 58.87 on AIDABench. • A deeper path without duplicate weights: Its 24-layer middle block runs twice with shared weights, producing 56 layer executions from 32 distinct decoder layers. • Trained for long, multimodal work: Training recipes combine text, images, code, and tool-use trajectories at sequence lengths up to 262K tokens. • Acceleration and serving paths are available: MTP is supported now, with documented Transformers, LightLLM, SGLang, and vLLM deployment paths. Note: This is a Preview release; the technical report is still forthcoming. Examples created with Raccoon Work 👇
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Intern-Decision scales multimodal structured decision-making across 0.8B, 2B, and 4B models. 🤖 https://modelscope.ai/collections/Shanghai_AI_Laboratory/Intern-Decision 🏆 The three models average 79.38, 84.68, and 90.02 across seven decision benchmarks. Intern-Decision-4B surpasses Jev at 88.74 while achieving better probability calibration. ⚡ Reported mean latency is 33.98 ms, 33.28 ms, and 44.16 ms respectively, compared with 109.70 ms for Jev in the same local HF setup. 🧠 One forward pass answers multiple choice, score, and yes/no questions with typed JSON and calibrated probability distributions. 🖼️ Text states and up to eight images enable routing, tool selection, scoring, and multimodal workflow control. 📜 Apache 2.0. Applicable Qwen3.5 upstream notices must be retained.
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Jina-OCR-v1 parses entire pages into structured Markdown at 2.57 pages per second.🚀 🤖 https://modelscope.ai/models/jinaai/jina-ocr-v1 📄 https://modelscope.ai/papers/2609.03181 🏆 Scores 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench, 7.4 points above DeepSeek-OCR on the latter. ⚡ Delivers the highest throughput among 14 evaluated systems at concurrency 32. 📑 Preserves text, formulas, tables, and reading order in a single pass. 🧠 The 3.4B MoE activates only 570M parameters per token, while FastMTP accelerates decoding without changing the autoregressive output. 📜 CC BY-NC 4.0. Commercial use requires permission.
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Two LoRAs, one clean layer-editing workflow: extract an object as a transparent layer, then remove it from the original image. 🤖 https://modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-LayerExtract 🤖 https://modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-LayerRemove ✂️ LayerExtract isolates a prompt-specified subject and exports it on a transparent background for independent editing. ✨ LayerRemove takes the source image and a reference layer, removes the corresponding object, and reconstructs the scene behind it. ⚡ Both are compact Qwen-Image-2.1 LoRAs that can be hot-swapped within the same DiffSynth-Studio pipeline. 🧩 Together they turn flattened images into editable assets for compositing, repositioning, replacement, and design workflows. 📜 LoRA weights: Apache 2.0. Qwen-Image-2.1 base-model terms also apply.
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IQuest-Q1 lands with 320B MoE scale, 15B active parameters, and a 512K context window for long-horizon coding agents. 🤖 https://modelscope.ai/models/IQuestLab/IQuest-Q1 🧠 Built for agentic coding, complex reasoning, and multi-step tool use across long-running workflows. 🏆 Scores 84.5 on CyberGym, 83.2 on Terminal-Bench 2.1, 64.6 on DeepSWE v1.1, and 63.0 on NL2Repo. ⚡ 256 experts with 8 activated per token, hybrid sliding-window/full attention, and recursive MTP for faster decoding. 🛠️ Production deployment supports SGLang and vLLM, with integrations for Claude Code, Codex CLI, and OpenAI-compatible APIs. 📜 Weights released under the IQuest-Q1 License.
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Audio8 ASR Infinite keeps transcribing around the clock with bounded memory and latency. 🤖 https://modelscope.ai/models/Edge0/Audio8-ASR-Infinite 🎙️ A rolling KV Cache enables unlimited-length Chinese and English transcription without accumulated drift. 🏆 At 480 ms delay, it reaches 1.75 CER on AISHELL-1, 2.89 on AISHELL-4, and 3.04/6.81 WER on LibriSpeech test-clean/test-other. ⚡ Select an 80, 120, or 160 ms audio clock and tune delay from 240 to 560 ms. Semantic VAD distinguishes pauses from real turn endings. 📜 Preview release under Apache 2.0, with continuous deployment through an adapted vLLM stack.
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A 5B world model built for real-time interaction. Astronex-World 1.0 is now available.📜 Apache 2.0. 🤖 https://modelscope.ai/models/Astronex/Astronex-World 📊 Scores 70.0 on WBench Full, outperforming evaluated models with 13.6B and 14B parameters while coming within 0.9 points of a 22B model. ⚡ Streams 832×480 video at 24 fps in real time on a single 48GB GPU, with 1280×704 generation also supported. 🎮 Camera trajectories, 64-D continuous actions, embodiment IDs, and timed text events provide frame-aligned control over how the world evolves. 🧠 One backbone supports both bidirectional full-context generation and block-causal streaming with cross-block KV caching. 🛠️ The complete five-stage training pipeline runs on two 48GB GPUs; weights, code, sampling presets, and post-training recipes are included.
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Beyond ordinary faces. Krea 2 OpenCast LoRA is an open-weight adapter by Akatz Labs based on Krea-2-Raw, creating fictional characters with exaggerated features, sculptural hair, and lifelike skin and clothing. 🤖 https://modelscope.ai/models/akatz-ai/Krea-2-OpenCast-LoRA 🎭 Design an original cast: Describe facial geometry, hairstyle, wardrobe, expression, and lighting to create fictional human or dressed animal characters. 🧪 Start from a proven recipe: Browse 18 exact prompts and seeds, then edit the included portrait workflow to make the character your own. 🛠️ Run it in ComfyUI: Use Krea 2 Turbo with the OpenCast LoRA at strength 1.0. No trigger token or custom nodes needed. 📜 Krea 2 Community License Agreement applies, including commercial-use conditions.
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Having issues with Qwen-Image-2.1? Our official feedback form connects you directly with the Qwen Image research team. Share your prompts, images, or workflows to help us investigate and improve. 📝https://alidocs.dingtalk.com/notable/share/form/v01WgZOZA5DaVQPeqLX_dv19yqvsgs3oebp3pcjys_1qX0QQ0?source=link English & Chinese welcome.
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@InSpatio_AI releases InSpatio-World 1.5, turning images and videos into real-time, explorable 4D worlds. 🤖 https://modelscope.ai/models/InSpatio/inspatio-world-v1.5 📄 https://modelscope.ai/papers/2604.07209 🏆 The 1.3B model scores 68.72 on WorldScore-Dynamic, ranking #1 among evaluated real-time and interactive methods while reaching up to 24 FPS. 🌐 A single image, four images, a panorama, or a video can become a navigable scene with wide viewpoint changes and target camera trajectories. 🎥 Next-view prediction preserves motion, spatial structure, and visual style across space and time, with support for bullet-time creation. 📐 For video inputs, Depth Anything 3 automatically estimates missing depth and per-frame camera intrinsics and extrinsics. 🧠 Spatiotemporal autoregression maintains a persistent world state for consistent long-horizon exploration. 📜 Code: Apache 2.0. Dependencies retain their own licenses.
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IndexTeam releases Index-Translate, a multilingual translation family for text, speech, dubbing, and long documents. 🤖 https://modelscope.cn/collections/IndexTeam/Index-Translate 🏆 Index-Translate-9B scores 0.8789 on FLORES, 0.8209 on instTrans, and 0.7387 on MEME, showing strong general translation, instruction following, and cultural-context understanding. 🌍 Covers 150 languages while preserving terminology, formatting, structured data, and protected text instead of simply translating everything. 🎙️ Index-Echo supports S2TT and voice-conditioned S2ST; Index-Homura controls translation length for dubbing, with 81.92% of SandGlass samples staying within 10% of the target syllable count. 📚 Index-NativeLong translates complete documents in one pass, keeping names, terminology, and context consistent across books and long transcripts. 📜 2B and 9B models are released under Apache 2.0, with 35B-A3B preview results also available.
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Step 5 Preview is going open source! ⏳ A flagship foundation model built for real-world agentic tasks. Join the countdown: https://modelscope.ai/models/stepfun-ai/Step-5-Preview
点赞、转发、浏览是采集那一刻的快照，不是帖子最终数据；时间为北京时间。KPI 卡片里的同比只在账号有 8 周以上数据时显示， 否则只看近 4 周。“打法建议”只说明数据里的相关性，不代表因果。