Turn complex documents into data AI agents can use with OXR-1.0, SoMark's 1.3B open-weight model for parsing PDFs and document images into Markdown or JSON. 🤖 modelscope.ai/models/SoMark/… 🏆 SOTA across three parsing benchmarks: Ranks #1 in the project’s published comparisons on olmOCR-Bench, OmniDocBench, and DocParsingBench. ⚡ Small model, top-tier speed: Also leads the project’s 21-model speed comparison at 557.77 native tokens/s. 📄 Beyond plain OCR: Recognizes 13 element types, including tables, formulas, chemical equations, chemical structures, and code blocks. 🧩 Bring your own layout model: Layout analysis is independent and pluggable, so teams can customize structure handling while retaining OXR’s recognition and reading-order capabilities. 🌀 Trained to improve from failures: Recursive self-improvement during post-training turns real document parsing failures into new optimization signals. 📜 OXR Open Source License applies.

Sage-A1.5 is here, bringing 256K context, parallel research, and evidence checks to long-horizon scientific workflows. 🤖 modelscope.ai/collections/In… 🏆 SOTA across scientific benchmarks: Scores 90.0 on PinchBench, 81.0 on FrontierScience-Olympiad, and 48.3 on FrontierScience-Research, ahead of Kimi-K3, GLM-5.3, and GPT-5.6-sol in the reported comparison. 🧩 Agentic Orchestration: When enabled, delegates independent investigations to parallel subagents with isolated contexts, then integrates their evidence and findings to guide subsequent work. ✅ Verification: Optional Verification Mode reviews progress, checks evidence and assumptions, and redirects stalled searches within the model's own reasoning, without a separate critic model at inference. 🔬 Open Scientific Discovery: Supports exploratory research through tool-assisted analysis and produces inspectable research artifacts, extending its use beyond predefined question answering. 🛠️ Native tool use for research: Supports APIs, code interpreters, search engines, and external tools for tool-augmented scientific reasoning and professional workflows. ⚡ Available in BF16, FP8, and GGUF F16/Q8/Q4 formats, with Transformers, vLLM, and SGLang support. 📜 Apache 2.0.

Spark-X2.6-4B has landed: an open-source model with 1M context, three thinking budgets, and deployment-ready formats for local agents.@SparkLLM 🤖 modelscope.ai/collections/XH… 🏆 Stronger at 4B: In the team’s reported comparison table, it reaches a 63.3 average, ahead of Spark-X2.5-4B at 52.1 and Qwen3.5-9B at 49.1 across the evaluated suite. 🧠 Set the thinking budget: Switch between none, low, and high thinking modes to balance reasoning quality, latency, and cost for each task. ⚡ Built for efficient local inference: A hybrid attention design combines 1M context with resource-conscious deployment, while FP8, INT8, INT4, and GGUF releases offer practical precision and footprint tradeoffs. 🛠️ One model, multiple workflows: Use the Instruct checkpoint for everyday agent work or the Base model for continued training and customization. Supports Transformers, SGLang, vLLM, llama.cpp, Ollama, MLX, and LM Studio. 📜 Apache 2.0.

Introducing MemT-1.0, an open-weight model family with persistent, evolving memory built into model computation, enabling conversations and agents to reuse historical information without reloading the original context. 🤖 modelscope.ai/collections/Me… 🏆 Leads parametric memory methods: Across 11 memory benchmarks, MemT-1.0-Flash and MemT-1.0-Nano rank first and second among the evaluated methods. 🧠 Memory lives in the model: Dynamic local memory and trainable hyper memory retain past information without carrying the original conversation into every new inference. ⚡ Built for long-running agents: At 128K history, Nano delivers an official 1.86× end-to-end inference speedup and about 66× lower persistent storage than a full-context KV cache. 🛠️ Two backbones, one memory design: Nano is built on Qwen3.6-35B-A3B with about 3B active parameters; Flash builds on DeepSeek-V4-Flash with about 17B active parameters. Both support locally persistent memory sessions. 📜 Flash weights are released under MIT. Inherited backbone licenses still apply.

NVIDIA Nemotron Labs Dory1.0-4B is an open-weight reasoning model with up to 256K context, letting developers adjust inference compute within a single checkpoint. 🤖 modelscope.ai/models/nv-comm… 🔁 One checkpoint, adjustable compute: Run the shared recurrent block for different numbers of loops to balance latency and accuracy per task, with no retraining or model swap. 🏆 Stronger coding-agent results at 4B: At two loops, it reaches 59.5 on SWE-bench Verified in the vendor evaluation, ahead of Qwen3.5-4B at 54.7. 🧠 Extra thinking without longer answers: More loops add latent compute per token instead of simply generating more tokens. ⚡ Loop without compounding cache cost: Optional last-loop KV caching keeps one cache per recurrent attention layer, avoiding extra KV-cache overhead as loop count grows. 📚 Built for long-context workloads: 4B parameters, up to 256K context, and releases in BF16, MXFP8, NVFP4, and Q5_K_M formats. 📜 OpenMDW License Agreement v1.1.

VideoJev-4B brings direct decision-making to video understanding. Qwen3.5-4B based video decision model for temporal grounding and multiple-choice video QA. 🤖 modelscope.ai/models/MCG-NJU… 📄 modelscope.ai/papers/2610.18… 🏆 Video QA leader at its scale: Ranks #1 among sub-6B models on LVBench, Video-MME, LongVideoBench, and MLVU. ⚡ Direct decisions, not long generations: Scores candidate answers and time spans directly, making video QA and event localization easier to integrate into workflows. 🎬 Find the moment, pick the answer: Locates when an event occurs in video and answers multiple-choice questions with calibrated probabilities. 🧠 Long-video ready: Supports up to 256K context in the official video QA evaluation setup. 🛠️ Ready for local use: Released with BF16 weights, training and evaluation code, plus a quick-start demo. 📜 Apache 2.0.

Ming-3.0 is here: five open-weight audio models for controllable speech, long-recording analysis, and context-aware transcription. 🤖modelscope.cn/collections/in… 🏆 Voice cloning at the top of the open-weight ranking: Ming-3.0-tts-0.5B achieves open-weight SOTA on Artificial Analysis Controlled Voice with a 1069 Elo rating, using eight English reference voices. 🎙️ Design a voice, then direct the performance: The TTS models support voice design from descriptions or references, multilingual synthesis, and controllable delivery for podcasts, characters, and product experiences. 🧠 Turn long recordings into answers: Ming-3.0-audio-flash transcribes, summarizes, analyzes, and follows up on long audio while grounding answers in the original recording. It reaches 99.40 on BigBenchAudio S2T in the authors’ comparison. 📝 Transcribe with business context: Ming-3.0-asr-tiny accepts hotwords, domain terminology, and dialogue history to improve recognition for product names, dialects, and specialized conversations. ⚙️ Choose the fit: Flash models prioritize broader capability, while Tiny and 0.5B options target lower-cost, efficient deployment.

Xing4.0-29B-Intent turns a one-line request into structured intent, entities, relations, and action parameters, all through one schema-driven model. 🤖 modelscope.ai/models/XingChe… 🏆 Leads the authors’ extraction evaluation: Scores 58 across 6,000 samples from 48 Chinese and English public datasets, ahead of 18 evaluated general-purpose models. 🧠 Choice and extraction in one model: Handles both intent routing and structured semantic parsing, so applications can classify a request and extract the information needed to act on it. ⚙️ Built for reliable tool use: Outputs schema-constrained JSON rather than long-form replies, covering intent detection, entity recognition, relation extraction, and event extraction. 📱 Made for real product flows: Fits customer service routing, smart-device commands, terminal assistants, and agent or function-calling pipelines. 📜 Model weights, training data, and code are released under Apache 2.0. #XingIntent #IntentUnderstanding #SemanticParsing #FunctionCalling #AgenticAI #ModelScope

MiniMax H3 Character Swap LoRA is now available on ModelScope, an experimental adapter for recasting a selected person in video from an image reference. 🤖 modelscope.ai/models/akatz-a… 🎭 Swap the performer, keep the shot: Recast a creator, actor, or avatar from a character reference while retaining the original scene, movement, and camera work. 🧩 V1.5 is the recommended checkpoint: Improved character retention in the authors’ selected V1 comparison examples. 🛠️ Built for the H3 workflow: Load it with the MiniMax H3 Ref2VA base, text encoder, and VAEs in a compatible ComfyUI LoRA workflow. 📜 MiniMax H3 Community License Agreement applies.

DeepSeek-V4.1-Flash-1017 delivers stronger coding and agent performance with faster task completion and shorter outputs. Available on ModelScope: 🤖 modelscope.ai/models/deepsee…

NVIDIA Nemotron 3.5 Bolt has landed on ModelScope: a 74B-A9B open-weight VLM in a 24GB footprint. 🤖 modelscope.ai/collections/nv… 🏆 Strong agent results: The 24GB model reaches 50.98 on Terminal-Bench 2.1 and 66.75 on BrowseComp. NVIDIA positions it as the #1 open model in its class on PinchBench and #2 among open models on BrowseComp. ⚡ Built for local inference: Native 2-bit quantization fits the model into 24GB while retaining 9B active parameters per token. 👁️ Multimodal, long-context agents: Accepts text, images, and video with up to 256K shared context for prompts, tool results, and conversation history. 🧩 Choose the right variant: Use 24GB IQ2 for local inference, BF16 for post-training and custom quantization, or LiLiCorr variants for speculative decoding. 📜 OpenMDW License Agreement v1.1. Commercial and non-commercial use supported.

Introducing Hy-Memory-30B-A3B, a dedicated long-term memory writer with around 3B active parameters. 🤖 modelscope.ai/models/tencent… 📄 modelscope.ai/papers/2610.22… 🏆 Cross-framework leader: Scores 55.49 across Mem0, Hy-Memory, MemOS, and held-out EverMemOS in the fixed 20% evaluation matrix, ahead of Claude Opus 5 at 54.62. 🧠 Write memories, not chat replies: Decides what to retain, turns evidence into precise memory entries, and updates the persistent store when new information arrives. 🔄 Transfers beyond its training stack: Leads the held-out EverMemOS harness at 56.89, showing the writer can plug into unseen memory frameworks. 🛠️ Easy to integrate: Runs with vLLM and an OpenAI-compatible API, with a no-thinking mode for structured fact and preference extraction. 📜 Apache 2.0.

Meet MOSS-Image-Base: an 8.56B open-weight text-to-image foundation model built for more controllable generation. 🤖 modelscope.ai/collections/op… 🧩 More than natural-language prompts: Generate from descriptive prompts, tag sequences, or JSON layouts that specify text and object placement. 📝 Layout-aware images: Control captions, scene elements, and bounding boxes for posters, whiteboards, diagrams, and other structured visuals. 🔧 Built for customization: As the base checkpoint in the MOSS-Image family, it is designed for research, fine-tuning, and workflow-specific adaptation. 🖥️ Ready for local workflows: Includes Diffusers support, a Gradio interface, CPU offloading options, and a native ComfyUI plugin. 📜 Model weights and inference code are released under Apache 2.0.

Full GLM-5.3, compressed for multi-node local serving. Kindling AI releases an exlr8 K2/K3/K4 quantization of the 744B MoE model. 🤖 modelscope.ai/models/kindlin… 📦 Flexible memory targets: Choose profiles ranging from 219GB to 378GB, depending on the quality and memory budget of the deployment. ⚙️ One weight set, multiple precisions: K2, K3, and K4 expert weights ship together, letting users change the precision mix at load time without re-encoding the model. 🖥️ Built for distributed local inference: Designed for tensor parallelism across 3–7 unified-memory nodes, with TP=3 and TP=4 configurations already tested. 🧪 Quality-aware allocation: Hessian-calibrated manifests spend additional bits where they reduce divergence from the original BF16 model most effectively. 📌 Preview release. Performance and full BF16-alignment validation are still in progress. GLM-5.3 License applies.

NVIDIA Nemotron 3.5 Super VL is open released in BF16 and NVFP4: a 120B MoE multimodal agent model that activates just 12B parameters per token. ModelScope’s ms-swift supports both inference and fine-tuning, helping developers put the model to work and adapt it to specialized multimodal applications. 🤖 modelscope.ai/collections/nv… 💻 github.com/modelscope/ms-swi… 🏆 Agent performance: Outperforms the 5x larger Nemotron 3 Ultra on the reported Hermes Bench and LangChain evaluations. ⚡ Faster workflows: Delivers up to 5x higher throughput in its size class and completes evaluated agent tasks up to 4x faster. 👁️ Multimodal reasoning: Understands text, images, charts, documents, and video while reasoning, coding, and using tools. 🧰 Built to customize: Fine-tune with your own multimodal datasets using LoRA in ms-swift, or use NVIDIA NeMo for SFT and RL to adapt the model to domain data, tools, policies, and workflows. 🧠 Efficient deployment: NVFP4 with QAD, multi-token prediction, and cross-turn context reuse reduce repeated inference work. Supports vLLM, SGLang, TRT-LLM, and NIM across local, data center, and cloud environments.

🌍 Meet BlenderWorld from the Qwen Team, now on ModelScope! Explore 👉 modelscope.ai/datasets/Qwen/… Can visual agents turn real-world references into editable 3D scenes? 🧊 223 Blender tasks across 9 categories 🖼 Text, image & video inputs 📏 2,651 task-specific evaluation rubrics

Train your own few-step diffusion models faster, with OPD training workflow now offered in DiffSynth-Studio! Try example “TurTurbo” Qwen-Image-2.1 LoRAs trained with this pipeline: 🤖 modelscope.ai/models/DiffSyn… 📚 Docs: diffsynth-studio-doc.readthe… ⚡ Fast training feedback: with OPD, we are seeing training convergence typically within 2 GPU-hours. 🎯 Fewer sampling steps: distill FLUX.2-klein-base-4B and Qwen-Image-2.1 from 50 to 8 steps, or Qwen-Image-2.1-Turbo from 8 to 3, while upholding generation qualtiy. 🧠 Simple workflow: train on student-generated trajectories with teacher guidance, without pre-generating teacher trajectories or designing an additional discriminator. 📜 Release under Apache 2.0 license.

TaoMate-H3-FL2VA brings 3-step streaming audio-video generation to MiniMax H3. The TaoMate-H3 runtime reports 11.45× faster DiT inference and 10.6× faster time to first playable video than the original MiniMax H3 pipeline in its 480p benchmark. 🤖 modelscope.ai/models/TaoLive… 📄 modelscope.ai/papers/2607.24… ⚡ 11.45× faster DiT: Generates a 10-second 480p sample in 14.8 seconds of DiT time, compared with 169.6 seconds for the original MiniMax H3 pipeline in the reported setup. ▶️ Earlier playback: Produces the first playable video in about 17.3 seconds, a 10.6× improvement over the baseline. 🔊 Synchronized by design: Generates speech, sound, and video together on one timeline instead of assembling them afterward. 🎬 Continuous generation: Processes prompts in five-second blocks while preserving visual identity, voice, and motion across prompt changes. Supports portrait and landscape output at 480p, 768p, and aligned 1080p. 📜 A three-step LoRA for the MiniMax H3 FL2VA base model. MiniMax H3 Community License Agreement applies.

Fish Audio Drama 3 brings voice cloning, multi-character scenes, and in-context audio editing to a 35B-A3B open-weight MoE model. 🤖 modelscope.ai/models/fishaud… 🎭 Point-level control: Place cues such as [sighs], [laughs], and [whisper] exactly where the expression should happen. ✂️ Edit without starting over: Change a word or line while preserving the surrounding voice, timing, and delivery, then continue revising from previous takes. 👥 Built for full scenes: Generate scripts with up to 16 characters, clone voices across languages, and stream long-form speech automatically. ⚡ Production-ready streaming: Delivers the first audio in under 100 ms in the reported single-stream setup and supports high-concurrency serving through SGLang-Omni. 📜 Fish Audio Community License.

Step-5-Preview is now open weight, bringing a 1M-token context window to real-world agentic work. Its 600B sparse MoE architecture activates 27B parameters per token. 🤖 modelscope.ai/models/stepfun… 🏆 Strong open-model coding: Scores 49.0% on StepCodeBench, leading the open-weight models in the reported comparison across code generation, bug fixing, refactoring, and performance tuning. 📈 Built for professional work: Delivers strong results across software engineering, finance, and knowledge tasks, including 66.4 on FrontierFinance. 🧰 From research to deliverables: Coordinates large-scale web research, analyzes complex datasets, and produces finished reports, spreadsheets, websites, and other working artifacts. 🧠 Long-horizon agents: Supports a 1M-token context window, visual input, and workflows extending across thousands of interactions. 📜 Released under the StepFun Community License.