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Friday107 items

  4. StepFun StepFunFirst-partyModel releases
    AI score67

    StepFun Step 5 Preview demonstrates strong inference performance on NVIDIA GB300

    StepFun states that Step 5 Preview demonstrates strong inference performance on NVIDIA GB300 and looks forward to developers building on it. NVIDIA AI, in a quoted tweet, calls this StepFun's largest open-weight release, with a 600B MoE model capable of handling agent tasks up to 24 hours long, reaching nearly 8K tokens/sec/GPU on NVIDIA GB300 with NVFP4, and providing an NVFP4 checkpoint as well as deployment and post-training paths for SGLang, vLLM, NeMo Automodel, and NVIDIA NIM.

    Quoted postNVIDIA AI · @NVIDIAAI

    Congrats to StepFun on Step 5 Preview 🙌 Their largest open weights release. The 600B MoE model is built to handle agent tasks lasting up to 24 hours. The model runs at nearly 8K tokens/sec/GPU on NVIDIA GB300 using NVFP4. NVP4 checkpoint: https://t.co/poBcZXeWqY Deploy with @SGL_project - https://t.co/2iE8ytEk50  or @vLLM_project - https://t.co/PedItg9OR4 Post-train with NeMo Automodel: https://t.co/KxNt53Sb8k Run it with NVIDIA NIM using a single command: https://t.co/UOWllHNEMW

    #Industry news#Reasoning#English text is being prepared.

  5. stepfun_aiFirst-partyModel releases
    AI score66

    English translation is being prepared.

    Quoted postFeatherless AI · @FeatherlessAI

    Step 5 Preview is here. @StepFun_ai's new open-weights model sets a new Pareto frontier, matching Kimi K3 on the @ArtificialAnlys Intelligence Index! It’s built for long-horizon agent work, with frontier-level results on software engineering and professional knowledge work, especially finance. - 600B sparse MoE, 27B active per token - ~1/6 the cost per task of Claude Opus 5.5, per Artificial Analysis Now on Featherless, served at FP8. https://t.co/mznI5gFzHe

    #Industry news#Open-source ecosystem#English text is being prepared.

  9. TypeSafe AIFirst-partyModel releases
    AI score60

    TypeSafe AI releases Jev 1.14, preview version supports images and longer context

    TypeSafe AI announced that Jev 1.14 was released at 1:14 PM PT on 10.14, and `jev-preview` now points to `jev-1.14.0`. This version brings image multimodality, a new bounding box base type, and 4x context length; the official statement says overall improvements are modest, with multilingual support improving most noticeably, but images and other features remain preview functionality and are not recommended for direct production use. The team also disclosed its in-house evaluation approach and invited developers to report where Jev still feels clumsy.

    Same story · 8 reports

    #Product updates#Multimodal#Reasoning

  11. unslothaiFirst-partyModel releases
    AI score67

    English translation is being prepared.

    Quoted postNVIDIA AI · @NVIDIAAI

    Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵 https://t.co/BcbED05qSG

    #Model releases#Open-source ecosystem#English text is being prepared.

  12. UnslothFirst-partyModel releases
    AI score80

    Meta releases open-source model Muse Spark Open, supporting 1M context and multimodal input

    Meta releases open-source model Muse Spark Open, with weights released the same day. Muse-Spark-Open-816B-A42B supports text, image, video and audio input, with a context window of 1M. Unsloth provides a Dynamic 2-bit GGUF quantized version that can run on Mac Studio, along with a GGUF download address and usage guide.

    Quoted postMark Zuckerberg · @finkd

    Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple. https://t.co/U2wJNrKBOg

    Same story · 33 reports

    #Model releases#Multimodal#Open-source ecosystem

  13. thom_wolfModel releases
    AI score58

    English translation is being prepared.

    Quoted postAlexandr Wang · @alexandr_wang

    1/ muse spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. natively multimodal. built for coding and agentic work like long-horizon tasks, hard software engineering, multi-step reasoning. america! 🇺🇲 https://t.co/NjtOB7aY9D

    #Model releases#Open-source ecosystem#Multimodal

  14. levieModel releases
    AI score69

    English translation is being prepared.

    Quoted postAlexandr Wang · @alexandr_wang

    1/ muse spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. natively multimodal. built for coding and agentic work like long-horizon tasks, hard software engineering, multi-step reasoning. america! 🇺🇲 https://t.co/NjtOB7aY9D

    #Industry news#Open-source ecosystem#Agents

  15. Rohan PaulModel releases
    AI score82

    Meta open-sources 816B-parameter model Muse Spark Open on Hugging Face

    Meta has released the model weights of Muse Spark Open on Hugging Face, an 816B-parameter model aimed at coding and agent tasks. The open-source version shares the same base model as Muse Spark 1.3, with additional safety post-training to reduce the risk of misuse when used standalone without system guardrails, reversing Meta's strategy of its first proprietary closed frontier model in April this year. The model has 62 layers, each containing 256 expert networks, with each token routed to 8 of them, so deployment requires the memory of an 816B-parameter model but runs at the speed of a 42B model. A 3-bit quantized version tested on a 512GB M3 Ultra occupies about 344 GB, with generation speed of about 21 tok/s under short prompts.

    Quoted postMark Zuckerberg · @finkd

    Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple. https://t.co/U2wJNrKBOg

    Same story · 33 reports

    #Model releases#Open-source ecosystem#English text is being prepared.

  16. Rohan PaulModel releases
    AI score77

    Meta open-sources 816B-parameter model Muse Spark Open, adapted for Apple MLX to run on Mac

    Meta has open-sourced the 816B-parameter Muse Spark Open on Hugging Face, sharing the same base model as Muse Spark 1.3 with added safety post-training. The model has 256 expert networks per layer, routing each token to 8 of them, so deployment requires 816B-parameter-level VRAM but runs at the speed of a 42B model; the 4-bit NVFP4 version requires at least 700GB of GPU VRAM, and Apple has adapted it for its MLX framework, with two M5 Ultra Mac Studios each providing up to 512GB of unified memory and 1.2TB/s bandwidth to carry it. It tops the MCP-Atlas tool-calling test at 89.2, and the license allows free commercial use but requires attribution as Built with Muse Spark Open; API model sellers with over $20 million in revenue over 12 months must sign a separate agreement with Meta.

    Quoted postRohan Paul · @rohanpaul_ai

    Meta released the weights of Muse Spark Open, on Huggingface, an 816B-parameter model built for coding and agentic work. The open version shares the same base model as Muse Spark 1.3, with additional safety post-training to resist misuse as a standalone model without system guardrails, reversing Meta’s first proprietary, closed-weight frontier model strategy from April. Each of its 62 layers holds 256 expert networks and routes every token through 8, so deployment needs the memory of an 816B-parameter model but runs at the speed of a 42B model. That storage bill explains the Apple tie-up, because the M5 Ultra pushes 1.2 TB/s of unified memory and a Mac Studio can carry up to 512GB. A 3-bit quant tested on a 512GB M3 Ultra took about 344 GB and ran at about 21 tok/s generation on a short prompt.

    Same story · 33 reports

    #Model releases#Open-source ecosystem#English text is being prepared.

  17. fal_xModel releases
    AI score68

    English translation is being prepared.

    Quoted postNVIDIA AI · @NVIDIAAI

    Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵 https://t.co/BcbED05qSG

    #Product updates#Multimodal#Agents

  18. antlingagiModel releases
    AI score63

    English translation is being prepared.

    Quoted postSGLang · @sgl_project

    SGLang has Day-0 support for Ling-3.1-flash from @AntLingAGI! A 560B MoE hybrid reasoning model, 25B active, 1M context, built for real work: it turns a rough brief into a page, mobile UI, or interactive prototype. ✅ MXFP4 + block-FP8 quantization cuts weights from ~534GB to ~292GB, fitting on 4 Hopper GPUs ✅ Strong on real-world agent, office, and coding tasks, including GDPVal-AA, FrontierSWE, and Terminal-Bench 4.0. Get started with SGLang:

    #Industry news#Open-source ecosystem#English text is being prepared.

  19. antlingagiModel releases
    AI score56

    English translation is being prepared.

    Quoted postvLLM · @vllm_project

    🎉 Day-0 support for Ling-3.1-flash is here in vLLM! ~560B total parameters, ~25B active per token, and up to 1M context. From a rough brief to websites, mobile UIs, and interactive prototypes—serve it on your own infrastructure with vLLM. Congrats to @AntLingAGI!

    #English text is being prepared.#English text is being prepared.#Open-source ecosystem

  20. openbmbModel releases
    AI score69

    English translation is being prepared.

    Quoted postModelScope · @ModelScope2022

    MiniCPM-V 4.7 is now open source on ModelScope: a 35B-A3B MoE multimodal model built to run on-device without compromising performance. Apache 2.0. 🤖 https://t.co/NwwbEXRXGg 📱 First open-source MoE with smooth, lossless on-device inference, running at 13+ tokens/s on iPhone 17 Pro Max. 🔥 New Pareto frontier: in Instruct mode, it reaches a higher overall score in less inference time than Qwen3.6 35B-A3B. ⚡ Mixed 4x/16x visual token compression lets developers balance visual detail and inference cost per task. 🛠️ Two sizes (35B-A3B and 1B) with FP8, GPTQ, AWQ, BNB, and GGUF variants. Works with SGLang, vLLM, llama.cpp, Ollama, SWIFT, and LLaMA-Factory. 💻 Runs on iOS, Android, and HarmonyOS, with day-0 support for Rockchip, Houmo, Intel, AMD, and Arm.

    #English text is being prepared.#English text is being prepared.#Multimodal