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Friday

  1. Rohan PaulSelected

    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.

    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 memor

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

Thursday

  5. Hugging FaceSelected

    Swiss AI releases the Apertus 1.5 multimodal model, expanding context to 262k tokens

    Swiss AI has released Apertus 1.5, which builds on 1.0 by adding native image and audio understanding, a thinking mode, and a 262,144-token context window—4 times larger than 1.0. The model uses early fusion to convert images and audio into discrete tokens and feed them into the same sequence as text; the architecture is unchanged, and it only understands multimodal input while outputting text only. The team says the 8B version outperforms open-source models of the same scale on instruction following and multilingual tasks, its multimodal performance is close to Gemma 4 12B, and it has also released the training data mixture, preference data, and intermediate checkpoints from each stage.

    Swiss AI has released Apertus 1.5, which builds on 1.0 by adding native image and audio understanding, a thinking mode, and a 262,144-token context window—4 times larger than 1.0. The model uses early fusion to convert images and audio into discrete tokens and feed them into the same sequence as text; the architecture is unchanged, and it only understands multimodal input while outputting text only. The team says the 8B version outperforms open-source models of the same scale on instruction foll

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    #lab#Model releases#Multimodal#Open-source ecosystem

Wednesday

  3. 36Kr · AISelected

    Google Gemini 4 Argon is fully open to developers worldwide

    On October 13, US local time, Google Gemini 4 Argon opened to developers and enterprises worldwide, callable via platforms such as the Gemini API, Google AI Studio, and Google Antigravity; Gemini Pro and Ultra subscribers can also experience it in the Gemini app and AI Mode in Search. Google also disclosed several application cases, including using Argon to refactor a Chromium legacy code directory of about 6 million lines, raising Gemma 4 E2B inference throughput on a single A100 from about 37,900 tokens per second to over 108,000, and building a Real2Sim simulation environment from real robot teleoperation videos. Limited-time API pricing is 2 USD per million input tokens and 10 USD for output, rising to 4 USD and 20 USD respectively from December 1; the model offers a unified 1 million token context window.

    On October 13, US local time, Google Gemini 4 Argon opened to developers and enterprises worldwide, callable via platforms such as the Gemini API, Google AI Studio, and Google Antigravity; Gemini Pro and Ultra subscribers can also experience it in the Gemini app and AI Mode in Search. Google also disclosed several application cases, including using Argon to refactor a Chromium legacy code directory of about 6 million lines, raising Gemma 4 E2B inference throughput on a single A100 from about 37,

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    #hot-signal#Model releases#Google#Multimodal

Tuesday