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Alexandr Wang

@alexandr_wang • 769,494 subscribers

chief ai officer @meta, founder meta superintelligence labs, founder @scale_ai. rational in the fullness of time

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it’s ok sarah muse is thinking about you too

it’s ok sarah muse is thinking about you too

597,849 次观看

you can now add your muse to your instagram profile :) show off your superintelligent sidekick to all your friends!

you can now add your muse to your instagram profile :) show off your superintelligent sidekick to all your friends!

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1/ we just publicly released Muse Spark 1.3 max! we see significantly stronger coding and agentic performance on muse spark 1.3 max, so would strongly recommend trying it out even if you've already tried muse spark 1.3 high or muse spark 1.3 xhigh.

1/ we just publicly released Muse Spark 1.3 max! we see significantly stronger coding and agentic performance on muse spark 1.3 max, so would strongly recommend trying it out even if you've already tried muse spark 1.3 high or muse spark 1.3 xhigh.

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1/ today, with the launch of muse image and the preview of muse video, we wanted to share more samples from the model alongside research details and eval results see some more samples from muse video below (and see thread for research blog!)

1/ today, with the launch of muse image and the preview of muse video, we wanted to share more samples from the model alongside research details and eval results see some more samples from muse video below (and see thread for research blog!)

379,646 次观看

1/ muse spark 1.2 is a very strong multimodal model—it can do visual coding, robotics planning, and audio-visual understanding that all come together through agentic tools.

1/ muse spark 1.2 is a very strong multimodal model—it can do visual coding, robotics planning, and audio-visual understanding that all come together through agentic tools.

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4/ to achieve maximum memory efficiency, we quantize model weights to ~4-bit, getting the language model under 20GB with room for the kv cache, perception encoder, and drafter alongside it. a dflash drafter proposes blocks of tokens the main model verifies in parallel, so it stays responsive.

4/ to achieve maximum memory efficiency, we quantize model weights to ~4-bit, getting the language model under 20GB with room for the kv cache, perception encoder, and drafter alongside it. a dflash drafter proposes blocks of tokens the main model verifies in parallel, so it stays responsive.

69,532 次观看

2/It's a step up on reasoning and coding. compared to Muse Spark 1.2, Muse Spark 1.3 wastes fewer turns, uses ~20% fewer tool calls, ~25% fewer tokens, and holds onto requirements well during long-horizon tasks.

2/It's a step up on reasoning and coding. compared to Muse Spark 1.2, Muse Spark 1.3 wastes fewer turns, uses ~20% fewer tool calls, ~25% fewer tokens, and holds onto requirements well during long-horizon tasks.

35,480 次观看

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