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Introducing ESM Cambrian. Unsupervised learning can invert biology at scale to reveal the hidden structure of the natural world. We’ve scaled up compute and data to train a new generation of protein language models. ESM C defines a new state of the art for protein representation learning.

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Alex Rives

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207,098 görüntüleme • 1 yıl önce •via X (Twitter)

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ESM C models establish a frontier of performance as a function of parameter scale. We see large improvements across all parameter scales over previous state of the art models. Read more:

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Information about protein structure in ESM C representations improves predictably with increasing training compute, demonstrating linear scaling across multiple orders of magnitude. (We overtrained the 300M and 600M models past the predicted point of compute optimality).

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ESM C comes with major performance and efficiency benefits over ESM2. The 300M parameter ESM C delivers similar performance to ESM2 650M. The 600M delivers similar performance to ESM2 3B and approaches the capabilities of the ESM2 15B, with far greater efficiency. The 6B parameter ESM C outperforms all ESM2 models by a wide margin.

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Today we’re releasing ESM C 300M, and 600M with open weights. ESM C 6B is available immediately on EvolutionaryScale Forge for academic use, and AWS Sagemaker for commercial use. ESM C will be on NVIDIA BioNemo soon. We’re excited to see what you build with ESM!

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Nathan Benaich1 yıl önce

cool work!

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Alex Rives1 yıl önce

Thanks Nathan!

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Kevin K. Yang 楊凱筌1 yıl önce

Why is it called Cambrian?

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Alex Rives1 yıl önce

wouldn't want to spoil the mystery!

Surge Biswas profil fotoğrafı
Surge Biswas1 yıl önce

Congrats Alex and the evoscale team! Exciting

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Alex Rives1 yıl önce

Thank you Surge!

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